All is said, I bought a new member of my mini-pc at home, this time it's an [[http://www.orangepi.org/orangepi2/|Orange Pi 2]]: kinda similar to a Raspberry Pi2, but with more muscle, wifi onboard and (unfortunately) poorer software. Well OS is a Linux, I have no plans to send it to Mars yet, so I guess that I'll get what I can on internet and learn how to setup the rest.
Hardware
Power
* Vendor says 1.5-2A power is needed. I tested with 1A, mine turns the leds on, but nothing happens on the HDMI... hard to believe this guy needs 10W to run... Waiting for the 2A USB charger to arrive to confirm.
* Couple of days later I get my 2A power supply, and in fact it was the HDMI that was not working so maybe it did work with a 1A, but I'm too lazy to double check so sorry. 2A plug it is.
Wifi
* The Wifi module I have is a **Realtek RTL8189ETV** as per the specs, though it's writen //Createk CK-W-R189// ?
* Driver is noted //rtl8189es// in //dmesg//
* I read that to enable wifi connection from command line run: **sudo nmcli -a d wifi connect** and enter your wifi credentials, but to no avail
If found an old cheap Wifi dongle in a drawer, chip is a **Realtek rtl8188cus** that is apparently supported by the Raspi, so maybe I can make it work here too...
* Some guy struggling here [[http://blog.donity.com/k1/2011/02/22/gw-usvalue-ez-%E3%82%92-ubuntu-%E3%81%A7/]]
* Some guy who made a script BUT do not run it as is, you must edit it (I tried, it forced me to do a reinstall): [[https://www.raspberrypi.org/forums/viewtopic.php?f=91&t=6256&start=375]]
# lsusb
to list the usb
# modprobe -a 8188eu
to load module [[http://doc.ubuntu-fr.org/tutoriel/tout_savoir_sur_les_modules_linux]]
Operating system
Install
- Not necessarily what I will do but interresting read on [[http://www.orangepi.org/orangepibbsen/forum.php?mod=viewthread&tid=342|this forum]]
- I'm trying the Debian server (headless), just under 1GB uncompressed. I'll post my impressions here.
Software
Now to the fun part.
* Started with the OPI2 "Debian server" version: plugged on TV and ... nothing. Ethernet leds stay on, nothing on the TV (though some mode change happens, you see the TV react and change display mode) no text nothing. Waited, waited, retried. Nix.
* Then tried alternate distributions [[http://www.orangepi.org/orangepibbsen/forum.php?mod=viewthread&tid=342|here]]: still no ethernet, no wifi, no HDMI **but I had Serial interface** (3.3v by the way):
* - Debian jessie minimal: no ethernet, no wifi, no HDMI
* - Arch linux minimal: no ethernet, no wifi, no HDMI
* Then [[http://www.orangepi.org/downloadresources/|Raspbian]] and I got the **Ethernet working**! HDMI I didn't test (don't care), and still no wifi though but we're making progress...
Links
* Apparently there's another geek in Japan with a OPi2 [[http://blog.osakana.net/]]
Rebooted - March 2016
Ok, I decided to start over:
* OS: I put an armbian [[http://www.armbian.com/orange-pi-2/]] ... oh found [[http://www.cnx-software.com/2016/03/16/orange-pi-one-board-quick-start-guide-with-armbian-debian-based-linux-distribution/|this tutorial]] (afterward of course)
* It **MUST** run with a beefy USB power supply or the boot process will just fail and stop in the middle
* Upgrade all //apt-get update; apt-get upgrade//
* To enable Wifi with a small dongle, need to install nw tools: //apt-get install network-manager //
Setting up the network with //nmcli// ([[https://docs.fedoraproject.org/en-US/Fedora/20/html/Networking_Guide/sec-Connecting_to_a_Network_Using_nmcli.html|doc1]], [[https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/html/Networking_Guide/sec-Using_the_NetworkManager_Command_Line_Tool_nmcli.html|doc2]]):
* Existing NW setup: nmcli con show
* Status of NW connections: nmcli dev status
* Wifi NW around: nmcli dev wifi list
* Specific connection details: nmcli connection show id //connection-id//
* Wifi on/off : nmcli radio wifi on
* Wifi connect: nmcli c up id //connection-id//
* Change server name ([[http://www.howtogeek.com/167195/how-to-change-your-raspberry-pi-or-other-linux-devices-hostname/|doc]]): edit /etc/hosts, run /etc/init.d/hostname.sh, and finally reboot
My favorite //.profile// ([[http://bashrcgenerator.com/|profile generator]]):
alias m='emacs'
alias emcas='emacs'
alias cls='clear'
alias als='ls -alF'
#alias temp='/opt/vc/bin/vcgencmd measure_temp'
alias killoldssh='pkill -o -u $USER sshd'
alias cd..='cd ..'
alias temp='cat /sys/class/thermal/thermal_zone0/temp'
Re-Rebooted April 2020
Ok forget everything above, install [[https://www.armbian.com/orange-pi-2/|Armbian]], ubuntu or debian flavor, connect via screen/keyboard or via Serial (3.3v at 115200bps). Login "root" password "1234".
* No wifi out of the box but plug a cheapo Wifi dongle and it just works
* [[https://docs.armbian.com/User-Guide_Getting-Started/|Getting started]] - Connect to wifi: nmtui-connect //your-wifi//


