class: left, middle
 # **On Open Science and Reproducibility**

 ### **Luna L. Sánchez Reyes** ### University of California, Merced

 Mathematical Biology Seminar Series February 17, 2021 · Merced, California
 Find the code for these slides [here](https://github.com/lunasare/slides/blob/master/reproducibility-2021-02-17.Rmd).  ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- # Start with a flashy issue that was originated because people did not make their science open - retracts from Pruit - other scandals ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- background-image: url("talks/assets/2-data-2723105_1920.jpg") background-position: center background-size: cover class: white # Something closer to home (as a PhD student)  ??? original cat gif https://64.media.tumblr.com/7a2d701ba2e421cf08ab15ab72c11b77/tumblr_nuryp6vbb11uyd39io1_250.gifv - personally affected by unshared scientific datasets - time invested gathering data/repeating analyses - Fecher B., Friesike S. (2014) **Open Science: One term, five schools of thought**. *In:* Bartling S., Friesike S. (eds) Opening Science. Springer, Cham. - Why do "*open*" science? Was it "*closed*"??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: bg-teal    # What is *Open* Science?

 ## Key properties of Open Science

 - **Reproducible** (code and data alone are not enough) - Reusable - Shareable (research materials) - Discoverable - **Accessible/Available** physically: i.e., available without bureaucratic or administrative barriers economically: free of charge conceptually: easy to understand (with metadata, thorough explanations and descriptions) - Modular
 ## These characteristics allow research to be: - Transparent - accountable - efficient - responsive - effective - Inclusive - Ethical - Collaborative -- - Hard to do... ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: bg-teal # Who has time for that?  ??? - learning new concepts - > reading papers - understanding new methods - > reading papers - running analyses - > learning how to code - preparing talks - > reading papers - preparing papers - > reading papers - preparing figures - > learning more core - trying to get published - > reading papers - graduating - > crying - I wasn't even thinking about life after PhD ... ******************************************************************************** ******************************************************************************** ******************************************************************************** --- # Reported benefits of doing Open Science ??? In here I talk about scientific reports that demonstrate that sharing scientific data results on more citations. I then continue on the next slide with particular benefits I have discovered on my journey ******************************************************************************** ******************************************************************************** ******************************************************************************** --- # Benefits I have discovered personally - Networking - Finding collaborators - Being invited to give lectures ??? On this slide I talk about the way my academic life has been affected positively by making not only my science, but most of the products of my work open. ******************************************************************************** ******************************************************************************** ******************************************************************************** --- # Openness in documents - Modifiable - Machine-readable - Readibility --> exportability ??? readibility: the property of a virtual document that can be opened and fully read and accessed using virtually any software. counter-example: word cannot be fully read if opened with anything else that is not proprietary software of Microsoft (that costs money) exportability: the property of a document that can be exported to any format (html, latex, ...) ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-10 bg-light-blue .row[.content[ # Reproducibility ## **Why** should *everyone* make their science reproducible ]] .row.split-two[ .column[.content[
 ### It is good for science... potentially - It has the potential to serve as a minimum standard for judging scientific claims when a full independent replication study is not possible - Alleviate the crisis on science credibility
 ### Importance of reproducibility for you: - develop a framework for writing narratives around code and data - speed your research advance and discovery - redo analyses faster - make your research known faster - establish yourself as a trustworthy researcher(?) - understand other research faster ]] .column[.content.v40.center[
 ![Science cat](https://github.com/annakrystalli/talks/raw/master/assets/science-cat.jpg) ]] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: bg-teal center

 # **How** do I make my analyses reproducible? --

 ## 1) Version control --

 ## 2) Project file structure --

 ## 3) Use markup languages for document generation --- class: split-10 .row[.content[ #   1) Version control ]] .row.split-two[ .column[.content.center.v40[    ]] .column[.content[ Or management of changes to documents, computer programs, large web sites, and other collections of information.

 ## Why? - Prevent messy file-naming situations - Avoid loss of work, resources and time ## How? We are going to use an open source (free for use) version control software called `git`

 And a website that allows to store git outputs online while collaborating with others, called `Github` ]] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-10 .row.bg-pink[.content[ # Demo 🐱 ]]

 ## [Github](https://github.com/) ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-10 .row[.content[ #   2) A self-contained file structure ]] .row.split-two[ .row.split-two[ .column[.content.pad10px[
 ## Why? - Keep all materials associated with a particular analysis in the same place - The working directory can be set to the one root directory of the project - Fresh environment everytime you start working (= faster debugging and testing) ]] .column[.content[

  ]] ] .row[.content.center[
 ## How? Premise: One local project - one Github repo Option 1: `Fork and clone` a project from `Github`. Option 2: Create a `new project` in RStudio and initialize it with `git`. ]] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-10 .row.bg-pink[.content[ # Demo 🗃 ]] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-10 .row[.content[ #   3) A markup language for reports ]] .row.split-two[ .column[.content.center[
 ## Wait, **why** not Word? ![word clip](https://media.giphy.com/media/13V60VgE2ED7oc/giphy.gif) Have you tried opening a word document with a text editor??? It is ***not*** human-readable... For the purposes of version control, we prefer markup languages: human-readable, plain-text documents that use tags to define elements and give format to documents. ]] .column[.content.center.v30[
 ## How? There are many markup languages around: html, xml, markdown, latex We will focus on markdown for the purpose of this hands on. ]] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-1-2-1 .row.bg-amber[ ] .row[.content.vmiddle.center[ # markdown crash course! ]] .row.bg-amber[ ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-two .column.split-10[ .column.bg-pink[ ] .column.split-two.bg-blue[ .row.split-five[ .row[.content[ # markdown ]] .row[.content[ ##   uses hashtags #⃣ ]] .row[.content.vmiddle[ ###     to create headers ]] .row[.content.vmiddle[ ####           up to 5 levels, I think... ]] .row[.content[ #####                 yes!!! ]] ] .row.split-two[ .column[.content.v30[ - Use hyphens - or an asterisk - to create a list - of some - things ]] .column[.content.v30[ 1. Use numbers 1. to create a 1. numbered list 1. of some 1. other things ]] ] ] ] ] ] .column.slide-in-right.split-two.bg-amber[ .row.split-five[ .row[.content[ ```{} # markdown ``` ]] .row[.content[ ```{} ## uses hashtags #⃣ ``` ]] .row[.content[ ```{} ### to create headers ``` ]] .row[.content[ ```{} #### up to 5 levels, I think... ``` ]] .row[.content[ ```{} ##### yes!!! ``` ]] ] .row.split-two[ .column[.content.v30[   \- Use hyphens   \\\* or an asterisk   \- to create a list   \- of some   \- things ]] .column[.content.v30[ .transp[\]1. Use numbers .transp[\]1. to create a .transp[\]1. numbered list .transp[\]1. of some .transp[\]1. other things ]] ] ] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-two name: markdown2 .column.split-10[ .column.bg-pink[ ] .column.split-four.center[ .row.bg-light-blue[.content.font_medium[ markdown emphasizes text with *italics*, **bold**, or ***both*** using 1, 2, or 3 asterisks respectively, at each side of the text ]] .row.bg-blue[.content.font_medium.v20[ Add links to [external sites](https://www.markdownguide.org/basic-syntax/) or [internal sections](#internal) of the document ]] .row.bg-light-blue[.content.font_medium[ Add linked URLs <https://www.markdownguide.org/basic-syntax/> ]] .row.bg-blue[.content[ Create horizontal rules with three or more asterisks, dashes, or underscores ****** ______ ______ ]] ] ] .column.split-four[ .row.bg-amber[.content.v20[ ``` markdown emphasizes text with *italics*, **bold**, or ***both*** using 1, 2, or 3 asterisks respectively, at each side of the text ``` ]] .row.bg-orange[.content.v20[ ``` Add links to [external sites](https://www.markdownguide.org/ basic-syntax/) or [internal sections](#internal) of the document ``` ]] .row.bg-amber[.content.v30[ ``` Add linked URLs <https://www.markdownguide.org/basic-syntax/> ``` ]] .row.bg-orange[.content.v10[ ``` Create horizontal rules with three or more asterisks, dashes, or underscores ****** ------ ______ ``` ]] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-10 .row.bg-pink[.content[ # Demo 🚀 ]] .row[.content.pad10px[

 ```{} git add . git commit -m "a message describing the changes you made" git push ``` ## 🎉 You've made your first version control commit 🎉 ]] --- class: split-1-2-1 .column.bg-pink[ ] .column[.content.center.vmiddle[ # We're not stopping there... ]] .column.bg-pink[ ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class:split-10 .row.split-30.slide-in-left[ .column.bg-blue[ ] .column[.content.vmiddle[ #   Use R (markdown)! ]] ] .row.split-60[ .column[.content.center.font_medium[ .font2[Why?] .left[Effectively *understands* most markup languages] .right[Not only ***include*** code, but ***run*** the code] .left[Change formatting for any journal in seconds] .right[Include citations and format them] .left[Automatically include a reference list in any
 format you need] .right[Render to different file formats, including word 😜] .left[And last, but not least:
   Easily add emojis and icons] ]] .column.split-two[ .row[.content[ ] ] .row.slide-in-right[.content.center.vmiddle[ .font2.bg-blue[How?]
 .font3.bg-blue[RStudio] ]] ] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-30 .row.split-two[ .row[.content[ # Edit the text and code section
 ]] .row[.content[  ## .color-main3[Add citations] along the text using the .amber[@ tags] from the .bib file: ]] ] .row.split-two[ .column.slide-in-left.bg-purple[.content.v30[ ### This ```{} Characteristics of Open Science, following @Fecher2014: "Our professional network is valuable. It is also limited. Perhaps there are people who are well-placed to help us, in another university or company, in a different country, but we unfortunately do not know them. Surely science would proceed faster if we could reach those people? Or, better, if they could find us?"[@woelfle2011open; @Fecher2014] ``` ]] .column.slide-in-right[.content.v40[ ### Becomes this .font0-85[Characteristics of Open Science, following .color-main3[Fecher and Friesike (2014)]:
 “Our professional network is valuable. It is also limited. Perhaps there are people who are well-placed to help us, in another university or company, in a different country, but we unfortunately do not know them. Surely science would proceed faster if we could reach those people? Or, better, if they could find us?” .color-main3[(Woelfle, Olliaro, and Todd 2011; Fecher and Friesike 2014)].] ]] ] --- class: split-30 .row.split-two[ .row[.content[ # Edit the text and code section
 ]] .row[.content[ ## .blue[Add a list of references] at the end of the document by typing .amber[References]: ]] ] .row.split-two[ .column.slide-in-left.bg-indigo[.content.v10[ ### This ```{} "Our professional network is valuable. It is also limited. Perhaps there are people who are well-placed to help us, in another university or company, in a different country, but we unfortunately do not know them. Surely science would proceed faster if we could reach those people? Or, better, if they could find us?"[@woelfle2011open; @Fecher2014] References ``` ]] .column.slide-in-right[.content.v40.pad10px[ ### Becomes this .font0-85[ “Our professional network is valuable. It is also limited. Perhaps there are people who are well-placed to help us, in another university or company, in a different country, but we unfortunately do not know them. Surely science would proceed faster if we could reach those people? Or, better, if they could find us?” .purple[(Woelfle, Olliaro, and Todd 2011; Fecher and Friesike 2014)]. .font_medium[References] Fecher, Benedikt, and Sascha Friesike. 2014. “Open Science: One Term, Five Schools of Thought.” In Opening Science: The Evolving Guide on How the Internet Is Changing Research, Collaboration and Scholarly Publishing, edited by Sönke Bartling and Sascha Friesike, 17–47. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-00026-8_2. Woelfle, Michael, Piero Olliaro, and Matthew H Todd. 2011. “Open Science Is a Research Accelerator.” Nature Chemistry 3 (10): 745–48. 2 ] ]] ] --- class: split-30 .row.split-two[ .row[.content[ # Edit the text and code section
 ]] .row[.content[ ## .teal[Add R code] along the text using .amber[backticks] and .amber[curly braces]: ]] ] .row.split-two[ .column.slide-in-left.bg-teal[.content.v30[ ### This code chunk ````markdown ```{r eval = FALSE, echo = TRUE} library(ape) data(chiroptera) str(chiroptera) ``` ```` ### And this inline code You can know the number of tips in your Chiroptera tree with `length(chiroptera$tip.label)`. ]] .column.slide-in-right[.content.v30[ ### renders this ``` ## List of 3 ## $ edge : int [1:1344, 1:2] 917 918 919 920 920 921 922 922 921 923 ... ## $ tip.label: chr [1:916] "Paranyctimene_raptor" "Nyctimene_aello" "Nyctimene_celaeno" "Nyctimene_certans" ... ## $ Nnode : int 429 ## - attr(*, "class")= chr "phylo" ``` ### will render this My Chiroptera tree has 916 tips. ]] ] --- class: split-30 .row.split-two[ .row[.content[ # Edit the text and code section
 ]] .row[.content[ ## .green[Embed the output of R code] along the text using .amber[backticks] and .amber[curly braces]: ]] ] .row.split-two[ .column.slide-in-left.bg-light-green[.content.v30[ ### This code chunk ````markdown ```{r eval = TRUE} library(ape) data(chiroptera) str(chiroptera) ``` ````  ### And this inline code My Chiroptera tree has ``` `{r} length(chiroptera$tip.label)` ``` tips. ]] .column.slide-in-right[.content.v30[ ### renders this ``` ## List of 3 ## $ edge : int [1:1344, 1:2] 917 918 919 920 920 921 922 922 921 923 ... ## $ tip.label: chr [1:916] "Paranyctimene_raptor" "Nyctimene_aello" "Nyctimene_celaeno" "Nyctimene_certans" ... ## $ Nnode : int 429 ## - attr(*, "class")= chr "phylo" ``` ### will render this My Chiroptera tree has 916 tips. ]] ] --- class: split-40 .row.split-30[ .row[.content[ # Edit the text and code section
 ]] .row[.content[ ## .deep-orange[Add a plot] without showing the code by setting .amber[echo] to .amber[FALSE]
 Control its quality with the .amber[fig.retina] option and its size with .amber[fig.height] and .amber[fig.width]: ]] ] .row.split-60[ .column.slide-in-left.bg-deep-orange[.content[ ### This code chunk

