diff --git a/.gitignore b/.gitignore index 3592961..434dcb7 100644 --- a/.gitignore +++ b/.gitignore @@ -31,6 +31,7 @@ **/*.toc **/*.vrb **/*.out +**/slides.pdf **/main.pdf ## temp models diff --git a/data/README.md b/data/README.md index 2463f05..f47eb62 100644 --- a/data/README.md +++ b/data/README.md @@ -1,5 +1,40 @@ # Data +## Demo data Thu-24-Aug-2023 +Tree of demo dataset. Each pair of video and time-series were recorded for approximately 5 minutes. +``` +$ tree -s +[ 4096] . +├── [ 4096] participant01 +│   ├── [ 1126670892] participant01-test01-rep01-1g-5mins.avi +│   ├── [ 10503488] participant01-test01-rep01-1g-5mins.avi.csv +│   ├── [ 1093617412] participant01-test01-rep02-1g-5mins.avi +│   ├── [ 10490777] participant01-test01-rep02-1g-5mins.avi.csv +│   ├── [ 1540763444] participant01-test02-rep01-1g-5mins.avi +│   ├── [ 10083571] participant01-test02-rep01-1g-5mins.avi.csv +│   ├── [ 1503624576] participant01-test02-rep02-1g-5mins.avi +│   ├── [ 9792205] participant01-test02-rep02-1g-5mins.avi.csv +│   ├── [ 1289063688] participant01-test03-rep01-1g-5mins.avi +│   ├── [ 9975193] participant01-test03-rep01-1g-5mins.avi.csv +│   ├── [ 1260531560] participant01-test03-rep02-1g-5mins.avi +│   └── [ 10033743] participant01-test03-rep02-1g-5mins.avi.csv +└── [ 4096] participant02 + ├── [ 1251925628] participant02-test01-rep01-1g-5mins.avi + ├── [ 10188391] participant02-test01-rep01-1g-5mins.avi.csv + ├── [ 1241199998] participant02-test01-rep02-1g-5mins.avi + ├── [ 10043427] participant02-test01-rep02-1g-5mins.avi.csv + ├── [ 1423517518] participant02-test02-rep01-1g-5mins.avi + ├── [ 9913693] participant02-test02-rep01-1g-5mins.avi.csv + ├── [ 1283264068] participant02-test02-rep02-1g-5mins.avi + ├── [ 10211794] participant02-test02-rep02-1g-5mins.avi.csv + ├── [ 1315188186] participant02-test03-rep01-1g-5mins.avi + ├── [ 10055324] participant02-test03-rep01-1g-5mins.avi.csv + ├── [ 1437222374] participant02-test03-rep02-1g-5mins.avi + └── [ 9870705] participant02-test03-rep02-1g-5mins.avi.csv + +2 directories, 24 files +``` + ## Demo dataset Thu-27-Jul-2023/ Create and go to data demo path ``` diff --git a/docs/figures/00_template-vector-images/outputs/drawing-v00.png b/docs/figures/00_template-vector-images/outputs/drawing-v00.png new file mode 100644 index 0000000..11b622b Binary files /dev/null and b/docs/figures/00_template-vector-images/outputs/drawing-v00.png differ diff --git a/docs/figures/experiments-24-aug-2023-A/Makefile b/docs/figures/experiments-24-aug-2023-A/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-A/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/experiments-24-aug-2023-A/README.md b/docs/figures/experiments-24-aug-2023-A/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-A/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/experiments-24-aug-2023-A/outputs/README.md b/docs/figures/experiments-24-aug-2023-A/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-A/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/experiments-24-aug-2023-A/outputs/drawing-v00.png b/docs/figures/experiments-24-aug-2023-A/outputs/drawing-v00.png new file mode 100644 index 0000000..ec8e9f6 Binary files /dev/null and b/docs/figures/experiments-24-aug-2023-A/outputs/drawing-v00.png differ diff --git a/docs/figures/experiments-24-aug-2023-A/references/README.md b/docs/figures/experiments-24-aug-2023-A/references/README.md new file mode 100644 index 0000000..a572f91 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-A/references/README.md @@ -0,0 +1,3 @@ +# References + + diff --git a/docs/figures/experiments-24-aug-2023-A/vectors/drawing-v00.svg b/docs/figures/experiments-24-aug-2023-A/vectors/drawing-v00.svg new file mode 100644 index 0000000..be43f64 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-A/vectors/drawing-v00.svg @@ -0,0 +1,1023 @@ + +image/svg+xmlv04bcayzxyzxYZXYZXabcworld framesensor frame diff --git a/docs/figures/experiments-24-aug-2023-B/Makefile b/docs/figures/experiments-24-aug-2023-B/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-B/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/experiments-24-aug-2023-B/README.md b/docs/figures/experiments-24-aug-2023-B/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-B/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/experiments-24-aug-2023-B/outputs/README.md b/docs/figures/experiments-24-aug-2023-B/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-B/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/experiments-24-aug-2023-B/outputs/drawing-v00.png b/docs/figures/experiments-24-aug-2023-B/outputs/drawing-v00.png new file mode 100644 index 0000000..b4a4ab2 Binary files /dev/null and b/docs/figures/experiments-24-aug-2023-B/outputs/drawing-v00.png differ diff --git a/docs/figures/experiments-24-aug-2023-B/references/README.md b/docs/figures/experiments-24-aug-2023-B/references/README.md new file mode 100644 index 0000000..a572f91 --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-B/references/README.md @@ -0,0 +1,3 @@ +# References + + diff --git a/docs/figures/experiments-24-aug-2023-B/vectors/drawing-v00.svg b/docs/figures/experiments-24-aug-2023-B/vectors/drawing-v00.svg new file mode 100644 index 0000000..caff0ce --- /dev/null +++ b/docs/figures/experiments-24-aug-2023-B/vectors/drawing-v00.svg @@ -0,0 +1,954 @@ + +image/svg+xmlv04bTEST01 - Fordward alignmentTEST02 - Side alignmentTEST03 -Reverse alignmentPARTICIPANT 01PARTICIPANT 02EULER ANGLESQUATERNIONSTEXTURE ANALYSIS + + + + + +Time-series data for one experimentExperimentsCollected Data├── participant01│   ├── participant01-test01-rep01-1g-5mins.avi│   ├── participant01-test01-rep01-1g-5mins.avi.csv│   ├── participant01-test01-rep02-1g-5mins.avi│   ├── participant01-test01-rep02-1g-5mins.avi.csv│   ├── participant01-test02-rep01-1g-5mins.avi│   ├── participant01-test02-rep01-1g-5mins.avi.csv│   ├── participant01-test02-rep02-1g-5mins.avi│   ├── participant01-test02-rep02-1g-5mins.avi.csv│   ├── participant01-test03-rep01-1g-5mins.avi│   ├── participant01-test03-rep01-1g-5mins.avi.csv│   ├── participant01-test03-rep02-1g-5mins.avi│   └── participant01-test03-rep02-1g-5mins.avi.csv├── participant02│   ├── participant02-test01-rep01-1g-5mins.avi│   ├── participant02-test01-rep01-1g-5mins.avi.csv│   ├── participant02-test01-rep02-1g-5mins.avi│   ├── participant02-test01-rep02-1g-5mins.avi.csv│   ├── participant02-test02-rep01-1g-5mins.avi│   ├── participant02-test02-rep01-1g-5mins.avi.csv│   ├── participant02-test02-rep02-1g-5mins.avi│   ├── participant02-test02-rep02-1g-5mins.avi.csv│   ├── participant02-test03-rep01-1g-5mins.avi│   ├── participant02-test03-rep01-1g-5mins.avi.csv│   ├── participant02-test03-rep02-1g-5mins.avi│   └── participant02-test03-rep02-1g-5mins.avi.csv diff --git a/docs/figures/github_repo_rtt4ssa/Makefile b/docs/figures/github_repo_rtt4ssa/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/github_repo_rtt4ssa/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/github_repo_rtt4ssa/README.md b/docs/figures/github_repo_rtt4ssa/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/github_repo_rtt4ssa/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/github_repo_rtt4ssa/outputs/README.md b/docs/figures/github_repo_rtt4ssa/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/github_repo_rtt4ssa/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/github_repo_rtt4ssa/outputs/drawing-v00.png b/docs/figures/github_repo_rtt4ssa/outputs/drawing-v00.png new file mode 100644 index 0000000..22bbfe3 Binary files /dev/null and b/docs/figures/github_repo_rtt4ssa/outputs/drawing-v00.png differ diff --git a/docs/figures/github_repo_rtt4ssa/references/README.md b/docs/figures/github_repo_rtt4ssa/references/README.md new file mode 100644 index 0000000..b18be66 --- /dev/null +++ b/docs/figures/github_repo_rtt4ssa/references/README.md @@ -0,0 +1 @@ +# References diff --git a/docs/figures/github_repo_rtt4ssa/vectors/drawing-v00.svg b/docs/figures/github_repo_rtt4ssa/vectors/drawing-v00.svg new file mode 100644 index 0000000..14d869c --- /dev/null +++ b/docs/figures/github_repo_rtt4ssa/vectors/drawing-v00.svg @@ -0,0 +1,333 @@ + +image/svg+xml diff --git a/docs/figures/lightweight-transformer-A/Makefile b/docs/figures/lightweight-transformer-A/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/lightweight-transformer-A/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/lightweight-transformer-A/README.md b/docs/figures/lightweight-transformer-A/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/lightweight-transformer-A/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/lightweight-transformer-A/outputs/README.md b/docs/figures/lightweight-transformer-A/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/lightweight-transformer-A/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/lightweight-transformer-A/outputs/drawing-v00.png b/docs/figures/lightweight-transformer-A/outputs/drawing-v00.png new file mode 100644 index 0000000..244468a Binary files /dev/null and b/docs/figures/lightweight-transformer-A/outputs/drawing-v00.png differ diff --git a/docs/figures/lightweight-transformer-A/references/README.md b/docs/figures/lightweight-transformer-A/references/README.md new file mode 100644 index 0000000..c25f8db --- /dev/null +++ b/docs/figures/lightweight-transformer-A/references/README.md @@ -0,0 +1,5 @@ +# References + + + + diff --git a/docs/figures/lightweight-transformer-A/vectors/drawing-v00.svg b/docs/figures/lightweight-transformer-A/vectors/drawing-v00.svg new file mode 100644 index 0000000..11546e7 --- /dev/null +++ b/docs/figures/lightweight-transformer-A/vectors/drawing-v00.svg @@ -0,0 +1,372 @@ + +image/svg+xmlv04bcZUKETANG Laparoscope Trainer BoxLaparo AnalyticaSimendosimulator diff --git a/docs/figures/lightweight-transformer-B/Makefile b/docs/figures/lightweight-transformer-B/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/lightweight-transformer-B/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/lightweight-transformer-B/README.md b/docs/figures/lightweight-transformer-B/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/lightweight-transformer-B/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/lightweight-transformer-B/outputs/README.md b/docs/figures/lightweight-transformer-B/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/lightweight-transformer-B/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/lightweight-transformer-B/outputs/drawing-v00.png b/docs/figures/lightweight-transformer-B/outputs/drawing-v00.png new file mode 100644 index 0000000..51b8889 Binary files /dev/null and b/docs/figures/lightweight-transformer-B/outputs/drawing-v00.png differ diff --git a/docs/figures/lightweight-transformer-B/references/README.md b/docs/figures/lightweight-transformer-B/references/README.md new file mode 100644 index 0000000..a572f91 --- /dev/null +++ b/docs/figures/lightweight-transformer-B/references/README.md @@ -0,0 +1,3 @@ +# References + + diff --git a/docs/figures/lightweight-transformer-B/vectors/drawing-v00.svg b/docs/figures/lightweight-transformer-B/vectors/drawing-v00.svg new file mode 100644 index 0000000..e20e647 --- /dev/null +++ b/docs/figures/lightweight-transformer-B/vectors/drawing-v00.svg @@ -0,0 +1,377 @@ + +image/svg+xmlv04bcZUKETANG Laparoscope Trainer BoxLaparo AnalyticaSimendosimulator diff --git a/docs/figures/main/Makefile b/docs/figures/main/Makefile index a2fb8b7..3bf30e5 100644 --- a/docs/figures/main/Makefile +++ b/docs/figures/main/Makefile @@ -1,13 +1,29 @@ -#INKSCAPE?=inkscape --export-png # Inkscape (svg support) -#INKSCAPE?=inkscape --export-filename # Inkscape (svg support) -INKSCAPE?=inkscape --export-dpi=200 --export-filename -OS_VERS:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') -# https://stackoverflow.com/questions/30088319/using-shell-in-makefile-to-find-ubuntu-version +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= FIGURES_SVG=$(wildcard vectors/*.svg) FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) # Pattern rule for converting SVG to PNG and PDF png: $(FIGURES_PNG) @@ -18,8 +34,22 @@ pdf: $(FIGURES_PDF) outputs/%.pdf: vectors/%.svg $(INKSCAPE) $(@) $(<) +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + clean: ## output figure files - rm -f $(FIGURES_PNG) $(FIGURES_PDF) + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf -#test: -# @echo $(OS_VERS) +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/main/outputs/drawing-v00.png b/docs/figures/main/outputs/drawing-v00.png index 778cc61..1b53930 100644 Binary files a/docs/figures/main/outputs/drawing-v00.png and b/docs/figures/main/outputs/drawing-v00.png differ diff --git a/docs/figures/main/references/README.md b/docs/figures/main/references/README.md index 0e06aa3..fa43f77 100644 --- a/docs/figures/main/references/README.md +++ b/docs/figures/main/references/README.md @@ -1,13 +1,2 @@ # References -https://www.thebalancecareers.com/what-does-an-ultrasound-technician-do-526077 - -https://www.ncbi.nlm.nih.gov/books/NBK363066/figure/CDR0000774255__392/ - -https://www.cancer.gov/publications/dictionaries/cancer-terms/def/abdominal-ultrasound - -https://thefederalist.com/2017/01/25/four-senate-republicans-want-make-taxpayers-pay-abortions/ - -https://www.freepik.com/premium-vector/development-fetus-mother-s-womb-until-birth-newborn-baby_6003788.htm - -https://www.freepik.com/vectors/fetal-development diff --git a/docs/figures/main/vectors/drawing-v00.svg b/docs/figures/main/vectors/drawing-v00.svg index 2c7e6ac..a615bfb 100644 --- a/docs/figures/main/vectors/drawing-v00.svg +++ b/docs/figures/main/vectors/drawing-v00.svg @@ -222,16 +222,24 @@ style="fill:none;stroke:#000000;stroke-width:0.264583px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1" d="m -28.365707,196.90467 c 4.12901,-0.94284 5.548782,-2.56209 7.725972,-3.99224 1.329219,2.9107 3.482434,5.24316 5.958741,7.34889 l -0.88637,1.39168 c -3.352611,1.02577 -5.696629,2.75627 -7.849456,4.62036 l -1.943574,-0.30372 c -0.301009,-2.64241 -0.760246,-5.28482 -3.48197,-7.92723 z" id="path1136" - sodipodi:nodetypes="cccccccc" />v04FeedbackDeyzxVisualPropriceptionPatient motionFetus motionHand tremorsInvoluntary motionsControl signalyzxFeedbackaStandardplane frametNbbcYZXYZXYZXYZXcZUKETANG Laparoscope Trainer Boxhttps://www.amazon.co.uk/ZUKETANG-Laparoscope-Laparoscopic-Simulator-Instruments/dp/B0BR6CY9ZJLaparo AnalyticaaZUKETANG Laparoscope Trainer Box [£758]Simendosimulator + x="298.18414" + y="180.78384" /> diff --git a/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/Makefile b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/README.md b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/outputs/README.md b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/outputs/drawing-v00.png b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/outputs/drawing-v00.png new file mode 100644 index 0000000..5f698b8 Binary files /dev/null and b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/outputs/drawing-v00.png differ diff --git a/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/references/README.md b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/references/README.md new file mode 100644 index 0000000..a572f91 --- /dev/null +++ b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/references/README.md @@ -0,0 +1,3 @@ +# References + + diff --git a/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/vectors/drawing-v00.svg b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/vectors/drawing-v00.svg new file mode 100644 index 0000000..c70c7b5 --- /dev/null +++ b/docs/figures/results-timeseries_classification_from_scratch-24-aug-2023/vectors/drawing-v00.svg @@ -0,0 +1,588 @@ + +image/svg+xmlv04Fully Convolutional Neural Network Total params: 25,858Engine noise captured by a motor sensor.The demo dataset contains 2400 training instances and another 600 testing instances (80/20) +Epochs 200 +Batch 32 + +Training 2200 Epochs 100 +Batch 16 +Class-1: participant01Class+1: participant02 +Accuracy +Loss diff --git a/docs/figures/results-timeseries_classification_transformer-24-aug-2023/Makefile b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/Makefile new file mode 100644 index 0000000..3bf30e5 --- /dev/null +++ b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/Makefile @@ -0,0 +1,55 @@ + +OS_VERSION:=$(shell lsb_release -a 2>/dev/null | grep Description | awk '{ print $$2 "-" $$3 }') +$(eval $(shell grep VERSION_ID /etc/os-release)) +#ifeq ($(VERSION_ID), 22.04) +ifeq ($(OS_VERSION), Ubuntu-22.04.1) +EXPORT_ID = --export-png +else +EXPORT_ID = --export-filename +endif +# https://stackoverflow.com/questions/714100/os-detecting-makefile + +INKSCAPE?=inkscape --export-dpi=200 $(EXPORT_ID) + + +#dPDFSETTINGS=screen #lower quality, smaller size. (72 dpi) +#dPDFSETTINGS=ebook #for better quality, but slightly larger pdfs. (150 dpi) +#dPDFSETTINGS=prepress #output similar to Acrobat Distiller "Prepress Optimized" setting (300 dpi) +#dPDFSETTINGS=printer #selects output similar to the Acrobat Distiller "Print Optimized" setting (300 dpi) +dPDFSETTINGS=default #selects output intended to be useful across a wide variety of uses, possibly at the expense of a larger output file + +GS?=gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/$(dPDFSETTINGS) -dNOPAUSE -dQUIET -dBATCH -sOutputFile= + +FIGURES_SVG=$(wildcard vectors/*.svg) +FIGURES_PNG=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.png)) +FIGURES_PDF=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=.pdf)) +FIGURES_PDF_REDUCED_SIZE=$(subst vectors/,outputs/,$(FIGURES_SVG:.svg=_reduced_size.pdf)) + +# Pattern rule for converting SVG to PNG and PDF +png: $(FIGURES_PNG) +outputs/%.png: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +pdf: $(FIGURES_PDF) +outputs/%.pdf: vectors/%.svg + $(INKSCAPE) $(@) $(<) + +edit: + inkscape $(FIGURES_SVG) + +view-png: + eog $(FIGURES_PNG) + +view-pdf: + evince $(FIGURES_PDF) + +reduce-pdf-size: + $(GS)$(FIGURES_PDF_REDUCED_SIZE) $(FIGURES_PDF) + +clean: ## output figure files + rm -f $(FIGURES_PNG) $(FIGURES_PDF) outputs/*.pdf + +test: + echo $(VERSION_ID) + echo $(OS_VERSION) + echo $(EXPORT_ID) diff --git a/docs/figures/results-timeseries_classification_transformer-24-aug-2023/README.md b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/README.md new file mode 100644 index 0000000..1477bf8 --- /dev/null +++ b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/README.md @@ -0,0 +1,18 @@ +# Usage + +* save images, create svg files +``` +make png #or make pdf +eog versions/drawing-v$NN.png +inkscape vector/drawing-v$NN.svg +``` +where `$NN` is the version of the drawing. + +## Download template +Open a terminal and type: +``` +cd ~/Desktop && svn checkout https://github.com/mxochicale/images/trunk/00_template-vector-images +cd 00_template-vector-images && rm -rf .svn +``` + +Reference: [:link:](https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repo) diff --git a/docs/figures/results-timeseries_classification_transformer-24-aug-2023/outputs/README.md b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/outputs/README.md new file mode 100644 index 0000000..ca7b64d --- /dev/null +++ b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/outputs/README.md @@ -0,0 +1,6 @@ +# Versions +## v01 +![v](drawing-v01.png) + +## v00 +![v](drawing-v00.png) diff --git a/docs/figures/results-timeseries_classification_transformer-24-aug-2023/outputs/drawing-v00.png b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/outputs/drawing-v00.png new file mode 100644 index 0000000..9bf9cc8 Binary files /dev/null and b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/outputs/drawing-v00.png differ diff --git a/docs/figures/results-timeseries_classification_transformer-24-aug-2023/references/README.md b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/references/README.md new file mode 100644 index 0000000..a572f91 --- /dev/null +++ b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/references/README.md @@ -0,0 +1,3 @@ +# References + + diff --git a/docs/figures/results-timeseries_classification_transformer-24-aug-2023/vectors/drawing-v00.svg b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/vectors/drawing-v00.svg new file mode 100644 index 0000000..330fbe9 --- /dev/null +++ b/docs/figures/results-timeseries_classification_transformer-24-aug-2023/vectors/drawing-v00.svg @@ -0,0 +1,809 @@ + +image/svg+xmlParticipant 01The prototyping above was done using the simple timeseries classification.Participant 02L1L2L3L4Total params: 86,730Multi-head attention-based modelClass-1: participant01Class+1: participant02The demo dataset contains 2400 training instances and another 600 testing instances (80/20) +Accuracy +Loss +Epochs 300 +Batch 32 + diff --git a/docs/references/2020-Khogali-Jakary-inSurgeryEndoscopy-SPRINGER/README.md b/docs/references/2020-Khogali-Jakary-inSurgeryEndoscopy-SPRINGER/README.md new file mode 100644 index 0000000..2982a31 --- /dev/null +++ b/docs/references/2020-Khogali-Jakary-inSurgeryEndoscopy-SPRINGER/README.md @@ -0,0 +1,20 @@ +# Manuscript + +## Citations + + +[NUMBER_OF_CITATIONS] +[GOOGLE_CITATIONS_LINK] +[ACCESSED_DATE] + + +## Links + +## Authors + +## Notes + +## bibtex +``` + +``` diff --git a/docs/references/2020-Khogali-Jakary-inSurgeryEndoscopy-SPRINGER/article.pdf b/docs/references/2020-Khogali-Jakary-inSurgeryEndoscopy-SPRINGER/article.pdf new file mode 100644 index 0000000..d58f02c Binary files /dev/null and b/docs/references/2020-Khogali-Jakary-inSurgeryEndoscopy-SPRINGER/article.pdf differ diff --git a/docs/references/2022-li-in-arvix/README.md b/docs/references/2022-li-in-arvix/README.md new file mode 100644 index 0000000..405b4cc --- /dev/null +++ b/docs/references/2022-li-in-arvix/README.md @@ -0,0 +1,23 @@ +# Manuscript + +## Citations + +19 +https://scholar.google.com/scholar?cites=3876751087281584278&as_sdt=2005&sciodt=0,5&hl=en +Wed 20 Sep 17:12:03 BST 2023 + +[NUMBER_OF_CITATIONS] +[GOOGLE_CITATIONS_LINK] +[ACCESSED_DATE] + + +## Links + +## Authors + +## Notes + +## bibtex +``` + +``` diff --git a/docs/references/2022-li-in-arvix/article.pdf b/docs/references/2022-li-in-arvix/article.pdf new file mode 100644 index 0000000..ef1120c Binary files /dev/null and b/docs/references/2022-li-in-arvix/article.pdf differ diff --git a/docs/references/2022-mehta-in-ICLR/README.md b/docs/references/2022-mehta-in-ICLR/README.md new file mode 100644 index 0000000..5fcee47 --- /dev/null +++ b/docs/references/2022-mehta-in-ICLR/README.md @@ -0,0 +1,25 @@ +# Manuscript + +## Citations + +480 +https://scholar.google.com/scholar?cites=5434557493125510443&as_sdt=2005&sciodt=0,5&hl=en +Wed 20 Sep 16:47:35 BST 2023 + + +[NUMBER_OF_CITATIONS] +[GOOGLE_CITATIONS_LINK] +[ACCESSED_DATE] + + +## Links +https://openreview.net/forum?id=vh-0sUt8HlG + +## Authors + +## Notes + +## bibtex +``` + +``` diff --git a/docs/references/2022-mehta-in-ICLR/article.pdf b/docs/references/2022-mehta-in-ICLR/article.pdf new file mode 100644 index 0000000..447fc40 Binary files /dev/null and b/docs/references/2022-mehta-in-ICLR/article.pdf differ diff --git a/docs/references/2022-zhang-in-CVPR/README.md b/docs/references/2022-zhang-in-CVPR/README.md new file mode 100644 index 0000000..8a5e7b9 --- /dev/null +++ b/docs/references/2022-zhang-in-CVPR/README.md @@ -0,0 +1,26 @@ +# Manuscript + +## Citations + +71 +https://scholar.google.com/scholar?cites=4250030993072671612&as_sdt=2005&sciodt=0,5&hl=en + +Wed 20 Sep 17:03:58 BST 2023 + +[NUMBER_OF_CITATIONS] +[GOOGLE_CITATIONS_LINK] +[ACCESSED_DATE] + + +Zhang, Wenqiang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu, and Chunhua Shen. "TopFormer: Token pyramid transformer for mobile semantic segmentation." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12083-12093. 2022. + +## Links + +## Authors + +## Notes + +## bibtex +``` + +``` diff --git a/docs/references/2022-zhang-in-CVPR/article.pdf b/docs/references/2022-zhang-in-CVPR/article.pdf new file mode 100644 index 0000000..dc30eba Binary files /dev/null and b/docs/references/2022-zhang-in-CVPR/article.pdf differ diff --git a/docs/references/2023-menghani-in-ACMSurveys/README.md b/docs/references/2023-menghani-in-ACMSurveys/README.md new file mode 100644 index 0000000..5eb717c --- /dev/null +++ b/docs/references/2023-menghani-in-ACMSurveys/README.md @@ -0,0 +1,25 @@ +# Manuscript + +## Citations + +101 +https://scholar.google.com/scholar?cites=8548750458227111982&as_sdt=2005&sciodt=0,5&hl=en +Wed 20 Sep 16:50:21 BST 2023 + +[NUMBER_OF_CITATIONS] +[GOOGLE_CITATIONS_LINK] +[ACCESSED_DATE] + +Menghani, Gaurav. "Efficient deep learning: A survey on making deep learning models smaller, faster, and better." ACM Computing Surveys 55, no. 12 (2023): 1-37. + + +## Links + +## Authors + +## Notes + +## bibtex +``` + +``` diff --git a/docs/references/2023-menghani-in-ACMSurveys/article.pdf b/docs/references/2023-menghani-in-ACMSurveys/article.pdf new file mode 100644 index 0000000..bd878c9 Binary files /dev/null and b/docs/references/2023-menghani-in-ACMSurveys/article.pdf differ diff --git a/docs/slides/README.md b/docs/slides/README.md new file mode 100644 index 0000000..db947d4 --- /dev/null +++ b/docs/slides/README.md @@ -0,0 +1 @@ +# Slides diff --git a/docs/slides/fml4s-26-09-2023/.latexmkrc b/docs/slides/fml4s-26-09-2023/.latexmkrc new file mode 100644 index 0000000..fbc046c --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/.latexmkrc @@ -0,0 +1,66 @@ +# vim: set filetype=perl: + +$pdf_mode = 1; +$pdflatex = 'lualatex --shell-escape --synctex=1 --file-line-error %O %S'; +$max_repeat = 5; +my @clean_ext = qw( + %R-blx.aux + %R-blx.bib + _minted-%R + acn + acr + alg + aux + bbl + bcf + blg + brf + cb + cb2 + cpt + cut + dvi + fdb_latexmk + fls + fmt + fot + glg + glo + gls + glsdefs + idx + ilg + ind + ist + lb + listing + loa + loe + lof + log + lol + lot + lox + nav + out + pdfsync + pre + run.xml + snm + soc + synctex + synctex(busy) + synctex.gz + synctex.gz(busy) + tdo + thm + toc + upa + upb + vrb + xcp + xdv + xmpi + xyc +); +$clean_ext = join ' ', @clean_ext; diff --git a/docs/slides/fml4s-26-09-2023/Makefile b/docs/slides/fml4s-26-09-2023/Makefile new file mode 100644 index 0000000..8fa2d99 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/Makefile @@ -0,0 +1,36 @@ +fileinfo := LaTeX Makefile +author := Miguel Xochicale +## Usage +# run "make" in the terminal to build local pdf +# run "make view" to see pdf file +# run "make clean" when you finished your local changes + +## References +# https://gist.github.com/dogukancagatay/2eb82b0233829067aca6 +# https://www.latex4technics.com/?note=5fu0y6 +# https://www.drewsilcock.co.uk/using-make-and-latexmk + +## Tools +LATEXMK = latexmk +PDFLATEX = pdflatex +LATEX_FLAGS = -shell-escape -halt-on-error -file-line-error +RM = rm -f +EVINCE = evince + +##Project name +#PROJNAME=slides +PROJNAME=slides + +## Rules +$(PROJNAME).pdf: $(PROJNAME).tex + $(LATEXMK) -pdf -pdflatex="pdflatex -shell-escape -synctex=1 -file-line-error %O %S" $< + +view: + $(EVINCE) $(PROJNAME).pdf + +mostlyclean: + $(LATEXMK) -silent -C + +clean: mostlyclean + $(LATEXMK) -silent -c + $(RM) main.pdf *.log *.bbl *.run.xml -rf _minted-main *.pyg example.out diff --git a/docs/slides/fml4s-26-09-2023/README.md b/docs/slides/fml4s-26-09-2023/README.md new file mode 100644 index 0000000..2d33220 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/README.md @@ -0,0 +1,34 @@ +# Slides + +## Abstract +"Towards lightweight transformer-based models with multimodal data for low-latency surgical applications" has been accepted for 15 minutes presentation at the Fast Machine Learning for Science! +Abstract: Surgical data technologies have not only been successfully integrated inputs from various data sources (e.g., medical devices, trackers, robots and cameras) but have also applied a range of machine learning and deep learning methods (e.g., classification, segmentation or synthesis) to data-driven interventional healthcare. However, the diversity of data, acquisitions and pre-processing methods, data types, as well as training and inference methods has presented a challenging scenario for implementing low-latency applications in surgery. Recently, transformers-based models have emerged as dominant neural networks, owing to their attention mechanisms and parallel capabilities when using multimodal medical data. Despite this progress, state-of-the-art transformers-based models remain heavyweight and challenging to optimise (with 100MB of parameters) for real-time applications. Hence, in this work, we concentrate on a lightweight transformer-based model and employ pruning techniques to achieve a balance in data size for both training and testing workflows, aiming at enhancing real-time performance. We present preliminary results from a machine learning workflow designed for real-time classification of surgical skills assessment. We similarly present a reproducible workflow for data collection using multimodal sensors, including USB video image and Bluetooth-based inertial sensors. This highlights the potential of applying models with small memory and parameter size, enhancing inference speed for surgical applications. Code, data and other resources to reproduce this work are available at https://github.com/mxochicale/rtt4ssa + +Further details of the talk: https://indico.cern.ch/event/1283970/contributions/5550640/ +Full program: https://indico.cern.ch/event/1283970/timetable/#20230926.detailed + +## Timing +You have been allocated either 15 minutes (standard) or 5 minutes (lightning) for your talk. + +## Questions from the audience: +1. Do you know what's the inference seed of your model? +2. What latency values are expecting in clinical settings? + +Some related work Exploring medical applications of fast ML with a novel FPGA firmware framework"" > https://indico.cern.ch/event/1283970/contributions/5550639/ + +## Building tex abstract with: +Commit changes +``` +git add -A +git commit -m 'genesis of slides' +git push origin generated-pdfs +``` + +## Local build +### Requirements +* Install latest version of (i.e., Tex Live 2020 [:link:](https://github.com/mxochicale/latex/tree/master/installation)). +* sudo apt-get install python-pygments #https://tex.stackexchange.com/questions/40083/how-to-install-minted-in-ubuntu + +## local build +make clean && make && evince main.pdf + diff --git a/docs/slides/fml4s-26-09-2023/beamerthememx.sty b/docs/slides/fml4s-26-09-2023/beamerthememx.sty new file mode 100644 index 0000000..d7a372f --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/beamerthememx.sty @@ -0,0 +1,165 @@ +% Name : beamerthememx.sty +% Author : Miguel Xochicale (miguel.xochicale@klc.ac.uk) +% Author : Séverin Lemaignan (severin.lemaignan@epfl.ch) +% License : This file may be distributed and/or modified under the +% GNU Public License. + +\ProvidesPackage{beamerthememx}[2021/05/14] + +\usepackage{fontawesome} +\usepackage{multimedia} + +\usepackage[scale=2]{ccicons} +\usepackage{qrcode} +\usepackage{tikz} +\usepackage{tikzpagenodes} +\usetikzlibrary{positioning} + +%%% FONTS +\usefonttheme{serif} + +%%%%% Special commands +\newcommand\BigSizeFont{\fontsize{25}{7.2}\selectfont} %https://tex.stackexchange.com/questions/33969 + +% table related packages +\usepackage{array} + +%% Colours +\definecolor{foreground}{RGB}{255,255,255} +\definecolor{background}{RGB}{24,24,24} +\definecolor{title}{RGB}{107,174,214} +\definecolor{gray}{RGB}{155,155,155} +\definecolor{subtitle}{RGB}{102,255,204} +\definecolor{hilight}{RGB}{102,255,204} +\definecolor{vhilight}{RGB}{255,111,207} + +\setbeamercolor{titlelike}{fg=title} +\setbeamercolor{subtitle}{fg=subtitle} +\setbeamercolor{institute}{fg=gray} +\setbeamercolor{normal text}{fg=foreground,bg=background} +\setbeamercolor{section in toc}{fg=subtitle} + +\setbeamercolor{item}{fg=foreground} % color of bullets +\setbeamercolor{subitem}{fg=gray} +\setbeamercolor{itemize/enumerate subbody}{fg=gray} + +\setbeamertemplate{section in toc}{\hspace*{1em}\inserttocsectionnumber.~\inserttocsection\par} +\setbeamertemplate{subsection in toc}{\hspace*{2em}\inserttocsectionnumber.\inserttocsubsectionnumber.~\inserttocsubsection\par} + +%--------------------------------------------------------------------- +% custom commands +%--------------------------------------------------------------------- +\newcommand{\@githubrepository}{} +\newcommand{\githubrepository}[1]{\def\@githubrepository{#1}} + +% Titlepage structure +\def\maketitle{\ifbeamer@inframe\titlepage\else{\setbeamertemplate{background}{}\frame[plain]{\titlepage}}\fi} +\def\titlepage{ +\usebeamertemplate{title page} +} + + + +\setbeamertemplate{title page}{% + + \begin{minipage}[b][\paperheight]{\textwidth} +% % Add background to title page +% \AddToShipoutPictureFG*{\includegraphics[width=\paperwidth]{background.pdf}} +% \begin{minipage}[b][\paperheight]{\textwidth} + \vspace*{10mm} +% \includegraphics[height=14mm]{logo}\par +% \vspace*{5mm} + \ifx\insertsubtitle\@empty% + \else% + {\usebeamerfont{title}\usebeamercolor[fg]{title}\inserttitle\par}% + \fi% + \ifx\insertsubtitle\@empty% + \else% + {\usebeamerfont{subtitle}\usebeamercolor[fg]{subtitle}\insertsubtitle\par}% + \vspace*{5mm} + \fi% + \ifx\insertdate\@empty% + \else% + {\usebeamerfont{date}\usebeamercolor[fg]{date}\insertdate\par}% + \fi% +% + \vfill + \vspace*{30mm} + + \ifx\insertauthor\@empty% + \else% + {\usebeamerfont{author}\usebeamercolor[fg]{author}\insertauthor\par}% + \fi% + \ifx\insertinstitut\@empty% + \else% + \vspace*{3mm} + {\usebeamerfont{institute}\usebeamercolor[fg]{institute}\insertinstitute\par}% + \fi% + + \vspace*{10mm} + \end{minipage} + + + \begin{tikzpicture}[overlay, remember picture] + \node[ + above right=0.35cm and -0.2cm of current page footer area.south west, + anchor=south west, + inner sep=-10pt] { + \fontsize{4pt}{7.2}\selectfont + \begin{tabular}{lm{.8\textwidth}} + \href{http://creativecommons.org/licenses/by/4.0/}{\ccby} & + This slices is licensed under a + \href{http://creativecommons.org/licenses/by/4.0/} + {Creative Commons ``Attribution 4.0 International''} license. + \par Get source of this slides and see further references + from \url{\@githubrepository} + \end{tabular} + }; + \node[ + above left=0.35cm and 0cm of current page footer area.south east, + anchor=south east, + inner sep=0pt]{\qrcode[height=1.5cm]{\@githubrepository}}; + \end{tikzpicture} + +} + + +%--------------------------------------------------------------------- +% Footline +%--------------------------------------------------------------------- +\usenavigationsymbolstemplate{} % hides small icons in bottom right corner use to navigate the presentation +\setbeamertemplate{footline} +{ + +%\begin{beamercolorbox}[wd=\textwidth,ht=3ex,dp=1.5ex,leftskip=0.3cm,rightskip=0.3cm]{structure}% +%\usebeamerfont{page number in head/foot}% +%\hfill\insertframenumber% +%\end{beamercolorbox} + + \raisebox{5pt}{\makebox[\paperwidth]{ + \hfill\makebox[20pt]{ + \color{gray} + \scriptsize\insertframenumber} + } + } +} + +%\hspace*{5pt}} + +%--------------------------------------------------------------------- +% Add Paper using {\paper{}. begin{beawer} ... end{beamer} } +%--------------------------------------------------------------------- +\newcommand\paper[1]{ + \setbeamertemplate{footline} + { + \begin{beamercolorbox}[wd=\textwidth,ht=3mm,dp=03mm,leftskip=0.3cm,rightskip=0.3cm]{black}% + \usebeamerfont{page number in head/foot} + \color{gray} + [#1]\mbox{} + \color{gray} + \scriptsize + \hfill + \insertframenumber + \end{beamercolorbox}% + } +} \ No newline at end of file diff --git a/docs/slides/fml4s-26-09-2023/content-tex/conclusions-and-future-work.tex b/docs/slides/fml4s-26-09-2023/content-tex/conclusions-and-future-work.tex new file mode 100644 index 0000000..0d76282 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/content-tex/conclusions-and-future-work.tex @@ -0,0 +1,63 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Conclusions and future work} + +\begin{frame} + \frametitle{Table of Contents} + \tableofcontents[currentsection] +\end{frame} + +\subsection{Conclusions} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +Sciortino et al. in Computers in Biology and Medicine 2017 https://doi.org/10.1016/j.compbiomed.2017.01.008; +He et al. in Front. Med. 2021 https://doi.org/10.3389/fmed.2021.729978 +} +\begin{frame}{Conclusions} + +\begin{itemize} +\item Proposed a data-collection protocol for video and tracking data +\item Prototyped CNN and transformer based models for time-series classification of multimodal data +\end{itemize} + +\end{frame} +} + + +\subsection{Future work} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +Menghani, Gaurav. "Efficient deep learning: A survey on making deep learning models smaller, faster, and better." ACM Computing Surveys 55, no. 12 (2023): 1-37. +} +\begin{frame}{Future work} + +\begin{itemize} +\item Benchmarks for efficient architectures \\ (training for lightweight models and inference costs) +\item Make use of fpga-dev-kit for surgical skill assessment +\item Contribute to open public datasets +\end{itemize} + +\end{frame} +} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{GitHub repository} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +GitHub repository \url{https://github.com/mxochicale/rtt4ssa} +} +\begin{frame}{GitHub repository: rtt4ssa} +Real-time transformer-based models for surgical skills assessment + + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{github_repo_rtt4ssa/outputs/drawing-v00} + \end{figure} + +\end{frame} +} diff --git a/docs/slides/fml4s-26-09-2023/content-tex/experiments-demo-datasets.tex b/docs/slides/fml4s-26-09-2023/content-tex/experiments-demo-datasets.tex new file mode 100644 index 0000000..c019f63 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/content-tex/experiments-demo-datasets.tex @@ -0,0 +1,44 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Experiments and demo datasets} + +\begin{frame} + \frametitle{Table of Contents} + \tableofcontents[currentsection] +\end{frame} + +% \subsection{Clinical background} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +Khogali-Jakary et al., in Surgical Endoscopy (2020) +} + +\begin{frame}{Data collection} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{experiments-24-aug-2023-B/outputs/drawing-v00} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +Haralick, RM.; Shanmugam, K., "Textural features for image classification" in IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621. +%GLCM > https://youtu.be/wYZzpE1pPn0?feature=shared&t=784 +% National-Health-Service 2021. Screening for down’s syndrome, edwards’ syndrome and patau’s syndrome. \url{https://www.nhs.uk/pregnancy/your-pregnancy-care} +} + +\begin{frame}{Multimodal data}{Video[3-channels; 480Hx650W] and motion time-series data} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{experiments-24-aug-2023-A/outputs/drawing-v00} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} + + diff --git a/docs/slides/fml4s-26-09-2023/content-tex/introduction.tex b/docs/slides/fml4s-26-09-2023/content-tex/introduction.tex new file mode 100644 index 0000000..9af71ae --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/content-tex/introduction.tex @@ -0,0 +1,46 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Introduction} + +\begin{frame} + \frametitle{Table of Contents} + \tableofcontents[currentsection] +\end{frame} + + + +\subsection{Clinical background} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +ZUKETANG-Laparoscope-Laparoscopic-Simulator-Instruments; +Simendo simulator; +Laparo Analytic; +Gustavo A. Alonso-Silverio et al. in Surgical Education: Training for the Future 2018. +} + +\begin{frame}{Training Surgical Skills} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{main/outputs/drawing-v00.png} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} + + +\subsection{Research aims} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +%\paper{Wright-Gilbertson M. 2014 in PhD thesis} +\begin{frame}{Research aims} +\begin{itemize} +\item Design of reproducible workflow for data collection using multimodal sensors (e.g., USB video image and Bluetooth-based inertial sensors) +\item Investigate DL models with small memory and parameter size to enhance inference speed for surgical applications +% \item +\end{itemize} + +\end{frame} +} + diff --git a/docs/slides/fml4s-26-09-2023/content-tex/lightweight-transformer-based-models.tex b/docs/slides/fml4s-26-09-2023/content-tex/lightweight-transformer-based-models.tex new file mode 100644 index 0000000..feb2889 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/content-tex/lightweight-transformer-based-models.tex @@ -0,0 +1,69 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Lightweight transformer-based models} + +\begin{frame} + \frametitle{Table of Contents} + \tableofcontents[currentsection] +\end{frame} + +% \subsection{Clinical background} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +Menghani, Gaurav. "Efficient deep learning: A survey on making deep learning models smaller, faster, and better." ACM Computing Surveys 55, no. 12 (2023): 1-37. +% https://dl.acm.org/doi/full/10.1145/3578938 +} + +\begin{frame}{Efficient deep learning} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{lightweight-transformer-A/outputs/drawing-v00} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +% Li, Yanyu, Ju Hu, Yang Wen, Georgios Evangelidis, Kamyar Salahi, Yanzhi Wang, Sergey Tulyakov, and Jian Ren. "Rethinking vision transformers for mobilenet size and speed." arXiv preprint arXiv:2212.08059 (2022). +(a) Li et al. "Rethinking vision transformers for mobilenet size and speed." arXiv preprint arXiv:2212.08059 (2022). + +% EfficientFormerV2 Dec-2022 https://arxiv.org/abs/2212.08059 > https://github.com/snap-research/EfficientFormer +% Zhang, Wenqiang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu, and Chunhua Shen. "TopFormer: Token pyramid transformer for mobile semantic segmentation." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12083-12093. 2022. +(b) Zhang et al. "TopFormer: Token pyramid transformer for mobile semantic segmentation." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12083-12093. 2022. +% Topformer Apr-2022 https://arxiv.org/abs/2204.05525 > https://github.com/hustvl/TopFormer +} + + +\begin{frame}{Lightweight transformer-based models} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{lightweight-transformer-B/outputs/drawing-v00} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} + + +%OTHER RELEVANT REFERENCES +% CFPNet-M: A Light-Weight Encoder-Decoder +% Based Network for Multimodal Biomedical Image +% Real-Time Segmentation +% +% The +% CFPNet-M achieves segmentation results on all five +% medical datasets that are comparable to existing methods, +% yet require only 8.8 MB memory, and just 0.65 million +% parameters, which is about 2% of U-Net. Unlike other deep- +% learning segmentation methods, this new approach is +% suitable for real-time application: its inference speed can +% reach 80 frames per second when implemented on a single +% RTX 2070Ti GPU with an input image size of 256×192 pixels. + + +% Papa, Lorenzo, Paolo Russo, Irene Amerini, and Luping Zhou. "A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking." arXiv preprint arXiv:2309.02031 (2023). +% https://arxiv.org/abs/2309.02031 + diff --git a/docs/slides/fml4s-26-09-2023/content-tex/results.tex b/docs/slides/fml4s-26-09-2023/content-tex/results.tex new file mode 100644 index 0000000..b9e7431 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/content-tex/results.tex @@ -0,0 +1,41 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Preliminary Results} + +\begin{frame} + \frametitle{Table of Contents} + \tableofcontents[currentsection] +\end{frame} + +% \subsection{Clinical background} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +\url{https://keras.io/examples/timeseries/timeseries_classification_from_scratch} by hfawaz +} + +\begin{frame}{Timeseries classification with CNN model} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{results-timeseries_classification_from_scratch-24-aug-2023/outputs/drawing-v00} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +{ +\paper{ +\url{https://keras.io/examples/timeseries/timeseries_classification_transformer/} by Theodoros Ntakouris +} + +\begin{frame}{Timeseries classification with a Transformer model} + \begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{results-timeseries_classification_transformer-24-aug-2023/outputs/drawing-v00} + % \caption{The sonographer-probe-patient control system} + \end{figure} +\end{frame} +} diff --git a/docs/slides/fml4s-26-09-2023/gif-slides/pdf2gif.sh b/docs/slides/fml4s-26-09-2023/gif-slides/pdf2gif.sh new file mode 100644 index 0000000..1fb7da2 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/gif-slides/pdf2gif.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +# pdf2gif.sh +# chmod +x pdf2gif.sh + +# References +# http://phyletica.org/imagemagick/ + + + +# cp pdf to /slides-gif +echo '(1) cp pdf to /slides-gif' +cp ../*.pdf . + +# pdf2png +echo '(2) pdf2png' +convert -density 600 *.pdf -strip -resize @1048576 PNG8:slide-%02d.png + +# png2gif +echo '(3) png2gif' +convert -layers OptimizePlus -delay 75 slide-0?.png slide-??.png -loop 0 slides.gif +#convert -layers OptimizePlus -delay 75 slide-0?.png slide-1[01234].png -delay 300 slide-1[567].png -loop 0 slides.gif +# The options in the command above specify to spend 3/4 of a second on the first 15 slides, and then 3 seconds on the last three slides. + +# delete png images +echo '(4) remove *.png *.pdf' +rm *.png *.pdf diff --git a/docs/slides/fml4s-26-09-2023/lauch-slides.bash b/docs/slides/fml4s-26-09-2023/lauch-slides.bash new file mode 100644 index 0000000..f2104d9 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/lauch-slides.bash @@ -0,0 +1,8 @@ +countertimer=$1 +warningtime=$2 +warningtime_default=2 +countertimer_defaul=1 + +#pdfpc -w -d {$countertimer:=$countertimer_defaul} -l {$warningtime:=$warningtime_default} main.pdf +#pdfpc -w -d {$countertimer} -l {$warningtime} main.pdf +pdfpc -w -d 1 -l 2 main.pdf diff --git a/docs/slides/fml4s-26-09-2023/local-tex-build.bash b/docs/slides/fml4s-26-09-2023/local-tex-build.bash new file mode 100644 index 0000000..624bb20 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/local-tex-build.bash @@ -0,0 +1,3 @@ +make clean +make +make view diff --git a/docs/slides/fml4s-26-09-2023/preamble.tex b/docs/slides/fml4s-26-09-2023/preamble.tex new file mode 100644 index 0000000..b3c2d22 --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/preamble.tex @@ -0,0 +1,153 @@ +% utility packages +\usepackage{etoolbox} +\usepackage{multicol} +\usepackage{relsize} +\usepackage{fontawesome} + +% better text justifying +\usepackage{microtype} +% justify text inside list environment +% Ref: http://liam0205.me/2017/04/11/justifying-in-beamer-s-lists/ +\usepackage{ragged2e} +\makeatletter +\patchcmd{\itemize}{\raggedright}{\justifying}{}{} +\patchcmd{\beamer@enum@}{\raggedright}{\justifying}{}{} +\patchcmd{\@@description}{\raggedright}{\justifying}{}{} +\makeatother + +% table of content with numbers and justification +% https://tex.stackexchange.com/questions/188773 +\setbeamertemplate{section in toc}{\hspace*{1em}\inserttocsectionnumber.~\inserttocsection\par} +\setbeamertemplate{subsection in toc}{\hspace*{2em}\inserttocsectionnumber.\inserttocsubsectionnumber.~\inserttocsubsection\par} + +% math related packages +\usepackage{amsmath} +\usepackage[ruled,vlined]{algorithm2e} +\SetAlCapNameFnt{\scriptsize} +\SetAlCapFnt{\scriptsize} +\SetAlFnt{\scriptsize} + +% figure related packages +\usepackage{graphicx} +\usepackage[scale=2]{ccicons} +\usepackage{qrcode} +\usepackage{tikz} +\usepackage{tikzpagenodes} +\usetikzlibrary{positioning} + +% table related packages +\usepackage{array} +\usepackage{booktabs} +\usepackage{multirow} +\usepackage{colortbl} +\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}} + +% code highlight +\usepackage{listings} +%\usepackage{minted} +%\definecolor{mintedbg}{HTML}{E5E9F0} +%\setminted{autogobble,bgcolor=mintedbg,fontsize=\small} +%\setmintedinline{bgcolor=mintedbg,fontsize=\smaller} +%\newminted{bash}{} +%\newminted{latex}{} +%\newmintinline{bash}{} +%\newmintinline{latex}{} +%\newcommand{\texdoc}[2]{\href{#2}{\bashinline|texdoc #1|}} + +% hyperref setting +\hypersetup{ + unicode, + psdextra, + bookmarksnumbered=true, + bookmarksopen=true, + bookmarksopenlevel=3, + bookmarksdepth=4, + pdfcenterwindow=true, + pdfstartview={Fit}, + pdfpagemode={FullScreen}, + pdfpagelayout={SinglePage}, +} +\usepackage{bookmark} + +% beamer theme +\usetheme{metropolis} +\metroset{block=fill,numbering=fraction} + +% caption style +\usepackage{subcaption} +\setlength\abovecaptionskip{3pt} +\setbeamerfont{caption}{size=\scriptsize} +\renewcommand{\figurename}{Fig.} +\captionsetup{labelformat=empty,labelsep=none,textfont={bf,it}} + +% Ref: https://github.com/gpoore/minted/blob/master/source/minted.dtx +\newenvironment{latexexample} +{\VerbatimEnvironment\begin{VerbatimOut}[gobble=3]{example.out}}{\end{VerbatimOut}% + \begin{center} + \begin{minipage}{0.47\linewidth}% + \inputminted[resetmargins,fontsize=\scriptsize]{latex}{example.out}% + \end{minipage}% + \hspace{0.05\linewidth}% + \begin{minipage}{0.47\linewidth}% + \begin{framed} + \setlength{\parindent}{2em}% + \input{example.out}% + \end{framed} + \end{minipage}% + \end{center} +} + +\newenvironment{mathexample} +{\VerbatimEnvironment\begin{VerbatimOut}[gobble=3]{example.out}}{\end{VerbatimOut}% + \begin{center} + \begin{minipage}{0.47\linewidth}% + \inputminted[resetmargins,fontsize=\scriptsize]{latex}{example.out}% + \end{minipage}% + \hspace{0.05\linewidth}% + \begin{minipage}{0.47\linewidth}% + \begin{framed} + \[ \input{example.out} \] + \end{framed} + \end{minipage}% + \end{center} +} + +\newenvironment{mathexamples} +{\VerbatimEnvironment\begin{VerbatimOut}[gobble=3]{example.out}}{\end{VerbatimOut}% + \begin{center} + \begin{minipage}{0.47\linewidth}% + \inputminted[resetmargins,fontsize=\scriptsize]{latex}{example.out}% + \end{minipage}% + \hspace{0.05\linewidth}% + \begin{minipage}{0.47\linewidth}% + \begin{framed} + \directlua{ + local first = true + for line in io.lines('example.out') do + if first then + first = false + else + tex.print('\\newline ') + end + tex.print('$' .. line .. '$') + end + } + \end{framed} + \end{minipage}% + \end{center} +} + + + +%--------------------------------------------------------------------- +% Add Paper using {\paper{}. begin{beawer} ... end{beamer} } +%--------------------------------------------------------------------- +\newcommand\paper[1]{ + \setbeamertemplate{footline} + { + \begin{beamercolorbox}[wd=\textwidth,ht=3mm,dp=03mm,leftskip=0.3cm,rightskip=0.3cm]{black}% + \usebeamerfont{page number in head/foot} + (#1)\mbox{}\hfill\insertframenumber + \end{beamercolorbox}% + } +} diff --git a/docs/slides/fml4s-26-09-2023/slides.tex b/docs/slides/fml4s-26-09-2023/slides.tex new file mode 100644 index 0000000..f44f51b --- /dev/null +++ b/docs/slides/fml4s-26-09-2023/slides.tex @@ -0,0 +1,69 @@ +\documentclass[xcolor={dvipsnames},aspectratio=169,10pt]{beamer} +\usetheme{mx} +\usepackage{graphicx} +\graphicspath{{../../figures}} +%\input{preamble.tex} + + +\title{ +Towards lightweight transformer-based models with multimodal data for low-latency surgical applications +} +\subtitle{Fast Machine Learning for Science Workshop 2023} + +\author{ +Sujon Hekim, Stephen Thompson, and \\ +{\bf Miguel Xochicale, PhD} (\faTwitter @\_mxochicale \faGithub @mxochicale) +} + +\date{ +September 26, 2023; 17:00 - 17:15 +% \today +} + +\institute{ + Advanced Research Computing Centre and WEISS \\ + University College London + } + +\titlegraphic{ + \begin{tikzpicture}[overlay, remember picture] + \node[% + above right=0.35cm and -0.2cm of current page footer area.south west, + anchor=south west, + inner sep=0pt] {% + \usebeamerfont{footline} + \begin{tabular}{lm{.8\textwidth}} + \href{http://creativecommons.org/licenses/by/4.0/}{\ccby} & + This slices is licensed under a + \href{http://creativecommons.org/licenses/by/4.0/} + {Creative Commons ``Attribution 4.0 International''} license. + \par Get source of this slides and see further references + from \url{https://github.com/mxochicale/rtt4ssa}. + \end{tabular} + }; + \node[% + above left=0.35cm and 0cm of current page footer area.south east, + anchor=south east, + inner sep=0pt]{\qrcode[height=1.5cm]{https://github.com/mxochicale/rtt4ssa}}; + \end{tikzpicture} +} + +\begin{document} + +\maketitle + +\begin{frame} +\frametitle{Table of Contents} + \tableofcontents +\end{frame} + +\input{content-tex/introduction} +\input{content-tex/lightweight-transformer-based-models} +\input{content-tex/experiments-demo-datasets} +\input{content-tex/results} +\input{content-tex/conclusions-and-future-work} + +\maketitle + +\end{document} + diff --git a/rtt4ssa/data_analysis/A_analysis_of_data_from_multiple-files.ipynb b/rtt4ssa/data_analysis/A_analysis_of_data_from_multiple-files.ipynb new file mode 100644 index 0000000..98e4911 --- /dev/null +++ b/rtt4ssa/data_analysis/A_analysis_of_data_from_multiple-files.ipynb @@ -0,0 +1,1048 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "20ac01bc", + "metadata": {}, + "source": [ + "# Analysis of Video and Image Data for cropping and texture features\n", + "Author(s): Miguel Xochicale @mxochicale \n", + "Contributor(s): Sujon Hekim\n", + "\n", + "## History\n", + "* 17th May 2023: Add prototype\n", + "* 10th Aug 2023: Adds saving dataframes in cvs files\n", + "* 26th Sep 2023: Reads data from Thu-24-Aug-2023\n", + "\n", + "## Summary\n", + "\n", + "\n", + "### How to run the notebook\n", + "1. Go to repository path: `$HOME/repositories/`\n", + "Open repo in pycharm and in the terminal type:\n", + "```\n", + "git checkout main # or the branch\n", + "git pull # to bring a local branch up-to-date with its remote version\n", + "```\n", + "\n", + "2. Launch Notebook server. Go to you repository path: cd $HOME/repositories/ and type in the pycharm terminal:\n", + "```\n", + "mamba activate *VE \n", + "jupyter notebook --browser=firefox\n", + "```\n", + "which will open your web-browser.