The Unemployment Numbers as Sham: Where Are the Freelancers?
When the unemployment numbers are released the first Friday of each month, they drive headlines. Retailers cross their fingers. The business world looks for signs that interest rates may rise again.
The problem is that the unemployment numbers are wrong. They just aren't keeping up with the changes we're seeing in the new workforce. The Bureau of Labor Statistics' employment surveys were designed (back in the 1940s) to keep track of who has a full-time job, who doesn't, and who's looking.
But the way we work has changed dramatically since then. People are abandoning the 40-hour workweek -- some by choice, some by circumstance -- and becoming freelancers, working gig to gig, project to project. At last count, in 2006, more than 42 million people were considered independent workers. That's nearly one-third of the workforce.
The BLS surveys haven't kept up. They don't capture this type of independent, variable employment because they're not asking the right questions. The baseline question in the household survey is, "Last week, did you do any work for either pay or profit?"
Imagine Caroline, a freelance web designer who just finished a project a week ago and has a gig with a new client starting in a few days. Last week, she did not "work for pay or profit," but she wasn't exactly unemployed, either; she had a job lined up. What would the BLS make of her?
The BLS still uses a standard workweek as the measure of employment, but there's a whole workforce out there that doesn't fit easily into that box. For many freelancers, full-time employment is really a series of short-term (or part-time) gigs and projects.
Imagine Marcus, an independent anesthesiologist who works 20 hours a week for a doctors' group, teaches as an adjunct professor at a local medical school and is a serious amateur photographer, selling photos on Etsy.
His BLS interview would go off the rails at the question, "What is the main reason you do not want to work full-time?" Marcus has crafted the flexible work-life he wants, but the BLS assumes that he (and all Americans) should want to work one, full-time job.
Traditional work is being replaced by fractional work and micro-gigs, and the BLS isn't capturing this massive economic movement. It's impossible to know how many people are being miscounted, undercounted or left out entirely. With freelancing on the rise, we can't just leave this new workforce out of our economic data. The BLS surveys need to be updated to reflect the way people work now.
The issue isn't really about whether the official unemployment number would go up or down, but about rethinking what it means to be "employed" to address whether your income provides a sustainable life. The unemployment numbers currently tell us whether a person is or is not going to a job, but they don't tell us much about the quality of that job -- from an economic or social viewpoint -- which is important information to have to understand the economy and the labor force.
Instead of focusing on whether someone's job is full-time or part-time, how about asking if they have enough work to sustain a life?
We do have an employment problem in this country, but we're not going to figure out what it is until we start asking the right questions.


This page will help you decide which Epicodus track to take, answer the brief questionnaire and click submit.


{jenkins-plugin-info:cryptomove}
The CrytpoMove Key Vault Jenkins plugin sources your keys and secrets stored on CryptoMove and runs a command with your keys and secrets available as environment variables with the name and value.
The plugin has three inputs:
- `command` - the command you want to run with your CryptoMove keys sourced as environment variables.
- `token` - the authentication token from CryptoMove you want to use. All of the keys that the token provides read access to will be sourced as environment variables.
- `email` - the email of the CryptoMove user who owns the token


I love building and leading teams that routinely deliver solutions for the biggest challenges. The tightest latencies, the largest scales, the highest availability, the largest customer base, the intricate complexity, the fastest and most reliable delivery, the simplest and most usable UI, the automated wide and deep testing from unit through CI, integration, acceptance, performance, scale, load the automated testing. Running teams that do it and then do it even better is the best feeling around, I love getting up to go to work on getting even better, building and delivering more faster better.
Engineer/manager/director built and led teams that defined, built, shipped and supported dominant API driven multitier horizontally scalable network and web based products and services.
Hired, trained, and mentored teams from near startup (10 engineers, 1 product) to large (100+ engineers, 30+ products) divisions. My teams delivered mission critical applications to Fortune 500 firms, taking leading market share in several $50M+ markets with installed bases in the millions: Attachmate's EXTRA!, Cisco's Unity Connection and Media Sense. Managed and promoted engineers and managers, fixed performance issues, rewarded and promoted engineers while keeping morale, productivity and quality high and turnover low. In-sourced and outsourced, built and bought and acquired, the teams I manage deliver tech that works and sells and beats the competition. At Attachmate and Cisco I delivered industry best support operations and achieved outstanding customer satisfaction for decades. Delivered mission critical apps to Fortune 500 firms, dominating large fast growing markets. Managed engineers and managers, keeping productivity and quality high and turnover low. Built industry best support operations and achieved outstanding customer satisfaction.
Defined release requirements with product management then recruited and led teams that delivered on these commitments, from real time robots seventies to 3270 and 5250 emulators incuding APIs and multiuser database engine internals and network locking in the eighties, through servers, gateways and multitier automation, management and provisioning systems in the nineties, fast install device management and remote access for airline checkin counters and ssh clients this century, through real time media recording and unified messaging systems from on-premise to virtual to in the cloud this decade. They all share a history of success: market share captured, new markets created and dominated, mature markets sewn up and maximized for minimal cost.
Saved costs in development using ever improving open source platforms, frameworks, product and tools. Building with improving building blocks that get important features in performance, scale, redundancy and security delivered for you at no cost, we continue to build more and better results faster with fewer issues at ever improving scale and availability. We've consistently delivered more - scale, features, reliability, multi-tenancy, transparency and monitoring, security, all improve rapidly together when we get it right and I'm proiud to say I've built, led and managed many teams that did it right. The latest CI/CD tools, container and container management systems, and cloud integration options, when used properly, give us even finer control and flexibility in easy to manage and observe loosely coupled horizintally scalable systems. Costs drop, reliability, capacity, quality and velocity improve as demonstrated repeatedly at Cisco and Capital One Investing.
Continued delivering more sooner, more predictably and with fewer problems by improving processes, tech and communications. Moved to agile approaches, going from months long chronically late delivery cycles to demoing new features in hours and delivering them in days repeatedly and predistably using agile processes. Better tools, instrumentation, automation and systems give us locally tested, tested-on-push, review gated style checked static bug checked commit-tested automatically tested in both mocked and real world environments pipelined through ever more elaborate and careful test processes getting continuous integration and testing cycles into the minutes, and deployment cycles into the hours. Multiple deployments per day of live systems with millions of users without any issues is now routine.
Defined requirements and delivered on these commitments, taking market share from competitors in mature markets, capturing tens of millions of dollars in increased sales. Built continuous deployment pipelines with automated testing and automatic deployment to the cloud on AWS EC2 and Rackspace. Drove automated test improvements, increasing unit test coverage and improving test quality, automating end-to-end testing of web based UI components and deploying live integration test environments to improve back end component and system level testing, routinely catching defects within a few hours of code commit before they ever affect any customers. Automated latency and resource monitoring, enabling routine detection and correction of problems before customers get a chance to notice them.
Successfully designed, built, tested, deployed and supported complex API driven network and web based products, advocating for and delivering appropriate and innovative customer solutions to challenging real time multitier issues. Improved customer satisfaction by training Cisco's industry leading TAC (support) teams on new products and features and answering late night calls resolving critical customer issues. Provided public APIs and supported successful use by customers.
Revised processes to achieve faster results with higher quality. Achieved outstanding results using agile and waterfall, scrum, Kanban, and related techniques. Led ISO certification, team and organizational growth, patents, intellectual property, localization, licensing, certification and export/import compliance, as well as successfully off-shoring and on-shoring products.
Architect and Development Lead for multiple simultaneous releases of servers and clients. Built ground breaking products with scalable distributed systems using SQL and NoSQL database approaches including MongoDB and Cassandra. Built and continuously deployed cutting edge real time media micro-services with scalable stateless open stack components and design.
Achieved and sustained 5 nines availability for industry leading messaging and media servers with 10,000+ media users each.
Maintained compliance with strict security and personally-identifiable-information standards, passing Cisco's security reviews and scans before going live with each product.
Managed patent research and applications in USA, Europe and Canada. Negotiated patent dispute with IBM, reducing cross-licensing fees by $19 million.
Professional Experience:
Manager, Software Engineering 2016 - present Capital One Investing
Software Engineer   2007-2016 Cisco Systems Inc.
Staff Software Engineer 2002-2007 Attachmate Corp.
Director of Development 2000-2002 Attachmate Corp.
Staff Software Engineer 1999-2000 Attachmate Corp.
Development Manager 1992 -1999 Attachmate Corp.
Senior Software Engineer 1989 - 1992 Attachmate Corp.
Contract Sr. Software Engineer 1987 - 1989 various
Implemented components of the California Highway Patrol Computer Aided Dispatch system. Supported stringent reliability, fail-over and availability requirements, with innovative solutions.
Senior Software Engineer 1985 - 1987 Cosmos
Contract Sr. Software Engineer 1981 - 1985 various