```{r eval = TRUE, echo = FALSE, fig.height = 5, fig.retina = 3} plot(chiroptera, type = "c", cex = 0.3) ```
 ]] .column.slide-in-right[.content[ ### renders this  ]] ] --- class: split-30 .row.split-two[ .row[.content[ # Edit the text and code section
 ]] .row[.content[ ## .deep-purple[Add a table] with some formatting with the function .amber[kable] from the .amber[knitr] package: ]] ] .row.split-40[ .column.slide-in-left.bg-deep-purple[.content[ ### This code chunk

```{r eval = TRUE, echo = FALSE} knitr::kable(iris[1:5, ], caption = 'A caption') ```
 ]] .column.slide-in-right[.content[ ### Becomes this

 Table: A caption | Sepal.Length| Sepal.Width| Petal.Length| Petal.Width|Species | |------------:|-----------:|------------:|-----------:|:-------| | 5.1| 3.5| 1.4| 0.2|setosa | | 4.9| 3.0| 1.4| 0.2|setosa | | 4.7| 3.2| 1.3| 0.2|setosa | | 4.6| 3.1| 1.5| 0.2|setosa | | 5.0| 3.6| 1.4| 0.2|setosa | ]] ] --- class: split-40 .row.split-two[ .row[.content[ # Edit the text and code section
 ]] .row[.content[ ## Control your .deep-orange[code variables] globally by setting options on the .amber[setup chunk], i.e., the first R code chunk on the .Rmd file: ]] ] .row.split-60[ .column.slide-in-left.bg-deep-orange[.content.v40[
```{r setup} knitr::opts_chunk$set(fig.retina = 3, warning = FALSE, message = FALSE) ```
 ]] .column.slide-in-right[.content.v40.center[ ### These can be individually overriden on each chunk, in case you want to show a code warning, for example. ]] ]  ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- class: split-20 .row[.content.vmiddle[ #   Rendering the .blue[documents] in many formats! ]] .row.split-three[ .column.bg-red[.content.center[  ]] .column.bg-blue[.content.center[  ]] .column.bg-orange[.content.center[  ]] ] ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- background-image: url("https://media.giphy.com/media/DUtSpDzxZZwPu/giphy.gif") background-position: 50% 65% background-size: 650px ## Questions? ??? ******************************************************************************** ******************************************************************************** ******************************************************************************** --- name: internal class: split-two .column[.content.vmiddle[ ## References - [The Turing Way](https://the-turing-way.netlify.app/introduction/introduction.html) - [Anna Krystalli's Reproducible Research Talk](https://annakrystalli.me/talks/r-in-repro-research-dc.html#1) ]] .column[.content.center[ ### [Go back](#markdown2) to the markdown crash course 🚀  ]] ??? ******************************************************************************** ******************************************************************************** ********************************************************************************


Palette
Your Free Tool To Front End
Hard remembering
color codes
and basic
Site Settings
? Well we Are There For You.


Code Samples in Web Pages: How to Not be Doing It Wrong.
Observe:
To list files, do this:
$ ls -laF
Compare that to this:
To list files, do this:
ls -laF
You spot the difference? The second one is much more user friendly, because you won’t pick up the $
when you copy and paste, and only takes a teeny bit of CSS.


Senior Machine Learning Engineer (NLP) - San Francisco, CA 94108
At Ginger, we aim to provide better mental health care to humanity at a scale larger than has ever been possible before. This is no small task and as an expanding team we are working on a number of initiatives to achieve this, including aggressively building tools to simultaneously grow our reach and improve quality of care.
As a core part of a close-knit data science team you’ll:
- Design, train and validate algorithms to derive as much actionable information from our data (text, media, activity etc) as possible.
- Engineer informative NLP features that allow us to summarize salient passages, infer quality of care, etc.
- Develop and deploy algorithms that boost coach and clinician awareness of member goals, status, history and probable future conditions
- Write new algorithms to recommend specific personalized health-promoting content (e.g. modules in the app) based on a range of features
- Integrate streaming activity and sleep data directly from the phone or via services to expand our capacity to provide meaningful insight and care.
- Collaborate with engineers to integrate algorithms efficiently with backend production services
- Work with product team to define solutions and integrate them into the roadmap
- Help us scale our services using GPUs and modern distributed processing tools in the cloud (AWS)
- Dig into data with ad hoc analysis as necessary for technical, clinical, customer etc needs
- Improve and help define the data team’s processes and tooling
- See that we remain at the bleeding edge of advanced analytical capability in our effort to provide unparalleled levels of personalized care.
- Employ data to keep our eyes on the prize: improved mental-health outcomes for all members.
Necessary Skills:
- Machine Learning (ML) 5+ years
- DS/ML Project Lead 2+ years
- Python 4+ years
- Natural Language Processing (NLP) 3+ years
- Deep Net ML using PyTorch, TensorFlow, etc. 1+ years
- Deploying to production systems with active customers 3+ years
- Strong foundation in statistics
- Mathematically fluent
- PhD in technical field or experiential equivalent
Preferred Skills:
- Modern NLP tools such as spaCy, CoreNLP, Gensim, NLTK 3+ years
- Deep Net ML using PyTorch, TensorFlow, etc. 3+ years
- Deep learning for text understanding and generation 2+ years
- Distributed ML via Spark or similar 2+ years
- ML on GPUs 2+ years
- SciPy+Pandas 4+ years
- Python 6+ years
- Amazon Web Services (AWS) 3+ years
- Docker
- AWS Lambda, Sagemaker
- Time Series Analysis 2+ years
- Strong data visualization skills
- Significant experience in the healthcare space
At Ginger, it’s our mission to create a world where everyone has the support they need to improve their mental well-being and live happier healthier lives.
Every Ginger user is connected to a behavioral health coach who responds via text, within minutes. Coaches provide personalized, proven, coping strategies and help individuals set and achieve goals. Our licensed therapists and board-certified psychiatrists provide clinical care and medication management over video sessions, ensuring easy access and measurable progress.
Pinterest, Buzzfeed and numerous other companies trust Ginger to provide mental health benefits to their employees. Fast Company called Ginger one of The World’s Top 10 Most Innovative Companies in Healthcare, and the World Economic Forum named us a Technology Pioneer.
We are a mission-driven team that offers competitive compensation. We believe individuals that bring different perspectives to challenges are more innovative, collaborative, and create better solutions. To that end, we're striving to build a workplace that actively embraces diverse people, ideas, talents and experiences.


|
UnitySlippyMap
A slippy map implementation written in C# for Unity3D.
|
An abstract class representing a map layer. One can derive from it to add custom content to the map. More...
Public Member Functions | |
| abstract void | UpdateContent () |
| Updates the content. More... | |
Public Attributes | |
| Map | Map |
| The UnitySlippyMap.Map instance to which this UnitySlippyMap.Layers.Layer instance belongs. More... | |
Protected Attributes | |
| float | minZoom |
| The minimum zoom. More... | |
| float | maxZoom |
| The max zoom. More... | |
Properties | |
| float | MinZoom [get, set] |
| Gets or sets the minimum zoom. More... | |
| float | MaxZoom [get, set] |
| Gets or sets the max zoom. More... | |
An abstract class representing a map layer. One can derive from it to add custom content to the map.
|
pure virtual |
Updates the content.
Implemented in UnitySlippyMap.Layers.TileLayer.
| Map UnitySlippyMap.Layers.Layer.Map |
The UnitySlippyMap.Map instance to which this UnitySlippyMap.Layers.Layer instance belongs.
|
protected |
The max zoom.
|
protected |
The minimum zoom.
|
getset |
Gets or sets the max zoom.
The max zoom.
|
getset |
Gets or sets the minimum zoom.
The minimum zoom.


