What is it about?
“When Congress passes laws, federal agencies implement those laws through regulations. These regulations vary in subject and include everything from ensuring water is safe to drink to setting health care standards. Regulations.gov is the place where users can find and comment on regulations.” - Regulation.gov
Currently, all the comments are analyzed by government officials. While process works when the volume of comments is low, it fails when a particular regulation receives a large volume of comments. For example, the docket for the Consumer Financial Protection Bureau’s proposed rule on ‘Payday, Vehicle Title, and Certain High-Cost Installment Loans’ received 421,260 comments. Supposing an average of 1 minute is spent per comment, processing these comments would take approximately 878 8-hour work days. In this project, we aim to automatically summarize regulatory documents and cluster comments on those documents to aid policymakers in making better decisions based on the comments. We also hope this project will allow other interested parties to explore the documents and the comments being submitted.
How does it work?
- Document keyword extraction
- This algorithm extracts the keywords in a document. This helps the tool in giving users a high level idea of the topics that the document is talking about, and also acts as an input to other algorithms.
- Key sentences from document
- These are the sentences from the document that have the highest importance score, based on three criteria: highest number of our important keywords; longest length (indicating an abundance of information included); and presence of words indicating the sentence is related to the problem the regulation is attempting to solve.
- Cluster comments based on topic
- This algorithm clusters the comments on a document using k-means. The number of clusters is determined by the number of common parents for the top unigrams in the main document. The output for this algorithm is a collection of comment clusters based on the topic
- Pull out central comment cluster sentence
- As it is hard to determine the semantic meaning behind a cluster, we identified and selected the comment closest to the k-means centroid as a representative of that cluster.
- Differentiating cluster phrases
- Having clustered the comments, we merged all of the comments in each cluster into one ‘mega-comment’ and then used TF-IDF across these ‘mega-comments.’


Our unique school programme model also includes educating teachers and parents in identification and intervention methods. This will aid in early detection and better management of mental health.
According to the ASSOCHAM(2015) recent study, the rate of emotional problems such as anxiety and depression has increased by 45-50% among corporate employees in the last 8 years.
Hence in an effort to address these concerns, our corporate programmes have been designed to focus on spreading awareness regarding mental illness and their treatement.
Different communities that are part of the larger society need to be aware of basic mental health concerns. Educating them and providing them with the right tools and necessary support can enhance the over-all mental well-being of the society.
Hence our aim is to address different aspects of mental health and it's concerns among all sects of people.
Relive-India's community programmes are adaptable to cater any particular community.


Chapter 13 Workbooks
Brian Kim, Christoph Kern, Jonathan Scott Morgan, Clayton Hunter, Avishek Kumar
This final chapter provides an overview of the Python workbooks that accompany this book. The workbooks combine text explanation and code you can run, implemented in Jupyter notebooks107, to explain techniques and approaches selected from each chapter and to provide thorough implementation details, enabling students and interested practitioners to quickly get up to speed on and start using the technologies covered in the book. We hope you have a lot of fun with them.
13.1 Introduction
We provide accompanying Juptyer workbooks for most chapters in this book. The workbooks provide a thorough overview of the work needed to implement the selected technologies. They combine explanation, basic exercises, and substantial additional Python and SQL code to provide a conceptual understanding of each technology, give insight into how key parts of the process are implemented through exercises, and then lay out an end-to-end pattern for implementing each in your own work. The workbooks are implemented using Jupyter notebooks, interactive documents that mix formatted text and Python code samples that can be edited and run in real time in a Jupyter notebook server, allowing you to run and explore the code for each technology as you read about it.
The workbooks are centered around two main substantive examples. The first example is used in the Databases, Dataset Exploration and Visualization, Machine Learning, Bias and Fairness, and Errors and Inference workbooks, which focus on corrections data. The second example includes the APIs, Record Linkage, Text Analysis, and Network Analysis workbooks, which primarily use patent data from PatentsView108 and grant data from Federal RePORTER109 to investigate innovation and funding.
The Jupyter notebooks are designed to be run online in a cloud environment using Binder110 and don’t need additional software installed locally. Individual workbooks can be opened by following the corresponding Binder link, and everything can be done in your browser. The full set of workbooks is available at (https://workbooks.coleridgeinitiative.org), and additional workbooks may be added over time and made available in this repository.
The workbooks can also be run locally. In that case, you will need to install Python on your system, along with some additional Python packages needed by the workbooks. The easiest way to get this all working is to install the free Anaconda Python distribution111. Anaconda includes a Jupyter server and precompiled versions of many packages used in the workbooks. It includes multiple tools for installing and updating both Python and installed packages. It is separate from any OS-level version of Python, and is easy to completely uninstall.
13.2 Notebooks
Below is a list of the workbooks, along with a short summary of the content that each covers.
13.2.1 Databases
The Databases notebook builds the foundation of using SQL to query data. Much of the later notebooks will involve using these tools. This workbook also introduces you to the main data source that is used in the online workbooks, the North Carolina Department of Corrections Data (https://webapps.doc.state.nc.us/opi/downloads.do?method=view). In this notebook, you will:
Build basic queries using SQL,
Understand and perform various joins.
13.2.2 Dataset Exploration and Visualization
The Dataset Exploration and Visualization notebook further explores the North Carolina Department of Corrections data, demonstrating how to work with missing values and date variables and join tables by using SQL in Python. Though some of the SQL from the Databases notebook is revisited here, the focus is on practicing Python code and using Python for data analysis. The workbook also explains how to pull data from a database into a dataframe in Python and continues by exploring the imported data using the numpy
and pandas
packages, as well as matplotlib
and seaborn
for visualizations. In this workbook, you will learn how to:
Connect to and query a database through Python,
Explore aggregate statistics in Python,
Create basic visualizations in Python.
13.2.3 APIs
The APIs notebook introduces you to the use of Internet-based web service APIs for retrieving data from online data stores. This notebook walks through the process of retrieving data about patents from the PatentsView API from the United States Patent and Trademark Office. The data consist of information about patents, inventors, companies, and geographic locations since 1976. In this workbook, you will learn how to:
Construct a URL query,
Get a response from the URL,
Retrieve the data in JSON form.
13.2.4 Record Linkage
In the Record Linkage workbook, you will use Python to implement the basic concepts behind record linkage using data from PatentsView and Federal RePORTER. This workbook will cover how to pre-process data for linkage before demonstrating multiple methods of record linkage, including probabilistic record linkage, in which different types of string comparators are used to compare multiple pieces of information between two records to produce a score that indicates how likely it is that the records are data about the same underlying entity. In this workbook, you will learn how to:
Prepare data for record linkage,
Use and evaluate the results of common computational string comparison algorithms including Levenshtein distance, Levenshtein–Damerau distance, and Jaro–Winkler distance,
Understand the Fellegi–Sunter probabilistic record linkage method, with step-by-step implementation guide.
13.2.5 Text Analysis
In the Text Analysis notebook, you will use the data that you pulled from the PatentsView API in the API notebook to find topics from patent abstracts. This will involve going through every step of the process, from extracting the data to cleaning and preparing to using topic modeling algorithms. In this workbook, you will learn how to:
Clean and prepare text data,
Apply Latent Dirichlet Allocation for topic modeling,
Improve and iterate models to focus in on identified topics.
13.2.6 Networks
In the Networks workbook you will create network data where the nodes are researchers who have been awarded grants, and ties are created between each researcher on a given grant. You will use Python to read the grant data and translate them into network data, calculate node- and graph-level network statistics and create network visualizations. In this workbook, you will learn how to:
Calculate node- and graph-level network statistics,
Create graph visualizations.
13.2.7 Machine Learning – Creating Labels
The Machine Learning Creating Labels workbook is the first of a three-part Machine Learning workbook sequence, starting with how to create an outcome variable (label) for a machine learning task by using SQL in Python. It uses the North Carolina Department of Corrections Data to build an outcome that measures recidivism, i.e. whether a former inmate returns to jail in a given period of time. It also shows how to define a Python function to automate programming tasks. In this workbook, you will learn how to:
Define and compute a prediction target in the machine learning framework,
Use SQL with data that has a temporal structure (multiple records per observation).
13.2.8 Machine Learning – Creating Features
The Machine Learning Creating Features workbook prepares predictors (features) for the machine learning task that has been introduced in the Machine Learning Creating Labels workbook. It shows how to use SQL in Python for generating features that are expected to predict recidivism, such as the number of times someone has been admitted to prison prior to a given date. In this workbook, you will learn how to:
Generate features with SQL for a given prediction problem,
Automate SQL tasks by defining Python functions.
13.2.9 Machine Learning – Model Training and Evaluation
The Machine Learning Model Training and Evaluation workbook uses the label and features that were created in the previous workbooks to construct a training and test set for model building and evaluation. It walks through examples on how to train machine learning models using scikit-learn
in Python and how to evaluate prediction performance for classification tasks. In addition, it demonstrates how to construct and compare many different machine learning models in Python. In this workbook, you will learn how to:
Pre-process data to provide valid inputs for machine learning models,
Properly divide data with a temporal structure into training and test sets,
Train and evaluate machine learning models for classification using Python.
13.2.10 Bias and Fairness
The Bias and Fairness workbook demonstrates an example of using the bias and fairness audit toolkit Aequitas in Python. This workbook uses an example from criminal justice and demonstrates how Aequitas can be used to detect and evaluate biases of a machine learning system in relation to multiple (protected) subgroups. You will learn how to:
Calculate confusion matrices for subgroups and visualize performance metrics by groups,
Measure disparities by comparing, e.g., false positive rates between groups,
Assess model fairness based on various disparity metrics.
13.2.11 Errors and Inference
The Errors and Inference workbook walks through how one might think critically about issues that might arise in their analysis. In this notebook, you will evaluate the machine learning models from previous notebooks and learn about ways to improve the data to use as much information as possible to make conclusions. Specifically, you will learn how to:
Perform sensitivity analysis with machine learning models,
Use imputation to fill in missing values.
13.2.12 Additional Workbooks
An additional set of workbooks that accompanied the first edition of this book is available at (https://github.com/BigDataSocialScience/Big-Data-Workbooks). This repository provides two different types of workbooks, each needing a different Python setup to run. The first type of workbooks is intended to be downloaded and run locally by individual users. The second type is designed to be hosted, assigned, worked on, and graded on a single server, using jupyterhub
(https://github.com/jupyter/jupyterhub) to host and run the notebooks and nbgrader
(https://github.com/jupyter/nbgrader) to assign, collect, and grade.
13.3 Resources
We noted in Section Introduction: Resources the importance of Python, SQL, and Git/GitHub for the social scientist who intends to work with large data. See that section for pointers to useful online resources, and also see https://github.com/BigDataSocialScience, where we have collected many useful web links, including the following.
For more on getting started with Anaconda, see the Anaconda documentation.112
For more information on IPython and the Jupyter notebook server, see the IPython site (http://ipython.org/), IPython documentation (http://ipython.readthedocs.org/), Jupyter Project site (http://jupyter.org/), and Jupyter Project documentation (http://jupyter.readthedocs.org/).
For more information on using jupyterhub
and nbgrader
to host, distribute, and grade workbooks using a central server, see the jupyterhub
GitHub repository (https://github.com/jupyter/jupyterhub/), jupyterhub
documentation (http://jupyterhub.readthedocs.org/), nbgrader
GitHub repository (https://github.com/jupyter/nbgrader/), and nbgrader
documentation (http://nbgrader.readthedocs.org/).