PHENOMENON
A film review by Michael Dequina
Copyright 1996 Michael Dequina
(PG) *** (out of ****)
In the summers of 1990 and 1994, two offbeat, quieter films managed to become major word-of-mouth sleeper hits amid the loud, frenetic action blockbusters--Ghost and Forrest Gump, respectively. This year, comparable sleeper status may possibly come to Phenomenon, a slight but agreeable blend of comedy, drama, and whimsy.
John Travolta plays George Malley, a simple-minded, goofball auto mechanic who, after seeing a bright light on his 37th birthday, develops not only vast intelligence but the ability to move objects with his mind. Naturally, George's new abilities cause a stir in the Northern California town he calls home, attracting the attention of Berkeley scientists. However, there is one person he can't seem to get to pay him enough attention--chair maker and single mom Lace (Kyra Sedgwick), the Jenny to his Forrest, if you will.
A lot of Phenomenon resembles another recent Buena Vista release that dealt with supernatural powers, Powder. But one of the big reasons why this new film is as enjoyable and fun as the other was unwatchable and ridiculous is its healthy sense of humor. A great move on screenwriter Gerald diPego's part was to keep George's change a mere physical one, not also a personality change. George may become a genius, but he is still a goofball, as shocked as anyone else at his power. Since he remains just an average guy in spirit, George becomes a much more convincing and likable character than the straightfaced albino "saint" that is Powder. Vignettes in which George uses his powers to help others, such as where he tries to find a lost boy and another moment where he tries to fix his lonely Diana Ross-loving best friend (Forest Whitaker) up, are funny and cutesy without being overly cloying.
But that is not to say that there are not moments when director Jon Turteltaub (While You Were Sleeping) lays on the sap a bit too heavily. By far the worst part of the film is an interminable "foreplay" scene in which Lace gives George a haircut and shave while the easy listening sound of Aaron Neville fills the soundtrack; the moment is so blatantly designed to be touching and sweet that it left me wanting to gag. In fact, Phenomenon's calculated manipulative tendencies are what keeps it from falling short of the level of a Gump. When the tale takes the obligatory melodramatic turn in the third act, virtually all of the offbeat humor vanishes, and I found myself not caring as much about the story as I did earlier.
Still, Travolta's effortless charm and charisma kept me emotionally involved in the picture. He is one of those stars who is instantly likable from frame one, and he is put to ideal use here--funny, sweet, with a good sense of self-aware humor. It's hard to imagine anyone other than Travolta (except, perhaps, Tom Hanks) in the role; he's such a perfect fit. The supporting cast, including Sedgwick, Whitaker, and Robert Duvall, make the most of their small but pivotal roles. Star Trek's Brent Spiner is particularly memorable in a hilarious bit where he (as an FBI researcher) attempts to test George's IQ.
Phenomenon is not as satisfying or enjoyable as a Ghost or a Gump, but it certainly has its charms, and that may very well be enough to insure its place among the top box office grossers of the summer.
-- Michael Dequina<EMAIL_ADDRESS>Visit Mr. Brown's Movie Site at http://members.tripod.com/~MrBrown/
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


Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python.
| Version: | 0.3.1 |
| Depends: | R (≥ 3.0) |
| Imports: | lubridate (≥ 1.5), parallel (≥ 3.0), data.table (≥ 1.9), manipulate (≥ 1.0), shiny (≥ 1.0) |
| Suggests: | knitr (≥ 1.15), rmarkdown (≥ 1.1), R.rsp (≥ 0.40) |
| Published: | 2018-11-26 |
| Author: | Dan Dediu [aut, cre], Alexandra Dima [aut], Samuel Allemann [aut] |
| Maintainer: | Dan Dediu <ddediu at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/ddediu/AdhereR |
| NeedsCompilation: | no |
| Citation: | AdhereR citation info |
| CRAN checks: | AdhereR results |
| Reference manual: | AdhereR.pdf |
| Vignettes: |
AdhereR: Adherence to Medications Calling AdhereR from Python3 Using AdhereR with various database technologies for processing very large datasets |
| Package source: | AdhereR_0.3.1.tar.gz |
| Windows binaries: | r-devel: AdhereR_0.3.1.zip, r-release: AdhereR_0.3.1.zip, r-oldrel: AdhereR_0.3.1.zip |
| OS X binaries: | r-release: AdhereR_0.3.1.tgz, r-oldrel: AdhereR_0.3.1.tgz |
| Old sources: | AdhereR archive |
Please use the canonical form https://CRAN.R-project.org/package=AdhereR to link to this page.


social distancing is a must. we cant go to school. it is dangerous. headache. lack of oxygen. dry coughing. snaazing. you have to isolate your self. it spreads fast. (severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2) causes coronavirus disease 2019 ( COVID-19 ). The virus that causes COVID-19 spreads easily among people, and more continues to be discovered over time about how it spreads.
it has scared thre world alot . it has no cure. it has affected many people. coronavirus has killed many people. we have to wear masks everytime. schools have have been closed.s we can not play with our friend. 'CO' stands for corona, 'VI' for virus, and 'D' for disease. Formerly, this disease was referred to as '2019 novel coronavirus' or '2019-nCoV. The COVID-19 virus is a new virus linked to the same family of viruses as Severe Acute Respiratory Syndrome (SARS) and some types of common cold.


MANTEL BAROMETER CLOCK
CHIME SETTINGS
CHIME ENABLE
Select Chime Options
Chime Hour Only
Chime Hour and Half Hour
Chime Hour and Quarter Hour
Select Time Zone
UCT-12
UCT-11
UCT-10
UCT-9
UCT-8
UCT-7
UCT-6
UCT-5 (Ridgefield, CT Standard)
UCT-4 (Ridgefield, CT DST)
UCT-3
UCT-2
UCT-1
UCT (London/GMT)
UCT+1 (London/BST)
UCT=2
UCT+3
UCT+4
UCT+5
UCT+6
UCT+7
UCT+8
UCT+9
UCT+10
UCT+11
UCT+12
CALIBRATION
Striker starting position adjustment
Clockwise
Other Way
Previous
Next


Getting Started
The getting started guide walks you through how to manually setup a admin dashboard. flashboard actually create your back-end package services such as rest-full api of your models based on loopback node.js framework and generate admin dashboard automatically for data moderation.
Introduction
flashboard can handle your whole back-end includes services, rest-full api and admin dashboard based on loopback node.js framework.
Simply can define your models with several options and get your rest-full api and admin dashboard automatically.
Admin dashboard implemented by vue.js 2 framework and muse-ui as ui-component library that allows you have a multiple themes for your user interface.
You have a database-connector options that allows you to connect your back-end to each database you want such as mongodb or mysql and etc.
Admin demo : http://<IP_ADDRESS>/flashboard
demo admin credential:
email<EMAIL_ADDRESS>password: qwertyuiop
Let’s start to build your own back-end services.
Installation
- Before you begin, make sure you have Node.js and MongoDB installed. For best results, use the latest LTS (long-term support) release of Node.js.
- Running below command in your empty project directory path :
$ git clone https://github.com/vah7id/flashboard.git
- First install your services packages by run below command on your project directory :
$ npm run preinstall
- Install your admin generator packages by run below command on root directory:
$ npm install
- Running back-end services first before running admin dashboard :
$ npm run service
- At last you can run your admin dashboard by running below command:
$ npm run dev
for deploy on production you can run build command :
$ npm run build
Admin Default User
In first-time you start the project api/server/boot/script.js
create default admin account as you can see below :
module.exports = function (app) { var _ = require('lodash'); var User = app.models.User; var Role = app.models.Role; var RoleMapping = app.models.RoleMapping; var ACL = app.models.ACL; User.create([{ username: 'flashboard', email<EMAIL_ADDRESS>password: 'qwertyuiop', type: 'admin' }], function(err, users) { Role.find({ name: 'admin' }, function(err, results) { if (err) console.log(err); Role.create({ name: 'admin' }, function(err, role) { if (err); //debug(role); role.principals.create({ principalType: RoleMapping.USER, principalId: users[0].id }, function(err, principal) { if (err) console.log(err); }); }); }); }); };
You can change the initial credential in script.js before start your project.
If you miss this step nevermind, you can login by default admin credentials then create new admin account in dashboard from users section for yourself and manage accounts in there.
Models
After run your project you can see built-in models on your dashboard such as posts, galleries and etc. for starting to create your own website models you should go to next section of documentation.