{% extends 'quizard/base.html' %} {% load i18n %} {% block content %}
{% blocktrans with score=score total_points=assignment.total_points %} You have already completed this assignment. You scored {{ score }} out of {{ total_points }} points. {% endblocktrans %}


Overview of the ICJ
Established under the United Nations Charter in 1945, the International Court of Justice has since been the judicial panel of the UN. The court’s primary purpose is to mediate any multinational dispute under the boundaries of international law. The ICJ also acts as a principal advisor for any UN body and other specialized organizations regarding legal concerns in international and/or domestic actions.
The more comprehensive guide, with all procedural guides and related information can be found here (Download it here).
Modes of Address
Unlike the other major organs that make up the United Nations, the ICJ Court does not require parties to use third-person address. However, the Court’s Modes of Address is still restricted to the highest of formality.
Court members that occupy the seat of presidency shall be addressed as “Mr./Madame President/Vice President” or “President/Vice President”.
Any members of the panel of judges shall be addressed as “Your Honor” or “Judge Surname” or “Judge”.
Should there be a need to address the Registrar, they shall be addressed as “Registrar Surname” or “Registrar”.
As for the prosecuting and defending party, there are two modes of address that needs to be taken into account. When addressing a specific advocate of a party, the advocate needs to be referred to as “Advocate” or “Counsel”. When addressing an entire party, the party should be referred to as “Applicant/Respondent” or address by the name of the country they are representing.
Any witnesses that testify during court hearings may be referred to, and may use, first-person address.
Roles and Responsibilities
Court Officers:
The Presidency
- President – The President takes precedence over all court proceedings, direct the accounts of the case and administers over the Court panel (in weighing of evidence and ruling of verdict). During judicial deliberations, the President will cast a vote for the final verdict alongside the judges.
- Vice President – The Vice President acts as a direct assistance to the President, both in proceedings and essential materials of the Court. They are also the presiding officer in the absence or vacancy of the President. The Vice President will have casting vote in the final verdict.
Registrar – As a court officer, the registrar’s duty is to ensure all administrative proceedings in the case has been carefully managed. This includes providing the Presidents and the Judges with copies of the stipulation, memoranda, evidence packets, and any other documents necessary during court proceedings. They are also to keep track of the hearing by means of written reports/minutes.
Judiciary Panel
Advocate
Witness
Documents
All ICJ documents should be submitted prior to the inception of the conference. These documents present a general summary and arguments of each side. During the conference, these documents will primarily be utilized for argumentative support and background check purposes.
Important Note:


{% extends "layout.html" %} {% block bar_heading %} Get an annual MOT reminder {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} You’ve unsubscribed You won’t get any more MOT reminders at {{ data['email'] }} for {{ data['VRM'] }}. Sign up for another MOT reminder {% endblock %}
You won’t get any more MOT reminders at {{ data['email'] }} for {{ data['VRM'] }}.
Sign up for another MOT reminder


The Invisible Man
by H.G. Wells
Chapter 21
Additional Information
- Year Published: 1897
- Language: English
- Country of Origin: England
-
Source:
Wells, H.G. (1897).
. London, England; Pearsons Magazine.
-
Readability:
- Flesch–Kincaid Level: 7.5
- Word Count: 1,803
- Genre: Science Fiction
- Keywords: duality of man, good vs. evil, science, scientific experimentation
- ✎ Cite This
Downloads
"In going downstairs the first time I found an unexpected difficulty because I could not see my feet; indeed I stumbled twice, and there was an unaccustomed clumsiness in gripping the bolt. By not looking down, however, I managed to walk on the level passably well.
"My mood, I say, was one of exaltation. I felt as a seeing man might do, with padded feet and noiseless clothes, in a city of the blind. I experienced a wild impulse to jest, to startle people, to clap men on the back, fling people's hats astray, and generally revel in my extraordinary advantage.
"But hardly had I emerged upon Great Portland Street, however (my lodging was close to the big draper's shop there), when I heard a clashing concussion and was hit violently behind, and turning saw a man carrying a basket of soda-water syphons, and looking in amazement at his burden. Although the blow had really hurt me, I found something so irresistible in his astonishment that I laughed aloud. `The devil's in the basket,' I said, and suddenly twisted it out of his hand. He let go incontinently, and I swung the whole weight into the air.
"But a fool of a cabman, standing outside a public house, made a sudden rush for this, and his extending fingers took me with excruciating violence under the ear. I let the whole down with a smash on the cabman, and then, with shouts and the clatter of feet about me, people coming out of shops, vehicles pulling up, I realised what I had done for myself, and cursing my folly, backed against a shop window and prepared to dodge out of the confusion. In a moment I should be wedged into a crowd and inevitably discovered. I pushed by a butcher boy, who luckily did not turn to see the nothingness that shoved him aside, and dodged behind the cab-man's four-wheeler. I do not know how they settled the business, I hurried straight across the road, which was happily clear, and hardly heeding which way I went, in the fright of detection the incident had given me, plunged into the afternoon throng of Oxford Street.
"I tried to get into the stream of people, but they were too thick for me, and in a moment my heels were being trodden upon. I took to the gutter, the roughness of which I found painful to my feet, and forthwith the shaft of a crawling hansom dug me forcibly under the shoulder blade, reminding me that I was already bruised severely. I staggered out of the way of the cab, avoided a perambulator by a convulsive movement, and found myself behind the hansom. A happy thought saved me, and as this drove slowly along I followed in its immediate wake, trembling and astonished at the turn of my adventure. And not only trembling, but shivering. It was a bright day in January and I was stark naked and the thin slime of mud that covered the road was freezing. Foolish as it seems to me now, I had not reckoned that, transparent or not, I was still amenable to the weather and all its consequences.
"Then suddenly a bright idea came into my head. I ran round and got into the cab. And so, shivering, scared, and sniffling with the first intimations of a cold, and with the bruises in the small of my back growing upon my attention, I drove slowly along Oxford Street and past Tottenham Court Road. My mood was as different from that in which I had sallied forth ten minutes ago as it is possible to imagine. This invisibility indeed! The one thought that possessed me was -- how was I to get out of the scrape I was in.
"We crawled past Mudie's, and there a tall woman with five or six yellow-labelled books hailed my cab, and I sprang out just in time to escape her, shaving a railway van narrowly in my flight. I made off up the roadway to Bloomsbury Square, intending to strike north past the Museum and so get into the quiet district. I was now cruelly chilled, and the strangeness of my situation so unnerved me that I whimpered as I ran. At the northward corner of the Square a little white dog ran out of the Pharmaceutical Society's offices, and incontinently made for me, nose down.
"I had never realised it before, but the nose is to the mind of a dog what the eye is to the mind of a seeing man. Dogs perceive the scent of a man moving as men perceive his vision. This brute began barking and leaping, showing, as it seemed to me, only too plainly that he was aware of me. I crossed Great Russell Street, glancing over my shoulder as I did so, and went some way along Montagu Street before I realised what I was running towards.
"Then I became aware of a blare of music, and looking along the street saw a number of people advancing out of Russell Square, red shirts, and the banner of the Salvation Army to the fore. Such a crowd, chanting in the roadway and scoffing on the pavement, I could not hope to penetrate, and dreading to go back and farther from home again, and deciding on the spur of the moment, I ran up the white steps of a house facing the museum railings, and stood there until the crowd should have passed. Happily the dog stopped at the noise of the band too, hesitated, and turned tail, running back to Bloomsbury Square again.
"On came the band, bawling with unconscious irony some hymn about `When shall we see his Face?' and it seemed an interminable time to me before the tide of the crowd washed along the pavement by me. Thud, thud, thud, came the drum with a vibrating resonance, and for the moment I did not notice two urchins stopping at the railings by me. `See 'em,' said one. `See what?' said the other. `Why -- them footmarks -- bare. Like what you makes in mud.'
"I looked down and saw the youngsters had stopped and were gaping at the muddy footmarks I had left behind me up the newly whitened steps. The passing people elbowed and jostled them, but their confounded intelligence was arrested. `Thud, thud, thud, When, thud, shall we see, thud, his face, thud, thud.' `There's a barefoot man gone up them steps, or I don't know nothing,' said one. `And he ain't never come down again. And his foot was a-bleeding.'
"The thick of the crowd had already passed. `Looky here, Ted,' quoth the younger of the detectives, with the sharpness of surprise in his voice, and pointed straight to my feet. I looked down and saw at once the dim suggestion of their outline sketched in splashes of mud. For a moment I was paralysed.
"`Why, that's rum,' said the elder. `Dashed rum! It's just like the ghost of a foot, ain't it?' He hesitated and advanced with outstretched hand. A man pulled up short to see what he was catching, and then a girl. In another moment he would have touched me. Then I saw what to do. I made a step, the boy started back with an exclamation, and with a rapid movement I swung myself over into the portico of the next house. But the smaller boy was sharp-eyed enough to follow the movement, and before I was well down the steps and upon the pavement, he had recovered from his momentary astonishment and was shouting out that the feet had gone over the wall.
"They rushed round and saw my new footmarks flash into being on the lower step and upon the pavement. `What's up?' asked someone. `Feet! Look! Feet running!' Everybody in the road, except my three pursuers, was pouring along after the Salvation Army, and this blow not only impeded me but them. There was an eddy of surprise and interrogation. At the cost of bowling over one young fellow I got through, and in another moment I was rushing headlong round the circuit of Russell Square, with six or seven astonished people following my footmarks. There was no time for explanation, or else the whole host would have been after me.
"Twice I doubled round corners, thrice I crossed the road and came back upon my tracks, and then, as my feet grew hot and dry, the damp impressions began to fade. At last I had a breathing space and rubbed my feet clean with my hands, and so got away altogether. The last I saw of the chase was a little group of a dozen people perhaps, studying with infinite perplexity a slowly drying footprint that had resulted from a puddle in Tavistock Square, -- a footprint as isolated and incomprehensible to them as Crusoe's solitary discovery.
"This running warmed me to a certain extent, and I went on with a better courage through the maze of less frequented roads that runs hereabouts. My back had now become very stiff and sore, my tonsils were painful from the cabman's fingers, and the skin of my neck had been scratched by his nails; my feet hurt exceedingly and I was lame from a little cut on one foot. I saw in time a blind man approaching me, and fled limping, for I feared his subtle intuitions. Once or twice accidental collisions occurred and I left people amazed, with unaccountable curses ringing in their ears. Then came something silent and quiet against my face, and across the Square fell a thin veil of slowly falling flakes of snow. I had caught a cold, and do as I would I could not avoid an occasional sneeze. And every dog that came in sight, with its pointing nose and curious sniffing, was a terror to me.
"Then came men and boys running, first one and then others, and shouting as they ran. It was a fire. They ran in the direction of my lodging, and looking back down the street I saw a mass of black smoke streaming up above the roofs and telephone wires. It was my lodging burning; my clothes, my apparatus, all my resources indeed, except my cheque-book and the three volumes of memoranda that awaited me in Great Portland Street, were there. Burning! I had burnt my boats -- if ever a man did! The place was blazing."
The Invisible Man paused and thought. Kemp glanced nervously out of the window. "Yes?" he said. "Go on."