\n", + "\n", + "## References \n", + "1. https://stackoverflow.com/questions/45704999/how-to-convert-vector-wrapped-as-string-to-numpy-array-in-pandas-dataframe\n", + "2. https://github.com/YuxinZhaozyx/pytorch-VideoDataset/blob/master/datasets.py (Future work)\n", + "3. https://stackoverflow.com/questions/65446464/how-to-convert-a-video-in-numpy-array\n", + "4. https://matplotlib.org/stable/gallery/specialty_plots/mri_with_eeg.html#sphx-glr-gallery-specialty-plots-mri-with-eeg-py \n", + "5. https://www.researchgate.net/publication/326881329_Medical_image_security_enhancement_using_two_dimensional_chaotic_mapping_optimized_by_self-adaptive_grey_wolf_algorithm \n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "caf80206", + "metadata": {}, + "source": [ + "## Setting imports and datasets paths" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3190058c", + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-24T22:41:47.145415Z", + "start_time": "2023-06-24T22:41:47.136830Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/mxochicale/repositories/datasets/in2research2023/Thu-24-Aug-2023\n", + "PyTorch Version: 2.0.0.post200\n", + "pandas Version: 2.0.3\n", + "numpy Version: 1.25.2\n", + "cv2 Version: 4.8.0\n", + "skimage Version: 0.21.0\n" + ] + } + ], + "source": [ + "from rtt4ssa.utils.utils import video_to_tensor, compute_texture_array_and_plot\n", + "from rtt4ssa.utils.utils import data_frame_of_texture_analysis\n", + "from rtt4ssa.utils.utils import get_and_plot_imu_data_analysis\n", + "\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import cv2\n", + "import skimage\n", + "from typing import Tuple, List\n", + "\n", + "HOME_PATH = os.path.expanduser(f'~')\n", + "USERNAME = os.path.split(HOME_PATH)[1]\n", + "REPOSITORY_PATH='repositories/rtt4ssa'\n", + "\n", + "###########################\n", + "###SET DATA_PATH \n", + "#DATA_PATH='repositories/datasets/in2research2023/Thu-27-Jul-2023' \n", + "DATA_PATH='repositories/datasets/in2research2023/Thu-24-Aug-2023'\n", + "FULL_REPO_DATA_PATH = HOME_PATH +'/' + DATA_PATH\n", + "\n", + "## Printing Versions and paths\n", + "print(FULL_REPO_DATA_PATH)\n", + "print(f'PyTorch Version: {torch.__version__}')\n", + "print(f'pandas Version: {pd.__version__}')\n", + "print(f'numpy Version: {np.__version__}')\n", + "print(f'cv2 Version: {cv2.__version__}')\n", + "print(f'skimage Version: {skimage.__version__}')\n", + "\n", + "# ###########################\n", + "# ### experiments_13-Jul-2023\n", + "# AVI_FILE_01 = 'test01.avi'\n", + "# CSV_FILE_01 = 'test01.avi.csv'\n", + "# FULL_PATH_AND_AVI_FILE_01 = os.path.join(FULL_REPO_DATA_PATH , AVI_FILE_01)\n", + "# FULL_PATH_AND_CSV_FILE_01 = os.path.join(FULL_REPO_DATA_PATH , CSV_FILE_01)\n", + "# print(f'FULL_REPO_DATA_PATH: {FULL_REPO_DATA_PATH}')\n", + "# print(f'FULL_PATH_AND_CSV_FILE: {FULL_PATH_AND_CSV_FILE_01}')\n", + "# print(f'FULL_PATH_AND_AVI_FILE: {FULL_PATH_AND_AVI_FILE_01}')" + ] + }, + { + "cell_type": "markdown", + "id": "dfc94bcd", + "metadata": {}, + "source": [ + "# Reading video and plotting frames" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ec9f4372", + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-24T22:43:30.904233Z", + "start_time": "2023-06-24T22:43:27.564573Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " VIDEO_FEATURES\n", + " video_name=/home/mxochicale/repositories/datasets/in2research2023/Thu-24-Aug-2023/participant01/participant01-test01-rep01-1g-5mins.avi\n", + " Frame_height=480, frame_width=640 fps=120 nframes=51328 \n", + " \n", + " \n", + "num_frames: 9999\n", + "height: 480\n", + "width: 640\n" + ] + } + ], + "source": [ + "PARTICIPANTNN_TESTNN = 'participant01/participant01-test01-rep01-1g-5mins' #51328\n", + "# PARTICIPANTNN_TESTNN = 'participant01/participant01-test01-rep02-1g-5mins' #51178\n", + "# PARTICIPANTNN_TESTNN = 'participant01/participant01-test02-rep01-1g-5mins' #49183\n", + "# PARTICIPANTNN_TESTNN = 'participant01/participant01-test02-rep02-1g-5mins' #47577\n", + "# PARTICIPANTNN_TESTNN = 'participant01/participant01-test03-rep01-1g-5mins' #48688\n", + "# PARTICIPANTNN_TESTNN = 'participant01/participant01-test03-rep02-1g-5mins'#48789\n", + "\n", + "# PARTICIPANTNN_TESTNN = 'participant02/participant02-test01-rep01-1g-5mins'#49490\n", + "# PARTICIPANTNN_TESTNN = 'participant02/participant02-test01-rep02-1g-5mins'#49219\n", + "# PARTICIPANTNN_TESTNN = 'participant02/participant02-test02-rep01-1g-5mins'#48043\n", + "# PARTICIPANTNN_TESTNN = 'participant02/participant02-test02-rep02-1g-5mins'#49606\n", + "# PARTICIPANTNN_TESTNN = 'participant02/participant02-test03-rep01-1g-5mins'#48875\n", + "# PARTICIPANTNN_TESTNN = 'participant02/participant02-test03-rep02-1g-5mins'#48050\n", + "\n", + "\n", + "CSV_FILENAME_FOR_TEXTURE_ANALYSIS=PARTICIPANTNN_TESTNN+'.csv'\n", + "FULL_PATH_AND_AVI_FILE = os.path.join(FULL_REPO_DATA_PATH, PARTICIPANTNN_TESTNN+'.avi')\n", + "FULL_PATH_AND_CSV_FILE = os.path.join(FULL_REPO_DATA_PATH, PARTICIPANTNN_TESTNN+'.avi.csv')\n", + "\n", + "start_frame_number = 000\n", + "end_frame_number = 10000\n", + "total_number_of_frames = end_frame_number - start_frame_number\n", + "\n", + "\n", + "video, frames_timestam = video_to_tensor(FULL_PATH_AND_AVI_FILE, start_frame_number, end_frame_number)\n", + "\n", + "num_frames, height, width = video.shape\n", + "print(f'num_frames: {num_frames}')\n", + "print(f'height: {height}')\n", + "print(f'width: {width}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "51aca3e3", + "metadata": {}, + "source": [ + "# Generating texture_analysis_array and plotting frames and histograms" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ed983d4d", + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-24T22:43:46.518000Z", + "start_time": "2023-06-24T22:43:40.615453Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "frame_i: 0, timestamp 00:00:0.000\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# # This takes a while as it goes check each frame, so you might consider to comment this lines\n", + "# display_factor = 10000 \n", + "# texture_analysis_array = compute_texture_array_and_plot(video, frames_timestam, display_factor)\n" + ] + }, + { + "cell_type": "markdown", + "id": "56537774", + "metadata": {}, + "source": [ + "# Plotting texture analysis of all frames in the video" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "2e3d2e4d", + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-24T22:43:59.892073Z", + "start_time": "2023-06-24T22:43:58.621981Z" + } + }, + "outputs": [], + "source": [ + "df_texture_analysis = data_frame_of_texture_analysis(texture_analysis_array, start_frame_number, end_frame_number)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "879bd02e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " frame_i Contrast Correlation Dissimilarity Energy Homogeneity \\\n", + "0 0 3.308281 0.999091 0.866927 0.058461 0.698238 \n", + "1 1 3.308281 0.999091 0.866927 0.058461 0.698238 \n", + "2 2 3.308281 0.999091 0.866927 0.058461 0.698238 \n", + "3 3 3.308281 0.999091 0.866927 0.058461 0.698238 \n", + "4 4 3.308281 0.999091 0.866927 0.058461 0.698238 \n", + "... ... ... ... ... ... ... \n", + "9994 9994 8.506214 0.998824 1.322124 0.060407 0.632122 \n", + "9995 9995 8.506214 0.998824 1.322124 0.060407 0.632122 \n", + "9996 9996 8.506214 0.998824 1.322124 0.060407 0.632122 \n", + "9997 9997 8.506214 0.998824 1.322124 0.060407 0.632122 \n", + "9998 9998 8.506214 0.998824 1.322124 0.060407 0.632122 \n", + "\n", + " ASM Contrast_normalised Correlation_normalised \\\n", + "0 0.003418 -1.701527 1.143479 \n", + "1 0.003418 -1.701527 1.143479 \n", + "2 0.003418 -1.701527 1.143479 \n", + "3 0.003418 -1.701527 1.143479 \n", + "4 0.003418 -1.701527 1.143479 \n", + "... ... ... ... \n", + "9994 0.003649 2.334566 -1.795028 \n", + "9995 0.003649 2.334566 -1.795028 \n", + "9996 0.003649 2.334566 -1.795028 \n", + "9997 0.003649 2.334566 -1.795028 \n", + "9998 0.003649 2.334566 -1.795028 \n", + "\n", + " Dissimilarity_normalised Energy_normalised Homogeneity_normalised \\\n", + "0 -2.310331 1.148982 3.591494 \n", + "1 -2.310331 1.148982 3.591494 \n", + "2 -2.310331 1.148982 3.591494 \n", + "3 -2.310331 1.148982 3.591494 \n", + "4 -2.310331 1.148982 3.591494 \n", + "... ... ... ... \n", + "9994 2.091282 1.532533 -1.896305 \n", + "9995 2.091282 1.532533 -1.896305 \n", + "9996 2.091282 1.532533 -1.896305 \n", + "9997 2.091282 1.532533 -1.896305 \n", + "9998 2.091282 1.532533 -1.896305 \n", + "\n", + " ASM_normalised \n", + "0 1.115628 \n", + "1 1.115628 \n", + "2 1.115628 \n", + "3 1.115628 \n", + "4 1.115628 \n", + "... ... \n", + "9994 1.530592 \n", + "9995 1.530592 \n", + "9996 1.530592 \n", + "9997 1.530592 \n", + "9998 1.530592 \n", + "\n", + "[9999 rows x 13 columns]\n" + ] + } + ], + "source": [ + "print(df_texture_analysis)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0e37dd6", + "metadata": {}, + "source": [ + "## Reading and ploting csv files" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "df06effe", + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-24T22:44:12.264545Z", + "start_time": "2023-06-24T22:44:11.535541Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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Sample_numberEuler_angleEuler_val
00alpha76.263512
11alpha76.268105
22alpha76.277130
33alpha76.290985
44alpha76.309441
............
29595gamma-165.901642
29696gamma-165.882309
29797gamma-165.863464
29898gamma-165.835419
29999gamma-165.817444
\n", + "

300 rows × 3 columns

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" + ], + "text/plain": [ + " Sample_number Euler_angle Euler_val\n", + "0 0 alpha 76.263512\n", + "1 1 alpha 76.268105\n", + "2 2 alpha 76.277130\n", + "3 3 alpha 76.290985\n", + "4 4 alpha 76.309441\n", + ".. ... ... ...\n", + "295 95 gamma -165.901642\n", + "296 96 gamma -165.882309\n", + "297 97 gamma -165.863464\n", + "298 98 gamma -165.835419\n", + "299 99 gamma -165.817444\n", + "\n", + "[300 rows x 3 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ndf" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1c831cbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Sample_numberQuaternionQ_val
00q00.068419
11q00.068369
22q00.068327
33q00.068299
44q00.068282
............
39595q3-0.772171
39696q3-0.771887
39797q3-0.771593
39898q3-0.771242
39999q3-0.770964
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9595169288266419953802916928826641495121461212.785[0.06551546603441238, 0.27430564165115356, -0....[76.59014129638672, 29.883014678955078, -165.9...76.59014129.883015-165.9016420.0655150.274306-0.569400-0.772171
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axs[0,0].set_ylim((0,15)) \n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Correlation', ax=axs[0,1])\n", + "# # # axs[0,2].set_ylim((0.997,0.999)) \n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Dissimilarity', ax=axs[0,2])\n", + "# # axs[0,1].set_ylim((0.75,1.5)) \n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Energy', ax=axs[1,0])\n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Homogeneity', ax=axs[1,1])\n", + "# df_texture_analysis.plot(x='frame_i', y='ASM', ax=axs[1,2])\n", + "\n", + "# plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# ### Ploting all texture features \n", + "# fig, axs = plt.subplots(2,3, figsize=(12, 6))\n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Contrast_normalised', ax=axs[0,0] )\n", + "# # axs[0,0].set_ylim((0,15)) \n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Correlation_normalised', ax=axs[0,1])\n", + "# # # axs[0,2].set_ylim((0.997,0.999)) \n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Dissimilarity_normalised', ax=axs[0,2])\n", + "# # axs[0,1].set_ylim((0.75,1.5)) \n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Energy_normalised', ax=axs[1,0])\n", + "\n", + "# df_texture_analysis.plot(x='frame_i', y='Homogeneity_normalised', ax=axs[1,1])\n", + "# df_texture_analysis.plot(x='frame_i', y='ASM_normalised', ax=axs[1,2])\n", + "\n", + "# plt.show()\n", + "\n", + "\n", + "\n", + "# ### Ploting single texture feature\n", + "# ax = plt.gca()\n", + "# df_texture_analysis.plot(x='frame_i', y='ASM', ax=ax)\n", + "# # plt.ylim((0.003,0.005))\n", + "# plt.grid()\n", + "# plt.show()\n", + "\n", + "\n", + "# ax = plt.gca()\n", + "# df_texture_analysis.plot(x='frame_i', y='ASM_normalised', ax=ax)\n", + "# plt.grid()\n", + "# plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2474439c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4a0a184", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rtt4ssa/data_analysis/analysis_of_video_image_cropping_texture_features.ipynb b/rtt4ssa/data_analysis/Analysis_of_video_image_cropping_texture_features.ipynb similarity index 99% rename from rtt4ssa/data_analysis/analysis_of_video_image_cropping_texture_features.ipynb rename to rtt4ssa/data_analysis/Analysis_of_video_image_cropping_texture_features.ipynb index fdb5199..35c33d2 100644 --- a/rtt4ssa/data_analysis/analysis_of_video_image_cropping_texture_features.ipynb +++ b/rtt4ssa/data_analysis/Analysis_of_video_image_cropping_texture_features.ipynb @@ -10,7 +10,9 @@ "Contributor(s): \n", "\n", "## History\n", - "* 17th May 2022: Add prototype\n", + "* 17th May 2023: Add prototype\n", + "* 10th Aug 2023: Adds saving dataframes in cvs files\n", + "* 26th Sep 2023: Reads data from Thu-24-Aug-2023\n", "\n", "## Summary\n", "\n", diff --git a/rtt4ssa/data_analysis/analysis_of_data_from_multiple_files_plotting.ipynb b/rtt4ssa/data_analysis/B_analysis_of_data_from_multiple_files_plotting.ipynb similarity index 99% rename from rtt4ssa/data_analysis/analysis_of_data_from_multiple_files_plotting.ipynb rename to rtt4ssa/data_analysis/B_analysis_of_data_from_multiple_files_plotting.ipynb index ae2e7c1..4878904 100644 --- a/rtt4ssa/data_analysis/analysis_of_data_from_multiple_files_plotting.ipynb +++ b/rtt4ssa/data_analysis/B_analysis_of_data_from_multiple_files_plotting.ipynb @@ -12,6 +12,7 @@ "## History\n", "* 17th May 2023: Add prototype\n", "* 10th Aug 2023: Adds saving dataframes in cvs files\n", + "* 26th Sep 2023: Reads data from Thu-24-Aug-2023\n", "\n", "## Summary\n", "\n", @@ -51,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "id": "3190058c", "metadata": { "ExecuteTime": { @@ -64,12 +65,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/mxochicale/repositories/datasets/in2research2023/Thu-27-Jul-2023/plotting_data/\n", - "PyTorch Version: 2.0.0.post200\n", - "pandas Version: 2.0.3\n", - "numpy Version: 1.25.2\n", - "cv2 Version: 4.8.0\n", - "skimage Version: 0.21.0\n" + "/home/mxochicale/repositories/datasets/in2research2023/Thu-24-Aug-2023/plotting_data/\n", + "pandas Version: 2.0.3\n" ] } ], @@ -88,18 +85,14 @@ "\n", "###########################\n", "###SET DATA_PATH \n", - "DATA_PATH='repositories/datasets/in2research2023/Thu-27-Jul-2023' # DATA_PATH='scripts/sensor-fusion'\n", + "# DATA_PATH='repositories/datasets/in2research2023/Thu-27-Jul-2023' \n", + "DATA_PATH='repositories/datasets/in2research2023/Thu-24-Aug-2023'\n", "FULL_REPO_DATA_PATH = HOME_PATH +'/' + DATA_PATH\n", "PLOTTING_DATA_PATH=HOME_PATH+'/'+DATA_PATH+'/plotting_data/'\n", "\n", "## Printing Versions and paths\n", "print(PLOTTING_DATA_PATH)\n", - "print(f'PyTorch Version: {torch.__version__}')\n", - "print(f'pandas Version: {pd.__version__}')\n", - "print(f'numpy Version: {np.__version__}')\n", - "print(f'cv2 Version: {cv2.__version__}')\n", - "print(f'skimage Version: {skimage.__version__}')\n", - "\n" + "print(f'pandas Version: {pd.__version__}')\n" ] }, { diff --git a/rtt4ssa/data_analysis/analysis_of_data_from_multiple-files.ipynb b/rtt4ssa/data_analysis/analysis_of_data_from_multiple-files.ipynb deleted file mode 100644 index 44f2d8d..0000000 --- a/rtt4ssa/data_analysis/analysis_of_data_from_multiple-files.ipynb +++ /dev/null @@ -1,995 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "20ac01bc", - "metadata": {}, - "source": [ - "# Analysis of Video and Image Data for cropping and texture features\n", - "Author(s): Miguel Xochicale @mxochicale \n", - "Contributor(s): Sujon Hekim\n", - "\n", - "## History\n", - "* 17th May 2023: Add prototype\n", - "* 10th Aug 2023: Adds saving dataframes in cvs files\n", - "\n", - "## Summary\n", - "\n", - "\n", - "### How to run the notebook\n", - "1. Go to repository path: `$HOME/repositories/`\n", - "Open repo in pycharm and in the terminal type:\n", - "```\n", - "git checkout main # or the branch\n", - "git pull # to bring a local branch up-to-date with its remote version\n", - "```\n", - "\n", - "2. Launch Notebook server. Go to you repository path: cd $HOME/repositories/ and type in the pycharm terminal:\n", - "```\n", - "mamba activate *VE \n", - "jupyter notebook --browser=firefox\n", - "```\n", - "which will open your web-browser.