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import play.api.Configuration @import models.pages.Summary @import uk.gov.hmrc.govukfrontend.views.html.components._ @import uk.gov.hmrc.govukfrontend.views.html.layouts.{govukLayout, govukTemplate} @import uk.gov.hmrc.hmrcfrontend.config.AccessibilityStatementConfig @import uk.gov.hmrc.play.views.html.layouts.{Footer, Head} @import views.html.govuk.{head => headTemplate, scripts => scriptsTemplate} @this( govukTemplate: govukTemplate, govukHeader : GovukHeader, govukLayout: govukLayout, head: headTemplate, scripts: scriptsTemplate, govukPhaseBanner: govukPhaseBanner, govukBackLink: GovukBackLink, govukSummaryList: GovukSummaryList, footer: Footer, govukFooter: govukFooter, accessibilityStatementConfig: AccessibilityStatementConfig, configuration: Configuration, uiLayoutHead: Head, govukErrorSummary: GovukErrorSummary ) @( pageTitle: Option[String] = None, pageHeading: String, theForm: Form[_] = null, showBanner: Boolean = false, showH1: Boolean = true, showSection: Boolean = false, showFeedback: Boolean = true, showFooter: Boolean = true, showAccountInfo: Boolean = false, summary: Option[Summary] = None, beforeContentBlock: Option[Html] = None, backLinkUrl: Option[String] = None, showHistoryBack: Boolean = false, withTimeoutDialog: Boolean = true, gridClasses: String = "govuk-grid-column-two-thirds" )(contentBlock: Html)(implicit request: Request[_], messages: Messages) @beforeBLock = { @if(showFeedback) { @govukPhaseBanner( PhaseBanner( classes = "dontprint", tag = Option(Tag(Text("BETA"))), content = HtmlContent( messages("label.phase2", controllers.feedback.routes.FeedbackController.feedback.url) ) ) ) } @if(showAccountInfo && summary.isDefined){ @views.html.includes.account_info_header(summary.get) } @backLinkUrl.map{ url =>


java.lang.Object | +--DGWeapon
DGWeapon is designed to model a weapon or other piece of equipment the character can carry. All fields must be set at construction; the fields are all public fields therefore there are no accessors.
| Field Summary | |
short |
AccUp
Increase Accuracy by this much (ignore 0) |
static short |
Atk_Wep
Type value for all attack weapons (swords, knives, etc.). |
short |
AtkUp
Increase Attack by this much (ignore 0) |
static short |
Def_Wep
Type value for all defense weapons (shields, mail, etc.). |
boolean |
Defined
True if a defined weapon is present, false if not |
short |
DefUp
Increase Defense by this much (ignore 0) |
short |
HPUp
Increase HP by this much (ignore 0) |
short |
MagUp
Increase Magic by this much (ignore 0) |
short |
MPUp
Increase MP by this much (ignore 0) |
short |
SpdUp
Increase Speed by this much (ignore 0) |
short |
Type
The type of weapon (see Atk_Wep and Def_Wep) |
| Constructor Summary | |
DGWeapon()
Default constructor. |
|
DGWeapon(short typ,
short hp,
short mp,
short atk,
short mag,
short def,
short spd,
short acc)
The main constructor. |
| Methods inherited from class java.lang.Object |
|
| Field Detail |
public final short AccUp
public static final short Atk_Wep
public final short AtkUp
public static final short Def_Wep
public final boolean Defined
public final short DefUp
public final short HPUp
public final short MagUp
public final short MPUp
public final short SpdUp
public final short Type
| Constructor Detail |
public DGWeapon()
public DGWeapon(short typ, short hp, short mp, short atk, short mag, short def, short spd, short acc)
typ
- Sets Typehp
- Sets HPUpmp
- Sets MPUpatk
- Sets AtkUpmag
- Sets MagUpdef
- Sets DefUpspd
- Sets SpdUpacc
- Sets AccUp


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FsmMultipleActions
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Cases for several actions attached to different transitions from same state with different when conditions attached.
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- Class in
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FsmSupport
(def, def, def, def)
- Constructor in
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- Class in
grails-app.domain
Domain class to held some test of the FsmSupport
FsmSupportDummy
()
- Constructor in
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- Class in
src.groovy
FsmSupportException
(String)
- Constructor in
FsmSupportException
FsmSupportIntegrationTests
- Class in
test.integration
FsmSupportIntegrationTests
()
- Constructor in
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- Class in
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FsmSupportUnitTests
()
- Constructor in
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G
grailsVersion
- Property in
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(def)
- Constructor in
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hasErrors
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()
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(def)
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()
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mappings
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on
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onConfigChange
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P
pluginExcludes
- Property in
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property
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Q
R
record
()
- Method in
FsmSupport
If no initialState has been provided we'll try to get it from the object-property itself!!
registerTransition
(def)
- Method in
FsmSupport
relationships
- Property in
FsmSupport
Will hold relationships to any object & property any FsmSupport has been tied to.
reset
()
- Method in
FsmSupport
S
setAction
(def)
- Method in
FsmMultipleActions
setUp
()
- Method in
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setUp
()
- Method in
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status
- Property in
Foo
status
- Property in
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status
- Property in
FsmSupportDummy
T
target
- Property in
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tearDown
()
- Method in
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tearDown
()
- Method in
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testAnyFromTransition
()
- Method in
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testBasicFsm
()
- Method in
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testComplexFsm
()
- Method in
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testComplexFsm
()
- Method in
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()
- Method in
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testErrors
()
- Method in
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testFireEventsFromExistingStateWithErrors
()
- Method in
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testFirstAction
()
- Method in
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testInitialValues
()
- Method in
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testReloadedStates
()
- Method in
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()
- Method in
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title
- Property in
FsmGrailsPlugin
to
(def)
- Method in
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toState
- Property in
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toString
()
- Method in
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transactional
- Property in
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- Property in
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transactional
- Property in
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- Property in
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()
- Constructor in
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version
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watchedResources
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(def)
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_Install
- Class in
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_Install
()
- Constructor in
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- Class in
scripts
_Uninstall
()
- Constructor in
_Uninstall
_Upgrade
- Class in
scripts
_Upgrade
()
- Constructor in
_Upgrade
Groovy Documentation


This is a demo for Hovercraft! You can view it as a finished presentation at http://regebro.github.com/hovercraft/
It’s also useful as an example, in which case it’s supposed to be read as source code.
You can render this presentation to HTML with the command:
hovercraft hovercraft.rst outdir
And then view the outdir/index.html file to see how it turned out.
If you are seeing this text, and not reading this as source code, you are doing it wrong! It’s going to be confusing and not very useful.
Use The Source, Luke! But first you probably want to read through the official documentation at https://hovercraft.readthedocs.io/
Ibex: Making Scikit-Learn Run On Top Of Pandas¶
Ami Tavory, Shahar Azulay, Tali Raveh-Sadka
Making presentations is no fun!¶
Note
Welcome to the presenter console!
GUI tools are inflexible¶
- It’s hard to reorganize or import text
- Slow and memory hungry
- You get caught up in design early in the process.
Note
Here you have a view of the current slide, a preview of the next slide and your notes.
Use reStructuredText!¶
- You can use your favorite text-editor!
- Many tools available: Landslide, S5
- Convenient (and powerful!)
Note
You also have a clock and a timer, so you know how much time you have left.
But then there was Prezi¶
Sliding from left to right is no longer enough. You need to be able to…
Note
If you click on the timer it restarts from zero. This is handy when you are rehearsing the presentation and need to make sure it fits in the time allocated.
| data-y: | r1000 |
|---|
…pan…¶
Note
If you have more notes than fit in the console, you can scroll down, but more handily, you can scroll the text up by pressing space bar.
| data-rotate: | 90 |
|---|
…rotate…¶
Note
If there isn’t more text to scroll up, space bar will go to the next slide. Therefore you, as a presenter, just press space every time you run out of things to say!
| data-x: | r0 |
|---|---|
| data-y: | r500 |
| data-scale: | 0.1 |
…and zoom!¶
Note
Zooming is cool. But one day it will grow old as well. What will we do then to make presentations interesting?
| data-x: | r-800 |
|---|---|
| data-scale: | 1 |
But Prezi is a GUI¶
So we are back to square one.
(And it is closed source to boot)
Note
It’s probably back to making bad jokes again.
What about impress.js?¶
It’s open source!
Supports pan, tilt and zoom!
| id: | ThreeD |
|---|---|
| data-y: | r1200 |
| data-rotate-x: | 180 |
…and manual positioning¶
So inserting a slide means
repositioning all the following slides!
Note
The endless repositioning of slides is what prompted me to write Hovercraft! in the first place.
| id: | thequestion |
|---|---|
| data-x: | r0 |
| data-y: | r-1200 |
Is there no solution?¶
Of course there is!
Note
What would be the point of this slide show if I didn’t have a solution? Duh!
| data-rotate-y: | 180 |
|---|---|
| data-scale: | 3 |
| data-x: | r-2500 |
| data-y: | r0 |
Position slides¶
- Automatically!
- Absolutely!
- Relative to the previous slide!
- Along an SVG path!
Note
That SVG path support was a lot of work. And all I used it for was to position the slides in circles.
Presenter console!¶
- A view of the current slide
- A view of the next slide
- Your notes
- A clock
- A timer
Note
You found the presenter console already!
Hovercraft!¶
# .. image:: images/hovercraft_logo.png
The merge of convenience and cool!
Note
A slogan: The ad-mans best friend!
| data-x: | 0 |
|---|---|
| data-y: | 2500 |
| data-z: | 4000 |
| data-rotate-x: | 90 |