Affirmation and Stereotype Threat
Exploring your values
The common feeling's I experience when I am happiest most often have involved many of the following values:
- Accomplishment
- Compassion
- Confidence
- Commitment
- Having a family
- Integrity
- Self-reliance
In general, I try to live up to these values:
Disagree 1 ----- 2 ----- 3 -----
- 4
With some of the events I have experienced in the past I would say that commitment has led up to many of the other values. My personal belief is that very often the effort and commitment lead to many of the more meaningful values. As a parent, commitment is one value I try to stress to my kids. Sometimes this can tend to get lost amongst the focus on just getting everything right.
Advice
What was the last topic that someone asked for your advice on?
The last topic that someone asked me for advice on was a series of home-improvement tasks
Values and Stereotype threat
How do you feel when you think about your values? Do you think it can help you mediate stereotype threat if you recognize you feel it?
Can you think of any other exercises you can use to help you when you are feeling down (for whatever reason) at EDA?
I have been practicing 'mindfulness' for a while now since being directed to it several years ago. I found it extremely useful in times when I'm feeling a bit down.


Ocean [ad01], ocean, in Crystal Sea
Last updated Turn #146
Ocean [ad01], ocean, in Crystal Sea
------------------------------------------------------------------------
29: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
29: 30% damaged, arrived from Ocean [ad00], owner:
29: Phantasm [n4b], Imperial Empire, duke, with two workers,
29: eight sailors
30: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
30: 30% damaged, departed for Dark Desert [ad02].
30: The fog has cleared.
Routes leaving Ocean:
North, plain, to Concha Valley [ac01], Diamond Island, 2 days
East, desert, to Dark Desert [ad02], Diamond Island, 2 days
South, to Ocean [ae01], 6 days
West, to Ocean [ad00], 6 days


| CS 229 Machine Learning |
4-Way-Stop Wait-Time Prediction. David Held. [pdf]
A Better BCS. Rahul Agrawal, Sonia Bhaskar and Mark Stefanski. [pdf]
A Framework for Assessing the Feasibility of Learning Algorithms in Power-Constrained ASICs. Alexander Neckar. [pdf]
A Framework for Recognizing Hand Gestures. David Knight and Matthew Tang. [pdf]
A Machine Learning Approach for Future Career Planning. Yu Lou, Ran Ren and Yiyang Zhao. [pdf]
A Machine Learning Approach to Address the Issue of False Positives and False Negatives in Active Structural Health Monitoring (SHM). Surajit Roy, Fatih Sunor and Kranthi Kode. [pdf]
A Machine Learning Framework for Biochemical Reaction Matching. Tomer Altman, Eric Burkhart, Irene M. Kaplow and Ryan Thompson. [pdf]
A Novel Machine Learning Based Prediction Model for Energy Expenditure in relation to Varying Load, Incline, and Body Composition. Katherine Swanson, Aubrey Gress and Vlad Andrei.. [pdf]
A Reinforcement Learning Approach for Pricing Derivatives. Thomas Grassl. [pdf]
A Spam Classifier For Biology: Removing Noise from Small RNA Datasets. Diane Wu. [pdf]
A Step-by-Step Approach to Footstep Detection. Francisco Cai, David Philipson and Salik Syed. [pdf]
A Survey of Document Clustering Techniques & Comparison of LDA and moVMF. Yu Xiao. [pdf]
Accent Classification. Phumchanit Watanaprakornkul, Chantat Eksombatchai and Peter Chien. [pdf]
Action Recognition in Video. Pierre Kreitmann.[pdf]
Across-Subject Classification of Single EEG Trials. Blair Bohannan, Jorge Herrera and Lewis Kaneshiro. [pdf]
Adult Website Classifier. Saikat Sen. [pdf]
An Unsupervised Approach to Email Label Suggestions. David Harwath, Nikhil Johri and Edouard Yin. [pdf]
Applying Entropy Encoding to High Frequency Trading. Yijia Zhou. [pdf]
Assessing the Value of eBay Listing Features. Leon Lin and Vasant Ramachandran. [pdf]
Audio Source Separation Using Probabilistic Latent Component Analysis. Yinyi Guo and Mofei Zhu. [pdf]
Autism. Nelson Ray and Praveen Bommannavar.[pdf]
Automated Agent for the Game RoShamBoFu. Adrian Marple. [pdf]
Automated Classification of Galaxy Zoo Images. Michael Broxton. [pdf]
Automated Parameterization of the Joint Space Dynamics of a Robotic System. Josh Petersen. [pdf]
Automatic Virtual Camera View Generation for Lecture Videos. Derek Pang, Sameer Madan, Serene Kosaraju and Tarun Vir Singh. [pdf]
Autonomous Generation of Bounding Boxes for Image Sets of the Same Object. Jean Feng, Shui Hu, and Marc Rasi. [pdf]
Battery Management in Datacenter Using Machine Learning. Hyun Goo Kang. [pdf]
Beating Elo. Jeffrey Pennington. [pdf]
Beating the NCAA Football Point Spread. Brian Liu and Patrick Lai. [pdf]
Caloric and Nutritional Information Using Image Classification. Arne Bech. [pdf]
Classification and Quantification of Matrix Micro-cracks in Composite Structures. Cecilia Larrosa, Kuldeep Lonkar and Santhosh K. Shankar. [pdf]
Classification of Book Titles. Gongmyung Lee. [pdf]
Classification of Fast Magnetic Resonance Image Reconstruction Using Matching Pursuit Family Algorithm. Aldi Gunawan.[pdf]
Classification of handwritten digits by the set of partial linear and quadratic models. Youngsik Kim.[pdf]
Classification of RNA splice junctions based on genomic signals. Mavis Li, Yun (Sammy) Long, John Chong Mu. [pdf]
Classification of Road Images for Lane Detection. Mingyu Kim, Insun Jang and Eunmo Yang. [pdf]
Classifying Parts Of Songs. Nick Colonnese.[pdf]
Classifying Structural MRI Scans of Patients with Major Depressive Disorder . Estelle Comment, Guillaume Davo, Joseph Marrama, Christie Paz. [pdf]
Comparison of Supervised & Unsupervised Learning in Ôs Estimation between Stocks and the S&P500. J. Wei, Y. Hassid, J. Edery, and A. Becker. [pdf]
Diagnosis of Fragile X and Turner's Syndrome. Kevin Miller and Raj Paudel. [pdf]
Domain Adaptation for Relation Extraction. Daniel Posch, Sheldon Chang, and Matthew Watson. [pdf]
Emotion Recognition with Deep-Belief Networks. Tom McLaughlin, Mai Le, and Naran Bayanbat. [pdf]
Enhancing Automated Question Classification. William Mee and Seung-Yeoul Yang. [pdf]
EPiC: Earthquake Prediction in California. Caroline Suen, David Lo, and Frank Li. [pdf]
Extracting Latent User Preferences Using "Sticky" Sliders. Garrett Eastham. [pdf]
Fast Detection of Risk Signals in Post-marketing Drug Surveillance Using Costs in Claims Data: A Machine Learning Approach. Yihan Guan and Yiliang Jin. [pdf]
Feature selection and dimensionality reduction of neural data to model adaptation in the retina. Neda Nategh. [pdf]
Forecasting Avalanches in the Pacific Northwest. Wes Dyer. [pdf]
Geometric Understanding of Indoor Scenes. Moontae Lee. [pdf]
Graduate School Application Evaluation Based on SVM. Wei Li, Yu Zhou, and Yi Yang. [pdf]
Handwritten Character Recognition. Saurabh Mathur. [pdf]
Hearst Challenge : A Framework to Learn Magazine Sales Volume. Reza Omrani. [pdf]
Hessian Free Deep Learning. Subodh Iyengar. [pdf]
Human Accuracy Analysis on the Amazon Mechanical Turk. Chun-Chang Chen and Justin Hsu and Stefan Wager. [pdf]
Identifying Keywords in Random Texts. Ibrahim Alabdulmohsin and Gokul Gunasekaran. [pdf]
Identifying Looping Components of Audio Mixtures. Sean Coffin. [pdf]
Identifying Morphological Markers for Differential Diagnosis of Liver Lesion. Francisco Gimenez, Ting Liu, and Yi Liu. [pdf]
Information Flows on Twitter. Yu-Wei Lin. [pdf]
Innocent Until Proven Guilty: Linguistic Predictors of Court Verdicts. Anastasia Svetlichnaya. [pdf]
Janitor Bot - Detecting Light Switches. Jiaqi Guo and Haizi Yu. [pdf]
JIT Compilation of Common Kernels in Sequoia using Supervised Learning. Michael Bauer and Kushal Tayal. [pdf]
Joint Supervised Learning of Ratings and Rankings. Rafael Moreno Ferrer, Avinayan Senthi Velayutham, and Ying Wang. [pdf]
Learning the Genetic Causes for Schizophrenia through Copy-Number Variations. Xin Gao, Shan Liu. [pdf]
Learning the Structures and Predicting Behavoir of Potential Function Based Multi-Agent Formations. Doug Beck, Tzong-han Lee and Chun-Yeon Lin. [pdf]
Local Image Histograms for Learning Exposure Fusion. Justin Solomon and Ravi Sankar. [pdf]
Low-Cost Localization of Mobile Robots Through Probabilistic Sensor Fusion. Brian Chung. [pdf]
Machine Learning Approaches to Automatic BI-RADS Classification of Mammography Reports. Bethany Percha. [pdf]
Machine Learning for Sentiment Analysis on the Experience Project. Raymond Hsu, Bozhi See, and Alan Wu. [pdf]
MAUL: Machine Agent User Learning. Robert Holley and Daniel Rosenfeld. [pdf]
Measuring Occlusion Invariances in Deep Networks. Kyunghee Kim. [pdf]
Modeling Activity Patterns. Nipun Dave, Aditya Gudipati, and Shantanu Kurhekar. [pdf]
Multiclass Clustering using a Semidefinite Relaxation. Jason Lee. [pdf]
Multiple Experts with Binary Features. Ye Jin and Lingren Zhang. [pdf]
Musical Pitch Identification. Manoj Deshpande. [pdf]
Neuro-Fuzzy Inverse Forward Models. Brian Highfill. [pdf]
Neural Prediction Challenge: Primary Auditory Cortex. Kevin Nam Truong, MoorXu and Daniel Hawthorne. [pdf]
Object Detection with Partial Occlusion Based on a Deformable Parts-Based Model. Johnson Hsieh and Alexander Chia. [pdf]
Object Identification in Dynamic Environments. Andrew Chou, Elliott Jin, and Wendy Mu. [pdf]
Online Dynamics Estimator for Adaptive UAV Control with Uncertain Model. William Grossman and Marcus Hammond. [pdf]
Online learning of control policies for dynamical systems based on input/output data recorded on the fly. Roberto A. Bunge. [pdf]
Optical Character Recognition for Handwritten Hindi. Aditi Goyal, Kartikay Khandelwal, and Piyush Keshri. [pdf]
Optimizing Leakage Power using Machine Learning. Shuhan Bao. [pdf]
OS fingerprint classification using a support vector machine. David Fifield. [pdf]
Parallel Unsupervised Feature Learning with Sparse AutoEncoder. Milinda Lakkam and Siranush Sarkizova. [pdf]
Parallelizing Machine Learning Algorithms. Juan Batiz-Benet, Quinn Slack, Matt Sparks, and Ali Yahya. [pdf]
Parallelizing the Sparse Autoencoder. Tom Jiang. [pdf]
Parking Spot Detection from Aerial Images. Mehmet Ozan Kabak and Ozhan Turgut. [pdf]
Particle classification. Kahye Song. [pdf]
Predicting and Understanding Bronchopulmonary Dysplasia in Premature Infants. Laney Kuenzel. [pdf]
Predicting Magazine Sales Using Machine Learning. Mei Ling (Zan) Chu, Freeman Fan, Yisha Peng. [pdf]
Predicting Market Fluctuations via Machine Learning. Michael Lim and Yong Su. [pdf]
Predicting the Success of a Retirement Plan Based on Early Performance of Investments. Darrell Cain and AJ Minich. [pdf]
Predicting Course Grades. Martin Hunt, Sharon Lin, and Chinmay Kulkarni. [pdf]
Predicting Tags for URLs Based on Delicious Data. Jayesh Vyas and Varun Katta. [pdf]
Predicting Wireless Channel Utilization at the PHY. Jeffrey Mehlman and Aaron Adcock. [pdf]
Prediction of outcome for two team games. Gundu Gowtham and Riyaaz Shaik. [pdf]
Rating Modules on cnx.org: Using Network Structure as a Feature in Regression. Austen Head. [pdf]
Automated Energy Distribution In Smart Grids. John Yu and Michael Chun. [pdf]
Recognizing facial expressions from videos using Deep Belief Networks. Adithya Rao and Narendran Thiagarajan. [pdf]
Recognizing Hand Gestures with a 3D Camera. Michael Marx, Michael Fenton, and Gage Hills. [pdf]
Recommending Accounts to Twitter Users. Siddharth Vijayakrishnan. [pdf]
Recycler Bot. Jiahui Shi. [pdf]
Reinforcement Learning with Deep Architectures. Daniel Selsam. [pdf]
Reinforcement Learning for Scheduling Threads on a Multi-Core Processor. Saurabh Shrivastava. [pdf]
Reservoir Uncertainty Assessment Using Machine Learning Techniques. Jincong He. [pdf]
RSS Feed Recommendation. Charles Chen. [pdf]
Scene Text Understanding. Sanjeev Satheesh. [pdf]
Semi-Supervised Learning of Named Entity Substructure. Alden Timme. [pdf]
Sentiment Analysis for Market Movements. Jeffrey Wong. [pdf]
Short-Term Electricity Demand Forecasting Using Independent Component Analysis. Jeff Chern, Matthew Ho, and Edwin Tay. [pdf]
Short Term Load Forecasting With Dynamic Pricing. Yu Xian Lim, De Wei Koh and Jonah Tang. [pdf]
Signature Recognition on Multitouch Computers. Chris Moore. [pdf]
Soft Co-clustering via Extension of PLSA. Dakan Wang. [pdf]
Storage Efficient NL-Means Burst Denoising for Programmable Cameras. Mirek Kukla and Brendan Duncan. [pdf]
Subspace Clustering with Applications to Dynamical Vision. Adel Javanmard and Mahdi Soltanolkotabi. [pdf]
Task Recommendation on Wikipedia. Eric Huang and Hyung Jin Kim. [pdf]
Textbaxed Soccer Simulator. Sergei Chevtsov. [pdf]
Topic Modeling in Financial Documents. Patrick Grafe. [pdf]
Toward Mechanized Music Pedagogy. Lingfeng Yang. [pdf]
Transferring Learning in Large Scale Datasets. Huizhong Chen, Bowen Meng and Ying Chang. [pdf]
Travel Time Estimation Using Floating Car Data. Raffi Sevlian. [pdf]
Twitter Hash Tag Prediction Algorithm. Tianxi Li and Yu Wu. [pdf]
Twitter Relevance Filtering via Joint Bayes Classifiers from User Clustering. Alexander L. Churchill, Emmanouel G. Liodakis and Simon H. Ye. [pdf]
Twitter Spammer Profile Detection . Grace Gee and Hakson Teh. [pdf]
Unsupervised Multi-factor Risk Models for US Equities. Kyle Kelley. [pdf]
Unsupervised Learning of Multi-modal Features: Images and Text. Maurizio Calo Caligaris. [pdf]
Unsupervised learning technique for audience privacy protection in video lectures. Juthika Dabholkar, Xunjia Lu, Harsh Nayyar, Sijia Zheng. [pdf]
Using AdaBoost for Real-Time Object Detection on Programmable Graphics Hardware. Farooq Mela. [pdf]
Using the Deformable Part Model with Autoencoded Feature Descriptors for Object Detection. Hyunghoon Cho and David Wu. [pdf]
Using Local Steering Kernels to Detect People in Videos. Arthur Louis Alaniz II and Christina Marianne Mantaring. [pdf]
Using Machine Learning for Identification of Art Paintings. Alexander Blessing and Kai Wen. [pdf]
Using Social Networks to Improve Movie Ratings Predictions. Suhaas Prasad. [pdf]
Using Two Lenses for Depth Estimation and Simulation of Low Depth-of-Field Lenses. Andy L. Lin. [pdf]
VC Robot. Mar Hershenson. [pdf]
Vocal-based Musical Genre Classification. Brian Hyun Hun and Arun Miduthuri. [pdf]