\n", - "\n", - "## References \n", - "1. https://stackoverflow.com/questions/45704999/how-to-convert-vector-wrapped-as-string-to-numpy-array-in-pandas-dataframe\n", - "2. https://github.com/YuxinZhaozyx/pytorch-VideoDataset/blob/master/datasets.py (Future work)\n", - "3. https://stackoverflow.com/questions/65446464/how-to-convert-a-video-in-numpy-array\n", - "4. https://matplotlib.org/stable/gallery/specialty_plots/mri_with_eeg.html#sphx-glr-gallery-specialty-plots-mri-with-eeg-py \n", - "5. https://www.researchgate.net/publication/326881329_Medical_image_security_enhancement_using_two_dimensional_chaotic_mapping_optimized_by_self-adaptive_grey_wolf_algorithm \n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "caf80206", - "metadata": {}, - "source": [ - "## Setting imports and datasets paths" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3190058c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-06-24T22:41:47.145415Z", - "start_time": "2023-06-24T22:41:47.136830Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/mxochicale/repositories/datasets/in2research2023/Thu-27-Jul-2023\n", - "PyTorch Version: 2.0.0.post200\n", - "pandas Version: 2.0.3\n", - "numpy Version: 1.25.1\n", - "cv2 Version: 4.8.0\n", - "skimage Version: 0.21.0\n" - ] - } - ], - "source": [ - "from rtt4ssa.utils.utils import video_to_tensor, compute_texture_array_and_plot\n", - "from rtt4ssa.utils.utils import data_frame_of_texture_analysis\n", - "from rtt4ssa.utils.utils import get_and_plot_imu_data_analysis\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "import matplotlib.pyplot as plt\n", - "import cv2\n", - "import skimage\n", - "from typing import Tuple, List\n", - "\n", - "HOME_PATH = os.path.expanduser(f'~')\n", - "USERNAME = os.path.split(HOME_PATH)[1]\n", - "REPOSITORY_PATH='repositories/rtt4ssa'\n", - "\n", - "###########################\n", - "###SET DATA_PATH \n", - "DATA_PATH='repositories/datasets/in2research2023/Thu-27-Jul-2023' # DATA_PATH='scripts/sensor-fusion'\n", - "FULL_REPO_DATA_PATH = HOME_PATH +'/' + DATA_PATH\n", - "\n", - "## Printing Versions and paths\n", - "print(FULL_REPO_DATA_PATH)\n", - "print(f'PyTorch Version: {torch.__version__}')\n", - "print(f'pandas Version: {pd.__version__}')\n", - "print(f'numpy Version: {np.__version__}')\n", - "print(f'cv2 Version: {cv2.__version__}')\n", - "print(f'skimage Version: {skimage.__version__}')\n", - "\n", - "# ###########################\n", - "# ### experiments_13-Jul-2023\n", - "# AVI_FILE_01 = 'test01.avi'\n", - "# CSV_FILE_01 = 'test01.avi.csv'\n", - "# FULL_PATH_AND_AVI_FILE_01 = os.path.join(FULL_REPO_DATA_PATH , AVI_FILE_01)\n", - "# FULL_PATH_AND_CSV_FILE_01 = os.path.join(FULL_REPO_DATA_PATH , CSV_FILE_01)\n", - "# print(f'FULL_REPO_DATA_PATH: {FULL_REPO_DATA_PATH}')\n", - "# print(f'FULL_PATH_AND_CSV_FILE: {FULL_PATH_AND_CSV_FILE_01}')\n", - "# print(f'FULL_PATH_AND_AVI_FILE: {FULL_PATH_AND_AVI_FILE_01}')" - ] - }, - { - "cell_type": "markdown", - "id": "dfc94bcd", - "metadata": {}, - "source": [ - "# Reading video and plotting frames" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ec9f4372", - "metadata": { - "ExecuteTime": { - "end_time": "2023-06-24T22:43:30.904233Z", - "start_time": "2023-06-24T22:43:27.564573Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - " \n", - " VIDEO_FEATURES\n", - " video_name=/home/mxochicale/repositories/datasets/in2research2023/Thu-27-Jul-2023/participant01/participant01-test04.avi\n", - " Frame_height=480, frame_width=640 fps=120 nframes=7923 \n", - " \n", - " \n", - "num_frames: 999\n", - "height: 480\n", - "width: 640\n" - ] - } - ], - "source": [ - "PARTICIPANTNN_TESTNN = 'participant01-test05'\n", - "\n", - "CSV_FILENAME_FOR_TEXTURE_ANALYSIS=PARTICIPANTNN_TESTNN+'.csv'\n", - "FULL_PATH_AND_AVI_FILE = os.path.join(FULL_REPO_DATA_PATH, 'participant01/'+PARTICIPANTNN_TESTNN+'.avi')\n", - "FULL_PATH_AND_CSV_FILE = os.path.join(FULL_REPO_DATA_PATH, 'participant01/'+PARTICIPANTNN_TESTNN+'.avi.csv')\n", - "\n", - "start_frame_number = 000\n", - "end_frame_number = 1000\n", - "total_number_of_frames = end_frame_number - start_frame_number\n", - "\n", - "\n", - "video, frames_timestam = video_to_tensor(FULL_PATH_AND_AVI_FILE, start_frame_number, end_frame_number)\n", - "\n", - "num_frames, height, width = video.shape\n", - "print(f'num_frames: {num_frames}')\n", - "print(f'height: {height}')\n", - "print(f'width: {width}')\n" - ] - }, - { - "cell_type": "markdown", - "id": "51aca3e3", - "metadata": {}, - "source": [ - "# Generating texture_analysis_array and plotting frames and histograms" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ed983d4d", - "metadata": { - "ExecuteTime": { - "end_time": "2023-06-24T22:43:46.518000Z", - "start_time": "2023-06-24T22:43:40.615453Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "frame_i: 0, timestamp 00:00:0.000\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_factor = 500 \n", - "texture_analysis_array = compute_texture_array_and_plot(video, frames_timestam, display_factor)\n" - ] - }, - { - "cell_type": "markdown", - "id": "56537774", - "metadata": {}, - "source": [ - "# Plotting texture analysis of all frames in the video" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2e3d2e4d", - "metadata": { - "ExecuteTime": { - "end_time": "2023-06-24T22:43:59.892073Z", - "start_time": "2023-06-24T22:43:58.621981Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "df_texture_analysis = get_and_plot_data_frame_of_texture_analysis(texture_analysis_array, total_number_of_frames)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "879bd02e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " frame_i Contrast Correlation Dissimilarity Energy Homogeneity \\\n", - "0 0 4.623543 0.998238 0.860221 0.066374 0.749130 \n", - "1 1 5.164254 0.998033 0.991047 0.060407 0.705082 \n", - "2 2 5.164254 0.998033 0.991047 0.060407 0.705082 \n", - "3 3 5.164254 0.998033 0.991047 0.060407 0.705082 \n", - "4 4 5.164254 0.998033 0.991047 0.060407 0.705082 \n", - ".. ... ... ... ... ... ... \n", - "994 994 6.035873 0.998382 1.079457 0.058355 0.685384 \n", - "995 995 6.035873 0.998382 1.079457 0.058355 0.685384 \n", - "996 996 5.943225 0.998386 1.091875 0.057412 0.681350 \n", - "997 997 5.943225 0.998386 1.091875 0.057412 0.681350 \n", - "998 998 5.943225 0.998386 1.091875 0.057412 0.681350 \n", - "\n", - " ASM \n", - "0 0.004406 \n", - "1 0.003649 \n", - "2 0.003649 \n", - "3 0.003649 \n", - "4 0.003649 \n", - ".. ... \n", - "994 0.003405 \n", - "995 0.003405 \n", - "996 0.003296 \n", - "997 0.003296 \n", - "998 0.003296 \n", - "\n", - "[999 rows x 7 columns]\n" - ] - } - ], - "source": [ - "print(df_texture_analysis)\n" - ] - }, - { - "cell_type": "markdown", - "id": "d0e37dd6", - "metadata": {}, - "source": [ - "## Reading and ploting csv files" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "df06effe", - "metadata": { - "ExecuteTime": { - "end_time": "2023-06-24T22:44:12.264545Z", - "start_time": "2023-06-24T22:44:11.535541Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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T26ojxJFAIGhztGSlWnUUCgVhXmH1Pv+y8Mvk9ZHcI2y8uJG8irwaFVprL6y1C3Gk0WmY9Pcks32jYkbhomz+Lz1Tz5GoWLN/TIfOBnsEA0bPEUjz1QKdAms8zxYIcSQQCNocpmNDYn1jL3G2bfFy8eLy6MvZmLIRgIfXPWzxvOTiZLLKsszKo23BroxdZtsKFGbJ6M2JWc6REEc1OJ57nJSSFIZFDSO1OJWCygL6hfWTqyFbmtwKE8+Rh+Q58nY1iqMSVYmZ4LUlrSbnKCkpiXvvvZf4+Hg8PDzo0KEDL7/8MiqVyuy8Q4cOMXz4cNzd3YmJieG9996zkcUCgcAWlGvK5XETHQM6NsvYEGvz+tDXLe4fGzuWe3veK2+b5iTZiuXnlsvrnkE9+WXiLySEJFjltQyl/CDCatU5kHWAKcumMHPDTAYsHMB1S65j2qppXLP4Go7mHLWJTZY8Rz4uPvK+YrX9VKy1GnF04sQJdDod33zzDUePHuXjjz/m66+/5rnnnpPPKSoqYty4cbRr1469e/fy/vvv88orr/Dtt9/a0HKBQNCSnMk3jg1p6ZBaYwlwD2DzLZsZ126cvG9K1ym8N+I9+oT2kfcdyD5gA+uM6PQ6NqVsAsDT2ZMfr/yRHsE9rPZ6IiHbMnq9ng/2fIBGr6lxLKkoiVuX3cq4P8ax9sJaNDoNOr1ObkxqTbLLsuW1LI5cjeKoRGU/Sdn2f8tUTyZMmMCECRPk7fbt23Py5Em++uorPvjgAwAWLlyISqVi7ty5uLq60qNHDw4cOMBHH33E/fffbyvTBQJBC2KLSrXmwN/dnw9HfkixqhgvFy+547apV+Zg1sFan6/RaVidtJowrzCrVI6BFMYxjPIYFDEINyc3q7yOAdP+VKKU38iaC2s4mF37ewEgvTSdxzc8TqB7IHkVeSgVSvqH9SfcK5x+Yf0YFDGIwspCcspzCPUMpXNA5waH44pVxejRywUH6aXp8jHDgObqYTV7odWII0sUFhYSGGi8s9i+fTsjRozA1dVV3jd+/Hjeffdd8vPzCQgIqHGNyspKKisr5e2iIvvqxSAQCBqGrSrVmgvTO22QvEpxvnEkFSVxIu8EGp3GYqjw+S3Ps/y8FPL69opvGRw5uNltM3iNAIZGDW3261fHRemCj6sPxapi4Tmq4kTeCZ7Z/Iy8/cHlH6DX64n3i0ePnvtX3282i86Qq6XT6+R8sSVnl1i8du+Q3lwVfxWBHoHsydiDi9IFtU7Nnow95FRIIuqtYW/RNbAra5PX8vyW56nUVjKtxzQe7vOwLI5cla7GajU7Dau1WnF05swZ/ve//8leI4CMjAzi480brYWFhcnHLImjt99+m1dffdW6xgoEghbDtFLNkTxHddEpoBNJRUmodCpSilNo59vO7C7/ZN5JWRgBPPjfg+y8fSeuTq6WLtcoVFoVf5z6A5CSsIdHDW+2a9dFkHuQEEdV5Ffkc8/Ke+QQWf+w/oxrN87svbDp1k2UqcvYlLqJpWeXsjN9JxXa+o1eOZh9sE6PVGFlITf9exMJwQkcyjkk7//u8Hfo0ZNWkgZAhHeEbJO9eo7sPufo2WefRaFQ1Pk4ceKE2XNSU1OZMGECN910E/fdd1+TXn/27NkUFhbKj4sX7bMTrUAguDR6vZ7T+dIA1zDPMLkRnaNjOpbj6sVXk/BTgtmXmEG0GNDoNXy458NmtWHZuWVklWcBUul+hHdEs16/NgwVayXqElRa1SXObt38e/ZfM+/La0NesxgK83TxZELcBD4f8zlbb9vKX9f8xbbbtrHgqgVcGX8lAN2DunNl/JVM7jiZnkE9G2SHqTAyMOfwHMo0ZYAxpAbmnlB7GiFi956jWbNmcffdd9d5Tvv27eV1Wloao0aNYsiQITUSrcPDw8nMzDTbZ9gODw/HEm5ubri5WTduLhAIWob00nT5y8MRQ2q10cGv5syye1fdyyejPmHRyUVsuLihxvGfT/zM9Z2ub/Lv4WjuUd7Z+Y5ZMvi0ntOadM2GUL1irSE9pFoTer2ev07/JW9/PfZrYnxjLvk8VydXOgV0AqSwWe+Q3rw3omYVd055DquSVlFYWYhCoSDALYAgjyB8XX1xUbpQrCrmn7P/sD55PRq9BhelC0/2f5LfTv7G2cKzZtdq72f8zq7e58hesHtxFBISQkhISL3OTU1NZdSoUfTr148ffvgBpdLcMTZ48GCef/551Go1Li5SQ7I1a9bQpUsXiyE1gUDQurBl80drYkngVGoreeC/B8z2jY4ZjZ+bH3+f+RuAG/+9kSf7P8md3e9sVO+bUnUpj617jMwy401n39C+NQboWhOzLtmVbVccHco5JIuQvqF9mz3nK9gjmNu73V7nOZfHXE5eRR4n8k7Qyb8TIZ4heLp48uLWF83OM/3bE2E1K5OamsrIkSOJjY3lgw8+IDs7m4yMDDIyMuRzpkyZgqurK/feey9Hjx5l0aJFfPrpp8ycafuusgKBwPo4aqXapYj3i2dG79rnsxl4MPFBbu16q9m+D/Z8QMJPCby09SVK1aX1fs3/LvzHoJ8HmQkjBQoe7mO5SaW1EF2yJX498au8vq7TdTazI9A9kCGRQwjxlJwaY2LHEOph3pjUVDybJmTb03y1ViOO1qxZw5kzZ1i7di3R0dFERETIDwN+fn6sXr2a8+fP069fP2bNmsVLL70kyvgFgjaCaaVa58DWI44AHkp8iMN3HebwXYd5ZfArZseubn81O6fspEtgF7oHdWfu+Lk1nv/3mb8Z9PMgOWm2Ls7kn2HWxllm+8bGjuXrK742G3fSErT1+WqFlYW8tfMtlp5bCkhz7Ez7YdkaH1cfFk5cKIfuBoQPMA+rmXiORM6RFbj77rsvmZsEkJCQwObNm61vkEAgsDuO5R4DwN3JnVgf+x4b0hRu6HwDUT5RqLQqBkcMrjHM87Lwy1h/83pG/TaqxnPnHZ3H7IGz67z+4jOL5UaaAHf3uJtZ/WfV8Qzr0dZHiHy09yOzXKPpvabj6eJpQ4tqEu4VzqJJiziRe4LuQd3NQrheLl4oUKBHb1dhtVYjjgQCgaAucstzSS1JBaRKHEcYG9IUBkUMqvN4sEcwB+88yPG84xzNOcrrO6QRJXsz99b5PL1ez5oLa+TtyR0n80DvB+p4hnUJdGv5LtmZpZmM/WMsHs4e/DD+B6t2Aa+LgooC/j37r7x9W9fbmNp9qk1suRQuShd6hfSqsV+pUOLl4kWJukSE1QQCgaClOZxzWF5ba9aXo6FUKOkR1IObu9xMR/+OAJwtOFvnKIkTeSdIK5VCb0Mih/D60Ndt6qloac/RybyTjPtTCluVa8p5YesL6PV6q7+uJZaeW4papwbg+k7X89zA5xxS9BtCa/YUVhPiSCAQtAkOZRt7r/QKrnkH29aJ95Ma5Gr0mjrzjnZn7JbXI2NGWtusS9KS89XOF57n7pV3m4UUzxScYU/mnhYXSCqtinnH5snbd3a/s0VfvzkxlPPbk+fI8SSmwO5Ye2EtuRW5XNfxuhq5DQKBvWDamE54jmoS5xsnr5OKkoj1tZyTdTzvuLzuHdLb2mZdkuql/NZkwbEFFr/A71l1DwCP9nmU6b2mc67wHKGeoTVGvVRqK9mZvpP0knRCPEPQI431iPGJIbssmzMF0lDktJI0/Nz8GB49XJ5LVp3fT/1ORqlUjT0ieoRZI1BHw/AzVmorUWlVzdq5vbEIcSRoEkvPLWX2Zil587P9nzEmdgzt/doztt1YoryjbGydQGDE0Bk70D2QMM+22QunLuL84uT1+cLzjIgeYfG8E3nSRAJnhbMcirMlrk6ueLt4U6IusarnSKvTsjZ5LQBuTm6svnE196++36wC8rP9n/HZ/s8ku5SufDzqY4ZEDiG5KJnDOYf56uBXct5bfQj2COb7cd/T3r+92f49GXv4ZO8n8vZDiQ814SezPdUr1oI8gmxojYQQR4JGodfrWXB8Ae/tNnZSLawslKsmvj30LX9f+zehnqG1XUIgaDGKVEVyPkp7v/aNanjY2onxMXZTNngkqqPVablQdAGAdr7t7OIOHyTvUYm6xKo5RweyD5BbkQvA0MihBLoH8u24b3lp60tsTNlY43yVTsVTG58i2COY5OLkRr1mTnkO96+5nyWTl8h5XWcLzvLIukfkeWiT2k+ie1D3Rv5U9oFpl+wSdYkQRwLH5dN9n/L9ke/N9ikVSjkWX6Qq4tcTv/Jo30dtYZ5AYMaFwgvyup1vOxtaYr+EeBgnEWSXZ1s8J7MsU04Ari3sZgsC3AO4WHyRIlURap0aF2Xzh/f3Ze6T16NipRYIge6BfD7mcwA+2vMRC48vRIdOTmgv05TVEEYJwQmMjBlJhbYCJ4UTp/NPk1aaRrhnONE+0bgoXcgpz+Gfs/8A0u98yC9DGBQxiEkdJrH07FI5tDcsahivDnH8weim4Ud7KecX4kjQYLLLspl31JgIODhiMB+O/JCCygK+P/w9f57+E4Cd6TttZaJAYEZSUZK8NiQeC8wxdDQG6W/cEqZf9PbUJ8qsS3Z5nlVGiJjmWiUE18xZm9l/Jk/0ewKFQkFBRQGT/5kse5pAGsY7rec0EkMS6+W5vLPHndyw5AYAtHotW9O2sjVtq3w8yjuKj0Z+ZDfeu6Zgj/PVRLWaoMH8c/YfNHrpzuj2brfz7bhv8XH1IcYnhleGvCJ/+RzLPUa5ptyWpgoEgLk4Ep4jy7g5ueHn5gdAVlmWxXMuFl+U16ZhOFtjmt/YkJyehmDIWXNzcqv1PWQQPf7u/swZN4dewb0I8Qjh/RHv89noz+gT2qfeId3OAZ25P6H26Q0zes/Aw9mjgT+FfWKP89WE50jQIPR6Pf+c+UfetjSIMDEkkfOF59HoNZzIO0Gf0D4taaJAUANDngyYV2UJzAnxCKGwspDs8mz0en2NL3J7FUfR3tHy+mLxRfqG9W3W66u1avlnj/eLx0npdMnndAzoyM8Tf7b4e6wvDyc+TKRXJCfzT/L7qd/lcF23wG5c3f7qRl3THjGtyCusLLShJUaE50jQIHZm7JTvwvuH9bf4AdkjyNgt1jCuQSCwJUmFSYBUYRXlI6ooa8OQd1SpraRIVVTjeEpxiry2K3HkYxRHKSUpdZzZOPZn7Uer1wI0uGS+Kcn/CoWCGzrfwHMDn2PNjWu4ufPN3NDpBr4a+1W9BJqjEORuTMC2djuG+iI8R4IG8fvJ3+X1LV1usXiOaSt9IY4E5ZpyDmUfoltQt1p7tlgT0worQ8KrwDLV844MYTYDBu+Jk8KJcO/wFrWtLkyFmqmAay7WX1wvry+PvrzZr18fgj2CeXHwizZ5bWsT6GHMGcstz63jzJZDiCNBvcmryGPdxXWAlAA5JnaMxfM6BXTCWeGMRq/haM7RljRRYGfsy9zH4+sfJ78yn2CPYH6+6mcivCNa1IaUkhS57Nke+vLYM6atN7LKsugYYPx96fV6WRxFeEXYlciM9I6U19YQR1tStwCSKBwaNbTZr9/WMUuot5PhwSKsJqg3/579V455X9vh2lq7Ybs5udEpoBMA54vOU6YuazEbBfbDltQtPLT2IdlNnlOew3NbnkOr07aoHafyT8lrw/tSYBnTcv7MskyzY/mV+ZSqSwHzMJY94OHsIdtumhfVHKSWpMqpBL1DetvE+9naMRVHphV+tkR4jgSXxNDw8YM9H8j7ru90fZ3P6R7UneN5x9HpdZzMP2m/SdnJO2HP9xA3DPpMBdEcsFn4+uDXfHHgixr792Tu4adjPzGt57RmfT2tTsvR3KMcyj6EHj255bnE+sZSqa006yTcLbBbs75uayPcyxgqq94I8lzBOXltj+0Qon2iyS7PJrcilzJ1WbMNw92aaiyfF14j6+Dt4o2r0hWVTmU3niMhjgSX5LP9nzHn8Bx5e1DEILNRA5boHtRd7nd0LPeYfYqj1L0wfzKoy+DQIkjaCld/Ci7utrbModmUsslMGPm4+jCz30xe2/4aevR8tPcjTuSd4I2hbzRpFl+pupSd6Ts5knOE30/9TkFlQZ3nKxVK+oX3a/TrtQVMPULVPTDnCo3iqL2f+TgLeyDGJ4b9WfsBydvTXF5CM3EUKcSRNVAoFAR6BJJRmiFyjgSOQW55LnOPzJW3g9yDmD1g9iWfZ1qxZpd5R1knYOHNkjAycOhXyD0NtywE35bNi2kt6PQ6Ptv3mbw9MnokLw1+iRDPEI7mHuWPU38AsPz8clRaFR+N/Ag9epSKuiP8OeU5HMw6yI70HVwoukB+ZT6n80/LFUT1YXLHySIkcglMS+IvFF8wO2bv4sjU9pTilGYRR2qdmp0ZUjPbALcAugUJz6O1iPGJIaM0g7yKPPIq8sxCbbZAiCNBnexI3yGPBBkVM4oPL/+wXnf7nQI64ax0RqPT2F/FWt55+OkaKMsx7lMoQa+TvElzxsD0/8A3svZr2ILDf8CWT8A/FrxDIbA99LkDPG37IWIgpzyHLw98KQ/i7BnUk89GfyaXMj+c+DAn805yOOcwAP8l/8d9q+/jcM5hOgd05s1hb9YYSXGu8BzfH/6eJWeXXPL1A90DuT/hftyd3DldcBpfV19CPKW+Pa5KV27temsz/8StD08XT6K8o0gtSeVE7gkqtZW4ObkByP9uIDUotDfq8no1loNZB+U8qyFRQy4p4gWNp0tAF3Zn7AbgRO4JhkQNsak9QhzZCRqdhpe3vcy1Ha5lQMQAW5sjsy1tm7y+vdvt9Q6DuDq50sm/E8fzjstJ2c2VA9AkKkvg1ylQUpVsGpEId/4DBcnS/sKLUJQKP98Ckz6GyL6gtNIHol5f/xynlL3w9wzQqSHT+CXF9s9h8pfQcSw55Tn4u/njrGz5P+v9Wfu5b/V9VGor5X0PJj5o1uMlyCOInyf+zPJzy3lm8zMA8l35gewDTPx7IsEewYR4hBDnG0daaRoHsw/W+prt/drTOaAznQI6Ee4VzuXRl9coPRc0nMvCLyP1TCoqnYq9mXsZEjkEtVbNidwTgNRh3N/d37ZGWsAavY5Mx3WIkJp16RncU17/33//x7UdruXhPg+b5cG1JEIc2Qlrk9ey5OwSlpxdwutDX2dyx8m2Ngm9Xi+LIw9njwbnDZkmZZ/IO9HsXWsbjFYNi2dAVpUnK6gTTP0bPPylx33r4LsxUJgMGYckD5JXKPhUzWlSl0NUf0jdAzoNDH8SovuDiwf4RkF982eOLoZVz0NRCrj7Q4fRMOp5CK6lzLw8H/64WxJG1SnJRL3gBt7qOog/KtMI8wzjo5EfEesTi7PS2awtf7Oj14OqBPYv5JOT31OJJIwUwD1RYxgWMdji0ybET+D7I9+bVZEZyCnPIac8x2yOFYCPiw/j4sbR0b8jfcP6Eu8X32pGJ9gbw6KGsfjMYgDWJ69nSOQQTuafRKVTAdAruJcNrasda/Q6Ms03Ghxp+f0saB4GRw5GgQI9ekAaU3V9p+uFOGrrrEpaJa/f2vkWkV6RNvUgzTs6z6w67bLwyxo84LB6UrZNxVFpDvx2F1yQ+pXg5gu3/WIekvIOpfCmOfz673Q6FmczpqwcSrOkh4HcM8b1koeNa49ASJwCgx+uPV9JXQEb3oatnxj3VRTA0b/g+BLoNw1GPgtewcbjBcmw4Abp/yB5uiZ9DFoVuo3vMT97Fwv8fMioTAOk8uvbl0sjXbxdvHlvxHsMjx7eoF9Vvcg6Lv0+c05y0sWFfdHSz+yp07EoNYO48z/AkbXQ6ybofi1E9JafqlQoefqyp5m+ejoAHf3a4+vmz76sfTVext/Nn3t73suNnW+0rtATyAyLGoaL0gW1Ts36i+t5buBz7M3cKx+3V3EU5B6Eu5M7FdqKZvEcmYr0boHdCPYIvsQzBE0h0D2Qy6MvZ0PKBnlfTnlO7U+wMkIc2QnvjXiP57Y8x4rzKyjXlHPv6nsJ9ghmfNx4pvea3qJ/mOuT15sJI4AhkQ2P/9pNp+zyAph3tdFjpHSBG76HYPOETZ1ex6OHv2SfJ+AZwrM6f26/cBiq7mTqfo08KcS1e47kURrysORRMnB2PSybCXnGpFYC4iWvUEWB5Ina/R0c/l0SWR1GS8d/nQI5VR4Wdz+4Zb6UcwR83XMMXx06XatJJeoSnvrvIZbqIwjucIWUo+QbBVH9wKkJf/rqCvjtLvQ5J/nVx5u3go0C8/G8AuI0Ui8sCpJh84fSI/F2uOxeKUypUDCwKJ9nC0rIRMcdeafxC+rMfJUrWv9Yrhg6m5SCcwSqK+ne4SqU3iG1GCKwBl4uXgyMGMiW1C1klmVyLPcY/579Vz4+MGKgDa2rHYVCQbRPNGcKzpBanIpOr2tSjtD2tO3y2m5K+AuSpfYjncaCR4CtrWl23h3xLnsy9+CidCHYI9hsoHBLI8SRneCsdOa1Ia9Rpi5jY8pGQFLNC48v5N+z/zJn3JwWq5RYkbTCbNvLxYsJcRMafJ1O/naQlK3Xw5/TjcLIOwxung+x5h/wh7IP8ci6R8x6bLznVITTVS9xi9oZRYfR4BcFGUek/+edhzP/QWURlGTDuQ2grQRNBax/A3ZIeUB0Ggcpu2HXN8YXUzjBuNdh0INStdy2z2Hrp6AulYTSji+lhymB7eHWX2RhpNKq+PnkL/Lhvm4hPJJ+kb2KCv729ibVRfrTLlXo+anwBDPX7TBey80P4odLIcGYgRA7uEH9ndTrXicn/wy7vL3MhJGX0oWrh70ACmc4tVL6/Rg4sFB6GMKP+UkYRxYXQGEq0wFSz8DRdRhroR6Vwp+dx0P85RDV19yzJrAKo2NHy12hX9j6AmcKJI9puFe4XVaqGTCII5VORWZpZpO6sRt+frCTfKPcs/DtKKgsBJ8I6HIlnFgGrt5w1ftSHqWTK3Sf3LSbH2twbgP894q0HvsKtB9p8TRPF09GRI9oIaPqRqHX6+txWywwUFRUhJ+fH4WFhfj6Nn9ZsFqnZs6hOWxO3cyJvBOoq/JMgj2C+fuav1skEfKqv66Sqz1m9J7B6JjRjRZmN/97M8fzjqNAwY4pO1o+KXvb57D6eWntGQT3roEg88GRO9N3MuO/GXL3b0t8M/abuqsnSrJh8wew61up6g3QALvc3WmnUROlqSo5jx0MEz+EsB7mzy/OkPKQjv4N1cvTPQKl6jkTu1clreLJjU8CMCFuAu9f/r6UE3VuIyRvJ/vAfCYEe6JSSqJnYHkFNxUVM66sHIsyKKQbJN4mfdAGtIP2o6S8q7ProSQLKgrQuQfwR/EJvio+SY5zzaGXzw54ltu7GSUPeefh2D+w6QNQFdf+u2sQCoi+DCa8A9H17Fmk00oCNaRLq7zbtgbZZdmM/n10jf2vDXmN6zpdZwOL6scHuz9g3rF5AMwZN6fRXi69Xs/I30aSV5GHl4sXm2/dbPtxKb9MgZPLLn1e10nSDaC1Ckkaik4HH3eH4nRp2y8WHjsANhic25DvbyGOGoi1xZEpF4su8uSmJ2Wvy7h243j/8vetWk6aX5HPiEWSck8MSWT+VfObdL1Xt78q97aZN2Fey+YdHfwV/nlIClkB3PGn5M0xQafXMfGviWY5CpM7Tkan15mVjwe5B7HmpjWX/oDMOg4b30V9eg3Xhvpw0cUFpV7PyHIVr3SfRsCIZ2v10mh0Gk6l7eb88T85knWASm0FI307MXz4iyiCzO/WZ6yZIVfSfDfuOwZFDDK/mKqUNzc9x6+p6yy+VkeVin4VlSRUqgjVaOiuUuGrq/2jQA+8HRjAL34+NY71DOrJV2O/ql24l+fD8X/hyJ9SSEBTLoUIBz8Cw2dB/nnpPKUzrHhaEoqegZLYK8+X8ryqC1dnDyk/a+hjdXu9Korgnwel13f2gDsXQ+yg2s8XyNy29DaO5B6RtwPdA1l701qbVEPWl99O/sbrO14H4MVBL3Jzl5spUZXw5s43WXNhDU/0e8JcwNfCmfwzXLdEEoGXR1/O52M+t6rdl6Q0Fz7sXPPvoDb8Y6W/r/7T6l8oYi1yz8L/qn3u3/6nFBpsYRry/W2/73IBMb4xfD76cyb/M5kiVRGrL6yGjfDW8Lfk3iPNjWkFkWlpZWPpHtRdXh/PO94y4khVCn/dDyeWGvcNuL+GMAKpVYFBGPm7+fP+5e8zMFy62xwWNYynNz0NSPN+5hyaQ+eAzoyIHlFrS4MS/xheCQ1mVZkx3KRTKFjn6YavcxmvW/gi1+v1bEzZyMd7PzZrtAfwe+Eebjn9My8EvSDvO5R9SBZGUd5RDAi3kLjv6sUTl79NyY7XWXpuaY3DZ1xdOePqyqKqbQ+djitLy5iZV4CfTlfj/G/9fS0KIzcnN14b+lrdHk2PAOh7p/QASbC4eBg/tE09eVMW1Xx+aQ6cXSd5so5VtV3QlMN/L8O2z6DXzdBuiOSVc/eD5G1ST6jsk1LfKoMnTlMOq1+E6Wtqt1UgMzBioJk4uqLdFXYtjACzPlkXiy+i1+t5YsMT7EiXwsrv7HqHPqF9zD6XLLEnc4+87h/W3zrG1pfSXFjzolEYJd4BngFSLmWvGyXPbNJm8+cUJMOKp+DiDrh+jm29SKk1Cy04vdom4qgh2Pc7XUCIZwgvD36ZpzY9hU6vY/WF1ezJ3MOLg17k8ujLmzR+wRJpJWny2rQ0