TI-83+ Link Protocol Guide v1.0 - Packet formats
Texas Instruments calculators always send data and responses in
self-contained "packets."
Packets have the following format:
Note - all 2-byte integers are transmitted little-endian Intel-style
(least significant byte first).
| Offset | Length | Description |
|---|---|---|
| 0 | 1 byte | Machine ID byte |
| 1 | 1 byte | Command ID byte |
| 2 | 2 bytes | Length of data (see note below) |
| 4 | n bytes | Data (if attached) |
| 4+n | 2 bytes | Checksum (if data is attached) |
The Machine ID byte identifies the machine that is sending the packet. It can have one of the following values:
| Value | Description |
|---|---|
| 02h | Computer sending TI-82 data |
| 03h | Computer sending TI-83 data |
| 23h | Computer sending TI-83+/TI-84+ data |
| 73h | TI-83+ or TI-84+ |
| 82h | TI-82 |
| 83h | TI-83 |
The Command ID byte identifies the request or response that the machine is sending. It can have one of the following values:
| Value | Description | Data Included |
|---|---|---|
| 06h | Variable Header (VAR) | A fixed-length, NUL padded variable header |
| 09h | Clear to send (CTS) | None |
| 15h | Data packet (DATA) | Screenshot/variable/backup data |
| 2Dh |
Silent - Request Versions (VER) | None |
| 36h | Skip/Exit (SKIP/EXIT) | A five-byte rejection code (see codes below) |
| 56h | Acknowledge (ACK) | None |
| 5Ah | Checksum Error (ERR) The previous packet must be sent again. |
None |
| 68h | Check ready (RDY) | None |
| 6Dh | Silent - Request Screenshot (SCR) | None |
| 88h |
Silent - Delete Variable (DEL) | A fixed-length, NUL padded variable header |
| 92h | End of Transmission (EOT) | None |
| A2h | Silent - Request Variable (REQ) | A fixed-length, NUL padded variable header |
| C9h | Silent - Request to Send Variable (RTS) | A fixed-length, NUL padded variable header |
Rejection codes (used with Command ID 36h) can have one of the following values:
| Value | Description |
|---|---|
| 01h | EXIT - The entire transmission has been cancelled. |
| 02h | SKIP - The current variable has been skipped. |
| 03h | OUT OF MEMORY (silent transmissions only) - The receiving calculator is out of memory. |
The checksum is a 16-bit value used to verify the integrity of the
data in the packet. It only present if data is present.
The checksum is calculated by taking the lower 16 bits of the sum of
the data bytes, as shown below:
int calculateChecksum(unsigned char* data, unsigned short datalength) {
unsigned short x, checksum;
for(x=0; x<datalength; x++) {
checksum+=data[x]; //overflow automatically limits to 16 bits
}
return checksum;
}
A Variable header contains information
about one variable in the calculator.
| Offset | Length | Description |
|---|---|---|
| 0 | 2 bytes | Size of actual variable data, in bytes |
| 2 | 1 byte | Type ID Byte (see type ID's below) |
| 3 | 8 bytes | Variable name, padded on the right with NULL characters (0h). This field is always tokenized. Not used for ID list. |
| 11 | 1 byte | Version - Always has a value of 0h. |
| 12 | 1 byte | Type ID Byte 2 - Always has a
value of 0h. Note: bit 7 is set if archived. Not used for getting
certificates. |
The type ID byte specifies the type of variable that is being transmitted. It can have one of the following values:
| Value | Description (click for variable format) |
|---|---|
| 00h | Real Number |
| 01h | Real List |
| 02h | Matrix |
| 03h | Y-Variable |
| 04h | String |
| 05h | Program |
| 06h | Edit-locked Program |
| 07h | Picture |
| 08h | GDB |
| 0Bh | Window Settings (See note below) |
| 0Ch | Complex Number |
| 0Dh | Complex List |
| 0Fh | Window Settings (See note below) |
| 10h | Saved Window Settings (See note below) |
| 11h | Table Setup (See note below) |
| 13h | Backup |
| 14h |
Used to delete a FLASH
application |
| 15h | Application Variable |
| 17h | Group Variable (only found on TI83+) |
| 19h | Directory (See note below) - only used when requesting a directory |
| 23h | FLASH Operating System |
| 24h | FLASH Application |
| 26h | ID list |
| 27h | Get Certificate |
| 29h |
Clock |
If the type ID byte specifies a backup, the variable header takes the following 9-byte format :
| Offset | Length | Description |
|---|---|---|
| 0 | 2 bytes | Size of first backup section (flags area), in bytes |
| 2 | 1 byte | Type ID Byte (13h in this case) |
| 3 | 2 bytes | Size of second backup section (data area), in bytes |
| 5 | 2 bytes | Size of third backup section (symbol area), in bytes |
| 7 | 2 bytes | Start User Area Address |
If the type ID byte is in the range 22...28h, the variable header takes the following 10-byte format :
| Offset | Length | Description |
|---|---|---|
| 0 | 2 bytes | Size of actual data, in bytes (LSB). |
| 2 | 1 byte | Type ID Byte (see type ID's below) |
| 3 | 2 bytes | Size of actual data, in bytes
(MSB). Usually 128/256 bytes. |
| 5 | 1 byte | Flag - Always has a value of 0h but bit 7 is set if archived (*). |
| 6 | 2 bytes | FLASH offset |
| 8 | 2 bytes | FLASH page |


Full Name [English]:
Stage Name [English]:
Former Full Name [English]:
Former Stage Name [English]:
Birth Date:
Birth Country Code:
Birth City [Can include more specific location]:
Gender:
Male
Female
Description:
Height:
Twitter:
Youtube:
Melon:
Instagram:
VLive:
Spotify Artist ID:
Fancafe:
Facebook:
Tiktok:
Zodiac:
Avatar:
Banner:
Blood Type:
Tags [If unsure, just put kpop, separate by commas]:
Group IDs [separated by commas, group id can be found by %card (group name)]:
Notes:
After clicking submit, copy the JSON you see on the screen and paste it on discord under the command %addidol.


Isar735.github.io
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--- layout: page title: About header-img: "img/about-bg.png" ---
WebDev student @FlatironSchool. Former Senior Community Director at @IVYconnect. Political enthusiast, language addict, and expert coffee drinker.


anki_vector.motors¶
Control the motors of Vector.
Classes
MotorComponent (robot) |
Controls the low-level motor functions. |
-
class
anki_vector.motors.
MotorComponent
(robot)¶ Controls the low-level motor functions.
-
set_head_motor
(speed)¶ Tell Vector’s head motor to move with a certain speed.
Positive speed for up, negative speed for down. Measured in radians per second.
To unlock the head track, call set_head_motor(0).
import anki_vector with anki_vector.Robot() as robot: robot.motors.set_head_motor(-5.0)
Parameters: speed ( float
) – Motor speed for Vector’s head, measured in radians per second.
-
set_lift_motor
(speed)¶ Tell Vector’s lift motor to move with a certain speed.
Positive speed for up, negative speed for down. Measured in radians per second.
To unlock the lift track, call set_lift_motor(0).
import anki_vector import time with anki_vector.Robot() as robot: robot.motors.set_lift_motor(-5.0) time.sleep(3.0) robot.motors.set_lift_motor(5.0) time.sleep(3.0)
Parameters: speed ( float
) – Motor speed for Vector’s lift, measured in radians per second.
-
set_wheel_motors
(left_wheel_speed, right_wheel_speed, left_wheel_accel=0.0, right_wheel_accel=0.0)¶ Tell Vector to move his wheels / treads at a given speed.
The wheels will continue to move at that speed until commanded to drive at a new speed.
To unlock the wheel track, call set_wheel_motors(0, 0).
import anki_vector import time with anki_vector.Robot() as robot: robot.motors.set_wheel_motors(25, 50) time.sleep(3.0)
Parameters: - left_wheel_speed (
float
) – Speed of the left tread (in millimeters per second). - right_wheel_speed (
float
) – Speed of the right tread (in millimeters per second). - left_wheel_accel (
float
) – Acceleration of left tread (in millimeters per second squared)None
value defaults this to the same as l_wheel_speed. - right_wheel_accel (
float
) – Acceleration of right tread (in millimeters per second squared)None
value defaults this to the same as r_wheel_speed.
- left_wheel_speed (
-


Pre-ordered List example
<tomalec-sortable-list order="4,3,2,0,1">
Please note that
order
is a sequence of elements' order. So the key is index from HTML, and value is CSS order - position on screen.
zero
one
two
three
four
Other examples
Basic one
Plain
Nested
Dynamic add nodes
Sortable
<body>
With Bootstrap


JWTError
public struct JWTError : Error, Equatable
A struct representing the different errors that can be thrown by SwiftJWT
-
A human readable description of the error.
Declaration
Swift
public let localizedDescription: String
-
Error when an invalid JWT String is provided
Declaration
Swift
public static let invalidJWTString: JWTError
-
Error when the JWT signiture fails verification.
Declaration
Swift
public static let failedVerification: JWTError
-
Error when using RSA encryption with an OS version that is too low.
Declaration
Swift
public static let osVersionToLow: JWTError
-
Error when an invalid private key is provided for RSA encryption.
Declaration
Swift
public static let invalidPrivateKey: JWTError
-
Error when the provided Data cannot be decoded to a String
Declaration
Swift
public static let invalidUTF8Data: JWTError
-
Error when the KeyID field
kid
in the JWT header fails to generate a JWTSigner or JWTVerifierDeclaration
Swift
public static let invalidKeyID: JWTError
-
Error when a PEM string is provided without the expected PEM headers/footers. (e.g. —–BEGIN PRIVATE KEY—–)
Declaration
Swift
public static let missingPEMHeaders: JWTError
-
Function to check if JWTErrors are equal. Required for equatable protocol.
Declaration
Swift
public static func == (lhs: JWTError, rhs: JWTError) -> Bool
-
Function to enable pattern matching against generic Errors.
Declaration
Swift
public static func ~= (lhs: JWTError, rhs: Error) -> Bool