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
News story
A simple checklist of what schools can do to instil good behaviour in the classroom has been developed and published today by Charlie Taylor - the headteacher of a special school with some of the toughest behaviour issues and the government’s expert adviser on behaviour.
A simple checklist of what schools can do to instil good behaviour in the classroom has been developed and published today by Charlie Taylor - the headteacher of a special school with some of the toughest behaviour issues and the government’s expert adviser on behaviour.
The behaviour checklist - entitled ‘Getting the simple things right’ - follows Charlie Taylor’s recent behaviour summit, where outstanding headteachers from schools in areas of high deprivation gathered to discuss the key principles for improving behaviour. What soon became clear was how much similarity there was between the approaches that the headteachers followed. Many of them emphasised the simplicity of their approach but they agreed that most important of all was consistency.
Actions from the checklist include:
Charlie Taylor said:
Without good behaviour teachers can’t teach and pupils can’t learn. There are schools in some of the toughest areas of the country who are getting discipline right. However, some schools struggle with managing and improving behaviour. Often the problem is that they aren’t being consistent with their behaviour policy such as ensuring that punishments always happen every time a pupil behaves badly.
As a headteacher I know that where there is inconsistency in schools, children are more likely to push the boundaries. If a pupil thinks there is a chance that the school will forget about the detention he has been given, then he is unlikely to bother to turn up. If he gets away with it, the threat of detention will be no deterrent in the future.
The checklist sets out for all schools the simple but essential things to get right to ensure strong discipline and therefore strong teaching. Teachers can run through the checklist first thing in the morning and again after lunch to ensure the correct preparations.
The behaviour checklist idea is based on the Checklist Manifesto - developed by Atul Gawande, a surgeon who was concerned that so many patients seemed to suffer serious complications in the days after their operation. He realised that many of these problems were caused by operating staff failing to follow basic procedures, for example, a surgeon forgetting that to wash his hands could cause an infection.
Mr Gawande developed a checklist that could be read out before each operation to ensure that all of the simple but essential procedures were followed. Surgeons were able to adapt the checklist to meet their own needs. The number of patients becoming seriously ill or dying after surgery dropped dramatically after the checklist was introduced.
The behaviour checklist is intended to help schools develop their own policies to improve discipline. It is not intended to be mandatory. Rather it gives schools the ability to develop the 10 or so essential actions needed to improve behaviour. The ideas themselves in the checklist are very simple. It is the process of using a checklist and ensuring consistency which can improve behaviour.
Woodend Park Primary School in Hillingdon, London, has been trialling the checklist approach. They have found that teachers who follow these guidelines find there is more consistency of approach to managing behaviour both in the classroom and around the school. It has led to improvements in behaviour as pupils know what is expected of them.
Avril Stockley, a teacher at the school who uses the checklist after helping to develop it, said:
We found the checklist a helpful way of making sure we were doing all the things to keep behaviour in order. We had found that sometimes we were forgetting to do simple things like praise children for good behaviour as well as discipline for poor behaviour.
We have found that when children know that teachers will stick to the behaviour policy and class routines, they feel safer and happy, and so not only behaviour improves but learning too.
Charlie Taylor’s checklist is also available in the Behaviour and attendance section of the Department for Education’s website.
Examples of some of the checklists schools have produced can be found on the Department for Education’s Facebook page .
Charlie Taylor, the government’s expert adviser on behaviour, has worked with children with behavioural difficulties throughout his teaching career. Since 2006 he has been headteacher of the Willows Special School in Hillingdon, London, which has received ‘outstanding’ ratings in its last two Ofsted inspections. In addition he has been a behaviour consultant in both primary and secondary schools, and has worked for the anti-bullying charity Kidscape. He has written for newspapers and journals, and is the author of two books on parenting young children and teenagers. He is married with three young children and lives in west London.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 19 October 2011
From: Department for Education
Part of: School behaviour and attendance