Overview / Installation
Overview
The SQLAlchemy SQL Toolkit and Object Relational Mapper is a comprehensive set of tools for working with databases and Python. It has several distinct areas of functionality which can be used individually or combined together. Its major components are illustrated below. The arrows represent the general dependencies of components:
Above, the two most significant front-facing portions of SQLAlchemy are the Object Relational Mapper and the SQL Expression Language. SQL Expressions can be used independently of the ORM. When using the ORM, the SQL Expression language remains part of the public facing API as it is used within object-relational configurations and queries.
Tutorials
- Object Relational Tutorial - This describes the richest feature of SQLAlchemy, its object relational mapper. If you want to work with higher-level SQL which is constructed automatically for you, as well as management of Python objects, proceed to this tutorial.
- SQL Expression Language Tutorial - The core of SQLAlchemy is its SQL expression language. The SQL Expression Language is a toolkit all its own, independent of the ORM package, which can be used to construct manipulable SQL expressions which can be programmatically constructed, modified, and executed, returning cursor-like result sets. It’s a lot more lightweight than the ORM and is appropriate for higher scaling SQL operations. It’s also heavily present within the ORM’s public facing API, so advanced ORM users will want to master this language as well.
Main Documentation
- Mapper Configuration - A comprehensive walkthrough of major ORM patterns and techniques.
- Using the Session - A detailed description of SQLAlchemy’s Session object
- Database Engines - Describes SQLAlchemy’s database-connection facilities, including connection documentation and working with connections and transactions.
- Database Meta Data - All about schema management using MetaData and Table objects; reading database schemas into your application, creating and dropping tables, constraints, defaults, sequences, indexes.
- Connection Pooling - Further detail about SQLAlchemy’s connection pool library.
- Column and Data Types - Datatypes included with SQLAlchemy, their functions, as well as how to create your own types.
- sqlalchemy.ext - Included addons for SQLAlchemy
Code Examples
Working code examples are included in the SQLAlchemy distribution, and there are also usage recipes on the SQLAlchemy wiki. A description of all the included example applications is at Examples.
API Reference
An organized section of all SQLAlchemy APIs is at API Reference.
Installing SQLAlchemy
Installing SQLAlchemy from scratch is most easily achieved with setuptools. Assuming it’s installed, just run this from the command-line:
# easy_install SQLAlchemy
This command will download the latest version of SQLAlchemy from the Python Cheese Shop and install it to your system.
Otherwise, you can install from the distribution using the setup.py script:
# python setup.py install
Installing a Database API
SQLAlchemy is designed to operate with a DB-API implementation built for a particular database, and includes support for the most popular databases. The current list is at Supported Databases.
Checking the Installed SQLAlchemy Version
This documentation covers SQLAlchemy version 0.6. If you’re working on a system that already has SQLAlchemy installed, check the version from your Python prompt like this:
>>> import sqlalchemy
>>> sqlalchemy.__version__
0.6.0
0.5 to 0.6 Migration
Notes on what’s changed from 0.5 to 0.6 is available on the SQLAlchemy wiki at 06Migration.


Testing PI:importwiki
Testing import of material from Wiki, This is in an experimental stage and some work remains to be done. The concept of dpath for selecting from a flat documentstructure is not waterproof, but it works on simple constructs.
This page selects material from a Wikipedia-page: http://en.wikipedia.org/wiki/Tiger
A section without references
White tigers
There is a well-known mutation that produces the white tiger, technically known as chinchilla albinistic, an animal which is rare in the wild, but widely bred in zoos due to its popularity. Breeding of white tigers will often lead to inbreeding (as the trait is recessive). Many initiatives have taken place in white and orange tiger mating in an attempt to remedy the issue, often mixing subspecies in the process. Such inbreeding has led to white tigers having a greater likelihood of being born with physical defects, such as cleft palates and scoliosis (curvature of the spine). Furthermore, white tigers are prone to having crossed eyes (a condition known as strabismus). Even apparently healthy white tigers generally do not live as long as their orange counterparts. Recordings of white tigers were first made in the early 19th century. They can only occur when both parents carry the rare gene found in white tigers; this gene has been calculated to occur in only one in every 10,000 births. The white tiger is not a separate sub-species, but only a colour variation; since the only white tigers that have been observed in the wild have been Bengal tigers (and all white tigers in captivity are at least part Bengal), it is commonly thought that the recessive gene that causes the white colouring is probably carried only by Bengal tigers, although the reasons for this are not known. Nor are they in any way more endangered than tigers are generally, this being a common misconception. Another misconception is that white tigers are albinos, despite the fact that pigment is evident in the white tiger's stripes. They are distinct not only because of their white hue; they also have blue eyes.
A section with references, only heading and images
Golden tabby tigers
A section accessed with xpath
| Tiger | |
|---|---|
| A Bengal Tiger (P. tigris tigris) in India's Ranthambhore National Park. | |
| Conservation status | |
|
Endangered (IUCN 3.1) |
|
| Scientific classification | |
| Kingdom: | Animalia |
| Phylum: | Chordata |
| Class: | Mammalia |
| Order: | Carnivora |
| Family: | Felidae |
| Genus: | Panthera |
| Species: | P. tigris |
| Binomial name | |
|
Panthera tigris
(Linnaeus, 1758) |
|
| Subspecies | |
|
P. t. tigris
|
|
| Historical distribution of tigers (pale yellow) and 2006 (green). | |
| Synonyms | |
|
Tigris striatus
Severtzov, 1858
|


--- title: "Categories" layout: default header: true footer: true permalink: "/categories.html" ---
{{ site.data.blog.description }}