$template{name="sample-readme-html-header" title="Adaptive Grid demo"}$
This sample demonstrates how a JPPF Grid can be grown or shrunk dynamically based on the current workload.
To achieve this, the demo will adjust two components of the grid topology:
The calculations for the number of client connections and the number of nodes are done as follows:
The demo itself will start with a single connection and a single node, then run a sequence of job batches, where the number of batches and the number of jobs in each batch are also configurable. It will display a message each tile the size of the connection pool or the number of nodes is changed.
You will first need to start a JPPF server and one node. For information on how to set up a node and server, please refer to the JPPF documentation.
To run the demo, in a command prompt or shell console, type "./run.sh" on Linux or "run.bat" on Windows. You will then see a number of meesages like these while the demo is running:
[demo] Starting the demo with maxAllowedNodes=10 and maxAllowedPoolSize=10 [demo] **** submitting jobs batch #1 (1 jobs) ***** [demo] job 'Job 1 - batch 1 (1/1)' result: execution successful [demo] **** submitting jobs batch #2 (20 jobs) ***** [demo] increasing the number of server connections to 10 [demo] increasing the number of nodes to 5 [demo] job 'Job 2 - batch 2 (1/20)' result: execution successful [demo] ... [demo] job 'Job 21 - batch 2 (20/20)' result: execution successful [demo] **** submitting jobs batch #3 (50 jobs) ***** [demo] increasing the number of nodes to 10 [demo] job 'Job 22 - batch 3 (1/50)' result: execution successful [demo] ... [demo] job 'Job 71 - batch 3 (50/50)' result: execution successful [demo] **** submitting jobs batch #4 (8 jobs) ***** [demo] decreasing the number of server connections to 8 [demo] decreasing the number of nodes to 2 [demo] job 'Job 72 - batch 4 (1/8)' result: execution successful [demo] ... [demo] job 'Job 79 - batch 4 (8/8)' result: execution successful [demo] demo has completed, resetting to initial grid configuration [demo] decreasing the number of server connections to 1 [demo] decreasing the number of nodes to 1
It is also interesting to monitor the grid with the administration console: the topology view will show the slave nodes that are started and stopped, and the job data view will show more or less jobs in the server queue, depending on the client connection pool size.
You can also play with the parameters of the demo that are configurable in the config/jppf.properties file:
# the maximum allowed connection pool size maxAllowedPoolSize = 10 # the maximum allowed number of nodes maxAllowedNodes = 10 # defines the number of job batches and their sizes jobBatches = 1 20 50 8 # duration of each task in millis taskDuration = 1500
If you need more insight into the code of this demo, you can consult the Java source files located in the AdaptiveGrid/src folder.
In addition, There are 2 privileged places you can go to:
$template{name="sample-readme-html-footer"}$


Section 3.1. "Telling the Monkeys What to Do"
Section 3.2. Forking Processes
Section 3.3. Threads
Section 3.4. Program Exits
Section 3.5. Interprocess Communication
Section 3.6. Pipes
Section 3.7. Signals
Section 3.8. Launching Programs on Windows
Section 3.9. Other System Tools


CQ3ObjectRef
Wrapper for Quesa objects.
The main purpose for using a C++ wrapper for a Quesa object is to
prevent memory leaks. The wrapper's destructor disposes the object.
This class is designed to be usable in STL container classes, in a way similar
to std::shared_ptr. One could base such a wrapper on boost::intrusive_pointer,
but I chose not to require Boost.
Due to a HeaderDoc bug, it is not possible to automatically
document more than one constructor. Besides the constructor from
a TQ3Object, there is a default constructor (which holds nullptr)
a copy constructor, and a move constructor.
This wrapper is not fully functional with objects that are not
reference-counted (such as Views and Picks). Calling the copy constructor
or copy assignment operator on a CQ3ObjectRef holding an object of a non-shared
type will result in a fatal assertion failure inside Q3Shared_GetReference.
© 1999-2023 Quesa Developers (Last Updated 1/27/2023)
HTML documentation generated by HeaderDoc


Pinged Issues Analytics
A way to see how issues are doing after we've pinged them
{{ data.total_pinged }}
Pinged Issues
{{ data.closed_pinged }}
Total Closed Issues
since we started pinging
{{ data.percentage_pinged }}%
Percentage Closed
Issue Url
Date Pinged
Status
New Comments
New State
{% for issue in data["pinged_issues"] %}
{{issue["html_url"]}}
{{issue["date_pinged"]}}
{{issue["status"]}}
{{issue["comment_delta"]}}
{{issue["state_delta"]}}
{%endfor%}


Switcher
Fully Featured
- Hello! Switch to tab 3
- Hello again! Next tab
- Bazinga! Previous tab
- You will never see me!
- Hello! The first item.
- Hello again! The second item.
- Bazinga! The third item.
- You will never see me!
Tab
- Hello!
- Hello again!
- Bazinga!
- You will never see me!
- Hello!
- Hello again!
- Bazinga!
- You will never see me!
- Hello!
- Hello again!
- Bazinga!
- You will never see me!
Button
Link
- Hello!
- Hello again!
- Bazinga!
- You will never see me!
- Hello!
- Hello again!
- Bazinga!
- You will never see me!
Subnav
- Hello!
- Hello again!
- Bazinga!
- Hello!
- Hello again!
- Bazinga!
Nav
- Hello!
- Hello again!
- Bazinga!
Animation


Touch the mountains so that they smoke!
6 Flash forth the lightning and scatter them;
send out your arrows and rout them!
7 Stretch out your hand from on high;
rescue me and deliver me from the many waters,
from the hand of foreigners,
8 whose mouths speak lies
and whose right hand is a right hand of falsehood.
9 I will sing a new song to you, O God;
upon a ten-stringed harp I will play to you,
10 who gives victory to kings,
who rescues David his servant from the cruel sword.
11 Rescue me and deliver me
from the hand of foreigners,
whose mouths speak lies
and whose right hand is a right hand of falsehood.
12 May our sons in their youth
be like plants full grown,
our daughters like corner pillars
cut for the structure of a palace;
13 may our granaries be full,
providing all kinds of produce;
may our sheep bring forth thousands
and ten thousands in our fields;
14 may our cattle be heavy with young,
suffering no mishap or failure in bearing;
may there be no cry of distress in our streets!
15 Blessed are the people to whom such blessings fall!
Blessed are the people whose God is the LORD!


{% extends "base.html" %} {% block content %}
Your ressource for client side infection
Learn more
Upload your file here.


Posts
-
Hello World
What is Lorem Ipsum? Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic...


Welcome
Maps
Events
Welcome to InstaRally
Mission Statement: Due to 2020's massive growth regarding protesting, Team InstaRally has decided to create this webpage in order to better accomodate these protests.


Thiruvananthapuram
Kollam
Pathanamthitta
Alappuzha
Kottayam
Idukki
Ernakulam
Thrissur
Palakkad
Malappuram
Kozhikode
Wayanadu
Kannur
Kasaragod


Key Features
Transpiled
It is just C#, with generated boilerplate.
It was already a great language,
Now you drive it.
Expressive
Say less to your compiler, get more back.
Your domain has its own lingo,
Use it.
Unobstrusive
Add a .xs file, compile, be happy.
Visual studio, command line,
Everywhere.
Community Oriented
The many always better than the few.
We get to create our dialects.
And choose the best.
Easy
Simple extensions in 200 lines of code.
Complex ones in 2000.
Easy to publish.
Open
Long ways to go, but we can get it there.
Get it, use it, make it better.
Make it yours.
Download
Get our prototype.
Command Line
Or download out command line interface
Contribute
Please.


500 years ago, having a fever could mean the difference between life and death, nowadays; a simple prescription will make your fever go away in a day. Women had a 50% or more chance of dying during childbirth, now it is a fraction of that. Sending messages to the other side of the world would take months, now reduced to seconds. Travelling took years, with a high chance of dying on the way, now we travel with leisure and watch a movie on the way. Truly, many would agree it is far better to live in the present than in the past, but is the rate of technological advancements moving too fast? Without a doubt technology has made many things more convenient. You need only Google something to see the benefit. It’s also made it possible for more people to find work and helped to create countless businesses. But is there too much technology for our own good?
Everyday there always seems to be some new technology around the corner; it would be fair to assume that technology is moving very fast. We, that human race have advanced more in the last 100 years than the last thousand. However, we must also see what areas we need to improve on, not only just with electronics or gadgets, but in other fields of technology such as medicine, astrology and even in our way of thinking about technology.
Our medical technology has advanced and continues to advance at a rapid rate; we now know a lot more about the human body than we did before. We can easily treat or prevent disease. But, we have only ever successfully eliminated two diseases, namely, Smallpox and Rinderpest, which is an incredible achievement and needs to be done more frequently in order to save more lives. The ongoing mistrust and campaign against medicine and vaccinations have had an effect on the advancing our medicines as instead of the funding of new medicines and vaccines, the medicinal community is wasting money on proving time and time again that the dangers of vaccination are unfounded and is not true.
Space Exploration used to be a very exciting thing to the general public. People were glued to their televisions when footage of the moon landing was released to the public. Organizations such as NASA have dedicated billions of dollars in an attempt to learn more and explore the vast universe in which we live in. In the present time, a new discovery regarding space has been met with mostly indifference from the public as they most likely do not see the importance of these discoveries. This, in turn, has led to organizations like NASA losing funding and moving slower than they ever have in the realms of space exploration.
So, is technology moving too fast? I believe it is not moving fast enough and is actually slowing down in favor of more commercial advancements to be used for entertainment purposes. I believe that the majority of the human race is slowing down the advancement of technology due to the hostile view of technology rooted in their traditional superstitions and belief. The spread of misinformation also plays a factor in this and humanity needs to align its understanding of technology to the rate of its advancements, because that is what holding technology back, a lack of understanding and interest. We must learn to be more open-minded and think of the future of our race if we are to make any progress.


See: Description
| Interface | Description |
|---|---|
| ProtocolCodecFactory |
Provides
ProtocolEncoder and ProtocolDecoder which translates
binary or protocol specific data into message object and vice versa. |
| ProtocolDecoder |
Decodes binary or protocol-specific data into higher-level message objects.
|
| ProtocolDecoderOutput |
Callback for
ProtocolDecoder to generate decoded messages. |
| ProtocolEncoder |
Encodes higher-level message objects into binary or protocol-specific data.
|
| ProtocolEncoderOutput |
Callback for
ProtocolEncoder to generate encoded messages such as
IoBuffer s. |
| Class | Description |
|---|---|
| AbstractProtocolDecoderOutput |
A
ProtocolDecoderOutput based on queue. |
| AbstractProtocolEncoderOutput |
A
ProtocolEncoderOutput based on queue. |
| CumulativeProtocolDecoder |
A
ProtocolDecoder that cumulates the content of received buffers to a
cumulative buffer to help users implement decoders. |
| ProtocolCodecFilter |
An
IoFilter which translates binary or protocol specific data into
message objects and vice versa using ProtocolCodecFactory ,
ProtocolEncoder , or ProtocolDecoder . |
| ProtocolCodecSession | |
| ProtocolDecoderAdapter |
An abstract
ProtocolDecoder implementation for those who don't need
ProtocolDecoder.finishDecode(IoSession, ProtocolDecoderOutput) nor
ProtocolDecoder.dispose(IoSession) method. |
| ProtocolEncoderAdapter |
An abstract
ProtocolEncoder implementation for those who don't have any
resources to dispose. |
| SynchronizedProtocolDecoder |
A
ProtocolDecoder implementation which decorates an existing decoder
to be thread-safe. |
| SynchronizedProtocolEncoder |
A
ProtocolEncoder implementation which decorates an existing encoder
to be thread-safe. |
| Exception | Description |
|---|---|
| ProtocolCodecException |
An exception that is thrown when
ProtocolEncoder or
ProtocolDecoder cannot understand or failed to validate
data to process. |
| ProtocolDecoderException |
An exception that is thrown when
ProtocolDecoder
cannot understand or failed to validate the specified IoBuffer
content. |
| ProtocolEncoderException |
An exception that is thrown when
ProtocolEncoder
cannot understand or failed to validate the specified message object. |
| RecoverableProtocolDecoderException |
A special exception that tells the
ProtocolDecoder can keep
decoding even after this exception is thrown. |
Copyright © 2004–2016 Apache MINA Project. All rights reserved.