trF0Cegir0/n15483GS0asi/IHkhFt5ofizxDikMUw21Ts3n+413hK8MecXMA3Nl/JUEewRzz6p7APjyoJQHNDBiIN+M/Qanam7hA1kHeGLDE7VWWCw+s5iiyiLuS7hPFp5Hc47ywZ4PzD6Mq7Po5CLCvcIZHjWc3IpcntzwpHzs9m631+pJ9HTx5O3hb/PG0DdYf3E9f53+i82pmy2eW65U8pePN2lhXfk6Kw+n7BPgFUrR4Af5qOw0f6Ybn/fmZbNJjB7G6gurGR41vOGdiN0b6HH1CoaEm6XH6BclgWPI4SrLhZ1fSQ9AaiRQhzM8ZZeUFB9ov3kz9sKA8AF8f+R7efu6jvYbTjPQzqedvD6ed5zpq6ezK2OX2Tm3LL2FTgGdCHQL5PWhr9fMS9Lp2HPsN3nzMpcA6fPFFh6Y7JPww5XS+1w26B6pqMJA7GDJMxreC/YvkG4YDBz5EyL7wJBHWs7m6qRZEEcpu1vejgYiwmoNpCXDaqZsStnEkxufpFxTLu/rFtiNr8Z+RZBHULO9zlcHvpJFwGejPmNU7KgmXa9MXcbAnyVPTEJwAgsnLmyyjTLphyA/Cc5vkpJ9TUeBALh4wbWfQ0/LQ3J/PPIjH+79EIAOfh3445o/atwZ6/V6piybYnYHDRDnG4evqy8DIwZyc5ebmX9sPguPL6wxzmLBVQvYkbaDzw8YRZiL0oW3hr3F8vPLWX9xvdn5nQM6kxCSQP+w/uzP2s+ikxY8KVV08OvAzxN/bnAeV6W2kkptJcdyj3E4+zC/nviVrHJju4KHEh9iRqeb2Vt4jue3vUBqSap87Il+T3BPz3sa9HpW4eQK2P4FXNgq53hZJLizNFS4sliqBAQY+yoMe7xFzHRk1Do101dNZ1/WPh7r+xjTe023tUmXRKfXMeyXYRSr65fj1iOoB/OvnG92k6nf8gljT35DlrMznjodWy+k4BzcGaYulooxWpIfJ5l7hcIT4P821R5K1umkOZH7Fxjblji5wYD7IOEWiEiwvs3V+X685MEC6TNZXSr1d3v2Qp1PswYi58iK2EocgRROeeC/ByhSFcn7+ob25fvx3zebu/vlbS/z1+m/APj96t/pGti1ydc0JHh7OHuwY8qOpudM6XSw5BE4sKDu825ZAN2utnioUlvJ+D/Gk1uRi1KhZMGVC+gVYrl/y7nCczyw5gHSStMsHq9O54DOfDLqE9nzptKqeHX7q3WOwIj1ieWJfk8wJnaM3FVaq9Ny4783ypVCpvQP689HIz8iwL15Eox3Z+xm+urp8qgY02ZsIFUsPtrnUW7reptZ12ubU5oDF3dB0hZI2y8lfms1UnVbv7uMHqK8c/BZVRPTyL5w//rarymQ0ev1VGgrHKrhpulnmIG3hr1FjE8MM/6bIY8DMeXLMV8yPHo4OnUFR77oze0BUk+3weXlfJuRLZ3k3w7u+lcqWGgJzqyFBSY3dl2uknqc+dSzKeKKZ008qkhVsoMfkvKR2o+U/u/kWlNo1eUl02mholBKrnb1lq6hUEj5gcUZkm2mLUw0KngnVgpp+7eDgDg4L1Vj8/T5Fh99JMSRFbGlOAKp8+vvp37nlxO/yF4kwx92c3D/6vvZni7199hy65ZmGcfw+PrHWZu8FoDl1y0nxrcJ4Tp1OSx/CvZbSBSPHSIl26pKpRBMdO25An+e+pNXtr8CSInuH478sM6X1el1KFBw67Jba21L4Obkxr0972V6r+kWw527M3bz7OZnySozemkC3AJ4uM/DXNfxOovPSSpM4s2db8o5EwCRXpH8OunXZhNGBr45+I2Zh8tAn9A+vDP8HSK97WzWXEP5ehhkVI1eeXS/CK21UnLLc7l12a1klGbg7uTOuyPeZXSsVHmXVpLGmgtrpP2735WrgV2ULjyX+AgLD33HGY3R6/Sgf28e2L8UOVQbEC+NG6qPQNLr4dBvkHkE+t8DgfH1GxmUnwQHfoaN7xr3Tf5K6n/WENTl8Me9l65wC4iXWpwUJEOxyQ1gUCdw9TI+NBWQdlBqJWCKR4BUOGGgy1XQ6QroM1XK+Zs7XtqfcItUdHGgKnrw4A4IbdlBvkIcWRFbiyMDy84t49nNzwJSLsBrQ19rlute/ffVJBUl4eHswc4pO5vFS/DFgS/4+uDXAHwy8hPGtBvTuAupymD+dUYXLUj5RNH9pTLtevbr0el1XLv4WpKKkgD4ZeIv9U4+P5h9kMfXP46/mz/39bqPbWnb2J6+ne5B3Xmq/1M1BqdWR61V8/yW51mRtILLwi/jjaFv1Et0qLQq5h6Zi1avZUrXKc0ujEDyVH2w5wM2p24muSgZPXqGRw3n01GfNntum03Y8rGx18roF2DEUzY1R2A9ytRlXCy+SDvfdrg7u1s8Z/GZxby49cU6rzNvwjz6ekRITWRzq3Imw3pKHhgXD+g8wegpKcmSensFdYKYy2DNS1L/MgNOblIvNEMzVq1aEhZBHaQwU1muVEyQXm2eYIcxcPvvjS99zz4FK5+RihpakvjLJU/RPqm1Atd8LjW0NeRE3bVU6rfWgghxZEXsRRxVaCoYsWgE5ZpyAtwCWH/z+hoJwg1Fr9czYOEAKrQVtPdrzz+T/2kWW9dcWMPMDTMBeLD3gzyQ+EDjLrT+bdhYlVjt7A5Xfwq9Gz5pfV3yOh5b/xgghad+mPBDg56v1+ubLBq1Om2T/72sSVpJGqklqfQN7WvXdjaIgmT4pCp0GtoDHtxW9/mCVk+JqoQnNz5pNmDWQDvfdiyZvERKAyhMgbkTpMHU1el5o9S81bTpaXMR2Rem/tX0/lw6nZRzl7JL+lkKkqU8PDcfKLhY/z5kHgEQ2EHybpXlgEIJnsHmI5YsoXSBWSelVIg1L0n7bvoRerRskr8o5W8DuDu7MyxqGGsurCG/Mp+d6TvlJoX5Ffk8tv4xiiqLeG3oaySE1C8JL78ynwptBQARXo3vLFudTv7GaqbTBY2sWKsogh1V8XOlM0xbbl6xUU/0ej1zDs+Rt6f1nNbgazSHN83eBUekd6Tjh9Gq4x8L0QOkL4iso1JPqhZ26wvsC29Xbz7qeg9PnvmPA25uDKqo4NrE+0kLiGV8/HhjfqRfNEx4GxbdUfMiR/6o34spncEnskpg1eGT8AqRRFH3a6Q2Fc4Nm2lp+bWV0PsW6WGJ0lxp4LaLp9R01sVDEk/eoZKYcnKRQmtuvkbvvFr6rsDFxDOXe1aaE7nxPfMCmZ43gFeQ5CEzUFEtPGdnCHHkwFwZfyVrLkg9W74+9DWDIgehVCj59tC37M/aD0it/5dMrj0R2BTTSdbRPtHNZmeMT4w8ENLSJHaZsjzJDZ11DGIGSENcXTxAUwnfjjTGuhNubZQwAlh9YTWHc6S8k84BnRke1bJuXYGN6XmDJI4AjvwFo5+3rT0C26LX47nuDb7MrEq6Hv+WFDKzRLerYdInUnhWXQal2ZbP842ColRJDI1+UWpUWp4veV0UCilxuTgdgrtIMxlzz0j5QS6ekggLiG/5vkReQdLDFLeqQc+15Ve5WAhXBnWAYU9I3rQNb0u9ycJ6SuNNADz8jecKcSSwFqNiRhHrE0tycTL7s/bT+6fedPTvaFbddL7wPOkl6ZecMaTVac3FkXfziSMnpRMd/DtwNPcoF4svUqGpMM8D0Krh6GJY95px+vzp1bB3HoR0lZL6yo0zzxrbs6NIVcTbO9+Wtx9MfNC+Kq8E1qfHZFj5LKCXesCMeq5Bc+UErYydXxurp7xCoP+9dZ/ff5r0AEjZC6dXSUKn41gp1yi4szTXrDxfSr42VGOZVmX5hBsrzlyrBFFrwz8GJn9Zc7+7SYFPeUGLmdMYhDhyYJyVzrwy5BW5SSFgsez7aO7ROsXRp/s+NQs1AUT5NG8/D4M40ul1JBUlGVsEqErh51tqdngFyf1cPcY/5BEIbVx7gYXHF5JbITVTGxUzitExNWdHCVo5PuFS36OkzZB3Vrqzjehta6sEtmDXd1VCuYpxb1j2htRGdL/a5/uJGX6WMRVHdu45spPJdILGcln4ZUztPrXOc+rqTH2x6GINYQRSyKk56eBvHBFxtuCs8cCyJ82FkX+sVLYaYiEX5IrX4IrXG/X65Zpyfj7+MwBOCieevuxp4TVqq5gmgZ5cYTs7BLZBp5PGy6x42rhv8MONKu4QNBCRcyRoSZ7s/ySJIYmodCpyy3PR6rX0C+vHHcul5EFL3iQDy87X7IER4hFCrE/dJekNpYOfBXFUnCF1c4Wqbtb/kyZKO7tB79uq4vYuUtjDK6RJ4Y9/zvxDQWUBABPiJzRrTpXAweg8AZZJ1ZOcWikNsBW0fnQ6KYy282soMOnOPOB+yWsksD5mnqMCm5lRH4Q4agUoFUrGxY0z26fWqXFWOKPRa2oMMzWg0+tYfGaxvB3vF48SJTP7z2x2r4qp50gWa/vnG4eCDnpASpY1oFA0Wyy+VF1qNiNqWo+GV6gJWhF+UdIcqozDUlftonTwbb7qTIEdUVkMv0+TKhOLUmoeD0+QPNLCi9wyOFBYTYijVoqL0oVY31jOFZ7jQtEFNDpNjREjH+z5QJ6bNSRyCN9c8Y3V7In0jsTD2YNyTTnH846j12pR7P2p6qjCOLHdCny892MySjMAGB41nC6BXS7xDEGrp/OVxm7Zp1dLo0YErY8dX8GZNTX3dxgtlcn3mGw+7kJgXZROUjuAyiK7T8gWOUetmPZ+0ngEtU5NSnEKBRUFGHp+bkrZxPxjxhEcN3e+2aq2KBVKeodIia8ZpRmkHPtd6qsB0HGM1eYV7UzfKQ9v9XD24LmBz1nldQQORucJxvWplbazQ2A91OWws9oNn0cgTP1beiTeJoSRLTB4j+zccyTEUSsm3i9eXl+9+GqGLxrOExueQK1T88WBL+Rjt3S5RZ49ZE0GRgyU1+sOzjUe6GedMFeZuoyXt70sbz/e93GRaySQiOwDXqHS+twG6YtU0Lo4tVLq4gzQ8QqYvk6aqddBVKnaFENSthBHAlvR3r/mYM21yWsZ+/tYeXhqt8BuPD/w+Rap3BobO1ZeLy5PlnrEeodLE9Rr4UjOESb9PYl+8/tx+aLL+d/+/8mT4y2h1qnZnbGbvIo8Pj/wuRw27BfWj1u7imoUQRVKJXSuytNTl8F5C60kBI5N8k7jeuD/SWX3pk0IBbbB4DnSVtr1TYnIOWrFGMJq1cmrMDZU/L/e/9diJe1xfnEkhiRyIPsAZ1xdOObqQo++d0qt6S1QoirhsfWPyVPs8yry+PbQt6xOWk2geyAZpRl4uXoR6hmKu5M7pepSs+n1Btyc3HhtyGvGUQACAUh5R/sXSOsjfxjFkqB1kLLbuG5kR32BFaielG2noc1W+W1RWVlJYmIiCoWCAwcOmB07dOgQw4cPx93dnZiYGN577z3bGNkCxPnGmW0rMBdBCcEJLd4I8Vo/YzL04oAQGPKwxfMM0+sNwsiUpKIk9mXtI600jdP5p9maupW1yWstCiOAGb1nEOvbvK0JBK2ADqONzfoO/yF1Yhe0DjSVUoNPkDpXm3aoFtgWBxkh0irF0dNPP01kZM2hmUVFRYwbN4527dqxd+9e3n//fV555RW+/fZbG1hpfTxdPEkINg6d/WLMFzw74FmcFE74ufnx8pCXW7YRok7H+P3/4K6TwmLLfbxRu3jWOE2v1/PM5mdYd3EdICVSr7xhJZM7Tr7kS7gqzYc0joweyT0976nlbEGbxtXTOEdLr4U/p0ul3wLHJ/0gaFXSOvoy29oiMMdBRoi0urDaihUrWL16NX/++ScrVph3v124cCEqlYq5c+fi6upKjx49OHDgAB999BH333+/jSy2Ls8NfI63dr5F18CuDI0aynDFcMbHjcfT2RNPC8LEqlzYik/OKUYrglju7UWRtoLt6dsZET3C7LT/kv+TB+q6Obnx8ciPifKOYvaA2YR7haPRaegV3IvuQd3xc/MjtzyXjNIMNHoNA8IHkFOew/xj84n0juTGTjeKcJqgdoY+LnXJTt0Leefg7xlw04+1hnoFDkLaAeM6qq/NzBBYwEF6HbUqcZSZmcl9993H4sWL8fSs+cW/fft2RowYgaur0bswfvx43n33XfLz8wkIaH3zcHoE92DhxIVm+4I9gm1jzMFfAJhQWsZyby8AVp5fKYsjnV7HoexDZsNhXxr8EkOjhgKSJ+yhxJoTs6N9os2q0EI9Q5nVf5bVfgxBK8LJBW6YA9+MhMpCOLEUfpoMt8wXoRhHxhBSA4hItJkZAgs4yAiRVnNLrdfrufvuu5kxYwb9+/e3eE5GRgZhYWFm+wzbGRkZFp9TWVlJUVGR2UPQCFSlcOwfAIZqXfF2kcTR+ovrqdRWcq7wHJP/mczUFVPJLs8GYFDEIK5uf7XNTBa0EQLbw01zpVE1ABe2wNfD4NQqu66mEdSBLI4UENbdpqYIquEgI0TsXhw9++yzKBSKOh8nTpzgf//7H8XFxcyePbtZX//tt9/Gz89PfsTExDTr9dsMx/8FVQkArj0mMzp2DAAl6hLWJ69n1oZZnC88L58e5B7ES4NeEsNhBS1Dx7EwbYU0ww+kuX4/3wxvR0vDkbUa29onqD9atTQuBCC4E7h62dYegTkOIo7sPqw2a9Ys7r777jrPad++PevWrWP79u24ubmZHevfvz+333478+bNIzw8nMzMTLPjhu3w8HCL1549ezYzZ86Ut4uKioRAagwHfjauE6cwXqliydklADy16Sn5kLeLN1O7T+WGTjcQ5hVW/SoCgfWIuQz+bxPMvx6yq75cdRrY/R0Up8ONc6WhyAL7JvOoMRk7PKHucwUtj4NUq9m9OAoJCSEkJOSS53322We88YZxsnJaWhrjx49n0aJFDBwodWYePHgwzz//PGq1GhcXyYW+Zs0aunTpUmu+kZubWw3BJWggBRfh/CZpHdgeYgYyWKfB19WXIpUxTOnm5MaCqxaYDakVCFoU30i4by0cWgQpe+Hwb9IX7YmlkmgaNEP6wrXSuBtBM2Da30hUqtkfHibftWV5tZ9nY+w+rFZfYmNj6dmzp/zo3LkzAB06dCA6WkrWnTJlCq6urtx7770cPXqURYsW8emnn5p5hgRW4NCvIPXDht5TQKHAxcmF6zpeZ3baE/2eEMJIYHtcvaD/PTD5C5iyCAxVnRe2wKI74IsBcGGbbW0U1E7KHuNaiCP7w9OkIKg0x3Z2XIJWI47qg5+fH6tXr+b8+fP069ePWbNm8dJLL7XaMn67QK+HA78Yt3vfIi9n9J7BxPYT6ejfkWk9p3Fb19tsYKBAUAcdRsMdf5nnSWgq4NcpkHvWdnYJaie1Shw5uUJ4T9vaIqiJZyAYGhKXZtvUlLqw+7BaY4mLi5Mn0JuSkJDA5s1ijlKLkbIb8qq+ROKGg7+xU7W3qzfvDH/HRoYJBPWk3WB4ZD8cXwJLH5f2ledLXqT/2yR6ItkTZXmQe0ZahyeIHDF7ROkEnkHSUGDhORK0WaolYgsEDolXEPSfBs9ehJBu0r6sY7D+rYZfK+eMVAG3/m0oSm9eO9s6qfuMaxFSs18MVaGl2VJ0wQ4R4khgPdQVcPQvae3iBd2usa09AkFTcfeFyV8ihwW2fCR12K4vJdnw41VSBdzGd+CrwZBz2iqmtklSTfONLPe7E9gBXlV5R5pyqQeeHSLEkcB6nFxuLNXsfg24edvWHoGgOYjqC6OeM27vm1+/52k18Nd0KDFpJ1KeDwtvAlVZ89rYVjFNxo7qZzs7BHXjZVKBbqd5R0IcCayHaUitt0i2FrQihs8C76o+XKdXQf6FSz9n/3w4t8G47eoj/T//vHQjIWgaer2xjN8zGALibGqOoA687L9iTYgjgXUoSoeza6W1X6yUjC0QtBaUTtDvbmmt08DaV+vOnSjNgfVvGrfv+lea32bg0CKrmNmmyD1r7LgcfRmI7vr2i6nnqEyII0Fb4sAC0Oukde9bQSneaoJWxqAHwM1XWh/5E1Y8U7tAWv6UMXzQdRLEj5AeflXd9k+vqZ/3SVA7ZvlGIqRm15h5jkRYTdBW0OlM8jAU0OcOm5ojEFgFjwCY+BFycvaub+DX22t2/T30m7EwQX4Okvep711VJ+lh9Qug07aE5a2TC1uNa1GpZt84QM5Rq+1zJLAhZ9dCQdVdcIdRYtSCoPWScBPo1LD4QUAPJ5fBxxsgtJuU8+LmDfsXGs+f8C74mMwM7HMHbHpPGlFyfInUGmDMiy38QzSQyhIozZKqjIK7gLOrrS0CTSUck2Y14uIpxJG9YyaO7DOsJsSRoPnZ+qlx3f8e29khELQEiVMkcbP8Ken/6lIpxGMa5gHoM9WsQzwAvhFw7Zfw9/+BXgubPwD/GGM+k72x4ytY+xqoTarrLn8WRs22nU0ghSUN+UZdJ0kjYAT2i6cIqwnaGod+h6SqDuSBHaDLVba1RyBoCfrdDY/sg543mI8aMT0+6RPLz024CcYZh2bz72Pwy232N7E8dR+sfNZcGIHUr+nkStvYZMA0oT3hltrPE9gHpjlHJVm2s6MOhOdI0HycWi31cTEw+nkpr0IgaAv4x8CNc6W1Vg0Zh6AgWQrx+EXX/dzBD0JRKmz/XNo+uRyWzYLrvrWfYoZN79d+7M/p8MBW8I2E/CTp/y3lvSkvgFNV4swrBNqPbJnXFTQedz8p/Kkuk/5G7BAhjgTNQ+ZR+MMkhNZ7inQXLRC0RZxcpCaEDWlEOO4N8I2CVVUhqsO/Q0hXGPGkdWxsCOkHjb2YfCLhsQOgUMLrVR4AVTF8mmA83zNIapTpHQYdx4KLh/VsO/aPFM4E6HkjOImvNbtHoYDgTtL7quCCNE3Bxd3WVplhJ7ckAoemohDmXy99QIL0AT/pY9vaJBA4GgqF5EG6aZ5x3/YvpC+O+qDVSHlPb4TD3CsheWfz2bb5I+N62BPSQFcnF5h1ErzDa55flit5vhbdAV8NhYu7ms+W6hgqAUEKUQocg+Au0v/1OuNwcjtCiCNB0zm2BEoypHV4Ajy8x+7uAgQCh6HHZMkDAlCeB8cW1+95a16EXd9K86qSt8HccbDgRtg9R2qtkXmscfaU5sKJZdLaKwT63mk85hMO09fAZdMhdoj0929aiQTSF9+8qyXvcnNTlgfnq3Ic/WMhsm/zv4bAOoR0Nq6zT9rOjloQ/kdB0zm+xLi+6gNw9bSdLQJBa2DAfXDkD2m9e47USLUuso7Dji9r7j+zRnoYmPihJGQawu7vpHYFICU7V7/x8Y+VrmtAr4dVz8OOL4z7NBWw6QO46YeGvfalOLlcqvIDabC16IrtOBg8RwDZJ2xnRy0Iz5GgaVQUwtn10tonUvQXEQiag5iBENZTWqfsvnRYau+PxvXoF6TKOL/YmuetnA05Z+pvR2WJVL4PoHCCAfdf+jkKBUx4C544BjNPGD1Jx/5p/uTbYyY3Zt2vbd5rC6xLeE/jOv2g7eyoBSGOBE3j2BLjXWW3q+2nskYgcGQUCmk8iYEtn9R+7rkNsOs7ae3sDv2mQf9p8Oh+uPEHGDYTOl4hHdeqpHL8uubAmbJvnrF/UK+bGtbQ1S9K6uN02X3Stl4LO7+p//MvRUURnDPcmEVAVP/mu7bA+gTEG9tepO6r/3uyhRDfZILGo9dLLncDCTfbzhaBoLXR62bJGwtS5+0sC6GHwlT49Q5jaGnII8YeMk7O0PN6GPsy3DxPKpQAKcy2e47096uphI3vw8KbpeaOKpMeRno97PvJuD3s8cb9HJfdC05u0nrfT5KoaQ5OrTJWqXW7RtyYORoKBUT2kdalWXZX0i/eTYLGk7zD6A6NSGxY2bJAIKgbZ1cY/JBxe+O7Nc9Z+ayxSjRuOAyfZflarl7mjSaXPwlvhMJbkbD+DTi9CjZ/KPUrMtzBZxwy5oLEDpZGojQGr2BjzlRlEexf0LjrVOf4P8Z192ua55qCliVmkHGdvN12dlhAiCOBEa1Gym2oT2devR62mJTrD3pAJEMKBM1Nv7ukYbUglawf+Nl4rCQLTiyV1l6hcMv8uvsJ9bgO+t9r3NaqQKcxP+fkMvhxEpz+D04sN+5vas+yQQ8a1zu/loZTN4XyAslGkHKaYgc37XoC29BuiHF9YZvt7LCAEEcCifIC+OFK+P4K+PwyyDld+7mVxbDkYeluEyR3fY/rWsRMgaBN4eYD4940bi9+AOZOgOxTsGeu1CMGoM/tRhFVGwqFVFV2809SY0bPYKlZY+wQGDjDeN6FLbDwBmksiIFOVzTt5wjtCh1GS+uCC5C0qfHX0mnh30ellgUgJWKLTvyOSfRloKwqmrczz5Eo5W8LFKZIH7KWZj4B5J2D3+6EjMPSdkmm5HafurimN+jMWljyKBSlGPeNe0NqCicQCJqfxCnSXfWBqnBU8nb4wqQqVOEEfe+q37UUCklMVK/s0uulWYirnzfm8RiIHgABcY02X6bvnXB2nbTe8VXjxnxkn4JlM43zG509YPDDTbdNYBtcPSG8F6Ttl27IK4rA3dfWVgFCHLV+Vr8I2z6ThNEtCyF+uPGYplLqqLvvJ6BapcC5DfDLrZJHKXWvVJEW0tW8H4VCKY0I6Hl9C/wgAkEbRaGAaz+XQhCbP6zZTXjAfRAY3/TXGHi/lCC76X2jVzi0O9zwXd3PrS9dJoJvtHRjdWqlNIOtvqIrZQ+seEb6LDJ8VimdpVBiU392gW2J7COJI/RSDqvpd5QNUej1dlY/Z+cUFRXh5+dHYWEhvr72oXBl9HqpbX9JFgR1hNOrYdHtxuMunnDHX1JYLPcMHP6t6k1ZhU8E9L8H1r9Z89rViRsOV38KQR2a/+cQCASWUZVKNzSHFkk3J92vlUb1uPk08+tUDQQN7tS8IavNH0pVcSC1GBj7cj1sKYVPE6WKJgP+7eCaz8SQ2dbAvp9gySPS+orXYOhjVnuphnx/C89Ra6DgIvz3stTiX1PHHCZ1GfwwoeZ+Z3cpoXrgA+AdKgmmkybJmB6B0hgDkPKLxrwklRmL0lmBoGVx9YLJX8LEjyRvj7XC2a6eUp5Qc5N4B6x/S0oE3/sDDJ95aWF3Zq1RGLn6wJCHYcijohN/a8F05EvqPtvZUQ0hjhydgmT45nKjeLFEh9GgVRvj9KYExEs9UCJ6G/fdPF/yOhWmQOwgKSZcmCIJr4A4aeCkQCCwHY46u9AnTJobd+hXKM+Hfx+XOnrXFRpL2W1cX/8NdJ1odTMFLUhoN3DxAnVpVdjUPhDiyF7QaiQ3eUO9MStnmwsjv1hpGGRK1biBblfDNf8DpYt0x3biXwjpJuUJeYdJeQzV7z6dnKHrVeb7/GMa/jMJBAJBdYbPlMKC6KX5cUf+hDEv1t6jyfQLU3TBbn0onaS8owtboPAiFGdKItrGCHFkLxxYKJWnuvtJk63bXw59pkpCpzbyzhn7nHiHwYwtUlhMr4eLO8HJFaJMXJYT3pIeAoFAYCtCusDYV2Dtq1WtCPRSHpJXiFTRZopWY8yL9Iu1iy9NgRWI7ieJI4DUPXbhHRRJI/aCuqptf0WhFP5a9wZ83AOWPQnqcsvPObfBuB5wvySMQMpFiB1kLowEAoHAXhj2uHQzl3CLcd/SJySRlH3SuC9ps/GzMWZAi5ooaEFMPYKmYVQbIsSRveAZLL1BDLOUQEpa3P0dzLnC8iRtU3HUYZTVTRQIBIJmI6wHXP+tNCgXpM+7zR/CFwNg7pVwcBGcXGE8v3qoX9B6MB09ZRhJZWNEWM1eSLhJegDkX5CmYW//UuoCm3kYvhkhla72ulE6R6eD81VdZt39pNlmAoFA4GhMeBvyz5vf7CVvkx4GFEpoL24AWy2+kVKH9/J8yDxma2uABoqjoqL6T1O2ux5AjkRAO6lcvueN8PtdkHNKyuT/815JVY99VYrLludL58cNF+3zBQKBY+LiAbf/KSVnZ5+QZrrlnDQ/J+EW8Ay0jX0C66NQQFhPKYxakgGlOdLAYhvSIHHk7++P4hLDRfV6PQqFAq1W2yTDBEBYd7h/AyybBQd/kfZt+0z6APE0eePYQfKaQCAQNBonZ+h9q7Qe8zIkbYE1L0HaPmNvNUHrJqyHsd1M5lGpKMmGNEgcrV+/3lp2CGrD1QsmfwXR/aXOuHqd1IPIgNIZulxpO/sEAoGgOVEopBES96+/9FxIQeshtLtxnXXMscTR5Zfb1tg2i0IBl02HwPbw53RpRIiB6AGXnsYtEAgEjohftK0tELQUYT2N68wjtrOjiiYnZJeVlZGcnIxKZT7JOSEhoamXFlSnw2ip/PWna6U8JIUTjJpta6sEAoFAIGgaoV0BBaCXwmo2ptHiKDs7m2nTprFixQqLx0XOkZXwjYSHdkldY108pDitQCAQCASOjKuXNEYm7xxknZAagDrZrqC+0X2OHn/8cQoKCti5cyceHh6sXLmSefPm0alTJ5YsWdKcNgqqo1BIOUhCGAkEAoGgtWBoSWNoYWNDGi3L1q1bxz///EP//v1RKpW0a9eOK664Al9fX95++20mThQVVAKBQCAQCOpJ7GA4+pe0Tt4hzVyzEY32HJWWlhIaKo2rCAgIIDs7G4BevXqxb9++5rFOIBAIBAJB2yB2kHGdvN12dtAEcdSlSxdOnpQadfXu3ZtvvvmG1NRUvv76ayIiIprNQIFAIBAIBG2AsB7g6iOtL2yXJkHYiEaLo8cee4z09HQAXn75ZVasWEFsbCyfffYZb70lJr8LBAKBQCBoAEonaDdYWpdmwelVNjOl0TlHd9xxh7zu168fFy5c4MSJE8TGxhIcbNu23wKBQCAQCByQ/vdKjY49g6Ci0GZmNFocbdmyhWHDhsnbnp6e9O3bt1mMEggEAoFA0AbpNA4mfw3drwVXT5uZ0eiw2ujRo4mPj+e5557j2DH7mKIrEAgEAoHAgVEqIfE2mwojaII4SktLY9asWWzcuJGePXuSmJjI+++/T0pKSnPa12CWLVvGwIED8fDwICAggMmTJ5sdT05OZuLEiXh6ehIaGspTTz2FRqOxjbECgUAgEAjsjkaLo+DgYB5++GG2bt3K2bNnuemmm5g3bx5xcXGMHj26OW2sN3/++SdTp05l2rRpHDx4kK1btzJlyhT5uFarZeLEiahUKrZt28a8efP48ccfeeklMfFZIBAIBAKBhEKv1+ub40JarZYVK1bw4osvcujQoRYfH6LRaIiLi+PVV1/l3nvvtXjOihUrmDRpEmlpaYSFhQHw9ddf88wzz5CdnY2rq+slX6eoqAg/Pz8KCwvx9fVt1p9BIBAIBAKBdWjI93ejPUcGtm7dyoMPPkhERARTpkyhZ8+eLFu2rKmXbTD79u0jNTUVpVJJnz59iIiI4Morr+TIEeN03+3bt9OrVy9ZGAGMHz+eoqIijh61POiusrKSoqIis4dAIBAIBLakUqPleHoRpZUiLcQaNFoczZ49m/j4eEaPHk1ycjKffvopGRkZzJ8/nwkTJjSnjfXi3LlzALzyyiu88MILLF26lICAAEaOHEleXh4AGRkZZsIIkLczMjIsXvftt9/Gz89PfsTExFjxpxAIBAKBwDLfbTrH9V9uZeWRdK78dDNXfrqZKz7ayMW8Mlub1upotDjatGkTTz31FKmpqSxdupTbbrsNT8/mzy5/9tlnUSgUdT5OnDiBrqqT5vPPP88NN9xAv379+OGHH1AoFPz++++Nfv3Zs2dTWFgoPy5evNhcP5pAIBAIBPVi8+ls3lx+nH3JBcxYsI9z2aUApBVWMGfzORtb1/podJ+jrVu31uu8iRMnMmfOnEaPFJk1axZ33313nee0b99e7tbdvXt3eb+bmxvt27cnOTkZgPDwcHbt2mX23MzMTPmYJdzc3HBzc2uU7QKBQCAQNAdfrj9b67GtZ3Nb0JK2QaPFUX3ZtGkT5eXljX5+SEgIISEhlzyvX79+uLm5cfLkSbk5pVqtJikpiXbt2gEwePBg3nzzTbKysuShuWvWrMHX19dMVAkEAoFAYC9otDr2JefXevxMVgnZxZWE+Igb+eaiyQnZ9oKvry8zZszg5ZdfZvXq1Zw8eZIHHngAgJtuugmAcePG0b17d6ZOncrBgwdZtWoVL7zwAg899JDwDgkEAoHALknKLaNSYz6E9YruYdw1uJ28vfdC7eJJ0HCs7jlqSd5//32cnZ2ZOnUq5eXlDBw4kHXr1hEQEACAk5MTS5cu5YEHHmDw4MF4eXlx11138dprr9nYcoFAIBAILHMyo1hePzG2M4+O6YhCoWDlkQzmbb8AwKGUAib0tJweImg4rUocubi48MEHH/DBBx/Uek67du1Yvnx5C1olEAgEAkHj+Ht/Ck8sOihvdwn3QaFQANA7xk/e/+WGsygU8NiYzrg6t5qgkM0Qv0GBQCAQCOyQU5nFzPztoNm+PrH+8jrc150ATxd5+4v1Z/ls7emWMq9VI8SRQCAQCAR2yMIdFzCdYTG+Rxhhvu7ytkKhICHa3+w5i/ZcRKtrlsEXbZpGiSO1Ws0999zD+fPnL3nuc889R2BgYGNeRiAQCASCNotpif69w+L55JY+Nc65ub95Y+Ls4kp2J+VZ3bbWTqPEkYuLC3/++We9zp09ezb+/v6NeRmBQCAQCNokWcUVnMkqAaRQ2ouTuuPh6lTjvIkJEWx7djTPX9VN3rdHiKMm0+iw2uTJk1m8eHEzmiIQCAQCQcvz8ZpT3DV3F9vO5NjaFJmd54wCZ3D7oDrPjfT3YGx342gsUdbfdBpdrdapUydee+01tm7dSr9+/fDy8jI7/uijjzbZOIFAIBAIrMnx9CI+rUpi3ngqm2+m9mN8D9uXxP+1L0VeD7qEOAKIC/IkyMuV3FIVey/ko9PpUSoV1jSxVdNocfT999/j7+/P3r172bt3r9kxhUIhxJFAIBAI7J6d58xHb8zZfM7m4uhoWiHrT2YD4OfhwoD4S+ftKhQK+rULYPWxTIoqNHy96SwPjuxobVNbLY0WR/VJxhYIbIFaq2PV0QyiAzxJjPG3tTkCgcCO2V0tBLU/uYD/jmXSK9rPrDLM2qi1OtYez+TjNac5mWls+vjI6I64u9TMNbLEgPhAVh+T5oW+t/IkwzoG16hmE9SPJjeBVKlUnD9/ng4dOuDs3Kp6SgoclDeXHefHbUkAfD6lD5MSIm1rkEAgsEsq1Fq2nDbPM9Lo9Ez/aQ8Rfu6sfmIEPu4utTy7+dh0Kps75+6qsd/DxYlbLoux8AzLDO0YbLb9175UIY4aSaMTssvKyrj33nvx9PSkR48e8uT7Rx55hHfeeafZDBQI6svFvDLumLNTFkYgCaXiCrXtjBIIBHbLqqMZFJZb/nxIL6xgycG0Rl+7Qq3lgQV7Gf/xJg6lFFg8p6RSwwML9loURp6uTsy7Z0CDxFm3CF+eu6qrvL3trP0kmDsajRZHs2fP5uDBg2zYsAF3d6PrcezYsSxatKhZjBMI6ktmUQU3fLWNLdWqTdILK/hlV7KNrBIIBPbM4ZRCeT11ULsax9dUhagaw5vLjrPiSAYnM4t5YtEBtDo9ybllaHV6/juWydTvd9Lz5VWsOJJh9rxbL4th/ZMj2fT0qHrlGlXn/hEd6F2VTnAqs4SckspG/wxtmUbHwRYvXsyiRYsYNGiQPOcFoEePHpw9e7ZZjBMI6oNGq2PWbwfJKrb8IfD7nhTuG97e7H0qEAgExzOK5PX/Xd6eZYfTyStVyfs2nMym/xv/cUPfKJ69smu9P0NySir52eSm7Gx2KR2eq3umZ88oXxbeOwg/z6aH8Qa1D+TgxQIAdp3P46peEU2+Zluj0Z6j7OxsQkNDa+wvLS0VX0KCFuWPvSmyxyjY25WPbu7N6idG0L9dAACns0q4kFtmSxMFAoGdUanRciRVEkfB3m5EB3jyxZS+dIvwNTsvp6SSbzad43h6saXLyFSotZzIKEKn0/PvwbR6j/AYGB/IhidHsvSR4c0ijMC8L9L2s7l1nCmojUZ7jvr378+yZct45JFHAGRBNGfOHAYPHtw81gkE9WDRnovy+v0bezOqqyTaR3cLZU9VJcrm09nEBXtZfL5AIGh7LN6fKucbDawKXw3uEMSKx4ZzOrOYqz/fQoVaJ5+/8kg63SN9LV5LpdEx6X9bOJNVQkygBxfzyutlw1vX9eK2ATHN7lDoHxeIk1KBVqdnxzkhjhpDo8XRW2+9xZVXXsmxY8fQaDR8+umnHDt2jG3btrFx48bmtFEgqJUFOy6wP7kAgCh/D1kYAYzoFMJ7K08CsPl0DlMHx9nAQoFAYI/8ezBdXt87PN7sWKcwH9bOGsnOc7nM/O0gAHO3JnF5lxD6tTPPA8ovVfHd5nPyqA9TYdQx1JtPbknkWFoR1yRGotbq2JdcgL+Hi5wXZA283ZzpFeXHgYsFnM4q4d+DaYztFmZx/IjAMo0Oqw0bNowDBw6g0Wjo1asXq1evJjQ0lO3bt9OvX7/mtFEgqIFWp2fWbwd5YfERed91faLMzuke4UuglysguZY1Wh0CgUAAyGLG39OFvrEBNY5H+Xtwfd9oLouTjpVUarjhq+18s9GYU5tRWMGETzfx5QbLebbX942iZ5QfN18Wg7uLEz7uLlzeOcSqwsjA4A7G0Nojv+znnh93o6tnqE/QBHEE0KFDB7777jt27drFsWPHWLBgAb169Wou2wQCi6i1Ou75cTd/mrTXjw7w4MFRHczOUyoVct+P4koNB2sppxUIBG2L0koNGUUVALS/RLj9+r7RZtsfrTklr7/eeJbMIvNCkPiq613fJ4p7hpp7pFqSib0iMI3WbT+Xy8qjUmXctrM5bDuTU6PNSYVay7JD6bz671H2J7ft+WwNCqsVFRVd+qQqfH0tx2YFgqay+mgmG09ly9v/d3l7np1guZJkeKdg/q3qVbLpVE4Nl7hAIGh7nM8pldftQ7zrPPemftHM336BY+nS91+lRoder+dQSqFZTzWAL2/vazeVYT2j/Pjo5t48seigvO/Bhfvq/fwftiZxVa9wPru1D85OTfKjOCQN+on9/f0JCAio82E4RyCwFv8dN/YeuXtIHLOv7FZrQuPwTsaOsZtPZ1s8RyAQtC3OmYmjuj1Hzk5KPp/Sx2zfL7sucu0XW+XtEZ1DSHpnot0IIwPX9Ynm/NtXEenXuDEoyw9nkPjaGrKKKtDp9Kw8ks7qoxlkFVe0+ua6DfIcrV+/3lp2CAT14nh6EX/vTwWkpMPnrupW5/kRfh50DPXmTFYJB1MKKSxX4+dh/XEAAoHAfjmXXSKv2wfX7TkCybs0fVg8c7ZIM0Wf+/uw2fF3rrffdBKFQsGEnhHM3dq4eagllRoGvLW21uPB3m70a+fPO9cnEFCV49kaaJA4uvzyy61lh0AASA0d/zmQxqGUAuZtvwBInWtfnNSdlUczePSX/fK5E3qG4+p8aefn8E7BnMkqQavTs/1sLhN62nbitkAgsC3nso2eow6X8BwZGNklVBZH1Yn092gWu6zFbQNizMTRxF4RvHV9L7xcnajU6Phi/Rn+OZDGkA5BzBrXBT8PF1Yfy+CxXw9c8to5JZWsOprJvuRN/PvwMMIb6aWyNxpdyr9p06Y6j48YMaKxlxa0YWb9fpB/DpjPM5q/4wLzd1ww2+fp6sRT47vU65rDOwXzw9YkQAqtCXEkELRtzuVIniOlAmKDPOv1nKEdg+gR6cvRNPPc27uHxDW3ec1OpzAfvr6jH3/svcj7N/Y28/A4Oyl5ekJXnp7Q1ew51yZGkV+q4pV/j9XrNbKLK/nfutO8eZ39etEaQqPF0ciRI2vsM8370Gq1jb20oI2y90J+DWFUG3Pvvoww3/rdoQyMD8LFSYFaq68xe00gELQttDq97DmKCfTEzbl+vX8UCgVvX9+Lp34/RK9oP0J93NDo9Dw+tpM1zW02JvQMb/CN4d1D47mhXzQ5JSpySypRKKBvbAAVah1/7L1I90hfsosrmbFASvReezyLNybrW8WUjEaLo/x88zI/tVrN/v37efHFF3nzzTebbJigbaDX6/l9TwoLdl7gkMkQSIAATxeiAjzkFv8Ark5Klj82jI6hPvV+DS83Z/rGBrDzfB4XcstIzi2r992iQNCW+WztaTaczOKFSd0t9gJyRI6lFVGmkm7ee9TS8bo2EqL9WfVE24qK+Li74OPuIrcoAPBwdTJrqju0YxBbz+SSUVTBrN8O8uHNvR1eIDVaHPn5+dXYd8UVV+Dq6srMmTPZu3dvkwwTtH60Oj1P/X6Qv6oSrA1E+rmz/qmR8h3dyiPpJOeV4axUMqprqNkfaX0Z0TmEnefzAFh7IpNpNuw/IhA4AicziuWePs/8cYg1M1tHzunO88ZxGgPjg+o4U1Bfekf7s/WM9Hv9a38qF/PL+OjmRGICHfcmtNmbF4SFhXHy5MnmvqygFfLvwbQawgjg9ck9zVzdE3pGcP+IDtwzLL5RwghgbLcweW3aPFIgEFjmeLrRY3s6q4SsqqaJjs6KIxnyelB7IY6agzHdzIfQ707KZ9qPux16KkGjPUeHDh0y29br9aSnp/POO++QmJjYVLsEbYC1J7LkdZivG/3aBTCqSyhjTIRMc9El3IeeUb4cSS3iSGoRWUUVhNYzZ0kgaItcyC0z2z6YUsgV3R37b+ZMVgl7q4ZRdwnzoXPYpcv4BZemX7tAvp3aj/vnGyNGZ7JKWH4kg2t6R9rQssbTaHGUmJiIQqFArzef1TJo0CDmzp3bZMMErRu93jgt2svVia3PjLZ6F9ZRXULl/KXVxzK5Y1A7q76eQODIJOWWmm2fzykBmv/GpSVZdsg4bPam/tEOnxdjT4zrEU7SOxNZeSSDGQskkbR4f2rbE0fnz5v3e1AqlYSEhODu7th3FoKWISm3jOxiaSZR/7jAFmlPP6prKP9bdwaAH7ae5/aBseLDUSCoBdNGiWA+csNR2XMhT16P7yFaeliDcd3DiPBzJ72wgk2nsikoU+Hv6XjNIRv8jXTVVVdRWFhIu3btaNeuHb/88gt+fn7ExMTg7u5Obm4u3bt3t4atglaE6VBDw9Rra9Mnxl9+rbPZpey50LYHKwoEtaHX680aJQI1th0NrU7P/uQCAEJ93IgOsO/GjY6KUqlgUoI0RkWj07PSJMfLkWiwOFq1ahWVlcYpxG+99RZ5eUY1rtFoREK24JIYPqQA+rRQibBCoWDKwFh5+/c9F1vkdQUCRyO7pJLiSo3ZvpT8chtZ0zyczymhpOpn6hsbILzGVuRqk1CaaW6pI9FgcVQ9x6j6tkBQHw5cLABAoYCE6JptIazFhB4R+LhJ0eQVhzMcuppCILAWlrxE6YXlqB347+VMljFM2DWi/n3SBA2nZ6QfblWjnaqHZx0F6yd6CATVqFBr5TLhTqHe+Li33CBYD1cnRnQJAaC4UsOx9KJLPEMgaHtYEkc6PaQVOK73yFQcdQwVVWrWRKlUEBcktV1JzitDp3M8J0qDxZFCoajhjhTuSUFDOJJaiKbqj6VPTMt33R0UHyivd53Pq+NMgaBtYnq33yvK6Nm9mCfEkaB+xARKOV1qrZ6s4spLnG1/NLhaTa/Xc/fdd+Pm5gZARUUFM2bMwMtLUomm+UgCgSV2JRkFSWKsf4u//gCTrrg7zuUxfXj7FrdBILBnzplUpl3eOYTDqdJon4v5ZbU9xe45k20cNtvYZrKC+hMdYOyOnZJfRrifY1WyN1gc3XXXXWbbd9xxR41z7rzzzsZbJGj1rDdJ0BvSoeU71HYK9SbA04X8MjXbz+ZQodbi7lK/4ZMCQVvA4DnydHWiv0k16anMYluZ1CR0Oj1nsyTBF9uAYbOCxhPlb6wGTC0op78NbWkMDRZHP/zwgzXsELQRCspUcofaDiFetAtq+Ts4pVLB2G5h/L43hVKVlg0ns5jQM6LF7RAI7JEKtZaLVZVp8cFe9IkJwEmpQKvTs/1s7iWebZ+kFZZTrpaGzYqQWssQZdIqwRErHUVCtqBF2XgqG0Nu3uiuoXWfbEUmmZSa/mvSNbclqFBrOZFR1KBKuTKVhpm/HeDWb7ez5XSOFa1rOOdzSll+OF1U/rUSTmYUo636I+0a7oufpwsxVV90KfnlDlmhbJpv1EGIoxYhWogjgaB+aLQ6Hvv1gLw9uqvtRhEM6RBEgKdUJbfueBZlKs0lniGh0+lZdyKTkxlSeCGvVMX6E1nklarq9fy8UhVjPtzIhE82c8u3O+r9ugt2XOCvfansOJfHvfN2280Q0PxSFTd8tY0HF+7j5SVHbW2OoBk4mmas4OwR6QsYvQAllRqKKur3nrUnzJKxQ4Q4aglMw2opDpirJsSRoMWYs8U4csbH3dksl6GlcXFSMqGnND6gXK1lXT0blS3YeYF7ftzD1Z9vYd2JTK78dBPTftzN8HfXsfpo3Z1gT2UWc/9Pe0itKofeeyGfCZ9sZsPJ2l+7Qq3lnwOpfLDqlLyvUqMzmyxuC46nFzHy/fX0eX2NLAwX7kwW3qNWgCH5GqBnVaWaWf6IA3oB3lh2XF6LsFrLEOjliperlNt1MU+II4GgVky/0O8f3h6XFpinVheTEoyhtRWHaxcbFWotL/1zhKf/OCi3wldpdNzz4x4yi6TqzFKVlhkL9vLPgdQaz9fq9Dz1+0HGfbypxsiS5Lwy7v5hN8/9fVgOZZjy0MJ9PPbrAVTVRMcr/x7lREbz92g6m13CzzuTWbw/lbN1NG97e8UJknJrfuAdMvliFTgmB6satCoV0DOqynPkb6w8SnWwXkfVv5i7hIsGkC2BQqGQc0ov5jteA9FGD54VCBqCRquTGz/GB3vxyJhONrYIBsYHylVr605kUa7S4uFas4rlqw1n+Wn7hUteT6eHx349QIiPG0M6BMv7/z2Yxu97U8zOjfL3MPuS+XlnMgBvTu4p9w3LKKyotfW+Xg+3f7eT7bPH4OrcPCJz57lcbp+zU+5BBfDprYlcmxhldl5ppYYdtSTmbjuTQ98WGgcjsA4GMREb6Imnq/QVYZ4/4lhegF92JcvrSD93+WcSWJ/4YC+OpReh1elJyS93qBYKwnMkaBGScstQaaQ7h2520rrf2UkpT+YuV2vZeqZmorNWp2f+jtqFkbNSwd8PDuGGvtHyvtvn7ORwSqH8JbO+Wtisd4w/G58ayYJ7B9I5zOji/3lnMvGzl3Pdl1t5YtEBPlpjPqPQxUnB93f1x7NKwOWWqth2tvmSsz9ff8ZMGAF88t9p9Ho9Kfll7DyXS7lK+j0ZPFkKBcy8orN8/qI9Fx0yYVcgodbq5JlqAV7GSeqOmlx7JLWQLzeclbc/u62PDa1pe8QFGz2OSTmONbhYSGhBi2BIYAboEuZrQ0vMGdU1lF93SwNot5zJYUy3ULOO74dSCiwmW1+bGMneC/n877Y+9IkNoEOoNyuPpFOq0qLXw9WfbwHg+au6mfV1WvboMLqE+eDspGRYp2BWP3E5/xxI5fFFBzBoiv3JBWaDeQGevbIrY7qG0inMh9eu7cmTvx8EYMPJbEZ2aXrVX0GZii0WxOH5nFKu/HQzpzKL0emRPW0G5tzZn9FdQ/lzXwoXcsu4mFfOsHfXs2D6QIe6SxRIFJYb/20DPE3EUaB5Qz9HoXqYOyHa3zaGtFHiTFq1fLPpLKNsWKHcUFqV5+jUqVNce+21BAcH4+vry7Bhw1i/fr3ZOcnJyUycOBFPT09CQ0N56qmn0Ggcr/rC0Thpkh9jTzH/y+KMo0R+3JZE4mtr2F3VwbtCrWX2X4drPGd4p2A+vjmRLc+Mpk9VCMnX3YU/HhhS49w3lx+Xq3uGdQymR6QfztVyra5NjOKTWxJxcbI8hqdXlB8zLu9ApzDp9zauh7HKzzDAt6lsO5sri7N7h8Xz1nW95GMnMorl9gumwsjH3ZmhHYNRKBRMHdRO3p9aUM4t32yntFL8XTkaBWXGGwF/T+PMwzAfN5yV0vvTkTxHpjPi7hgU22whaEH9aB9iFEc7zuWx5GCaDa1pGK3qnTJp0iQ0Gg3r1q1j79699O7dm0mTJpGRISXRarVaJk6ciEqlYtu2bcybN48ff/yRl156ycaWt35OmHiOutqROAr0cmVsN6PYKCxXM/O3A+xOyqPriyvN7J53zwA+n9KH7+7sj1JZU8h0i/Bl01OjsHAIgJFVA28tcW1iFH8/OJSnJ3Th2Su70i5IulPvFOrN29f3MjvX192FuKrjx9Mb1i+pNky9RsM6BXNNYqTc6gCk8FmHEC+zL5cZl3eQO4vfOTiOWy+LkY9lFVfWCCcK7J8CE/Hr72H0HDk7KYnwl8Y/OJI4SjZJxn7tmp42tKRt0jHE/LN+2SHHEUetJqyWk5PD6dOn+f7770lISADgnXfe4csvv+TIkSOEh4ezevVqjh07xn///UdYWBiJiYm8/vrrPPPMM7zyyiu4urpe4lUEjcUwdsDDxYlYExe9PfDRLb156veDrDqaCUjDNW/6ervZOWG+blzeuXZxYyA2yJMDL4/jSEohU+bslPcnxvhz15C4Op/bM8pPLp2+f3h79IBTLUqrZ5QfSbllVGp0nM4qoVtE00KVhsaSrk5KBsYH4unqzILpA5m3LQkPFyemDm5Hx1AfiivU7EnKx8VJydCOxtEvrs5K3rkhgat6RXDn3F0A/Hcs06wiUGD/mHoGTcUxQLS/JxfzyiksV1NUocbX3aX60+0KvV4vz4LrEOJl8YZGYF38PF3oGeXLkVQpcuBIwrrVeI6CgoLo0qULP/30E6WlpWg0Gr755htCQ0Pp168fANu3b6dXr16EhRk9BePHj6eoqIijR0UDO2tRodbKd3AdQ73t7kPK192Fb6b256nxXWo955up9Z8M5OvuwpCOwSx7dBijuoTw2JhO/PnAkAa1LlAqFbUKI4DeJrkTB5sYWjufUyr/+/Rt5y9X8/SI9OO9G3vz6rU96Rgq3QH6uLswqmsowzoFm+VmGRjUPggfN+n5609mi75HDoZZWM3L/GbRNCnbEfrWZJdUUqGW3n/2dkPWlvj1/sHyOimn1GEKNlqNOFIoFPz333/s378fHx8f3N3d+eijj1i5ciUBAVJeSEZGhpkwAuRtQ+itOpWVlRQVFZk9BA1j65kcOWelkx03YDMNr4HU52XpI8NIemciiTH+Db5ej0g/fpg2gCeu6Fyn0GkMCdF+8vpgStN6C3214Yy8HtXE5G5XZyUjqsKHheVqFtRR6SewP8zDauaeoRizpGz79wCYCjghjmyHt5szo6o+E0pVWtIK7aO7/6Wwe3H07LPPolAo6nycOHECvV7PQw89RGhoKJs3b2bXrl1MnjyZq6++mvT0xs/Oevvtt/Hz85MfMTExl36SwIyP/zN2dx7SMbiOM21L5zBvgkzulmdf2U0Oc9kbPaP8ZMH1y65k4p5dVqMpZLlKyxOLDnDPj7tZfyKLH7aeN2vsqNPp+eS/U/y2R+rB5OPmzPUmLQkay+0DY+X1K/8e43uTzugC+ybfxHNkWq0GEBPoWJ4j03yjGCGObIqhmATgV5O+U/aM3ecczZo1i7vvvrvOc9q3b8+6detYunQp+fn5+PpK+Rdffvkla9asYd68eTz77LOEh4eza9cus+dmZkp5JuHh4RavPXv2bGbOnClvFxUVCYHUANIKyuV4c+cwbyYn2m8OikKh4IkrOvP28uMM7hDEPcPibW1SrXi5OTOue5hZ1/GHf97Pm5N7sudCPokx/qw/kcXf+6VS5vqMR3lsbCdCfNyabNuQDsFMTIhgWdVA3zeXHePaxEiCvc2vXViuJjm3jB6RvnYXam2rFJiU8vtXzzkKcDTPkdFGIY5sy62XxfDtpnMArDmWyaxxtacw2At2L45CQkIICbl0ImxZmXSXoFSaO8OUSiU6nRR3Hjx4MG+++SZZWVmEhkrhgzVr1uDr60v37t0tXtfNzQ03t6Z/YbRVDF/OABN7RdYoY7c37hjUjtsHxlrMp7E33pjck/TCCrmc/0xWCbd8u6NR1+oa7sPUwe0ufWI9eXZCV1kc6fSSOLu5v/GmokKtZeJnm0nJL+eZCV15YGQHdDo9CgUO8btvrdRWyg8QYyKOHM1zJMJqtqV9iDedQr05nVXCuexSdDq93d8Q2fc3VQMYPHgwAQEB3HXXXRw8eJBTp07x1FNPcf78eSZOnAjAuHHj6N69O1OnTuXgwYOsWrWKF154gYceekgIICth6rG4vm9UHWfaD47y5Rzk7cbih4byxZS+jXq+s1LBmK6hvH5tD/56cAhuzjVHpzSWmEBP/phhTMSsPm5k8+kc2fvw7soTLDuUTsKrq+nz+hqm/bCLJxYd4M+9KegszJsTWA+znKNqYbVQk15H6Q6QNyLCavaFIaFfpdWRU1ppY2sujd17jupLcHAwK1eu5Pnnn2f06NGo1Wp69OjBP//8Q+/evQFwcnJi6dKlPPDAAwwePBgvLy/uuusuXnvtNRtbbx+oNDrO5ZTQJcynWQSCSqOTJ3zHB3uJDygrMTEhgjXHIll8wHIPkYXTB+KsVNA7xh93FyeyiirILqmke4SvVYVgQrQ/rs5KVBpdjYG7P21PMtt+6Od98nr9yWxA8jrO+v0g/doF4OHihJNSwcZT0rEuYT6E+7nz8S2JBHqJFhzNhaEbvKuzUp6obkCpVBDq40ZaYQVZxfYvjgzerUAvV7zdWs1XncMSZVLtmJpfTqiPuw2tuTSt6h3Tv39/Vq1aVec57dq1Y/ny5S1kkWPx4MK9/Hc8i8mJkXxya9NnEB1LL5LnqfVpRLWXoP58cmsfhnQMJj7YC7VGx9S5u9Dr9Tw9oStDqyXBh/q6E+pr/Q8mV2clvaP92J2UT3JeGVlFFYT6upNfanlUSW3srSasAE5mFnMys5i+r68hyt+DSQkR3DMsnrAW+LlaE8fTi/hm41m5D1dulTgK8nK1KJxDfd1JK6wgp0SFWqtrUHuKlqRSoyWjSBJw4qbMPgg3+dvMLhaeI4GDUFyh5r/jUghs8YE0Xr22J34eTWvytuKwsUqwTzsxqd3amOb07H5+LEpFzdBIS3NZXCC7kyRx8791Z9h4Ktss3GGKm7OSqAAPovw92JOUT7laW6/XSC0o55tN51h5NINVj4/g/VUn5Qq5L2/vyxXdw1Aq6u4b1VZ5cfER9lzIZ/GBNAK8XMmvEke1eePCfI3pB9nFlUT6e1g8z9ak5pfL43BEvpF9EGRSkJFrYV6lvSHEkQAwH+8B0jTr6h6HhqDV6fljr1Qi7uqk5KqelqsBBdbBXkJNN/aLlqeiz7fQ8+i9GxM4nl5E5zAfbukfY5akmVNSSVpBOcUVGv47nklhmZoylZbO4T5EB3jw9B+HzK51IbeMri+uNNv34EJjuO69GxK4+TJRaWqgpFJjFu587NcD8rp2cWS8+88oqrBbcXQh1yTfKMA+bWxrmLZJyS0RniOBHaPX69l0OgedTl9jDta5nNImiaODKQXy3cHorqFmdw2CtkP7EG9Gdw212ErAx82Za3pHmnm8TAn2dpPL/y29F8d1D+PHbUnM25ZkNvaiNl799yjXJEbK8+DaOj1frj0FIagWcRThZxQaKfnl9I21T4/whVzjwFnTyfAC22H6HZBTIjxHAjtm8YFUnlh00OKxlPymleruTy6Q1yPqMZNM0Hp55eoeVGq0bD1jrFjzdnPm09sSmyRU/D1deXxsZx4f25l/DqQy+6/DlKmkUFxijD9JuaVm1VelKi37kwsY3CGotku2GQyzDmsj0MvyzUx8sFFonDeZeG9vXDAt4w8SYTV7INjbxHMkwmoCe+aHrUm1HisovfSdeF2cyTJ++HaL8KnjTEFrJzbIk4XTB5GSX0ZeqZTIGxfk1azexGsTo7g2MQqtTm+WW7TrfB63fLtdzj85mlYoxBEwY/5eee3j7kz3CF92ns+T94X7Wf63aR9iIo5ySiyeYw8km4TVhOfIPjDLOXKAsJp9lhoIrM7m09kcqmMmV15Z05T96UzjB2cHO56nJmg5ogM8SYj2p1+7QKuFWasnXQ+IDzTrt2Sai9JWOZpWyLkco9dn2SPDa4zJ6VjL32xsoCeGIjbTa9gbSVVhNTdnJaHN0PVd0HS8XJ1wc5YkR64DhNWEOGqDvLfyBFO/31XnOflNcHvq9XpOZ0niKNzXHV/3plW9CQRNITbQ6Dm44ACdna3NLhMPEUievbhgc+9KYozlXCJ3Fye5+utUZjEarc46RjYBtVbHxaoGo7GBnnbfibmtoFAo5ET//CbefLcEQhy1MfYn58vVQwYM0+ifGNsZn6pmaU3xHOWUqCismtFU2x2oQNBSBHu7ynesGYX2PxPM2hw28RjPv3cAAJ1N/k67hPnUWe3YM1LyMlWodTU+S+yBE+nFcn+1rhG+NrZGYIpX1fdLaaXGxpZcGpFz1MZYcyxTXveJ9ef3/xuMs5OSSo0WN2cn/tqfQnGlpkluzzNZxpCaEEcCW6NQKIjwcycpt8whxl5YE61Oz7qqylQXJwUD4gMBqR/Vjf2iOZ9TylvX9arzGokx/iyr6mH2+bozPDyqo115Z/YlG9sT9Iv1t50hghoYOpWXqrR2P19NeI7aGAdTCuT1V7f3kwfBGuZqhVTlghSWq6nU1K8JX3WOpxfJ685hIhlbYHvC/aT+PMUVGkoc4K7VWvxzIFWu4EuI9pf/7pVKBR/c1Js/HxhCl/C6/2ZNBxSrtDp6vrLKrn6npuKor2g+a1eYjnEpVdnPe8YSQhy1MU5mSF6dIC9X+QvDlBCT5MUzWSW8/M8R5m9PQq+v/wBQU3EkKtUE9oBpf56MNuw9Mp2/d9eQuEZdw93FiY9u7i1vl6m0/LoruammNRuGsKGbs5JuIqxmV5iKI3sS1JYQYbU2RH6pipyqEspOYZbDXaaVHU8sOsCpqqqzQC83xnQLpahcbTaXK69UhUank4cI6vV6jmdI4kipgK7h4sNJYHtMbwQyCivaZLhXp9NzoMqrEuztxtUJEY2+1lW9Ipj5m7FH2p/7UnFxUhIb5En/dgH42KgIQ6PVyeNp2od42+3st7aKl6nnSIgjgb1g2vittnCXqfA5ZVKO/9DP+wjxcSO7uJJrekcyKSECbzdn7p+/l5JKDUM6BBHg5crKIxlodZKXKS7YCw9X0Y1YYHsiTMRRWhtNyj6fW0pRhfSFlBjjb3GwbH1xd3FizRMjuOLjTYDkLX55yVH5+IMjO/D0hK5NM7gRpBaUo6n6/IkPFs0f7Q0fd6PkKK4Q4khgJ5wySZTuVIs46hBS+x21YZLykoNpLDmYZnZs29ncGucLl7bAXjCdCN4Ww2pzNp/jjWXH5e0+zZCo3CnMh9sHxrJwZ82Q2pcbznL3kDizm62WILPI2Fww0k/MVLM3vNyMN8ullY3LaW0phM+xDXHa1HNUS1ihOXOExnYLbbZrCQRNwTTnqK1VrO29kGcmjEDyHDUHt9QxyHfKnJ3N8hoNIc+kP1ugt30MXxYY8TLLOWraFAZrI8RRG+JkxqXDajEBjXdFh/u6MyA+kCh/DyYmRDApIbLR1xIImhPznKO2FVbbeDLbbNvVWcnAqhL+ppIQ7c/zV3UDpHzFD28yJmqfySrh+i+3Mm9bUrO8Vn0wbS4Y6CnEkb3haTJLsVxd03O0/HA6r/17zC68uyKs1oYwdK0O8XEjoJYmb0qlggk9wll5NEPed+qNK7mQW0pBuZr+7QI4nFrI+ZxSTmUWc9fgOPLL1Hi6OhETKGL8AvskyMsVFycFaq2+TXmOdDo9a45nme3b+sxouYVHc3DfiPZM7hOFl5sTnq7OrDiSwX/HpX5q+5IL2JdcwGVxgXSPtH6Y3dRzVNtnnMB2mA6arlCbd1efty1Jzls7k13CT/cMaFHbqiPEURshp6RS/uDoXEulmoEZIzvI4mjJw0NxdVaa5SglRPuTEO0vb7d0XoFA0FCUSgVhvu6k5JeTUdR2xNGOc7lyaw1fd2c2PT0Kfyt4VExbgLw+uYcsjgzsTc5vcXFUV5dvgW0wF0fmnqO5W8/L602nsm3eJFKIozaC6SDYTqF15xUlxvhz4vUJgPmbWSBwZCL8JHFUUKamXKVtE5WUpg0RX7mmh1WEUXUi/Dy4onuYWTf+UyYhfWuSL8SRXePuYvRYmnqONFodKfnm4e4LeWXEV5v515KInKM2wpnsho30cHdxEsJI0KoIN20E2Ua8RxdyjYN2W7Jb/Xd39mfTU6Pk7ZOZLSOO8kTOkV3jVovnKL2wQm4BY8C0mbAtEOKojXDG5MOpUxtsgCcQRPobw7+GRoGtHdOfs11Qy+YExgZ5yuG2syZtRKyJwXOkUICvh20aUQpqx93ZRByZjKfKKq6sca4QR4IW4XQ9ehwJBK0Z0x5eZ1roy9rWGMRRoJerTbpWd6z6neeWqszygaxFftXcOH8PF5zseKhpW8U0rFZpElYrKq9Z1n8sTYgjgZXR6/WcqIr5B3m5ili8oE1iKo7OZrd+cVSu0sqVebE2qiQ1HVPUEoK0oCqs1hK5VYKGU1tCdqElcSQ8RwJrk1FUId+1ia7VgraKaa5dW/AcmQrA9iG2SWw1FaTnc6z7O9fq9BRXzevyEyE1u6Q2cVRUUVMcpRdWcOBiQUuYZREhjtoA/1t3Rl73bYaxAQKBI+Ln4dLiOTC2ZL/JF0t3G90Umc60yyqqmVfSnBRXqNFX5fQKcWSf1FatVlhmFEcju4TI602nzBuYtiSilL+Vs/RQGj+bzD6a3CfKhtYIBLalfbAX2cWV5JaqKK5Q22x6fEtgWoTRK8rPJjaY9j+ylHTbnBSYfMH6e7bef1dHpraEbFPP0f3D2+Pl5szdQ+K4LK55Ork3BiGOWjlzNp83225fx2BZgaC1E+VvPmOtNYujczml8tpWf/emDWKziq3bPqHAJG/FX3iO7JL65BwFebvxxZS+LWqXJURYrZVTWhWDB3hyXGcbWiIQ2J4Ik3L+tILWPWPtfJU48nF3JthGQ1hNXzfb6p4jYzWcn0jItkvcnE2q1TQmYTUTceTrYR8+GyGOWjk6vbGx1kOjOtrQEoHA9kT4mXuOWisarU4Wf3FBXigUtilrd3N2kkNc1g6rmX7Bipwj+0SpVOBaNdevwqyU33gTby//dkIctXJyq6rUYgI9bPYBKRDYC6ZhtdbsOcoqrsTQcNg0KdoWhFblHWUVV6LX6y9xduMpFGE1h8CtKim7UlMzrOasVOBhJ5MZhDhqxWi0OjlJMcjL7RJnCwStH/OwWuv1HJl6xSJNBKEtCPWRfucqjc7MQ9DciIRsx8CQd2TWBLIqIdvPw8VubuKFOGrFmM4ZChKNHwUCs7Baa/YcpRcaf7ZwG3uOYkwaUG44lWW11xHiyDEw5B2Z9Tmq8hzZ08gXIY5aMabt+oNslJApENgTvu7OeLtJCZ+mAqK1kWHiObJ1WG1Cz3B5PW9bktVep6DcJCHbQ3ze2Suy56gqIVtn0rzT190+krFBiKNWTW6JyYRqEVYTCFAoFHLeUVphBTqd9XJgbIlpyNDUW2YLRnQKlgXayYxiq+UdFYmEbIfA0AjS4DkqUWnk5p3CcyRoEXJNPEe2KuUVCOyNSH9jDkxOqXUrqGxFRpHRK2Zrz5FCoaBruDTsulSlJdNKnbLzy4Q4cgQMjSA1Oj0arY7iCmMemo/wHAlagtwS44eQGDYrEEhEmCQoW3ukha0wDauF+trea9wSc+0MaQQ+7s64OouvNnvFzcW811GxSXdsXztqyireQa0Y85wj239ACgT2QIjJ34K1GxPaCoN3JtDLFTdn25dGdwr1kdenTMaaNCc5Vf+WweKzzq4xGyGi1ppVMArPkaBFyCkR1WoCQXVM5321RnGk0+nlUR1hvrYNqRnoGGb0HJ22gueoQq2Vk3pFCoF9YzZCxI49R/Yj0wTNTp5JPoWoVhMIJMyHoba+Xkd5ZSrUWinDNcwOQmoAnUzCaiczipr9+ub5lS33M+v1ejQaDVqt9tInCwAI8VQQ5SMJpNKyMsrKy+XtYA8FFRVN+5t0cXHByanp3lIhjlox5tVqQhwJBND6PUem+UbhduI58nF3ITrAg5T8ck5kFKPT6VEqm6/ZX05xy98IqlQq0tPTKSsra5HXay2Mi1UyNCwUgIq8DMKVOl4ZJW0HepVz/vz5up5+SRQKBdHR0Xh7N23YshBHrRg5QdHN2S7yDgQCeyDUVByVtC5xpNLo2Hk+T96ODfKs4+yWpVuELyn55ZSptCTnlREX7NVs1zYVuSHe1heEOp2O8+fP4+TkRGRkJK6urnbT2dne8SqqIL+qQXF0oCflKi2uVX+HUf4eeDchtKbX68nOziYlJYVOnTo1yYMkxFErJqfqDSdCagKBkeBWmpBdrtJy5aebSMo1ejI6hjTt7rk56Rruw5pjmYBUsdac4ijHROSaegathUqlQqfTERMTg6en/QhQR8ClUo+iKqjh6upGpV6DwlkKA7t7eODu1jRZEhISQlJSEmq1ukniSCRkt1JUGh1FVf0jREhNIDDi7uIkd+JtTeJo4c4LZsLI1UlJn9gAG1pkTryJGErKLW3Wa5v+O7ZkQrZSKb5CG4rSxMOm04PWpBGrUzN435rLgyf+ZVsp+WWiO7ZAUBsG70JrEUcVai1vLDtutm9g+8AW8aLUF1NP0bmc5hVHLe05EjQec3GkNxdHdqRI7MgUQXNi2tzOHprACQT2hOELtFSlpbTSepPiW4qU/Jpz4u4aHNfyhtRBexNx1NyNIE1zx1pzn6MNGzagUCgoKCiwtSmNxjQPP6FbZ+Z89YXxWDMm6TcVhxFHb775JkOGDMHT0xN/f3+L5yQnJzNx4kQ8PT0JDQ3lqaeeQqMx/+DbsGEDffv2xc3NjY4dO/Ljjz9a33gbYFqiHCrupAQCM0J8jEm7Oa0gKbvQZK4YwHNXdWVMt1AbWWMZf09X+bOouWesmf78AXacRnD33XejUChqPCZMmGBr01oMJzMBpEenb96wWnPhMOJIpVJx00038cADD1g8rtVqmThxIiqVim3btjFv3jx+/PFHXnrpJfmc8+fPM3HiREaNGsWBAwd4/PHHmT59OqtWrWqpH6PFMJ1fZC+N4AQCe6G1dckuNJlI/+jojtw/ooNdVk91qmoGWViupqBMfYmz649hPpdSAV6u9l2ZO2HCBNLT080ev/zyS4u9vkqluvRJVsQ0rKYHWSQ7VQlFe8FhxNGrr77KE088Qa9evSweX716NceOHWPBggUkJiZy5ZVX8vrrr/PFF1/Ib4avv/6a+Ph4PvzwQ7p168bDDz/MjTfeyMcff9ySP4pV2XI6h/t+2sNzfx+W94XbePCkQGBvmDeCdHxxZCo07HlUUGygsbLrYn7z9QcyiCNvN2e7+oK1hJubG+Hh4WaPgIAAkpKSUCgUHDhwQD63oKAAhULBhg0bar3eli1bGD58OB4eHsTExPDoo49SWmrM6YqLi+P111/nzjvvxNfXl/vvv/+SNj7zzDN07twZT09P2rdvz4svvohabXyPvfLKKyQmJjJ//nzi4uLw8/Pj1ltvpbjYOBqmuLiY22+/HS8vLyIiIvj4448ZOXIks5+eZXwhPeiqlkqlFC6cPn06ISEh+Pr6Mnr0aA4ePHhJe62Bw4ijS7F9+3Z69epFWFiYvG/8+PEUFRVx9OhR+ZyxY8eaPW/8+PFs37691utWVlZSVFRk9rBH9Ho9e5LyuHPuTrlc1kD7ZiyZFQhaA6GtrBGkqTjy97SfEQzViQ4wEUd5NfOkGothBIWPHY2faAnOnj3LhAkTuOGGGzh06BCLFi1iy5YtPPzww2bnffDBB/Tu3Zv9+/fz4osvXvK6Pj4+/Pjjjxw7doxPP/2U7777roYT4ezZsyxevJilS5eydOlSNm7cyDvvvCMfnzlzJlu3bmXJkiWsWbOGzZs3s2/fPqpLV9lzpFRw0003kZWVxYoVK9i7dy99+/ZlzJgx5OXl0dK0mj5HGRkZZsIIkLczMjLqPKeoqIjy8nI8PDyozttvv82rr75qJaubh5MZxdz9wy7SC2u2XXdWKsw+kAQCQevrkl1gknPj52G/AiE6wPgZaw3PkT0NLq2NpUuX1uje/NxzzzFlypQGX+vtt9/m9ttv5/HHHwegU6dOfPbZZ1x++eV89dVXuLtLUYPRo0cza9asOq5kzgsvvCCv4+LiePLJJ/n11195+umn5f06nY4ff/wRHx9pqPDUqVNZu3Ytb775JsXFxcybN4+ff/6ZMWPGAPDDDz8QGRlp5tmTwmrSev+u7ezatYusrCzc3KS/zw8++IDFixfzxx9/1Mvj1ZzY9J307LPP8u6779Z5zvHjx+natWsLWVST2bNnM3PmTHm7qKiImJgYm9ljiU/+O2VRGAFodPpqCXACgaC1iaNCk9Yd/p72m5AcYxJWS2kmcaTS6KjUSMEZRxBHo0aN4quvvjLbFxgY2KioxMGDBzl06BALFy6U9+n1ermDd7du3QDo379/g667aNEiPvvsM86ePUtJSQkajQZfX1+zc+Li4mRhBBAREUFWVhYA586dQ61WM2DAAPm4n58fXbp0obao58ljRygpKSEoKMhsf3l5OWfPnm2Q/c2BTd9Js2bN4u67767znPbt29frWuHh4ezatctsX2ZmpnzM8H/DPtNzfH19LXqNQIoPG1SsPZJVXFEjjBYT6CG7rB8d08kWZgkEdk1IKxshYuo58rdjz1GMFcJqplPdHSGs5uXlRceOHWvsLymR2huYVvGZ5vlYoqSkhP/7v//j0UcfrXEsNjbW7DXry/bt27n99tt59dVXGT9+PH5+fvz66698+OGHZue5uJj/rhUKBTqdjkthVqtmUrBYXlZKRESExfyq2irUrYlNxVFISAghISHNcq3Bgwfz5ptvkpWVRWioVMK6Zs0afH196d69u3zO8uXLzZ63Zs0aBg8e3Cw22ILf96SgMWmidfvAWJ6f2I1P/zuNWqtnxuX1E5cCQVsiwNMVJ6UCrU7fKjxHjpJzFOztiperE6UqLYdSClBrdbg0sfOfIaQGjuE5qg3Dd2F6ejp9+vQBMEvOtkTfvn05duyYRbHVWLZt20a7du14/vnn5X0XLlxo0DXat2+Pi4sLu3fvlkVaYWEhp06dYvjw4SiQQmqG/wIk9O5DRkYGzs7OxMXFNfnnaCoO805KTk4mLy+P5ORktFqt/Kbp2LEj3t7ejBs3ju7duzN16lTee+89MjIyeOGFF3jooYdkz8+MGTP4/PPPefrpp7nnnntYt24dv/32G8uWLbPhT9Z49Ho9f+5Lkbc3PTVKHjQ5+6putjJLILB7nJQKAjxdyClRyQOaHRmD50ihsG/viUKhYGTXUJYdSie/TM3RtCISY/ybdE1HE0eVlZVyHqwBZ2dngoODGTRoEO+88w7x8fFkZWWZ5f5Y4plnnmHQoEE8/PDDTJ8+HS8vL44dO8aaNWv4/PPPG2Vfp06dSE5O5tdff+Wyyy5j2bJl/P333w26ho+PD3fddRdPPfUUgYGBhIaG8vLLL6NUKqseCrPO2AAjR49m8ODBTJ48mffee4/OnTuTlpbGsmXLuO666xocGmwqDlOt9tJLL9GnTx9efvllSkpK6NOnD3369GHPnj0AODk5sXTpUpycnBg8eDB33HEHd955J6+99pp8jfj4eJYtW8aaNWvo3bs3H374IXPmzGH8+PG2+rGaxNnsEs5lSyWbA+IC7WoCt0Bg7xhmDuaWOr7nyJBz5OvuYvc5hv3bGee9HU0rbPL1HC2stnLlSiIiIswew4YNA2Du3LloNBr69evH448/zhtvvFHntRISEti4caPskenTpw8vvfQSkZGRjbbvmmuu4YknnuDhhx8mMTGRbdu21avCrTofffQRgwcPZtKkSYwdO5ahQ4fSrVs33N3dzXodGXByUrJ8+XJGjBjBtGnT6Ny5M7feeisXLlyoUUjVEij0zdmmtA1QVFSEn58fhYWFNRLUWprvNp3jzeXSPKUXJnZj+nARQhMI6stt3+5g+7lcAI69Nh5PV/v3OtRG4murKShTExvoyaanR9nanDrZcS6XW7/dAcDorqHMvfuyJl1v5ZEMZizYC8DTE7rw4MjmCzHVRkVFBefPnyc+Pl6uCBPUTWlpKVFRUXz44YcMn3gzFRqt2fEof49m6dFV179NQ76/HffTQMD6k1nyemQX+xoVIBDYO4Em09tzS1R4Bjrmx6Faq5Nzjux5dIaBruHGCqd1J7IoqlDj2wSPj6N5jtoK+/fv58SJEwwYMIDCwkI5inPttddSoLPgObIzj6fDhNUE5hRXqNl1XmqMFRvoSYcQ0ehRIGgIQSZCIteB845ME8rDHWDItL+nq9nn1aojGXWcfWlMc458HSDnyNa89dZbeHt7W3xceeWVzfpahuaTY8eOpbS0lM2bNxMcHIwlHWRv4ki8kxyMs9kl7DiXy/dbzstVaqO6hNh9y3yBwN4INBFHeQ6cd5RZZDpk2jFCPC9d3YO75kqtV3acy+Om/o3vHedoCdm2ZsaMGdx8880Wj9XW0qYx9OnTh71791o8ZkkIWcpDsiXineRA7EnK4/Y5O+WGZwau6B5uI4sEAsfFNL8hp9hxPUems+HCHMBzBDAwPhBnpQKNTt/kpGwRVmsYgYGBBAYG2tQGS0LI2cm+xJEIqzkIheVqHvv1gAVhFMaQDkG1PEsgENRGuK/Ry1Jbh3lHIMvUc+TrGJ4jdxcn4qpmPp7LKUWjvXTzwNoQniPHQ2nBc+SitC85It5JDkB+qYqbv9lOaoGxo+wV3cO4a3AcwzoF29AygcBxifQ3Com0guYbgtrSmHqOTAfq2judQr05k1WCSqPjYn458Y0ckF1SaRRH3m7iK80RqO4kclIqLAomWyLeSQ7Ah2tOcjpLai3v4+7MiseGi2GyAkETifI35lekFTquODLNOQpzEM8RSOJoRdX6RHpRo8VRmcoojrwcuB1DW6K6EHK2M68RiLCa3ZOSX8ai3RcBcHVSMv/egUIYCQTNgJ+HCx4uToBje44yi0xzjhxHHPWK9pfX+5LzG32dUpWxX46nm1NTTBK0ENUTsl3sLN8IhOfI7ihXafl641kUCnBxUvL+qpPysenD45vcal8gEEgoFAoi/d05m11KWkEFer3eIas+DWE1FydpJIqj0M+kU/bupMaLo/IqceSkVODaxDltgpbBWVFdHNnfv5v9WdSG0er0zFiwl0/XnuaT/06bCaNwX3f+7/IONrROIGh9RFaF1srVWgrL656Abq8YErJDfdwdStwFernSvqrf0dG0QirU2ks8wzKlVWE1T1cnh/r57ZWkpCQUCsUlh96a8uOPP+Lv71/v82t4jpztT4rYn0VtmO82n2PjqWyLxz68uTd+Ho5zVygQOAKRfsa8o1QHDK2pNDq5gWWog5Txm2KYs6bW6jmU0riS/rJKSVSJfCPHobo4chOeI0FtHEop4OM1p+TtkV1CuKV/DFf1CuevB4cwtKOoShMImpsIs4o1xyvnN0vGdpAGkKb0iTWG1g6nNlIcmXiOBI5BdU+Rq/AcCWrj/VUn5R5Gdw1ux4/TBvDujQl8eXs/+pp8gAgEguYj0rRizQE9RxfzyuR1TGDzdTduKXpG+snrY2lFjbpGWVXOkUjGrj8rV65k2LBh+Pv7ExQUxKRJkzh79qzFczds2IBCoWDZsmUkJCTg7u7OoEGDOHLkSI1zV61aRbdu3fD