LDA Model¶
class LdaModel¶
tmlib.lda.LdaModel(num_terms=None, num_topics=None, random_type=0)
This class provides some function to help you manipulate model (\(\lambda\) or \(\beta\)): you can save model, load model or display words of topics…
Parameters¶
num_terms: int, default: None
number of words in vocabulary file
num_topics: int, default: None
number of topics
random_type: int, default: 0
Initialize randomly array of \(\lambda\) (or \(\beta\)) (size num_topics x num_terms). If random_type = 0, model is initialized with uniform distribution. Otherwise, initialized with gamma distribution
Attributes¶
num_terms: int
num_topics: int
model: array 2 dimentions (num_topics x num_terms)
\(\lambda\) or \(\beta\). Depend on the learning method: \(\lambda\) in case Online VB, Online CVB0, Online CGS, Streaming VB and \(\beta\) for Online OPE, Online FW, Streaming OPE, Streaming FW, ML-CGS, ML-OPE, ML-FW
presence_score: a measure allow us to rank the topics. If a topic has the high value of presence_score, it means that corpus is related to that topic very much
Methods¶
__init__(num_terms=None, num_topics=None, random_type=0)
normalize ()
Used for estimating \(\beta\) from \(\lambda\). This function is usually used for regularized methods ML-CGS, ML-FW, ML-OPE
print_top_words (self, num_words, vocab_file, show_topics=None, display_result=None, type=’word’, distribution=False)
Display words of topics on the screen or save into file
Parameters:
num_words: int,
number of words of each topic is displayed
vocab_file: string,
path of file vocabulary
show_topics: int, default: None
number of topics is displayed. By default, all of topics are displayed
display_result: string, default: None
path of file to save words into, or ‘screen’ if you want to display topics on the screen. By default, if display_result=None, nothing happen and the method return the result as list python
type: string, default: ‘word’
You can print index of word in the vocabulary file by set type=’index’. If type=’word’ by default, the result returned is words (type string)
distribution:
If you only want to display words, then distribution=False. But you can display words along with term distribution of it by set distribution = True
load_model (path_file)
loading the learned model (\(\lambda\) or \(\beta\)) from file named path_file
save_model (path_file, file_type=’binary’)
saving model into a file named path_file. By default, the type of file is binary. We can change type of file to text by set file_type=’txt’
save (path_file)
you can save the object by using this function. All attributes of object LdaModel are saved in path_file and after that, you can restore object by using load function
load (path_file)
load the attributes from path_file to restore object LdaModel.


--- layout: archive title: Lean Prover Zulip Chat Archive permalink: archive/113489newmembers/25529Howtoprovelistlengthl10rat.html ---
I am having difficulty figuring out how to approach this.
by norm_cast; simp
Thank you.
{% endraw %} {% include archive_update.html %}


OpenAFP
Contents
Index
OpenAFP.Prelude.Instances
Synopsis
recOf
:: (
ChunkBuf
c n b,
Binary
(
Record
r)) => c -> IO r
apply
:: MonadIO m => (t -> IO a) -> t -> (a -> m b) -> m b
applyAFP
:: MonadIO m =>
N3
->
AFP_
-> (
forall
a .
Rec
a => a -> m b) -> m b
applyMCF
:: MonadIO m =>
N0
->
MCF_
-> (
forall
a .
Rec
a => a -> m b) -> m b
Produced by
Haddock
version ADDOCK_VERSION


In this Bite you make an iterator called EggCreator
by implementing the __iter__
and __next__
dunder methods (as dictated by the iterator protocol).
Have it receive an int
called limit
which is the max number of eggs to create. It then generates eggs of randomly chosen colors from the COLORS
constant.
Make sure you raise the right exception when you reach the passed in limit
.
So this is how the iterator would work + its output:
for egg in EggCreator(5): print(egg)
Would output (colors will vary due to randomness):
green egg yellow egg yellow egg blue egg red egg
Eggs theme? Yep, this Bite was produced the 31st of March 2018 - Happy Easter all!
Good luck and keep calm and code in Python!


Sir Collin Windawood:
Want to go to Giran, huh? Baulro sent you? I told him the story a while back.
You may be fresh out of school, but worldly experiences will help shape your future.
Giran is a busy place and unexpected things can happen, so stay calm and focused!
Meet Gatekeeper Roxxy and tell her I sent you. Good luck!