Dohyeon (Danny) Kim is a software engineer at Coupang. He has completed Master's degree at the Department of Industrial and Management Engineering at POSTECH (Pohang University of Science and Technology) in South Korea, under the supervision of Prof. Minseok Song. His research interests center on software engineering, process mining, data mining, and data visualization. He was the member of Analytics & Information Management Lab. (Prof. Minseok Song) from 2016 to 2018
.


How to Start Mining Anote?
In order to mine, send, receive and sell / exchange Anote, you need these two applications:
Telegram Instant Messenger
Waves Wallet
Once you have these two installed either or your mobile phone or on your computer, simply open one of our Telegram groups - Anonutopia or AnonutopiaBalkan (depending on your location and language) and our friendly Telegram bot AnoteRobot will lead you through the rest of the process. If you have any questions, please don't hesitate to ask in one of our Telegram groups.
The process is simple. You get subscribed to AnonShout Telegram channel where we show ads on a daily basis, together with a mining code. After you've seen the add, you call /mine command with AnoteRobot and provide it the daily mining code. And that's it, next day you repeat the process.
You can read more on how to use AnoteRobot here and AnonShout here.
Mining Power
Your mining power depends on your Anonutopia community activity. Basic mining power is 0.02 anotes per hour (A/h) and you get an additional 0.005 A/h for every referred user.
If you add 3 or more users, you become "Miner" and your mining power gets multiplied by 10.
So, for example,if you have 5 referred users, your basic mining power is 0.02 and you add 5 * 0.005 to that. You get 0.045 and since you have more than 3 referred users, it gets multiplied by 10 so your final mining power becomes 0.45 anotes per hour.
You can invite people to Anonutopia by sending them a link or a message that you get if you send "/ref" command to AnoteRobot.
Purchase Mining Power
You can purchase Anote mining power and add it to your basic mining power. Read more about that here.


THE USUAL SUSPECTS A film review by Bryant Frazer Copyright 1995 Bryant Frazer
St: Kevin Spacey, Gabriel Byrne, Chazz Palminteri Edited by John Ottman Cinematography: Tom Sigel Written by Christopher McQuarrie Directed by Bryan Singer U.S.A., 1995
I think I'll call this one RASHOMON DOGS.
RESERVOIR DOGS (1992), of course, is the epochal film by Quentin Tarantino that borrowed most of its ideas from the bank of movie history, and then put such a hip, self-aware spin on the proceedings that it pretty much revolutionized American movie-going, if not filmmaking. RASHOMON (1950) is the epochal film by Akira Kurosawa that told the story of a crime four different ways, from four different perspectives, and thoroughly debunked the veracity that had previously been ascribed to stories told with the motion picture camera. Like RASHOMON, THE USUAL SUSPECTS shows us key events only in flashback, and since we're hearing the narrative from a con man who's being interrogated by a cop, we--like the cop--have to wonder whether he's telling the truth. Like RESERVOIR DOGS, it gives us an assemblage of hoods who would be fairly distasteful and uninteresting if not for a skewed code of values, and relies on snappy dialogue and character tics to build their personalities.
The problem comes when screenwriter Christopher McQuarrie tries to cross Kurosawa with Tarantino, and winds up subverting both of them in search of a big payoff that apes Hitchcock. Director Bryan Singer works so hard at evoking an air of world-weary fatalism that it's a shame to find out, at the end, that the little things--like characters and story--don't matter as much as the oh-so-clever denouement.
The movie opens "last night," as a subtitle explains, when some kind of a gunfight has ended with the death of nearly everyone on-board a ship that's been burned to a crisp at the docks. In the "present day," customs agent Kujan (Chazz Palminteri, who was so good in BULLETS OVER BROADWAY but has less to do here) is grilling a survivor of the shoot-out nicknamed Verbal (Kevin Spacey) about the events that transpired since he and four other ex- convicts were gathered "six weeks ago" for a police lineup having something to do with a hijacked shipment of guns. The rest of the movie switches between Kujan's interrogation of Verbal and Verbal's recollection of how he and his co-conspirators found themselves in the middle of a $91 million deal at the docks. Among the neat tricks that this structure allows (it actually reminds me most of CITIZEN KANE) is a couple of scenes where Kujan gets a new lead on the case during real time and then bursts back into the room to pull his new knowledge on Verbal, who's visibly agitated that the cop knows so much.
What's Verbal got to hide? Well, that's unclear until we eventually learn about a mythical Turkish figure who's putting these hapless characters through their paces, and who Verbal fears will have him killed if Verbal spills the beans. This figure, named Kaiser Zose (sp? My kingdom for a press kit!), is probably the movie's most interesting creation. The only one of Verbal's companions who's of much interest is Gabriel Byrne's Keaton, the ex-con with a heart of gold who's trying to build a new life for himself. Of course, we've seen this character about a zillion times before (most recently played by David Caruso in Barbet Schroeder's KISS OF DEATH), but Byrne is endearing, and his lawyer girlfriend Edie (played by Suzy Amis, who has a small role, but a knack for picking interesting scripts) is testimony to his new domestic impulses. The script goes out of its way to paint Byrne's Keaton as a decent man trapped in a noirish downward spiral, and the most intriguing part of the movie may have to do with the questions that begin to swirl about his real intentions. Kevin Spacey's performance as Verbal is thoroughly engaging, which is fortunate, since he's the real story-teller. It's the rest of the gang, who act more like stereotypes from made-for-cable thrillers, that turn us off from caring too much about their plight.
Is it spoiling anything to say that this movie's raison d'etre is the surprise ending? I haven't seen a critic yet keep his or her mouth shut about it (shades of THE CRYING GAME), and your enjoyment of the movie may well hinge on exactly how much you appreciate the plot twists. THE USUAL SUSPECTS is one of those movies that's terrifically entertaining, but leaves you somewhat cold afterward. I can't imagine sitting through it a second time, although some folks say that's exactly what they did, to make sure they got everything straight. The dialogue is pretty stiff (with Palminteri's standard-issue whodunit monologue near the end a stand-out example), and it becomes hard to care about characters when we know the movie really doesn't.
The editing (by John Ottman) is very interesting, with certain scenes shot and put together seemingly to maximize confusion. In other places, the narrative becomes disjointed, with near-jump cuts from character to character threatening to befuddle the casual viewer. The direction also veers toward a jarring artiness, with a flashback dissolving into a coffee cup, the camera making Caligari angles out of an elevator shaft, and a few more apparent sub-Scorseseisms. All of this looked a little careless on the surface, but I suspect the artifice was carefully employed to maximize audience involvement with an edgy story line that is, really, coming out of the mouth of one of its characters. The technique seems to have worked -- the Friday night crowd I saw it with was quite willingly strung along, with folks sitting forward in their seats to pay closer attention, gasping at all the right moments and chattering happily, minds boggled as they spilled out onto the city sidewalks for the walk home.
Like this spring's SHALLOW GRAVE, THE USUAL SUSPECTS is a crowd-pleaser, but I'm similarly suspicious of the film's merits. Quentin Tarantino is a very clever filmmaker, but he's also a perfectionist auteur whose whacked-out persona shines loud and clear through his pictures, even the one (TRUE ROMANCE) he didn't direct. Clever by itself isn't enough, and it takes a little more raw energy to propel a film that's about an ensemble of characters who are doomed even before the first reel threads its way into the projector. And while Singer happily acknowledged the influence of RASHOMON in pre-release interviews, somebody here seems not to have noticed that the only reason the trick worked for Kurosawa is that lives were changed by the way that those stories were told. The only thing that really changes over the course of THE USUAL SUSPECTS is a policeman's ideas about who was responsible for a crime, and an audience's ideas about what the hell is going on.
DEEP FOCUS: Archived Movie Reviews by Bryant Frazer http://www.panix.com/~bfrazer/flicker/<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


we offer a nice place for families
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Software Developer in the project,Progression and Transmission of HIV (PATH), in collaboration with CDC(Centers for Disease Control and Prevention) which aims at developing agent-based simulation models to identify optimal combination interventions to achieve the NHAS goal.
Working with the data obtained from the simulation of these models on creating a data visualization dashboard.
• Programmed image processing for front end rendering using JSFeat and SVG elements.
• Design and execution of services in java on spring framework and in python using libraries like pysceneDetect and celery.
• Secondary research on competitive intelligence and industry benchmarks to inform product design.
Worked as a full stack developer with techno-managerial responsibilities including development, release and management of about 24 service cycles for eBay Global Deals project.
The project consisted of designing and developing the deals pages for serving international markets devoid of complete ebay website using java for backend and jsp/javascript/jQuery for front end development
Analyzed 10 years worth of billing data with the Monetization team and published the results internally on a web tool using Play, MongoDB and jQuery.