README A Brandwatch rake wrapper, Brake
Brake, A Cucumber options parser wrapper for Rake, because, cake was already taken!, Give Rake a Brake. Usage: brake <task> [options]
Cucmber options:
-t, --tags @high,@login Array: A list of all tagged test to be run
-d, --dry-run Bool: Invokes the formatters without executing steps
-v, --verbose Bool: Show the files and features loaded
-f, --format pretty String: How to format the output (Default: pretty)
-S, --strict Bool: Fail if there are and undefined or pending steps
-n, --name featureName String: Only execute features with a partial match to the given name
-g, --guess Bool: Guess best match for ambiguous steps
-x, --expand Bool: Expand scenario outline tables in output
Global options:
-e, --enviroment release String: The enviroment to run the tests against eg Release
-l, --log_level debug String: The log output level debug|info
-c, --controller chrome String: The type of controller to run (API, Chrome, Safari)
--[no-]headless Bool: To turn on/off headless mode (only works on Linux!)
--[no-]cleanup Bool: To turn on/off setup cleanup
--[no-]database Bool: Defines whether to log out to the database
--[no-]jenkins Bool: Defines whether to log out to the jenkins specific database
--retries 3 String: The number of retries to perform on element methods
--timeouts 10 String: The length of the timeouts on element methods
Web options:
-s, --screen 1280,1024 Array: The width,hight of the browser window
-p, --position 0,0 Array: The x,y coords of the browser 0,0 top left
Browser options:
-H, --[no-]highlight Bool: To turn on/off the highlight of elements
Brake options:
-V, --version Bool: Output the current version of brake
-I, --iterate 5 Int: Number of times to run the task
-U, --[no-]untangle Bool: Turns Debug on/off
-h, --help You're Looking at it
For further assistance please contact Ben Slaughter or Jonathan Chrisp<EMAIL_ADDRESS>or<EMAIL_ADDRESS>Release Notes: 0.1.0 Class structure created
0.1.6 Code cleanup Changed the names so that all the task names are strings rather than half symbol and half strings
0.2.0 Added this readme file Cleaned up the debug code Added the Version output to display the current version number Added the debug method so that the code looks cleaner and easier to understand Added the RuntimeError handling to mimic the same rake functionality Moved rake requier to be called before the Brakefile load
0.2.1 Ben - Added cucumber dry run Ben - Added -s as default (removes source)
0.2.2 Jon - Updated API task to run release as default
0.2.3 Ben - Removed '--require features' from the default args so now only -P disable profiles and -s disable source code line numbers Ben - Removed platform, this is no longer required
0.2.4 Ben - Some typos in comments and improved readme and code comments for yard docs Jon - Added headless, retries, highlight and cleanup options
0.3.0 Ben - Improved code, Cleaned up options parser, now a single block of code Ben - There are no more pre included tasks, it is either default or user entered Ben - Error checking on screen and position Ben - Brake passes a hash of arguments to the rake task Ben - Cucumber options added and task iterations
0.3.0 Ben - Format was missing the layout definition in the options parser
0.3.2 Ben - Added cucumber options: strict, name, guess, expand
0.3.3 Ben - Added Docs and fixed a few comment typos and changes for docs Ben - Added brake version option
0.3.4 Ben - Added the exit code to mimic what is returned from the task Ben - Fixed test iterate
0.3.5 Jon - Added the database option Jon - Added the jenkins option Jon - Cleaned up spelling errors
0.3.6 Jon - Added database flag to jenkins option so no longer to include database flag also
0.3.7 Jon - Updated gemspec, added new homepage as now on GitHub, created and changed license to MIT and added .gitignore file
END


Preparation time
1-2 hours
Cooking time
10 to 30 mins
Serves
Serves 4-6
For the terrine, place the leeks and spring onions into a wide shallow pan with the vegetable stock and bay leaf. Bring to the boil, then reduce the heat and simmer for 8-10 minutes, or until the vegetables are tender. Remove the vegetables from the pan and set aside.
Return the pan to the heat and boil to reduce the volume of the stock by two thirds.
Meanwhile, soak the gelatine leaves in cold water until softened. Strain, then add to the reduced stock. Whisk to combine, then strain into a bowl. Allow to cool, then season with salt and freshly ground black pepper and stir in the chervil.
Place into the fridge and chill for 30 minutes, or until just setting, but still with a good wobble.
On a work surface, place three large sheets of cling film one on top of the other. Lay the leeks in a row on the cling film, then top with the spring onions.
Spoon over the jelly, then roll the cling film up into a large cigar shape, twisting firmly to enclose at both ends. Place in the fridge for at least an hour until fully set.
For the dressing, whisk the mustard, vinegar and lemon juice together in a bowl. Slowly whisk in the rapeseed oil and season, to taste, with salt and freshly ground black pepper.
To serve, slice the terrine into wedges and place onto serving plates with some slices of ham and a drizzle of dressing.


Community Cast Pledges to Do a Movie If Dan Harmon Writes It
by Justin CaffierGreendale Community College’s most notorious study group reconvened once more this Sunday at Vulture Festival. Joel McHale, Alison Brie, Gillian Jacobs, Danny Pudi, Jim Rash, Yvette Nicole Brown, Ken Jeong, Chris McKenna, and Dan Harmon took the stage to reflect on their favorite episodes and behind the scenes moments from the show’s six-season run.
The series’ long-running semi-serious rallying cry of “six seasons and a movie” was addressed point blank when our moderator Jesse David Fox asked what it would take to produce a Community film. “One million dollars for Donald [Glover],” joked McHale.
“Who’s supposed to say ‘everyone do this?’” wondered Harmon. “That’s what I’ve always said.
Harmon groused that if he wrote the script, then half the cast would likely back out. To that, the whole cast present publicly vowed to attach themselves to the project should it ever be written.
McHale’s joke wasn’t the first time Glover’s absence from the panel was acknowledged. Earlier in the event, the actors shared their fondness for their former castmate. Brown called him “the most talented person [she’d] ever met,” noting his skills went well beyond acting, writing, and music, into disciplines like baking and crochet. Brie mentioned that one of the coolest things about working with Glover was getting to hear Childish Gambino songs before the rest of the world.
The anecdotes about the other missing cast member, Chevy Chase, weren’t quite as kind. Harmon referred to the actor as a “self-described legend,” and divulged that many of the dialogue for Chase’s character, Pierce Hawthorne, was pulled from meetings in Chase’s trailer.


27 October 1997
Disease Outbreak Reported
In 1997 leptospirosis spread to Surat district Gujarat State from Valsad district south of Surat where the diseases has been reported for many years. Cases started occurring mid-July. As of 11 September, 281 cases and 34 deaths (case-fatality rate, CFR 12%) had been reported in Valsad district and 132 cases with 14 deaths (CFR 11%) in Surat district. Cases are still being reported. Investigations at the Medical College, Surat showed that more than 40% of the clinically suspected cases had IgM antibodies and almost 60% of cases tested at the National Institute of Communicable Diseases in Delhi had IgG antibody.
Virtually all the patients are adults and from rural areas; 60% are males. Almost all families and most of the affected villages have reported only single cases. Valsad, Chikhali and Gandevi taluks in Valsad district, and Vyara and Mahuva taluks in Surat district are the worst affected areas. However, a high rate of seropositivity detected in persons without any illness or only mild fever in the last 3 months, also in villages considered unaffected during the present outbreak, indicates that the infection is much more extensive and widespread.
Based on a report from the National Institute of Communicable Diseases, Delhi, India


Gifts for good in the Stratford district
by Stratford District CouncilIt’s that time of year when cheer is in the air, and Stratford District Council is once again asking residents to donate a gift for those less fortunate.
The Mayoral Christmas Gift Appeal is a tradition in Stratford encouraging local residents to donate new or pre-loved toys and gifts in good condition.
Mayor Neil Volzke says that Christmas can be a difficult time for some in the community and the appeal helps to ease this.
“The generosity of Stratford residents is outstanding and I’m sure this year’s appeal will be another example of how our community continues to care for those less fortunate,” says Mayor Volzke.
Items needed include gifts suitable for older people and teenage boys, hard toys such as wooden or plastic made (rather than soft toys like teddy bears), hygiene products, and food is also useful.
Donations can be made under the Christmas tree at Council’s Service Centre on Miranda Street, or at the Stratford Library from Monday 2 December 2019.
Confidential nominations of families and individuals in need can be made by calling Peter Boyd on (06) 765 6099 or by emailing<EMAIL_ADDRESS>Gifts will be accepted up until Tuesday 17 December.


Just add a DIV with an ID named "noty-holder"
Then call you Noty with:
//1st Param = Message, 2nd Param = Type of alert createNoty('Hi! This is my message', 'info');


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Hammers.2C.20SMT.html ---
What is the status of hammers (like sledgehammer) in lean? Development in Coq seems to be somewhat stalled, but there are some interesting datasets:
https://coq.discourse.group/t/coqhammer-1-1-1-for-coq-8-9/327/12
https://coq.discourse.group/t/machine-learning-and-hammers-for-coq/303
Chantal Keller is doing nice work with on safely importing SMT proofs into Coq, and she is defining her own intermediate format. Could the same be used for lean?
https://github.com/smtcoq/smtcoq
I've been working on a sledgehammer-like tool. Here are the slides from FoMM in January: https://gebner.org/pdfs/2020-01-08_fomm20_leanhammer.pdf
However due to performance issues, I don't think this will ever make it into Lean3/mathlib. Any future work will happen in Lean 4.
Nice! How much of this can be modularized? You don't seem to be using any parts of sledgehammer, coqhammer, deephol, ...
BTW did you look at easycrypt? It uses why3 as an intermediate, and is quite pleasant to use.
No, everything I've had for FoMM is homegrown. Since then, I've hooked up the relevance filter from CoqHammer (because it's pretty generic and written in C++, which is nice since Lean is also C++). But in my tests it didn't perform any better than the dumb filters I was using before.
I don't think it makes sense to reuse translations at all. It's pretty short and straightforward code, but it depends very much on the logic of the proof assistant.
I didn't know about easycrypt. I'll take a look but it seems extremely specific ("reasoning about relational properties of probabilistic computations with adversarial code").
What are the relevant differences between classical Coq, isabelle and lean?
Easycrypt has an ambient logic: Classical HOL. There tactic language is ssr with an smt tactic. The libraries are fairly close copies of math-comp.
They have more rewriting build in than Coq.
Isabelle is very much different from the other two: the logic is simply-typed lambda calculus, which is much weaker than the type theories of Lean and Coq, and also much closer to what external provers expect. Additionally, type classes in Isabelle are much more restricted than in Lean: they are coherent, which means that there is a unique instance per type. Hence you just need predicates for type classes that say which type has a type class, and you can e.g. write add(a,b)
for addition (in a sound translation). In Lean and Coq, you can have multiple implementations of addition for a type so you might need to carry around an additional argument that says which type class instance you use.
The differences between Lean and Coq are smaller. Coq has more syntactic features that you need to encode: in a addition to lambdas, they also have fixpoints and match expressions. On the other hand, Lean makes an auxiliary definitions if you write a match
, and also adds equational lemmas that hide the actual implementation as recursors. You typically don't want to definitionally unfold the auxiliary definition, and only use the equational lemmas.
Thanks. I understood you translated lean to HOL (as in your slides). From that point on, one could imagine that similar tools could be used as for isabelle and easycrypt.
F* of course is another data point. However, I understand that the translation they are doing to smt solvers is not really well understood.
Last updated: Apr 24 2020 at 19:02 UTC