Salmon is one of a breed of new, very fast RNAseq counting packages. Like Kallisto and Sailfish, Salmon counts fragments without doing up-front read mapping. Salmon can be used with edgeR and others to do differential expression analysis (if you are quantifying RNAseq data).
Today we will use it to get a handle on the relative distribution of genomic reads across the predicted protein regions.
The goals of this tutorial are to:
Extra resources:
Download and extract the latest version of Salmon and add it to your PATH:
cd
wget https://github.com/COMBINE-lab/salmon/releases/download/v0.7.2/Salmon-0.7.2_linux_x86_64.tar.gz
tar -xvzf Salmon-0.7.2_linux_x86_64.tar.gz
cd Salmon-0.7.2_linux_x86_64
export PATH=$PATH:$HOME/Salmon-0.7.2_linux_x86_64/bin
Go to the data directory and download the prokka annotated sequences, assembled metagenome, and fastq files:
cd ~
mkdir -p data
cd data
curl -L -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/SRR1976948.abundtrim.subset.pe.fq.gz
curl -L -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/SRR1977249.abundtrim.subset.pe.fq.gz
curl -L -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/prokka_annotation_assembly.tar.gz
tar -xvzf prokka_annotation_assembly.tar.gz
Make a new directory for the quantification of data with Salmon:
mkdir quant
cd quant
Grab the nucleotide (*ffn
) predicted protein regions from Prokka and link them here. Also grab the trimmed sequence data (*fq
)
ln -fs ~/data/prokka_annotation/*ffn .
ln -fs ~/data/*.abundtrim.subset.pe.fq.gz .
Create the salmon index:
salmon index -t metagG.ffn -i transcript_index --type quasi -k 31
Salmon requires that paired reads be separated into two files. We can split the reads using the split-paired-reads.py
from the khmer package:
for file in *.abundtrim.subset.pe.fq.gz
do
tail=.fq.gz
BASE=${file/$tail/}
split-paired-reads.py $BASE$tail -1 ${file/$tail/}.1.fq -2 ${file/$tail/}.2.fq
done
Now, we can quantify our reads against this reference:
for file in *.pe.1.fq
do
tail1=.abundtrim.subset.pe.1.fq
tail2=.abundtrim.subset.pe.2.fq
BASE=${file/$tail1/}
salmon quant -i transcript_index --libType IU \
-1 $BASE$tail1 -2 $BASE$tail2 -o $BASE.quant;
done
(Note that –libType must come before the read files!)
This will create a bunch of directories named after the fastq files that we just pushed through. Take a look at what files there are within one of these directories:
find SRR1976948.quant -type f
Now, the quant.sf
files actually contain the relevant information about expression – take a look:
head -10 SRR1976948.quant/quant.sf
The first column contains the transcript names, and the fourth column is what we will want down the road - the normalized counts (TPM). However, they’re not in a convenient location / format for use; let’s fix that.
Download the gather-counts.py script:
curl -L -O https://raw.githubusercontent.com/ngs-docs/2016-metagenomics-sio/master/gather-counts.py
and run it:
python2 ./gather-counts.py
This will give you a bunch of .counts files, which are processed from the quant.sf files and named for the directory from which they emanate.
In Jupyter Notebook, open a new Python3 notebook and enter:
%matplotlib inline
import numpy
from pylab import *
In another cell:
cd ~/data/quant
In another cell:
counts1 = [ x.split()[1] for x in open('SRR1976948.quant.counts')]
counts1 = [ float(x) for x in counts1[1:] ]
counts1 = numpy.array(counts1)
counts2 = [ x.split()[1] for x in open('SRR1977249.quant.counts')]
counts2 = [ float(x) for x in counts2[1:] ]
counts2 = numpy.array(counts2)
plot(counts1, counts2, '*')


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Numbers 35:29
So these things shall be for a statute of judgment unto you throughout your generations in all your dwellings.


| Item | Description | Hours | Amount |
| 1 | Monthly service for Feb, 2005: (on site 2/23/2004)
On site 3 hours 2/23/2004
a) Checked virus protection and ran anti-spyware on all PCs. Found and eliminated 1 virus on Leonard's PC.
b) Miscellaneous phone support: remaining email setup for remote personnel. |
3 | $150.00 |
| 2 | Paid in Full 7/23/2005 |
($150.00) |


Disclaimer
This website is a non-profit initiative created in the spirit of helping each other. The developer of this website has put together a repository where people from all other parts of the country can upload information.
Due to limited resources or any institutional support, the developer associated with this website is unable to verify all the information that is being shared via the website
In the light of the same, please be aware of people misusing such platforms by asking for advance payments thereby duping people. I request you to perform your due deligence before making any payments. The developer of this website cannot be held responsible towards the authenticity or veracity of the information available on the website.


{% import "head.html" as head %} {% import "navbar.html" as navbar %} {% import "events.html" as events %} {% import "network.html" as network %} {{ head.head() }}
{{ navbar.navbar() }}Competitions like the Data Science Bowl 2017 and LUNA16 are empowering the community to aid in the fight against lung cancer in ways like never before.
DeepOncology is an open-source platform for lung cancer detection, designed to be easy to understand, extensible, and powerful. With the power of deep learning at your fingertips, who knows what progress we might make.
Once you choose hope, anything’s possible.


Meraki Miles
Suggested Queries
How is <Network Name> doing?
List clients for <Network Name>
Show clients on SSID <SSID Name>
Get clients on VLAN <VLAN ID>
Tell me about client <Client MAC/Name>
Client events for <Client MAC/Name>
List clients for <Network Name>
Show clients on SSID <SSID Name>
Get clients on VLAN <VLAN ID>
Tell me about client <Client MAC/Name>
Client events for <Client MAC/Name>
Demo Data
Network: Parker LAN
Client: 38:53:9c:4b:70:06
Client: Nathan iPhone
SSID: sandstorm
VLAN: 810
Client: 38:53:9c:4b:70:06
Client: Nathan iPhone
SSID: sandstorm
VLAN: 810


6 overrides of StopProcessCommand
System.Management.Automation (6)
engine\debugger\debugger.cs (2)
2387
public override void
StopProcessCommand
()
4361
public override void
StopProcessCommand
()
engine\hostifaces\PSTask.cs (1)
1338
public override void
StopProcessCommand
()
engine\remoting\client\Job.cs (1)
4015
public override void
StopProcessCommand
()
engine\remoting\client\remoterunspace.cs (1)
1985
public override void
StopProcessCommand
()
engine\remoting\server\ServerRunspacePoolDriver.cs (1)
2026
public override void
StopProcessCommand
()
6 references to StopProcessCommand
System.Management.Automation (6)
engine\debugger\debugger.cs (2)
3596
activeDebugger.
StopProcessCommand
();
4363
_wrappedDebugger.
StopProcessCommand
();
engine\hostifaces\PSTask.cs (1)
1340
_wrappedDebugger.
StopProcessCommand
();
engine\remoting\client\Job.cs (1)
4017
_wrappedDebugger.
StopProcessCommand
();
engine\remoting\server\ServerRunspacePoolDriver.cs (2)
2030
_wrappedDebugger.Value.
StopProcessCommand
();
2317
debugger.
StopProcessCommand
();