Service which provides metadata about cipher suite, such as available signature schemes,
digests and security providers.
Factory to create new instances of the cipher scheme map service.
The CipherSuiteFactory provides operations to create various cipher suite's services.
Crypto service which can sign as well as create new key pairs.
Factory to create new instances of the crypto service.
The optional interface which can be implemented to clear up the HSM when running TCK tests.
Factory to create new instances of the basic hashing service.
Encoding service which can encode and decode keys to or from byte arrays
or strings using PEM encoding format.
Factory to create new instances of the key encoding service.
Holding class for the returned by teh CryptoService wrapped key pair.
Holding class for the private key material. The interpretation what is stored in the key material
is up to the CryptoService implementing class - it could be the encrypted private key or its alias
in case if the HSM natively supports large number of keys.


|
|
| | Point3 () |
| |
| | Point3 (TYPE _x, TYPE _y, TYPE _z) |
| |
| | Point3 (const Point3 &p) |
| |
| | Point3 (TYPE v) |
| |
| | Point3 (const Point2< TYPE > &p, TYPE _z=0) |
| |
| | Point3 (const Point4< TYPE > &p) |
| |
| template<typename T > |
| | Point3 (const Point3< T > &p) |
| |
| template<typename T > |
| | Point3 (const Point2< T > &p, TYPE _z=0) |
| |
| template<typename T > |
| | Point3 (const Point4< T > &p) |
| |
| template<int M> |
| | Point3 (const Point< TYPE, M > &p) |
| |
| template<typename T , int M> |
| | Point3 (const Point< T, M > &p) |
| |
| template<typename P > |
| | Point3 (const P &p) |
| |
|
| void | Zero () |
| | Sets the coordinates as zero. More...
|
| |
| void | Get (TYPE *p) const |
| | Puts the coordinate values into the array. More...
|
| |
| void | Set (const TYPE *p) |
| | Sets the coordinates using the values in the given array. More...
|
| |
| void | Set (TYPE v) |
| | Sets all coordinates using the given value. More...
|
| |
| void | Set (TYPE _x, TYPE _y, TYPE _z) |
| | Sets the coordinates using the given values. More...
|
| |
|
| TYPE | LengthSquared () const |
| | Returns the square of the length. Effectively, this is the dot product of the vector with itself. More...
|
| |
| TYPE | Length () const |
| | Returns the length of the vector. More...
|
| |
| void | Normalize () |
| | Normalizes the vector, such that its length becomes 1. More...
|
| |
| Point3 | GetNormalized () const |
| | Returns a normalized copy of the vector. More...
|
| |
| TYPE | Sum () const |
| | Returns the sum of its components. More...
|
| |
| bool | IsZero () const |
| | Returns true if all components are exactly zero. More...
|
| |
| TYPE | Min () const |
| |
| TYPE | Max () const |
| |
| int | MinID () const |
| |
| int | MaxID () const |
| |
|
| void | Clamp (TYPE minValue, TYPE maxValue) |
| |
| void | ClampMin (TYPE v) |
| |
| void | ClampMax (TYPE v) |
| |
| void | Abs () |
| | Converts all negative components to positive values. More...
|
| |
|
| Point3 | operator - () const |
| |
|
| Point3 | operator+ (const Point3 &p) const |
| |
| Point3 | operator - (const Point3 &p) const |
| |
| Point3 | operator * (const Point3 &p) const |
| |
| Point3 | operator/ (const Point3 &p) const |
| |
| Point3 | operator+ (const TYPE v) const |
| |
| Point3 | operator - (const TYPE v) const |
| |
| Point3 | operator * (const TYPE v) const |
| |
| Point3 | operator/ (const TYPE v) const |
| |
|
| const Point3 & | operator= (const Point3 &p) |
| |
| const Point3 & | operator+= (const Point3 &p) |
| |
| const Point3 & | operator -= (const Point3 &p) |
| |
| const Point3 & | operator *= (const Point3 &p) |
| |
| const Point3 & | operator/= (const Point3 &p) |
| |
| const Point3 & | operator+= (const TYPE v) |
| |
| const Point3 & | operator -= (const TYPE v) |
| |
| const Point3 & | operator *= (const TYPE v) |
| |
| const Point3 & | operator/= (const TYPE v) |
| |
|
| bool | operator== (const Point3 &p) const |
| |
| bool | operator != (const Point3 &p) const |
| |
|
| TYPE & | operator [] (int i) |
| |
| const TYPE & | operator [] (int i) const |
| |
| TYPE & | Element (int i) |
| |
| const TYPE & | Element (int i) const |
| |
| TYPE * | Data () |
| |
| const TYPE * | Data () const |
| |
|
| Point3 | Cross (const Point3 &p) const |
| | Cross product. More...
|
| |
| Point3 | operator ^ (const Point3 &p) const |
| | Cross product. More...
|
| |
| TYPE | Dot (const Point3 &p) const |
| | Dot product. More...
|
| |
| TYPE | operator % (const Point3 &p) const |
| | Dot product. More...
|
| |
|
| Point2< TYPE > | XY () const |
| |


{% extends "base.html" %} {% block main %}
This website is suppose to show a proof-of-concept job board application using flask, mongodb, and heroku.
Feel free to test the job creation process by clicking here.


Background How you can create a portfolio website using Bootstrap, hosted for free on GitHub Pages. Resources Used Bootstrap HTML/CSS/JS Isotope GitHub Pages Tools Needed Text Editor/IDE such as VS Code GitHub Account Parent topic: The Basics


Waiting for customers to make a payment after having expressed interest in your products or services can be very worrying. Will the client keeps postponing his/her visit to the bank? Is she wondering if you get alerts or if you will know what she is paying for? You on the other hand, are wondering whether they forgot about your product or have totally lost interest in your service.
Why not make payment for your products or services straightforward by integrating payment into your website? This way, when customers select the Products or Services they want, they are automatically directed to a page where they can pay with their ATM cards, and you get payment confirmation and the exact products or services they have paid for.
Recurring Payments are also supported, so payment integration fits perfectly into Subscription services (Weekly, Monthly, Quarterly, Annually e.t.c.) and association levies.
Nearly everyone now has an ATM card, and more people are more comfortable using their cards for purchases, ranging from being willing to swipe their ATM cards in POS machines to online card purchases or utility payments. There is no better time to offer online payment in Nigeria. Differentiate yourself from your competition, accept Card and USSD payments today!
The days of wondering how a client outside Nigeria will pay for your products or services is thankfully coming to an end. You no longer need to make International clients go through the stress of going to the bank to do an International Fund transfer or Initiate a Western Union transfer. Most just abandon you and your product if payment becomes a hassle.


- AdaptiveAlpha (module), [1]
- adjoint_transform() (AlphaTransform.AlphaShearletTransform method)
- alpha() (AlphaTransform.AlphaShearletTransform method)
|
- AlphaShearletTransform (class in AlphaTransform)
- AlphaTransform (module)
- AlphaTransformUtility (module)
|


Leon's Resume for Linux SA
- Basic
Name: Leon Li (李勇)
Gender: Male Age: 29
- Contact
Mobile:<PHONE_NUMBER>0
Mail<EMAIL_ADDRESS>Objective
Linux System Administrator
Personal Description
I am a *nix lover, open-minded and easy-going. I have great passion in hacking, and strong ability in learning new things fast and well. Been using Ubuntu/Linux for 4 years, learned Bash/Shell, Python & C, desiring to learn more. Experienced in LAMP/LNMP, GIT, WORDPRESS, OPENVPN, LATEX, used a Ubuntu based VPS for 3 years. Vim & Google are my best friends.
I also love books, movies, music & workouts and all things which can make life better. Collected over 10,000 songs, and run 1,000km in 2 years.
Computer Skills
- Programming Languages Shell/Bash, Python, C
- Platforms Ubuntu Linux, Mac OS, Windows, Android
- Tools Vim, Git, GIMP, LaTeX, Office, Dropbox, Google, Wikipedia, iPod Classic, Kindle
- Social Twitter, IRC, Douban, V2EX.
Education
- 1990-2001 Been educated for 11 years, good at all my classes, but I quit from high school because I can’t afford the tuition fees. So I just learn things all by myself.
Experience
Customer Service Technology Engineer
- 2008.11-2012.5 In Optoma Corp. as repair engineer. Been good at repairing DMD projectors, done many onsite services well in North China and Northeast of China. Also worked as system admin, maintaining the infrastructure for 3.5 years.
Network Engineer
- 2006.8-2008.10 In Yurui Communication Tech Ltd,. as network engineer. Learned PBX, Intranet, Internet building, testing and cabling.
Bars & Restaurants
2004.9-2006.5 In ADRIA & Traktirr restaurants as waiter, foreman, intern manager, manager. Learned some Italian & Russian, retrieved a stolen wall painting.
2003.3-2004.7 In Yunmeng & The River bar in Sanlitun as waiter, bartender, manager. Learned and practised oral English, got along with all the guests.
Online Version
Call me. You won’t be disappointed!


Viscous water holds the secret to an ice skater’s smooth glide
Ice owes its trademark slipperiness to a layer of melt water with surprising properties.
Ice is slippery because it is lubricated by melt water as viscous as oil, according to experiments that teamed a tuning fork with a powerful microscope.
In the conventional explanation for ice’s slickness, objects melt the ice as they move across it, forming a thin layer of water on which those objects glide. But water is typically a poor lubricant because of its relatively low viscosity.
A team led by Alessandro Siria and Lyderic Bocquet at the École Normale Supérieure in Paris examined this melt water using a new ultra-high-resolution microscope equipped with a tuning fork. When the fork’s tip was made to vibrate, it dragged a glass bead back and forth across a layer of ice, allowing researchers to measure the friction generated by the bead’s passage. The bead also moved into and out of the ice, providing data on the viscosity of the meltwater film between the bead and the ice.
Surprisingly, the authors found that this melt water is up to 100 times more viscous than normal water, making it an excellent lubricant.