Main Features
-
With EaseDisplay you can keep control of your screen resolution and the way, every single app displays its content on your screen.
For more practical results, add EaseDisplay.app to your Login Items in System Preferences ➡ Users & Groups ➡ Login Items. This way EaseDisplay will run automatically on startup.


<script src="RGraph.common.core.js"></script> <script src="RGraph.bar.js"></script>Put this where you want the chart to show up:
<div> <canvas id="cvs" width="550" height="350">[No canvas support]</canvas> </div>This is the code that generates the chart - it should be placed AFTER the
canvas
tag(s):


Causes/Triggers:
Addiction- curiosity(usually in adolescence due to peer pressure and lack of education given in the home about substance use), mental disorders(becoming dependent on prescription meds),
presence in the home()
Tips/Coping/Managing:
creating a new life(rid yourself of your old ways and routines that were rooted and surrounded by the addiction)
honesty(),
cognitive therapy(getting in the know how and habit of changing negative thinking. esp, of oneself
Causes/Triggers:
Depression-biological(appear to have physical changes in brain), hormones(dramatic spikes and drops in hormones), inherited(common in people whose relatives suffer from the condition),
holidays(increase of family or isolation)
Tips/Coping/Managing:
Do something new( engaging in new activities releases dopamine), Set goals(small daily goals, pushes person to recognize achievements/accomplishments to do more), journal writing(allows person to express their anger, sadness, other emotions and analyze them)
Causes/Triggers/Managing
D.I.D-
trauma(predominantly onset in childhood, severe emotional, physical, mental, sexual abuse, witness to extreme violence)
fear(once an alternate personality has presented itself, they will take over for the dominant personality if they feel threatened) The outcome- it is believed that the brain creates another person/personality to cope with the trauma inflicted on the dominant personality
Tips/Coping/Managing:
Due to controversy around this disorder and disagreements within the mental health field whether or not the disorder actually exist, there aren't many treatment or coping options. The current mainstay is psychotherapy
Causes/triggers:
Anxiety- Trauma(endured or witnessed), Stress due to an illness(treatments, mortality), personality(certain personality types are more prone than others)
Tips/Coping/Managing:
Lantern(online tool that gives daily exercises and matches person with a personal coach)
Exercise(improve hormone balance, release hormone transmitters that improve mood), Yoga(breathing exercises and setting intentions)
Exposure therapy(guided by a psychologist, person confronts certain scenarios, to confront and decrease specific fears)


Gagah 271 Navy Training Vessel
Royal Malaysian Navy
Sample text supplies air conditioning, heating, cooling and ventilation products to a wide range of superyacht builders around the world. One of them is Hakvoort Shipyards in Monnickendam, the Netherlands, a venerable maritime institution with a history spanning nearly a century of yachtbuilding.
Having started out with traditional Dutch fishing boats, the yard evolved into a renowned builder of luxury yachts, becoming increasingly specialised along the way. We talked to managing director Klaas Hakvoort about the close partnership.
Teguh 272 Navy Training Vessel
Royal Malaysian Navy
Sample text supplies air conditioning, heating, cooling and ventilation products to a wide range of superyacht builders around the world. One of them is Hakvoort Shipyards in Monnickendam, the Netherlands, a venerable maritime institution with a history spanning nearly a century of yachtbuilding.
Having started out with traditional Dutch fishing boats, the yard evolved into a renowned builder of luxury yachts, becoming increasingly specialised along the way. We talked to managing director Klaas Hakvoort about the close partnership.
Sapurakencana Aman
Duration
Sample text supplies air conditioning, heating, cooling and ventilation products to a wide range of superyacht builders around the world. One of them is Hakvoort Shipyards in Monnickendam, the Netherlands, a venerable maritime institution with a history spanning nearly a century of yachtbuilding.
Having started out with traditional Dutch fishing boats, the yard evolved into a renowned builder of luxury yachts, becoming increasingly specialised along the way. We talked to managing director Klaas Hakvoort about the close partnership.
Interested in working with us?


HuffPost mocked for Thanksgiving article telling readers to tweak holiday because turkeys, travel impact planet
by Brian FloodThe liberal HuffPost wants Americans to change their Thanksgiving traditions because traveling to eat a turkey feast could harm the environment.
The story, written by Canada-based contributor Alexandra Emanuelli and published Tuesday, headlined “The environmental impact of your Thanksgiving dinner,” details the pros and cons of celebrating the American holiday.
“How much damage are we doing with our epic Thanksgiving meal every year? We spoke with three researchers to find out more about Thanksgiving’s carbon footprint,” Emanuelli wrote. “It turns out that your food isn’t the biggest holiday culprit of carbon dioxide emissions — traveling for the meal is.”
ABC NEWS' AMY ROBACH CAUGHT ON HOT MIC SAYING NETWORK SPIKED JEFFREY EPSTEIN BOMBSHELL
But traveling to celebrate holiday isn’t the only problem Emanuelli has with Thanksgiving traditions. The HuffPost contributor then declared that people shouldn’t “be discouraged from enjoying the holiday” but suggests nixing turkey because of its environmental footprint.
“Looking for a lower carbon alternative? Go for a plant-based source,” Emanuelli wrote. “So instead of more turkey, help yourself to an extra slice of pumpkin pie or an additional serving of squash soup.”
MAINSTREAM MEDIA LARGELY IGNORES ABC'S EPSTEIN SCANDAL; CNN’S SCANT COVERAGE IS LAMPOONED
The HuffPost also suggested using only locally sourced ingredients and urged readers cut down “the endless sides and apps” to help eliminate food waste. Emanuelli concluded her piece by suggesting people who refuse to give up turkey at least select smaller birds, while also urging readers to cut back on travel.
“Celebrating with friends and family locally or finding a spot that requires less travel for everyone are a few ways to lessen your impact on the environment while celebrating the holiday,” she wrote.
Comedian Michael Loftus mocked the HuffPost over the “leftist propaganda” during an appearance on Fox News on Friday.
“I need Thanksgiving, I need this. I need all the stuffing, I need sides, I need the turkey. I don’t need the Huffington Post telling me how to live my life,” Loftus said. “What’s the carbon footprint on a building full of idiots?”
CLICK HERE TO GET THE FOX NEWS APP
Others took to Twitter to mock the HuffPost’s thoughts on Thanksgiving:


Helper class for the burst_creator node. More...
#include <buffer_spike_detector.h>
Public Member Functions | |
| void | add (const neuro_recv::dish_state &d) |
| Adds a dish state to the buffer. | |
| BufferSpikeDetector () | |
| Sets values to their initial states. | |
| double | getBaseline (int index) |
| burst_calc::ranges | getRanges () |
| Returns a range object with values for each channel. | |
| double | getThreshold (int index) |
| void | init (int buffer_size, double stdev_mult) |
| Gives the object values needed for later calculations. | |
| bool | isBuffered () |
Private Member Functions | |
| void | calculate () |
| Calculates values for each channel. | |
Private Attributes | |
| double | baselines_ [60] |
| int | buffer_size_ |
| int | dishes_received_ |
| double | max_volts_ [60] |
| double | min_volts_ [60] |
| double | stdev_mult_ |
| double | stdevs_ [60] |
| double | sum_squares_ [60] |
| double | sums_ [60] |
| double | thresholds_ [60] |
| double | variances_ [60] |
Helper class for the burst_creator node.
Calculates baselines and spike thresholds for each of the 60 channels in a multi-electrode array.
Definition at line 22 of file buffer_spike_detector.h.
Sets values to their initial states.
Definition at line 14 of file buffer_spike_detector.cpp.
| void BufferSpikeDetector::add | ( | const neuro_recv::dish_state & | d | ) |
Adds a dish state to the buffer.
| d | the dish state to be added |
Definition at line 44 of file buffer_spike_detector.cpp.
| void BufferSpikeDetector::calculate | ( | ) | [private] |
Calculates values for each channel.
The values calculated are: baseline, variance, standard deviation, threshold. This method is not called until the buffer is full.
Definition at line 71 of file buffer_spike_detector.cpp.
| double BufferSpikeDetector::getBaseline | ( | int | index | ) | [inline] |
Definition at line 28 of file buffer_spike_detector.h.
Returns a range object with values for each channel.
The values stored are: baseline, threshold, min voltage, max voltage.
Definition at line 92 of file buffer_spike_detector.cpp.
| double BufferSpikeDetector::getThreshold | ( | int | index | ) | [inline] |
Definition at line 29 of file buffer_spike_detector.h.
| void BufferSpikeDetector::init | ( | int | buffer_size, |
| double | stdev_mult | ||
| ) |
Gives the object values needed for later calculations.
| buffer_size | the number of dish states used to calculate baselines and thresholds |
| stdev_mult | the standard deviation multiplier used when calculating thresholds |
Definition at line 34 of file buffer_spike_detector.cpp.
| bool BufferSpikeDetector::isBuffered | ( | ) | [inline] |
Definition at line 30 of file buffer_spike_detector.h.
double BufferSpikeDetector::baselines_[60] [private] |
Definition at line 38 of file buffer_spike_detector.h.
int BufferSpikeDetector::buffer_size_ [private] |
Definition at line 44 of file buffer_spike_detector.h.
int BufferSpikeDetector::dishes_received_ [private] |
Definition at line 45 of file buffer_spike_detector.h.
double BufferSpikeDetector::max_volts_[60] [private] |
Definition at line 43 of file buffer_spike_detector.h.
double BufferSpikeDetector::min_volts_[60] [private] |
Definition at line 42 of file buffer_spike_detector.h.
double BufferSpikeDetector::stdev_mult_ [private] |
Definition at line 46 of file buffer_spike_detector.h.
double BufferSpikeDetector::stdevs_[60] [private] |
Definition at line 40 of file buffer_spike_detector.h.
double BufferSpikeDetector::sum_squares_[60] [private] |
Definition at line 37 of file buffer_spike_detector.h.
double BufferSpikeDetector::sums_[60] [private] |
Definition at line 36 of file buffer_spike_detector.h.
double BufferSpikeDetector::thresholds_[60] [private] |
Definition at line 41 of file buffer_spike_detector.h.
double BufferSpikeDetector::variances_[60] [private] |
Definition at line 39 of file buffer_spike_detector.h.