29mbChAmkp6fLx3bv3s0VV1xBcHAwQQEB3HvTJI4fPohSocDdxf6kiP1Z1Eb5+JZEovw96NcugCfHd7G1OQJBm8DRy/mTco3iKDbI8eYrdgz1xpAmdDqruMHPV2l08hglTxfbhtX0ej1lKo1NHnq9vkG2lpaWMnPmTPbs2cPatWtRKpVcd9116HS1N+N86qmn+PDDD9m9ezchISFcffXVqNXGPL2ysjI++OAD5s+fz6ZNm0hOTubJJ5+UjxcXF3PXXXexZcsWduzYQY+unXnk7psJcNHiZIel/CJIaycEe7vx6/2DCPV1w81Z3AEJBC1BhJ9jh9X2XjBWeZlOu3cUPFydiA305EJuGaczS9Dp9A1qBmja48jWnqNytZbuL62yyWsfe208ng3IubrhhhvMtufOnfv/7d15XFTl/gfwz7DMMMMwDMuwyqqIKGooSlCaCopmpS3mRa6pkabpDVxTS9F8GVy3MjO9LVdITVJ/6vXmwiUVQ3MDARcUFDCsZBFlRxiY5/cHcpgji6DgDMP3/XrxenHOec4538cjw5fnPAsUCgXS0tIglUqbPCc8PBwjR44EAERHR6Nbt27Yv38/t06bUqnE1q1b0b173cChOXPm4NNPP+XOHzFiBP+e338HuVyO5PO/wf6VV1od+7OifelaF+ZgLqHEiJBnSP212q275RqM5Mn8WdTQctTbVqbBSJ5cT+u6pK5SWdvmTvEV6nMcUZ+jVrtx4waCgoLg6uoKmUwGZ2dnAEBOTk6z5/j6+nLfm5ubw93dHdeuXeP2SSQSLjECAFtbW+Tn53PbeXl5mD59Otzc3GBqagqZTIaysrIW76lJ1HJECOmyjAz10dNaioy8Mlz+sxjX7pTAoxMlGfkPJ4CUigxg3EmXzuhpLUVcWh4AICOvFA7mrZ/kltdypOHRamJDfaR9Gqixe7fFq6++CicnJ3z77bews7ODSqWCp6cnqquffAFmQ0P+aGqBQMB73TdlyhQUFhZi48aNcHJygkgkgq+v71PdsyN1zp8mQghpJ28M6IbII9cBAL9mFHSq5Kh+tFpnHMZfr77lCAAy8srg72Hd6nPLqxpajow13HIkEAg0nqC1RmFhIdLT0/Htt99iyJAhAIBTp0499ryzZ8/C0dERAHD//n1kZGTAw8Oj1fc9ffo0vv76a7z88ssAgNu3b+Pu3btPUINnQ/ufJCGEdKCXeiq45Cj1jyLNBtMGZVU13NIZnWnB2Ue5WTUkRzfy2tYpW/21mrgTJCbawMzMDBYWFvjmm29ga2uLnJwcLF68+LHnffrpp7CwsIC1tTU+/vhjWFpaYvz48a2+r5ubG7Zv3w5vb2+UlJRg4cKFEIu1d4Ql9TkihHRpblZSrtXh8OVc3txB2mzt0evc91adcI6jeq4KY9T3wc5o44g1/qKz1OeoNfT09BATE4OkpCR4enpi7ty5WLt27WPPi4yMRGhoKAYOHIjc3Fz897//hVAobPV9v//+e9y/fx8DBgzA5MmT8eGHH8LKyuppqtKhKNUmhHRpBvp68PewxsHUvwAAP57LwdyRPTUcVdOUtSqcunEXnvamiD7zO7e/to1DubWJkaE+nC2MkXW3HDfyylCrYo1mUG4Ov+WIkqPWCggIQFpaGm+fev+gpqYGePHFF5uc2wgApk6diqlTp/L2jR8/nncdLy8vXLhwgVfmrbfeamvozwy1HBFCurzQADfu+zOZhRqMpHmMMbwXnYhpURcwaPUvvGNjPG00FFX7cLOuGz5eVaPiTWz5OLyWo07aIZ1oJ0qOCCFdXneFFN3M6vo/nL91DwUPV7rXJjvP5TS59mJ3hTHGeNpqIKL2w++U3fpXazSUn3QUSo4IIQTAQKeGZXqmRZ3XYCSNlTxQ4pMDjV9peNrLEBs2tNWvobSVm1pydCO/rNXn8ZMjajnqCMOGDQNjDHK5XNOhPFOUHBFCCIARvRo6h175swRL9l3SYDR8yx5JjHxdLbDmzX44OPtFGGjhiuZt5f6ELUflVerzHFHLEWk/nf+nihBC2sGr/ex4I552nb+Nu2Waf73GGMN/Uv7itheM6oldM57H24Mc2rTUhjZzsTSGwcO6ZOQ9WcsR9Tki7YmSI0IIAaCnJ8DaCf15+y6qrV2mKfcrlLzt91/q3kzJzktooAdny7qFczML6kastQYN5ScdhZIjQgh5aIynDQY5N/Q9uphTpLlgHvrjfsPoraDBjjDUgddoTen5cMRadY0Kvxe2bp27cvU+R9RyRNqRbv6UEULIExAIBNg8aQC3nZyj+ZajP+43LMbqYK69Mwo/LfWZsq/ntq7fUUUVtRyRjkHJESGEqLGSGcFeXpeEpP5RBGWtSqPxqM/7082s9Yuydjae9qbc95f+KG7VOeU0Wo10EEqOCCHkEd4PX609UKpw7U6JRmNRbzmqn4tJF3nYNrQcZRW0rlN2fZ8jQ30BhAb066w1hg0bhrCwME2HofXofxMhhDyir1orRltGT3UE9T5HupwcWcsa1ofLa+UknBVVdS1H1Gr07ERFRXWJOY8oOSKEkEf0sJJy399sw6SEHaG+5UhkoAeFVKTRWDqSob4eLKV1C5nmFbdu8d/yhy1H1N+ItDdKjggh5BHdFdqRHDHGuOTI3kwMgUA35jVqjpVJXevR3bIqqFoxnJ9rOaKRam1SU1ODOXPmwNTUFJaWlli2bBm3SGxVVRUWLFgAe3t7GBsbw8fHB/Hx8QCA+Ph4TJs2DcXFxRAIBBAIBFixYgUAYPv27fD29oaJiQlsbGwwadIk5Ofna6iGT4/+RxFCyCPs5WIIDfTaNKy8I9wrr0alsi4B0OXO2PUUJiLgDlCjYrhfUQ2LFlrKGGPa1XLEGKBs/aK57cpQArQhcY6OjkZISAjOnz+PxMREzJgxA46Ojpg+fTrmzJmDtLQ0xMTEwM7ODvv378fo0aNx+fJl+Pn54YsvvsDy5cuRnp4OAJBK6/6QUCqVWLVqFdzd3ZGfn4958+Zh6tSpOHz4cIdUuaNRckQIIY/Q0xPA0VyCm/ll+P1eBVQq1iGzUf8n5U9c/P0+LKUi9LCSYrSnDa91iDeMX4f7G9WzMmlIhvJLq1pMjqpqVKhvXNKKPkfKCuAzO83ce+lfgNC41cUdHBzw+eefQyAQwN3dHZcvX8bnn3+OwMBAbNu2DTk5ObCzq6vLggULcPToUWzbtg2fffYZTE1NIRAIYGNjw7vmu+++y33v6uqKL7/8EoMGDUJZWRmXQHUmWvA/ihBCtI+zRV1yVF2jQm7JA9jJ2zc5OXrlDkJjUnj7Nv7tOYx7zp7b5o9U0/2WIysZPznysG2+bMmDhpnDjUVa0HLUiTz//PO8JNzX1xfr16/H5cuXUVtbi549e/LKV1VVwcLCosVrJiUlYcWKFUhNTcX9+/ehUtVNgZGTk4PevXu3fyU6GCVHhBDSBCeLhr/EbxWWt2ty9Mf9CszccbHR/g1xGXi1nx3XStVVRqrVq+9zBAAFjxmxll/ScFyhdp7GGErqWnA0de92UFZWBn19fSQlJUFfn59wttT6U15ejsDAQAQGBmLnzp1QKBTIyclBYGAgqqur2yW2Z42SI0IIaYKLZUNydCOvDH7dLZ/6mrUqhq9P3MT6uIwmj/9eWAHXpYfx/RRv+HtY43aXS47UW45aHrGWqzaizVqmBaP4BII2vdrSpHPnzvG2z549Czc3N3h5eaG2thb5+fkYMmRIk+cKhULU1tby9l2/fh2FhYWIjIyEg4MDACAxMbFjgn9GaLQaIYQ0oY+djPv+yp+tm7G5JeVVNei7IrZRYmTfRItUSHQibt0t73Kv1RTqyVFJyy1HeWrJk41MC1qOOpGcnBzMmzcP6enp2LVrFzZt2oTQ0FD07NkTwcHBeOedd7Bv3z5kZ2fj/PnziIiIwKFDhwAAzs7OKCsrw7Fjx3D37l1UVFTA0dERQqEQmzZtQlZWFg4ePIhVq1ZpuJZPh5IjQghpgoetDPoPX29d+evpZ8mOvZqLimr+X9xLX+6FUx8Nx9Gwxn+lD1sXj5yHS4eIDBrmANJllmodsO+Vt/w6Rn0uJGtTSo7a4p133kFlZSUGDx6M2bNnIzQ0FDNmzAAAbNu2De+88w7mz58Pd3d3jB8/HhcuXICjoyMAwM/PDzNnzsTEiROhUCiwZs0aKBQKREVFYc+ePejduzciIyOxbt06TVbxqdFrNUIIaYKRoT56KKRIzyvFjbxSPFDWwsjwyTv+Hr/On/Plt8UjuH5MvWxkuBU5FvHp+Zi67QJXJqugbhqBbl1gjiMAMDNuSADvV7ScHOWWqCVH2tDnqJOon7MIALZs2dLouKGhIVauXImVK1c2e40tW7Y0OjcoKAhBQUG8ffVzJ3VG1HJECCHN6GNf92qtRsWQkde6leKb8kBZi5MZBQAAmZEBbq4e02QH72HuVgj2cWy038Fc91+pAXX/NvWtdY9LjtRfOTb1apKQp0HJESGENMPTrmGNtdTbRc2Wq6iuQUFpFV7ZlIDxm083Wqz214wClD6om7DQ38MaBvrNf/R+Os4T5sb8V2gOXaC/EQAIBAKYSQwBAPfLlS2W/b2w7pWjqdgQpg/PIaS90Gs1QghpRn8HOff9tt9u4a2BDhCrzcZcUV2DBXtScfhyLu+8MRsTEBbghuddLTDI2RyRR65zx157ruWJAvX1BOhjJ0PCjbvcPgfzrtMyIpcIcbesGkUttBxVVtfiTnFdy5FjF2lVI89Wp2k5Wr16Nfz8/CCRSJpcETg1NRVBQUFwcHCAWCyGh4cHNm7c2KhcfHw8BgwYAJFIhB49eiAqKqrjgyeEdEoDHOV47mGClFVQjg1x6bzjH/3f5UaJUb0vfrmBv31zFt2XHkbW3bq+Q6ZiQ7zY4/FTArhZmfC2u0rLEQDIxXWtQOXVtVDWqposcyI9n5sd29Ne1mQZQp5Gp0mOqqurMWHCBMyaNavJ40lJSbCyssKOHTtw9epVfPzxx1iyZAm++uorrkx2djbGjh2L4cOHIyUlBWFhYXjvvfcQGxv7rKpBCOlEBAIB1k3oD6FB3UfltwnZ6Bsei9ziB2CMIfZq04lRcxaM6gnDFl6p1etpzZ9wT70FS9eZihtekRVXNv1q7Zdredz3YzxbmEabkCfUaV6r1fecb66lR31dF6BubZczZ85g3759mDNnDgBg69atcHFxwfr16wEAHh4eOHXqFLemDCGEPKqHlRTzRvbkXo2VVtVg/ObT+PfUQaiuaWjZmPaCM17pZ4cPdyXjz6JK3jVEBnqIfLMvXvfq1qp7jvG0xTe/ZuFWYTnCAnq2+9Il2ky9/1BRhZI3vB+o69z+86U7AOoWnPVxNX+m8ZGuodMkR0+iuLgY5uYNPzhnzpxBQEAAr0xgYCDCwsKavUZVVRWqqhomIyspefr5Tgghncv0Ia746vhNlFXVdarOLXmAj/7vEnf8HV8nhL/aBwDwQ8hgTP+hbhJH3+5161GtfM0TPaxav/imqcQQh0OHoLyqpsXFV3WRXNzQGb24snG/o+9PZXNJ6RA3BUQGtK4aaX86mxz99ttv+Omnn7hZPQEgNzcX1tbWvHLW1tYoKSlBZWUlxOLGf51FRES0ON8DIUT36esJ8Oui4Vh96Br+7+IfAIDLarNmq7fsdFdIcXz+sKe+p5Gh/lPNq9RZySXNv1bLLCjD2tiGfl8zXnJ9ZnGRrkWjfY4WL14MgUDQ4tf169cff6FHXLlyBePGjUN4eDhGjRr1VDEuWbIExcXF3Nft27ef6nqEkM7J3FiI9W/3x6Qm5iGieXbaj/yR12r1dp3Pgf/6k9x2LxsTDHA0e6axka5Doy1H8+fPx9SpU1ss4+ratr8M0tLS4O/vjxkzZuCTTz7hHbOxsUFeXh5vX15eHmQyWZOtRgAgEokgEnWtZm1CSPPeHGCPH8/l8PYN7anQUDS6R71Ddn1yNO+nFOxL/pNXztPeFIR0FI0mRwqFAgpF+32oXL16FSNGjMCUKVOwevXqRsd9fX1x+PBh3r64uDj4+vq2WwyEEN32nIMZJEJ9bp20jX97jvcLnTwdXnJUqUTS7/caJUYAMHt4j2cZFuliOs1Q/pycHKSkpCAnJwe1tbVISUlBSkoKysrKANS9Shs+fDhGjRqFefPmITc3F7m5uSgoKOCuMXPmTGRlZWHRokW4fv06vv76a+zevRtz587VVLUIIZ2Mvp4A015wBlA3OWNgHxvNBqRj5JKGDtkllUrsOMtvpVsY6I4zS0bAxdL4WYdGupBOkxwtX74cXl5eCA8PR1lZGby8vODl5YXExEQAwN69e1FQUIAdO3bA1taW+xo0aBB3DRcXFxw6dAhxcXHo378/1q9fj++++46G8RNC2mRuQE/s/8APsWFDu2Sn6Y5kobZ0yh/3K/FAWcttv/eiCz4Y1h22ptTH62mUlpYiODgYxsbGsLW1xeeff45hw4ZxI7e3b98Ob29vmJiYwMbGBpMmTUJ+fsPCyfHx8RAIBIiNjYWXlxfEYjFGjBiB/Px8HDlyBB4eHpDJZJg0aRIqKiq484YNG4Z//OMfCAsLg5mZGaytrfHtt9+ivLwc06ZNg4mJCXr06IEjR45w59TW1iIkJAQuLi4Qi8Vwd3dvcoLn9tZpkqOoqCgwxhp9DRs2DACwYsWKJo/funWLd51hw4YhOTkZVVVVyMzMfGyfJ0IIeZSBvh68HM0gEersgF+NsZeLYWJU9++a+kcRsh/OLm6gJ8BHY3pBIBBoMrxmMcZQoazQyBdjrE2xzps3D6dPn8bBgwcRFxeHhIQEXLx4kTuuVCqxatUqpKam4sCBA7h161aTvytXrFiBr776Cr/99htu376Nt99+G1988QV+/PFHHDp0CP/73/+wadMm3jnR0dGwtLTE+fPn8Y9//AOzZs3ChAkT4Ofnh4sXL2LUqFGYPHkyl1SpVCp069YNe/bsQVpaGpYvX46lS5di9+7dbX9IbSBgbf1X7eJKSkpgamqK4uJiyGQ0bT0hhLS3yd+f460tBwCuCuN2mSKhPTx48ADZ2dlwcXGBkZERAKBCWQGfH300Es+5SecgMWzdEjOlpaWwsLDAjz/+iLfeegtA3ZyAdnZ2mD59Or744otG5yQmJmLQoEEoLS2FVCpFfHw8hg8fjl9++QX+/v4AgMjISCxZsgSZmZncQKqZM2fi1q1bOHr0KIC6xona2lokJCQAqGsVMjU1xRtvvIEffvgBQN2UO7a2tjhz5gyef/75JuswZ84c5ObmYu/evY2ONfVs6rXl93enaTkihBDSNfS2bfyLi6ZLaB9ZWVlQKpUYPHgwt8/U1BTu7u7cdlJSEl599VU4OjrCxMQEL730EoC6vr/q+vXrx31vbW0NiUTCG2FubW3Nex336Dn6+vqwsLBA3759eecA4J23efNmDBw4EAqFAlKpFN98802jWNobtQkTQgjRKk11tna20O4O2GIDMc5NOqexe7eX8vJyBAYGIjAwEDt37oRCoUBOTg4CAwNRXc2fsdzQsGFkoUAg4G3X71OpVM2e09R59a9N68+LiYnBggULsH79evj6+sLExARr167FuXMd+29NyREhhBCt4qpovNTKFD8nDUTSegKBoNWvtjTJ1dUVhoaGuHDhAhwd6yY0LS4uRkZGBoYOHYrr16+jsLAQkZGRcHBwAABu4JMmnD59Gn5+fvjggw+4fZmZmR1+X3qtRgghRKu425jwtu1MjdDDyqSZ0qQtTExMMGXKFCxcuBAnTpzA1atXERISAj09PQgEAjg6OkIoFGLTpk3IysrCwYMHsWrVKo3F6+bmhsTERMTGxiIjIwPLli3DhQsXOvy+lBwRQgjRKqZiQ2wPaegTM30oraHWnjZs2ABfX1+88sorCAgIwAsvvAAPDw8YGRlBoVAgKioKe/bsQe/evREZGYl169ZpLNb3338fb7zxBiZOnAgfHx8UFhbyWpE6Co1WayMarUYIIc9GrYqhpFIJM7W5j7RBSyOiOqPy8nLY29tj/fr1CAkJ0XQ4T6W9RqtRnyNCCCFaSV9PoHWJkS5ITk7G9evXMXjwYBQXF+PTTz8FAIwbN07DkWkPSo4IIYSQLmbdunVIT0+HUCjEwIEDkZCQAEtLS02HpTUoOSKEEEK6EC8vLyQlJWk6DK1GHbIJIYQQQtRQckQIIYQQooaSI0IIIeQJ0GBv7dNez4SSI0IIIaQN6pe7qF85nmiP+iVO9PX1n+o61CGbEEIIaQN9fX3I5XJucVSJRMKtCUY0R6VSoaCgABKJBAYGT5feUHJECCGEtJGNjQ0ANFp1nmiWnp4eHB0dnzpZpeSIEEIIaSOBQABbW1tYWVlBqVRqOhzykFAohJ7e0/cYouSIEEIIeUL6+vpP3b+FaB/qkE0IIYQQooaSI0IIIYQQNZQcEUIIIYSooT5HbVQ/wVRJSYmGIyGEEEJIa9X/3m7NRJGUHLVRaWkpAMDBwUHDkRBCCCGkrUpLS2FqatpiGQGj+c/bRKVS4a+//oKJiUm7T/pVUlICBwcH3L59GzKZrF2vrS2ojrpB1+uo6/UDqI66gurYeowxlJaWws7O7rHD/anlqI309PTQrVu3Dr2HTCbT2f/k9aiOukHX66jr9QOojrqC6tg6j2sxqkcdsgkhhBBC1FByRAghhBCihpIjLSISiRAeHg6RSKTpUDoM1VE36Hoddb1+ANVRV1AdOwZ1yCaEEEIIUUMtR4QQQgghaig5IoQQQghRQ8kRIYQQQogaSo60xObNm+Hs7AwjIyP4+Pjg/Pnzmg6pWb/++iteffVV2NnZQSAQ4MCBA7zjjDEsX74ctra2EIvFCAgIwI0bN3hl7t27h+DgYMhkMsjlcoSEhKCsrIxX5tKlSxgyZAiMjIzg4OCANWvWdHTVAAAREREYNGgQTExMYGVlhfHjxyM9PZ1X5sGDB5g9ezYsLCwglUrx5ptvIi8vj1cmJycHY8eOhUQigZWVFRYuXIiamhpemfj4eAwYMAAikQg9evRAVFRUR1cPALBlyxb069ePmzfE19cXR44c4Y539vo1JTIyEgKBAGFhYdy+zl7PFStWQCAQ8L569erFHe/s9av3559/4u9//zssLCwgFovRt29fJCYmcsc7+2eOs7Nzo+coEAgwe/ZsAJ3/OdbW1mLZsmVwcXGBWCxG9+7dsWrVKt4yHlr3DBnRuJiYGCYUCtm///1vdvXqVTZ9+nQml8tZXl6epkNr0uHDh9nHH3/M9u3bxwCw/fv3845HRkYyU1NTduDAAZaamspee+015uLiwiorK7kyo0ePZv3792dnz55lCQkJrEePHiwoKIg7XlxczKytrVlwcDC7cuUK27VrFxOLxexf//pXh9cvMDCQbdu2jV25coWlpKSwl19+mTk6OrKysjKuzMyZM5mDgwM7duwYS0xMZM8//zzz8/PjjtfU1DBPT08WEBDAkpOT2eHDh5mlpSVbsmQJVyYrK4tJJBI2b948lpaWxjZt2sT09fXZ0aNHO7yOBw8eZIcOHWIZGRksPT2dLV26lBkaGrIrV67oRP0edf78eebs7Mz69evHQkNDuf2dvZ7h4eGsT58+7M6dO9xXQUGBztSPMcbu3bvHnJyc2NSpU9m5c+dYVlYWi42NZTdv3uTKdPbPnPz8fN4zjIuLYwDYiRMnGGOd/zmuXr2aWVhYsJ9//pllZ2ezPXv2MKlUyjZu3MiV0bZnSMmRFhg8eDCbPXs2t11bW8vs7OxYRESEBqNqnUeTI5VKxWxsbNjatWu5fUVFRUwkErFdu3YxxhhLS0tjANiFCxe4MkeOHGECgYD9+eefjDHGvv76a2ZmZsaqqqq4Mh999BFzd3fv4Bo1lp+fzwCwkydPMsbq6mNoaMj27NnDlbl27RoDwM6cOcMYq0sg9fT0WG5uLldmy5YtTCaTcXVatGgR69OnD+9eEydOZIGBgR1dpSaZmZmx7777TufqV1paytzc3FhcXBx76aWXuORIF+oZHh7O+vfv3+QxXagfY3U/9y+++GKzx3XxMyc0NJR1796dqVQqnXiOY8eOZe+++y5v3xtvvMGCg4MZY9r5DOm1moZVV1cjKSkJAQEB3D49PT0EBATgzJkzGozsyWRnZyM3N5dXH1NTU/j4+HD1OXPmDORyOby9vbkyAQEB0NPTw7lz57gyQ4cOhVAo5MoEBgYiPT0d9+/ff0a1qVNcXAwAMDc3BwAkJSVBqVTy6tirVy84Ojry6ti3b19YW1tzZQIDA1FSUoKrV69yZdSvUV/mWT/32tpaxMTEoLy8HL6+vjpXv9mzZ2Ps2LGNYtGVet64cQN2dnZwdXVFcHAwcnJyAOhO/Q4ePAhvb29MmDABVlZW8PLywrfffssd17XPnOrqauzYsQPvvvsuBAKBTjxHPz8/HDt2DBkZGQCA1NRUnDp1CmPGjAGgnc+QkiMNu3v3Lmpra3n/qQHA2toaubm5GorqydXH3FJ9cnNzYWVlxTtuYGAAc3NzXpmmrqF+j2dBpVIhLCwML7zwAjw9Pbn7C4VCyOXyRvG1Jf7mypSUlKCysrIjqsNz+fJlSKVSiEQizJw5E/v370fv3r11pn4AEBMTg4sXLyIiIqLRMV2op4+PD6KionD06FFs2bIF2dnZGDJkCEpLS3WifgCQlZWFLVu2wM3NDbGxsZg1axY+/PBDREdH8+LUlc+cAwcOoKioCFOnTuXu3dmf4+LFi/G3v/0NvXr1gqGhIby8vBAWFobg4GBejNr0DGnhWUJaMHv2bFy5cgWnTp3SdCjtzt3dHSkpKSguLsbevXsxZcoUnDx5UtNhtZvbt28jNDQUcXFxMDIy0nQ4HaL+L28A6NevH3x8fODk5ITdu3dDLBZrMLL2o1Kp4O3tjc8++wwA4OXlhStXrmDr1q2YMmWKhqNrf99//z3GjBkDOzs7TYfSbnbv3o2dO3fixx9/RJ8+fZCSkoKwsDDY2dlp7TOkliMNs7S0hL6+fqORB3l5ebCxsdFQVE+uPuaW6mNjY4P8/Hze8ZqaGty7d49XpqlrqN+jo82ZMwc///wzTpw4gW7dunH7bWxsUF1djaKiokbxtSX+5srIZLJn8otNKBSiR48eGDhwICIiItC/f39s3LhRZ+qXlJSE/Px8DBgwAAYGBjAwMMDJkyfx5ZdfwsDAANbW1jpRT3VyuRw9e/bEzZs3deY52traonfv3rx9Hh4e3OtDXfrM+f333/HLL7/gvffe4/bpwnNcuHAh13rUt29fTJ48GXPnzuVadLXxGVJypGFCoRADBw7EsWPHuH0qlQrHjh2Dr6+vBiN7Mi4uLrCxseHVp6SkBOfOnePq4+vri6KiIiQlJXFljh8/DpVKBR8fH67Mr7/+CqVSyZWJi4uDu7s7zMzMOrQOjDHMmTMH+/fvx/Hjx+Hi4sI7PnDgQBgaGvLqmJ6ejpycHF4dL1++zPthjouLg0wm4z7ofX19edeoL6Op565SqVBVVaUz9fP398fly5eRkpLCfXl7eyM4OJj7Xhfqqa6srAyZmZmwtbXVmef4wgsvNJpKIyMjA05OTgB04zOn3rZt22BlZYWxY8dy+3ThOVZUVEBPj59u6OvrQ6VSAdDSZ9jmLtyk3cXExDCRSMSioqJYWloamzFjBpPL5byRB9qktLSUJScns+TkZAaAbdiwgSUnJ7Pff/+dMVY3JFMul7P//Oc/7NKlS2zcuHFNDsn08vJi586dY6dOnWJubm68IZlFRUXM2tqaTZ48mV25coXFxMQwiUTyTIbVzpo1i5mamrL4+Hje8NqKigquzMyZM5mjoyM7fvw4S0xMZL6+vszX15c7Xj+0dtSoUSwlJYUdPXqUKRSKJofWLly4kF27do1t3rz5mQ2tXbx4MTt58iTLzs5mly5dYosXL2YCgYD973//04n6NUd9tBpjnb+e8+fPZ/Hx8Sw7O5udPn2aBQQEMEtLS5afn68T9WOsbhoGAwMDtnr1anbjxg22c+dOJpFI2I4dO7gynf0zh7G6UcqOjo7so48+anSssz/HKVOmMHt7e24o/759+5ilpSVbtGgRV0bbniElR1pi06ZNzNHRkQmFQjZ48GB29uxZTYfUrBMnTjAAjb6mTJnCGKsblrls2TJmbW3NRCIR8/f3Z+np6bxrFBYWsqCgICaVSplMJmPTpk1jpaWlvDKpqansxRdfZCKRiNnb27PIyMhnUr+m6gaAbdu2jStTWVnJPvjgA2ZmZsYkEgl7/fXX2Z07d3jXuXXrFhszZgwTi8XM0tKSzZ8/nymVSl6ZEydOsOeee44JhULm6urKu0dHevfdd5mTkxMTCoVMoVAwf39/LjFirPPXrzmPJkedvZ4TJ05ktra2TCgUMnt7ezZx4kTe/D+dvX71/vvf/zJPT08mEolYr1692DfffMM73tk/cxhjLDY2lgFoFDdjnf85lpSUsNDQUObo6MiMjIyYq6sr+/jjj3lD7rXtGQoYU5uikhBCCCGki6M+R4QQQgghaig5IoQQQghRQ8kRIYQQQogaSo4IIYQQQtRQckQIIYQQooaSI0IIIYQQNZQcEUIIIYSooeSIEEIIIUQNJUeEEJ0jEAhw4MABTYfx1KZOnYrx48drOgxCuhxKjgghT6SgoACzZs2Co6MjRCIRbGxsEBgYiNOnT2s6NEIIeSoGmg6AENI5vfnmm6iurkZ0dDRcXV2Rl5eHY8eOobCwUNOhkRYwxlBbWwsDA/r4J6Q51HJECGmzoqIiJCQk4J///CeGDx8OJycnDB48GEuWLMFrr70GANiwYQP69u0LY2NjODg44IMPPkBZWRl3jaioKMjlcvz8889wd3eHRCLBW2+9hYqKCkRHR8PZ2RlmZmb48MMPUVtby53n7OyMVatWISgoCMbGxrC3t8fmzZtbjPf27dt4++23IZfLYW5ujnHjxuHWrVutqmv9q61169bB1tYWFhYWmD17NpRKJVemqdd4crkcUVFRAIBbt25BIBBg9+7dGDJkCMRiMQYNGoSMjAxcuHAB3t7ekEqlGDNmDAoKChrFsHLlSigUCshkMsycORPV1dXcMZVKhYiICLi4uEAsFqN///7Yu3cvdzw+Ph4CgQBHjhzBwIEDIRKJcOrUqVbVnZCuipIjQkibSaVSSKVSHDhwAFVVVU2W0dPTw5dffomrV68iOjoax48fx6JFi3hlKioq8OWXXyImJgZHjx5FfHw8Xn/9dRw+fBiHDx/G9u3b8a9//Yv3yx4A1q5di/79+yM5ORmLFy9GaGgo4uLimoxDqVQiMDAQJiYmSEhIwOnTpyGVSjF69GhektGSEydOIDMzEydOnEB0dDSioqK4xKctwsPD8cknn+DixYswMDDApEmTsGjRImzcuBEJCQm4efMmli9fzjvn2LFjuHbtGuLj47Fr1y7s27cPK1eu5I5HRETghx9+wNatW3H16lXMnTsXf//733Hy5EnedRYvXozIyEhcu3YN/fr1a3PshHQpjBBCnsDevXuZmZkZMzIyYn5+fmzJkiUsNTW12fJ79uxhFhYW3Pa2bdsYAHbz5k1u3/vvv88kEgkrLS3l9gUGBrL333+f23ZycmKjR4/mXXvixIlszJgx3DYAtn//fsYYY9u3b2fu7u5MpVJxx6uqqphYLGaxsbGPreeUKVOYk5MTq6mp4fZNmDCBTZw4scn71TM1NWXbtm1jjDGWnZ3NALDvvvuOO75r1y4GgB07dozbFxERwdzd3Xn3Njc3Z+Xl5dy+LVu2MKlUympra9mDBw+YRCJhv/32G+/eISEhLCgoiDHG2IkTJxgAduDAgcfWlRBSh1qOCCFP5M0338Rff/2FgwcPYvTo0YiPj8eAAQO4FpVffvkF/v7+sLe3h4mJCSZPnozCwkJUVFRw15BIJOjevTu3bW1tDWdnZ0ilUt6+/Px83r19fX0bbV+7dq3JOFNTU3Hz5k2YmJhwLV7m5uZ48OABMjMzW1XXPn36QF9fn9u2tbVtFFNrqLfYWFtbAwD69u3L2/fodfv37w+JRMJt+/r6oqysDLdv38bNmzdRUVGBkSNHcnWTSqX44YcfGtXN29u7zfES0lVRjzxCyBMzMjLCyJEjMXLkSCxbtgzvvfcewsPDMWzYMLzyyiuYNWsWVq9eDXNzc5w6dQohISGorq7mftkbGhryricQCJrcp1KpnjjGsrIyDBw4EDt37mx0TKFQtOoaj4tJIBCAMcYro94nqanrCASCJve1pa71fbgOHToEe3t73jGRSMTbNjY2bvV1CenqKDkihLSb3r1748CBA0hKSoJKpcL69euhp1fXQL179+52u8/Zs2cbbXt4eDRZdsCAAfjpp59gZWUFmUzWbjGoUygUuHPnDrd948YNXgvZ00hNTUVlZSXEYjGAurpKpVI4ODjA3NwcIpEIOTk5eOmll9rlfoQQ6pBNCHkChYWFGDFiBHbs2IFLly4hOzsbe/bswZo1azBu3Dj06NEDSqUSmzZtQlZWFrZv346tW7e22/1Pnz6NNWvWICMjA5s3b8aePXsQGhraZNng4GBYWlpi3LhxSEhIQHZ2NuLj4/Hhhx/ijz/+aJd4RowYga+++grJyclITEzEzJkzG7U2Panq6mqEhIQgLS0Nhw8fRnh4OObMmQM9PT2YmJhgwYIFmDt3LqKjo5GZmYmLFy9i06ZNiI6Obpf7E9IVUcsRIaTNpFIpfHx88PnnnyMzMxNKpRIODg6YPn06li5dCrFYjA0bNuCf//wnlixZgqFDhyIiIgLvvPNOu9x//vz5SExMxMqVKyGTybBhwwYEBgY2WVYikeDXX3/FRx99hDfeeAOlpaWwt7eHv79/u7UkrV+/HtOmTcOQIUNgZ2eHjRs3IikpqV2u7e/vDzc3NwwdOhRVVVUICgrCihUruOOrVq2CQqFAREQEsrKyIJfLMWDAACxdurRd7k9IVyRgj74oJ4QQLebs7IywsDCEhYVpOhRCiI6i12qEEEIIIWooOSKEdGnqQ+Af/UpISNB0eIQQDaDXaoSQLu3mzZvNHrO3t+dGiRFCug5KjgghhBBC1NBrNUIIIYQQNZQcEUIIIYSooeSIEEIIIUQNJUeEEEIIIWooOSKEEEIIUUPJESGEEEKIGkqOCCGEEELUUHJECCGEEKLm/wGDU1qbVQhEbAAAAABJRU5ErkJggg==", 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", 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Sample_numberEuler_angleEuler_val
00alpha-106.816208
11alpha-106.809685
22alpha-106.782059
33alpha-106.753441
44alpha-106.720901
............
237827924gamma-29.572041
237837925gamma-29.582962
237847926gamma-29.605570
237857927gamma-29.622272
237867928gamma-29.617676
\n", - "

23787 rows × 3 columns

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" - ], - "text/plain": [ - " Sample_number Euler_angle Euler_val\n", - "0 0 alpha -106.816208\n", - "1 1 alpha -106.809685\n", - "2 2 alpha -106.782059\n", - "3 3 alpha -106.753441\n", - "4 4 alpha -106.720901\n", - "... ... ... ...\n", - "23782 7924 gamma -29.572041\n", - "23783 7925 gamma -29.582962\n", - "23784 7926 gamma -29.605570\n", - "23785 7927 gamma -29.622272\n", - "23786 7928 gamma -29.617676\n", - "\n", - "[23787 rows x 3 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ndf" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1c831cbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Sample_numberQuaternionQ_val
00q00.425897
11q00.425981
22q00.426065
33q00.426155
44q00.426259
............
317117924q30.486186
317127925q30.486642
317137926q30.487051
317147927q30.487375
317157928q30.487466
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31716 rows × 3 columns

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" - ], - "text/plain": [ - " Sample_number Quaternion Q_val\n", - "0 0 q0 0.425897\n", - "1 1 q0 0.425981\n", - "2 2 q0 0.426065\n", - "3 3 q0 0.426155\n", - "4 4 q0 0.426259\n", - "... ... ... ...\n", - "31711 7924 q3 0.486186\n", - "31712 7925 q3 0.486642\n", - "31713 7926 q3 0.487051\n", - "31714 7927 q3 0.487375\n", - "31715 7928 q3 0.487466\n", - "\n", - "[31716 rows x 3 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nqdf" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4658435d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Sample_numberepoch machine time (ns)Timestamp for frame capture.read (ns)Timestamp LPMSB2 (s)Quaternions_LPMSB2Euler_LPMSB2ABCq0q1q2q3
0016904621688486540491690462168849621185701.245[0.42589715123176575, 0.7636324167251587, 0.07...[-106.81620788574219, -52.48710632324219, -29....-106.816208-52.487106-29.6807670.4258970.7636320.0703530.480133
1116904621688811004031690462168849621185701.250[0.42598122358322144, 0.7636178731918335, 0.07...[-106.80968475341797, -52.4804573059082, -29.6...-106.809685-52.480457-29.6771530.4259810.7636180.0703690.480079
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7929 rows × 13 columns

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