Tonight I find myself with 3 substances that have been collecting dust for a little while - IAP, 2C-I and 2C-E. It's been a while, and I don't want to go overboard... but I want a little something. Not seeing any reports combining the two I decide on a combination of 2C-I and 2-CE. I have about 50mg of 2C-E, and about 3-5mg of residue of 2C-I. I decide on 10mg of 2-CE and what was left of the 2C-I - not even sure if I'll get anything out of it, but I have an early morning and feel the need for SOMETHING. So, here we go...
T+0:30 - 6oz 40 proof port (was a tad anxious, so I thought it would help a little)
T+0:00 - 10mg 2C-E in a gelcap and approx 5mg 2C-I dissolved in 6oz 40 proof Port.
T+0:30 - feeling first alert
T+1:00 - definitely feel it in me, but very weak. Decide to dose with another 5mg
T+1:30 - I'm at a definite +3. Wow. There's no way I could carry on a convo right now,
or, lol, drive.
T+2:30 - I don't think I've ever tripped this hard and maintained control of my faculties. Incredible OEV's (no CEV's) - I have a stucco ceiling and it looks like worms are crawling everywhere. Also, the walls are breathing like I've never seen before.
T+4:00 - Spent the last couple of hours just staring at my ceiling and walls. My mind is a complete jumble - anxious from time to time. Have a hint of that 'oh no, too much' feeling, but then it subsides. I'm glad I bumped it up 5mg, but any more and I might have lost it. A little hungry, so I had some crackers and cheese - nothing spectacular.
T+5:00 - Starting to come down. There are still marked visuals, but my mind is definitely clearing.
T+6:30 - Almost back to baseline. wow. what a trip.
I've used both of these chems independently and neither produced anything close to this. 2C-I was masked by the 2C-E, but the intensity was increased by maybe 2 orders of magnitude... and just from 3-5mg of 2C-I.
Just a follow up note - there was a significant 'hangover' the following day. I felt a little spaced out... almost like that after a night using DXM. No other side or after effects to speak of.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Ideas for using readxl to increase reproducibiliy and reduce tedium.
Reproducibility is much easier in theory than in reality. Here are some special dilemmas we face with spreadsheets:
.xls[x]
file, we’re in a pickle. The .xls[x]
file should obviously be preserved, and probably write-protected. But a faithful copy as CSV is a wonderful complement, as long as you can ensure the two are the same..csv
). readxl helps you get data directly out of a spreadsheet and into R, where you can record every step of your analysis as code. Below we show how to cache a CSV snapshot as part of this process.The examples below also demonstrate the use of functional programming or “apply” techniques to iterate over the worksheets in a workbook.
We load the tidyverse metapackage here because the workflows below show readxl working with readr, purrr, etc. See the last section for solutions using base R only (other than readxl).
We must load readxl explicitly because it is not part of the core tidyverse.
library(tidyverse)
#> ── Attaching packages ────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 2.2.1 ✔ purrr <IP_ADDRESS>00
#> ✔ tibble 1.4.2 ✔ dplyr <IP_ADDRESS>00
#> ✔ tidyr 0.8.0 ✔ stringr 1.3.0
#> ✔ readr 1.2.0 ✔ forcats 0.3.0
#> ── Conflicts ───────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(readxl)
Break analyses into logical steps, via a series of scripts that relate to one theme, such as “clean the data” or “make exploratory and diagnostic plots”.
This forces you to transmit info from step i to step i + 1 via a set of output files. The cumulative outputs of steps 1, 2, …, i are available as inputs for steps i + 1 and beyond.
These outputs constitute an API for your analysis, i.e. they provide clean entry points that can be used (and understood) in isolation, possibly using an entirely different toolkit. Contrast this with the alternative of writing one monolithic script or transmitting entire workspaces via save()
, load()
, and R-specific .rds
files.
If raw data is stored only as an Excel spreadsheet, this limits your ability to inspect it when solving the little puzzles that crop up in dowstream work. You’ll need to fire up Excel (or similar) and get busy with your mouse. You certainly can’t poke around it or view diffs on GitHub.
Solution: cache a CSV snapshot of your raw data tables at the time of export. Even if you use read_excel()
for end-to-end reproducibility, this complementary CSV leaves your analysis in a more accessible state.
Pipe the output of read_excel()
directly into readr::write_csv()
like so:
iris_xl <- readxl_example("datasets.xlsx") %>%
read_excel(sheet = "iris") %>%
write_csv("iris-raw.csv")
Why does this work? readr::write_csv()
is a well-mannered “write” function: it does its main job and returns its input invisibly. The above command reads the iris sheet from readxl’s datasets.xlsx
example workbook and caches a CSV version of the resulting data frame to file.
Let’s check. Did we still import the data? Did we write the CSV file?
iris_xl
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.10 3.50 1.40 0.200 setosa
#> 2 4.90 3.00 1.40 0.200 setosa
#> 3 4.70 3.20 1.30 0.200 setosa
#> # ... with 147 more rows
dir(pattern = "iris")
#> [1] "iris-raw.csv"
Yes! Is the data written to CSV an exact copy of what we imported from Excel?
iris_alt <- read_csv("iris-raw.csv")
#> Parsed with column specification:
#> cols(
#> Sepal.Length = col_double(),
#> Sepal.Width = col_double(),
#> Petal.Length = col_double(),
#> Petal.Width = col_double(),
#> Species = col_character()
#> )
## readr leaves a note-to-self in `spec` that records its column guessing,
## so we remove that attribute before the check
attr(iris_alt, "spec") <- NULL
identical(iris_xl, iris_alt)
#> [1] TRUE
Yes! If we needed to restart or troubleshoot this fictional analysis, iris-raw.csv
is available as a second, highly accessible alternative to datasets.xlsx
.
Some Excel workbooks contain only data and you are tempted to ask “Why, God, why is this data stored in Excel? Why not store this as a series of CSV files?” One possible answer is this: because the workbook structure keeps them all together.
Let’s accept that this happens and that it is not entirely crazy. How can you efficiently load all of that into R?
Here’s how to load all the sheets in a workbook into a list of data frames:
purrr::map()
to iterate sheet reading.path <- readxl_example("datasets.xlsx")
path %>%
excel_sheets() %>%
set_names() %>%
map(read_excel, path = path)
#> $iris
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.10 3.50 1.40 0.200 setosa
#> 2 4.90 3.00 1.40 0.200 setosa
#> 3 4.70 3.20 1.30 0.200 setosa
#> # ... with 147 more rows
#>
#> $mtcars
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21.0 6. 160. 110. 3.90 2.62 16.5 0. 1. 4. 4.
#> 2 21.0 6. 160. 110. 3.90 2.88 17.0 0. 1. 4. 4.
#> 3 22.8 4. 108. 93. 3.85 2.32 18.6 1. 1. 4. 1.
#> # ... with 29 more rows
#>
#> $chickwts
#> # A tibble: 71 x 2
#> weight feed
#> <dbl> <chr>
#> 1 179. horsebean
#> 2 160. horsebean
#> 3 136. horsebean
#> # ... with 68 more rows
#>
#> $quakes
#> # A tibble: 1,000 x 5
#> lat long depth mag stations
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -20.4 182. 562. 4.80 41.
#> 2 -20.6 181. 650. 4.20 15.
#> 3 -26.0 184. 42. 5.40 43.
#> # ... with 997 more rows
What if we want to read all the sheets in at once and simultaneously cache to CSV? We define read_then_csv()
as read_excel(...) %>% write_csv()
and use purrr::map()
again.
read_then_csv <- function(sheet, path) {
pathbase <- path %>%
basename() %>%
tools::file_path_sans_ext()
path %>%
read_excel(sheet = sheet) %>%
write_csv(paste0(pathbase, "-", sheet, ".csv"))
}
We could even define this on-the-fly as an anonymous function inside map()
, but I think this is more readable.
path <- readxl_example("datasets.xlsx")
path %>%
excel_sheets() %>%
set_names() %>%
map(read_then_csv, path = path)
#> $iris
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.10 3.50 1.40 0.200 setosa
#> 2 4.90 3.00 1.40 0.200 setosa
#> 3 4.70 3.20 1.30 0.200 setosa
#> # ... with 147 more rows
#>
#> $mtcars
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21.0 6. 160. 110. 3.90 2.62 16.5 0. 1. 4. 4.
#> 2 21.0 6. 160. 110. 3.90 2.88 17.0 0. 1. 4. 4.
#> 3 22.8 4. 108. 93. 3.85 2.32 18.6 1. 1. 4. 1.
#> # ... with 29 more rows
#>
#> $chickwts
#> # A tibble: 71 x 2
#> weight feed
#> <dbl> <chr>
#> 1 179. horsebean
#> 2 160. horsebean
#> 3 136. horsebean
#> # ... with 68 more rows
#>
#> $quakes
#> # A tibble: 1,000 x 5
#> lat long depth mag stations
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -20.4 182. 562. 4.80 41.
#> 2 -20.6 181. 650. 4.20 15.
#> 3 -26.0 184. 42. 5.40 43.
#> # ... with 997 more rows
dir(pattern = "^datasets.*\\.csv$")
#> [1] "datasets-chickwts.csv" "datasets-iris.csv" "datasets-mtcars.csv"
#> [4] "datasets-quakes.csv"
In a real analysis, starting with workbook "foo.xlsx"
, you might want to create the directory foo
and place the CSVs inside that.
What if the datasets found on different sheets have the same variables? Then you’ll want to row-bind them, after import, to form one big, beautiful data frame.
readxl ships with an example sheet deaths.xlsx
, containing data on famous people who died in 2016 or 2017. It has two worksheets named “arts” and “other”, but the spreadsheet layout is the same in each and the data tables have the same variables, e.g., name and date of death.
The map_df()
function from purrr makes it easy to iterate over worksheets and glue together the resulting data frames, all at once.
purrr::map_df()
to import the data, create an ID variable for the source worksheet, and row bind.path <- readxl_example("deaths.xlsx")
deaths <- path %>%
excel_sheets() %>%
set_names() %>%
map_df(~ read_excel(path = path, sheet = .x, range = "A5:F15"), .id = "sheet")
print(deaths, n = Inf)
#> # A tibble: 20 x 7
#> sheet Name Profession Age `Has kids` `Date of birth`
#> <chr> <chr> <chr> <dbl> <lgl> <dttm>
#> 1 arts David Bowie musician 69. TRUE 1947-01-08 00:00:00
#> 2 arts Carrie Fisher actor 60. TRUE 1956-10-21 00:00:00
#> 3 arts Chuck Berry musician 90. TRUE 1926-10-18 00:00:00
#> 4 arts Bill Paxton actor 61. TRUE 1955-05-17 00:00:00
#> 5 arts Prince musician 57. TRUE 1958-06-07 00:00:00
#> 6 arts Alan Rickman actor 69. FALSE 1946-02-21 00:00:00
#> 7 arts Florence Henders… actor 82. TRUE 1934-02-14 00:00:00
#> 8 arts Harper Lee author 89. FALSE 1926-04-28 00:00:00
#> 9 arts Zsa Zsa Gábor actor 99. TRUE 1917-02-06 00:00:00
#> 10 arts George Michael musician 53. FALSE 1963-06-25 00:00:00
#> 11 other Vera Rubin scientist 88. TRUE 1928-07-23 00:00:00
#> 12 other Mohamed Ali athlete 74. TRUE 1942-01-17 00:00:00
#> 13 other Morley Safer journalist 84. TRUE 1931-11-08 00:00:00
#> 14 other Fidel Castro politician 90. TRUE 1926-08-13 00:00:00
#> 15 other Antonin Scalia lawyer 79. TRUE 1936-03-11 00:00:00
#> 16 other Jo Cox politician 41. TRUE 1974-06-22 00:00:00
#> 17 other Janet Reno lawyer 78. FALSE 1938-07-21 00:00:00
#> 18 other Gwen Ifill journalist 61. FALSE 1955-09-29 00:00:00
#> 19 other John Glenn astronaut 95. TRUE 1921-07-28 00:00:00
#> 20 other Pat Summit coach 64. TRUE 1952-06-14 00:00:00
#> # ... with 1 more variable: `Date of death` <dttm>
Note the use of range = "A5:E15"
here. deaths.xlsx
is a typical spreadsheet and includes a few non-data lines at the top and bottom and this argument specifies where the data rectangle lives.
All at once now:
Even though the worksheets in deaths.xlsx
have the same layout, we’ll pretend they don’t and specify the target rectangle in two different ways here. This shows how this can work if each worksheet has it’s own peculiar geometry. Here’s the workflow:
purrr::map2_df()
to iterate over those two vectors in parallel, importing the data, row binding, and creating an ID variable for the source worksheet.path <- readxl_example("deaths.xlsx")
sheets <- path %>%
excel_sheets() %>%
set_names()
ranges <- list("A5:F15", cell_rows(5:15))
deaths <- map2_df(
sheets,
ranges,
~ read_excel(path, sheet = .x, range = .y),
.id = "sheet"
) %>%
write_csv("deaths.csv")
print(deaths, n = Inf)
#> # A tibble: 20 x 7
#> sheet Name Profession Age `Has kids` `Date of birth` `Date of death`
#> <chr> <chr> <chr> <dbl> <lgl> <chr> <chr>
#> 1 arts Davi… musician 69. TRUE 1947-01-08T00:… 2016-01-10T00:…
#> 2 arts Carr… actor 60. TRUE 1956-10-21T00:… 2016-12-27T00:…
#> 3 arts Chuc… musician 90. TRUE 1926-10-18T00:… 2017-03-18T00:…
#> 4 arts Bill… actor 61. TRUE 1955-05-17T00:… 2017-02-25T00:…
#> 5 arts Prin… musician 57. TRUE 1958-06-07T00:… 2016-04-21T00:…
#> 6 arts Alan… actor 69. FALSE 1946-02-21T00:… 2016-01-14T00:…
#> 7 arts Flor… actor 82. TRUE 1934-02-14T00:… 2016-11-24T00:…
#> 8 arts Harp… author 89. FALSE 1926-04-28T00:… 2016-02-19T00:…
#> 9 arts Zsa … actor 99. TRUE 1917-02-06T00:… 2016-12-18T00:…
#> 10 arts Geor… musician 53. FALSE 1963-06-25T00:… 2016-12-25T00:…
#> 11 other Vera… scientist 88. TRUE 1928-07-23T00:… 2016-12-25T00:…
#> 12 other Moha… athlete 74. TRUE 1942-01-17T00:… 2016-06-03T00:…
#> 13 other Morl… journalist 84. TRUE 1931-11-08T00:… 2016-05-19T00:…
#> 14 other Fide… politician 90. TRUE 1926-08-13T00:… 2016-11-25T00:…
#> 15 other Anto… lawyer 79. TRUE 1936-03-11T00:… 2016-02-13T00:…
#> 16 other Jo C… politician 41. TRUE 1974-06-22T00:… 2016-06-16T00:…
#> 17 other Jane… lawyer 78. FALSE 1938-07-21T00:… 2016-11-07T00:…
#> 18 other Gwen… journalist 61. FALSE 1955-09-29T00:… 2016-11-14T00:…
#> 19 other John… astronaut 95. TRUE 1921-07-28T00:… 2016-12-08T00:…
#> 20 other Pat … coach 64. TRUE 1952-06-14T00:… 2016-06-28T00:…
Rework examples from above but using base R only, other than readxl.
read_then_csv <- function(sheet, path) {
pathbase <- tools::file_path_sans_ext(basename(path))
df <- read_excel(path = path, sheet = sheet)
write.csv(df, paste0(pathbase, "-", sheet, ".csv"),
quote = FALSE, row.names = FALSE)
df
}
path <- readxl_example("datasets.xlsx")
sheets <- excel_sheets(path)
xl_list <- lapply(excel_sheets(path), read_then_csv, path = path)
names(xl_list) <- sheets
path <- readxl_example("deaths.xlsx")
sheets <- excel_sheets(path)
ranges <- list("A5:F15", cell_rows(5:15))
xl_list <- mapply(function(x, y) {
read_excel(path = path, sheet = x, range = y)
}, sheets, ranges, SIMPLIFY = FALSE)
xl_list <- lapply(seq_along(sheets), function(i) {
data.frame(sheet = I(sheets[i]), xl_list[[i]])
})
xl_list <- do.call(rbind, xl_list)
write.csv(xl_list, "deaths.csv", row.names = FALSE, quote = FALSE)