Base class for Reader and Writer, essentially just wrapping some utilities. More...
#include <Hi5.h>
Static Public Member Functions | |
| static void | handleStatus (herr_t status, const std::string &message, drain::Logger &mout, int lineNo=0) |
| static hid_t | getH5StandardType (const std::type_info &type) |
| static hid_t | getH5NativeDataType (const std::type_info &t) |
| Given type toOStr of a native C++ type, returns a native HDF5 data type. More... | |
| template<class T > | |
| static hid_t | getH5NativeDataType () |
| Given a native C++ type, returns a native HDF5 data type. | |
| static hid_t | getH5StringVariableLength () |
| static drain::image::Image & | getPalette (Hi5Tree &dst) |
| Creates a 256-element RGB palette to be referenced with linkPalette(). | |
| static void | linkPalette (const Hi5Tree &palette, Hi5Tree &dst) |
| Links the palette that has been (or will be) created with createPalette(). | |
| static void | writeText (const Hi5Tree &src, const std::list< Hi5Tree::path_t > &paths, std::ostream &ostr=std::cout) |
| Dumps the H5 structure, attributes and data properties. | |
| static void | writeText (const Hi5Tree &src, std::ostream &ostr=std::cout) |
| Dumps the H5 structure, attributes and data properties. (Calls writeText(src, src.getKeys(), ostr)). | |
| static void | readText (Hi5Tree &src, std::istream &istr=std::cin) |
| Constructs a tree from formatted text. See writeText(). More... | |
| static void | parsePath (const std::string &line, Hi5Tree::path_t &path, std::string &attrKey, std::string &attrValue) |
| Split full path string to path object and attribute key. More... | |
| static void | parsePath (const std::string &line, Hi5Tree::path_t &path, std::string &attribute) |
| static void | readTextLine (Hi5Tree &src, const std::string &line) |
| Assign a value with optional type specification. More... | |
| static void | deleteNoSave (Hi5Tree &src) |
| Delete branches that have been marked with noSave=true . | |
|
static |
Given type toOStr of a native C++ type, returns a native HDF5 data type.
TODO
|
static |
Split full path string to path object and attribute key.
| s | - string containing path and/or attribute |
Given a path like /dataset1/data3
|
static |
Constructs a tree from formatted text. See writeText().
The grammar should follow to that produced by writeText().
|
static |
Assign a value with optional type specification.
Creates a node in src, creates the desired attribute with given value of specified storage type. If the attribute is "image"
, creates an image with specified value (width
,height
) and type.
Create the node always


Values
Combating Stereotype Threat
August 2, 2015
T minus 15 days til phase 1 of DBC begins! When I first started shopping around for software boot camps, I found that Dev Bootcamp places a large emphasis on emotional intelligence, and the person as a whole rather than another product of the machine. This is what ultimately helped me make my decision to commit to this program, and is very deeply grounded into some of my core values.
Some of My Most Important Values:
- Family
- Inspiration
- Self-Reliance
- Courage
- Honesty
- Efficiency
- Excellence
- Close-Relationships
- Challenge
- Social Justice
- Passion
Honestly, I could keep this list going, but for the sake of this blog I will stop here. I feel like each of these attributes tell a piece of my story and shape how I make my decisions day by day. The only value I'm going to choose to focus on right now is honesty
Honesty
Honesty in the most traditional sense would be being true to others, but I think the part of honesty that I hold near and dear to my heart goes much deeper than speaking true words. Honesty includes being honest to oneself. Honesty means being brave enough to be true to the realities that I face and not misconstruing them to feel more comfortable about my environment. Honesty means looking at the world I live in and accept that it's far from perfect, but looking at my own two hands and deciding what I can do to make it a better place for me and those closest to me.
In the sphere of stereotype threat and how I will use my time at DBC to combat it, I really don't know what I will be able to do. I know that in order to make a larger and more meaningful impact in my community and the community of my students whom I love so much, I need to increase my sphere of impact. I need to build myself to be in a higher position of power because as a teacher there are only so many lives I can affect. I know my goals as a social justice educator and now living advocate for young women of colour and our opportunities within the tech sphere are extremely limited. I hope that the emotional intelligence we learn through DBC integrated with my daily reflection and practice of being honest, humble, and true to myself will help me grow into a position where I can truly make a difference. So that girls of colour don't need to be minorities amongst the minorities.
I could probably go on about this forever, but that's where I’ll leave it for now. I'm extremely passionate about social justice and I know that having the courage to be excellent and embrace my passion will all stem from my ability to remain honest with myself and what I am and am not able to do.


Acml2020
Our paper on averaging partially observed covariance matrices is accepted at ACML! This is a joint work with F. Yger, Q. Barthelemy and S. Sra.
Our paper on averaging partially observed covariance matrices is accepted at ACML! This is a joint work with F. Yger, Q. Barthelemy and S. Sra.


my name is Allan Pettigrew. I am proficient in user experience design, web design, motion design, and am interested in designing for augmented and virtual reality. I am a team player and love solving problems. If you want me to be a part of your team, contact me.


Preparation time
30 mins to 1 hour
Cooking time
1 to 2 hours
Serves
Serves 12
Dietary
This chocolate cake is said to be invented in Vienna by the chef Franz Sacher in 1832. It improves if left a day or two before cutting
Preheat the oven to 180C/350F/Gas 4. Grease a deep 23cm/9in round cake tin then line the base with greaseproof paper.
Break the chocolate into pieces, melt gently in a bowl set over a pan of hot water, stirring occasionally, then cool slightly. Beat the butter in a bowl until really soft, then gradually beat in the sugar until the mixture is light and fluffy. Add the cooled chocolate and the vanilla extract and beat again. Add the egg yolks, then fold in the ground almonds and sieved flour. The mixture will be quite thick at this stage.
In a separate bowl, whisk the egg whites until they are stiff but not dry. Add about one-third to the chocolate mixture and stir in vigorously. Gently fold in the remaining egg whites. Pour the mixture into the prepared tin and level the surface.
Bake in the oven for about 45-50 minutes, or until well risen at the top and the cake springs back when lightly pressed with a finger. Leave to cool in the tin for a few minutes then turn out, peel off the paper and finish cooling on a wire rack.
To make the topping, heat the apricot jam in a small pan and then brush evenly over the top and sides of the cold cake. Allow to set.
Make the icing by breaking the plain chocolate into pieces. Heat the cream until piping hot, remove from the heat and add the chocolate. Stir until the chocolate has melted, then cool till a coating consistency. Then pour the icing on to the centre of the cake. Spread it gently over the top and down the sides, and leave to set.
For the ‘icing’ writing, break the milk chocolate into pieces then melt gently in a bowl set over a pan of hot water. Spoon into a small paper icing bag or polythene bag and snip off the corner. Pipe ‘Sacher’ across the top and leave to set.


Fungus causes Seattle Children’s operating room closures
SEATTLE (AP) - Operating rooms at Seattle Children’s Hospital have been closed for the second time this year after a type of potentially dangerous fungus was discovered in air samples.
Hospital spokesperson Kathryn Mueller says latest discovery has closed three operating rooms and two procedural areas, forcing the hospital to postpone some surgeries and divert others to other hospitals.
KOMO reports that routine air tests on Nov. 10 revealed the presence of Aspergillus. Aspergillus is a common mold often found in the air - but it can cause complications for surgical patients.
All 14 of Seattle Childrens’ operating rooms will be closed later in the week as the hospital investigates. The latest outbreak of Aspergillus mold has been reported to state and county health officials.
It wasn’t immediately clear how many surgeries are being affected.
The latest discovery is the second time that the fungus has been detected in operating rooms at Seattle Children’s Hospital. The previous outbreak led to at least five infections and one death, hospital officials said at the time.
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CANDIDATE
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Journal
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Find A Stakepool
Stakepools allow you to participate in Decred Proof-of-Stake (PoS) mining without needing to worry
about losses due computer downtime, flaky internet connectivity, or inconsistent power.
about losses due computer downtime, flaky internet connectivity, or inconsistent power.
Notice: Missed tickets can be caused by a poorly connected Proof-of-work (PoW) miner and may not necessarily be the fault of the pool.
Warning: To protect de-centralization of the network, it is recommended to avoid pools that have over 5% of network votes.


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Defines a dynamic simulation case.
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__init__(self,
case,
frequency=50.0,
stepsize=0.01,
stoptime=1)
Constructs a new DynamicCase instance. |
source code
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csr_matrix
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getAugYbus(self,
U0,
gbus)
Based on AugYbus.m from MatDyn by Stijn Cole, developed at Katholieke
Universiteit Leuven. |
source code
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tuple
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generatorInit(self,
U0)
Based on GeneratorInit.m from MatDyn by Stijn Cole, developed at
Katholieke Universiteit Leuven. |
source code
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tuple
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exciterInit(self,
Xexc,
Vexc)
Based on ExciterInit.m from MatDyn by Stijn Cole, developed at
Katholieke Universiteit Leuven. |
source code
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tuple
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governorInit(self,
Xgov,
Vgov)
Based on GovernorInit.m from MatDyn by Stijn Cole, developed at
Katholieke Universiteit Leuven. |
source code
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tuple
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machineCurrents(self,
Xg,
U)
Based on MachineCurrents.m from MatDyn by Stijn Cole, developed at
Katholieke Universiteit Leuven. |
source code
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array
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solveNetwork(self,
Xgen,
augYbus_solver,
gbus)
Based on SolveNetwork.m from MatDyn by Stijn Cole, developed at
Katholieke Universiteit Leuven. |
source code
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Inherited from util._Serializable :
save ,
save_pickle ,
save_to_file_object
Inherited from object :
__delattr__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__str__
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