{% extends 'base_course.html' %} {% block subtitle %} {# I18N: Title of the webpage. #} - {{ gettext('FAQ') }} {% endblock subtitle %} {% block main_content %}
Google Communities allows you to post any discussion to either the discussion stream or any of the other Section level streams.
Anybody having the desire to learn about Big Data! No programming is required. However, we do provide some technology training using Python using some coding example.
Traditional forum works like a file system. Posts are placed in different categories. You can access them only if you are in that category.
Google community works like a tag system. You can access all posts at one time and you can also choose different tags to filter what post you want to see.
The category pandel is on the left. Select the category you want to see and click on it.
Now all the posts under this category are displaiyed on the right. You can click "All posts" to back.


SC CARSTYLER SRL
242424242
GPS:N47.145245 E22.33352
First select a tournament, then you can follow teams from their team page.


Pragya Singh Thakur made part of Rajnath Singh-led defence ministry panel
Sadhvi Pragya Singh Thakur has been made a part of the 21-member parliamentary consultative committee, which is headed by Defence Minister Rajnath Singh.
by , Ravish Pal SinghHIGHLIGHTS
- Pragya Singh Thakur has been nominated to a defence ministry panel
- The defence ministry panel is led by Rajnath Singh
- The Bhopal MP is a terror accused in the Malegaon blasts case
Controversial Bharatiya Janata Party (BJP) MP Sadhvi Pragya Singh Thakur has been nominated to the Parliamentary Consultative Committee of the Ministry of Defence.
The 21-member parliamentary consultative committee is headed by Union Defence Minister Rajnath Singh.
The Bhopal MP, who is an accused in the Malegaon blasts case, defeated former chief minister of Madhya Pradesh and Congress leader Digvijaya Singh in the Lok Sabha elections earlier this year.
The 21-member Parliamentary Consultative Committee also includes the likes of Opposition leaders Farooq Abdullah and Sharad Pawar.
Sadhvi Pragya has time and again courted controversy with her remarks. She had landed in a major controversy when she called Nathuram Godse, who assassinated Mahatma Gandhi, a patriot.
The BJP issued a showcause notice to Sadhvi Pragya over the remark.
Meanwhile, the Congress party has objected to this development and termed it as "unfortunate".
PC Sharma, Law Minister in Kamal Nath government in Madhya Pradesh, said that there is a difference in BJP's "words and actions".
"Modi ji had said that he will take action against her when she made controversial statements. There are many allegations against her. Making her a part of such an important committee is unfortunate," he said


--- layout: page title: Projects subtitle: Things I have worked on. description: permalink: /projects sitemap: priority: 0.9 listed: false ---


Graph Based Estimation of Information Theoretic Quantities
The estimation of information theoretic quantities is a problem of interest arising not only in information theory, but also feature selection, structure estimation for graphical models, and training and understanding deep neural networks. Common examples of these quantities include Shannon mutual information and KL divergence. In general, these quantities can be difficult to compute for continuous random variables, and most methods rely on an intermediate density estimator for the underlying distribution. These “plug-in” estimators suffer from bias near the support boundaries of the marginal densities and can be computationally prohibitive. Graph based estimation methods aim to skip the density estimation stage and directly estimate the quantity of interest by calculating a graph structure over the data sample. We give two examples from our past work below.
Estimating Multi-Class Bayes Error Using a Graph Based Estimator
Past work has used a minimal spanning tree or k-NN graph to estimate information-theoretic quantities of interest. One of the original algorithms in this area was for the estimation of Henze-Penrose (HP) divergence, which is a member of the broad class of \(f\)-divergences. This divergence measure has two important properties. First, it is possible to estimate the HP divergence directly from a minimal spanning tree across the data sample. As the minimal spanning tree can be computed in $O(n\log n)$, this approach to estimation is amenable to large datasets. Second, tight bounds have been proved that relate the HP divergence to the Bayes error rate of a binary classification problem. Thus, accurate estimation of the HP divergence allows for learning of the intrinsic hardness of the supervised learning task. Although tight bounds exist for the Bayes error rate for a binary classification task, this is not true for multi-class classification. In our work we derive tight bounds for the multi-class case using a generalized Henze-Penrose measure, and provide a graph based estimation procedure using a minimal spanning tree.
Curbing the Curse of Dimensionality with a New Estimator
Like other estimation methods, graph based estimation methods are subject to the curse of dimensionality. Specifically, as the feature dimension of the data increases, the bias of the estimate also increases. In recent work, we introduce a new estimator of the HP divergence, based on a different computed graph, that reduces the bias for growing dimension.
Relevant pubications:
- A Dimension-Independent Discriminant Between DistributionsIn 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, 2018 2018
- Learning to bound the multi-class bayes errorIEEE Transactions on Signal Processing 2020


Hi, I'm Liz Chaddock and welcome to my site! I'm currently a 4th year Computer Science student at UCSD. I am interested in mobile and web technologies that can improve people's lives.


- Command 1:
- Command 2:
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- Command 5:
Get the python commands you like!


{% extends 'wl/emails/base_email.html' %} {% block content %} Your Wildlife Licensing application has been reviewed and requires an amendment before approval. {% if amendment_reason %}
Amendment Reason: {{ amendment_reason }}
{% endif %}Amendment detail:
{{ amendment_detail }}
Click here to access to your application.
Note: If you haven't been on the Wildlife Licensing site recently you might have to login first before using the provided link.
{% endif %} {% endblock %}


{% load otree staticfiles %}
You will be paired randomly and anonymously with another participant.
In this study, one of you will be the proposer and the other will be the
responder. Prior to making a decision, you will learn your role,
which will be randomly assigned.
On each round the groups and roles will be shuffled.
Each round goes as follows:
There is {{ Constants.endowment }} to split. The proposer will
propose how to distribute this amount between himself/herself and the responder.
The responder will decide whether to accept or decline the proposal.
If the responder accepts the gil will be distributed according to the proposal.
If the responder declines, None of the players will get any gil.
Before the rounds start you will now be presented with a few questions to verify you understand the game.


{# User password reset page. #} {% block head_title %} Reset User {% end %} {% block title %} {% module Icon('reset') %} Reset {% module Icon('user') %} User {% end %} {% extends "base.html" %} {% block actions %} {% if current_user %}
{% else %} {% end %} {% end %} {% block content %}If you have forgotten your password, then you need to reset your account password, which means that your account will no longer have a password.
An activation code will be sent to your email address, with instructions on how to set your new password.
{% end %}