Run the query in the right panel.
The sample table that is used throughout this tutorial is called employee_salary. It contains a generated column that calculates the employees salary as the sum of their bonus and pay columns.
What you should see in this step is a message stating the tables was created successfully.


|
RemoveAbstractDefinitionsPass.php
|
<?php
/*
* This file is part of the Symfony package.
*
* (c) Fabien Potencier<EMAIL_ADDRESS>*
* For the full copyright and license information, please view the LICENSE
* file that was distributed with this source code.
*/
namespace Symfony\Component\DependencyInjection\Compiler;
use Symfony\Component\DependencyInjection\ContainerBuilder;
/**
* Removes abstract Definitions.
*/
class RemoveAbstractDefinitionsPass implements CompilerPassInterface
{
/**
* Removes abstract definitions from the ContainerBuilder.
*/
public function process(ContainerBuilder $container)
{
foreach ($container->getDefinitions() as $id => $definition) {
if ($definition->isAbstract()) {
$container->removeDefinition($id);
$container->log($this, sprintf('Removed service "%s"; reason: abstract.', $id));
}
}
}
}


| 1 | package fr.sii.ogham.core.resource.path; | |
| 2 | ||
| 3 | import fr.sii.ogham.core.util.EqualsBuilder; | |
| 4 | import fr.sii.ogham.core.util.HashCodeBuilder; | |
| 5 | ||
| 6 | /** | |
| 7 | * A generated path from a base path and a relative path. | |
| 8 | * | |
| 9 | * @author Aurélien Baudet | |
| 10 | * | |
| 11 | */ | |
| 12 | public class RelativePath implements ResourcePath { | |
| 13 | /** | |
| 14 | * The base path | |
| 15 | */ | |
| 16 | private final ResourcePath basePath; | |
| 17 | /** | |
| 18 | * The path to resolve against source path | |
| 19 | */ | |
| 20 | private final ResourcePath relativePath; | |
| 21 | /** | |
| 22 | * The resolution result | |
| 23 | */ | |
| 24 | private final String mergedPath; | |
| 25 | ||
| 26 | public RelativePath(ResourcePath base, ResourcePath relative, String mergedPath) { | |
| 27 | super(); | |
| 28 | this.basePath = base; | |
| 29 | this.relativePath = relative; | |
| 30 | this.mergedPath = mergedPath; | |
| 31 | } | |
| 32 | ||
| 33 | public RelativePath(ResourcePath base, String relativePath, String mergedPath) { | |
| 34 | this(base, new UnresolvedPath(relativePath), mergedPath); | |
| 35 | } | |
| 36 | ||
| 37 | /** | |
| 38 | * Get the path as string of the resource before any resolution attempt. | |
| 39 | * | |
| 40 | * This path is the relative path merged with the base path. | |
| 41 | * | |
| 42 | * @return the merge path | |
| 43 | */ | |
| 44 | @Override | |
| 45 | public String getOriginalPath() { | |
| 46 |
1
1. getOriginalPath : replaced return value with "" for fr/sii/ogham/core/resource/path/RelativePath::getOriginalPath → RUN_ERROR |
return mergedPath; |
| 47 | } | |
| 48 | ||
| 49 | /** | |
| 50 | * Get the base path | |
| 51 | * | |
| 52 | * @return the base path | |
| 53 | */ | |
| 54 | public ResourcePath getBasePath() { | |
| 55 |
1
1. getBasePath : replaced return value with null for fr/sii/ogham/core/resource/path/RelativePath::getBasePath → NO_COVERAGE |
return basePath; |
| 56 | } | |
| 57 | ||
| 58 | /** | |
| 59 | * Get the relative path | |
| 60 | * | |
| 61 | * @return the relative path | |
| 62 | */ | |
| 63 | public ResourcePath getRelativePath() { | |
| 64 |
1
1. getRelativePath : replaced return value with null for fr/sii/ogham/core/resource/path/RelativePath::getRelativePath → RUN_ERROR |
return relativePath; |
| 65 | } | |
| 66 | ||
| 67 | @Override | |
| 68 | public int hashCode() { | |
| 69 |
1
1. hashCode : replaced int return with 0 for fr/sii/ogham/core/resource/path/RelativePath::hashCode → NO_COVERAGE |
return new HashCodeBuilder().append(basePath).append(relativePath).hashCode(); |
| 70 | } | |
| 71 | ||
| 72 | @Override | |
| 73 | public boolean equals(Object obj) { | |
| 74 |
2
1. equals : replaced boolean return with true for fr/sii/ogham/core/resource/path/RelativePath::equals → NO_COVERAGE 2. equals : replaced boolean return with false for fr/sii/ogham/core/resource/path/RelativePath::equals → NO_COVERAGE |
return new EqualsBuilder(this, obj).appendFields("basePath", "relativePath").isEqual(); |
| 75 | } | |
| 76 | ||
| 77 | @Override | |
| 78 | public String toString() { | |
| 79 | StringBuilder builder = new StringBuilder(); | |
| 80 | builder.append("'").append(mergedPath).append("' (merged from ").append(relativePath).append(" relative to ").append(basePath).append(")"); | |
| 81 |
1
1. toString : replaced return value with "" for fr/sii/ogham/core/resource/path/RelativePath::toString → RUN_ERROR |
return builder.toString(); |
| 82 | } | |
| 83 | } | |
Mutations | ||
| 46 |
1.1 |
|
| 55 |
1.1 |
|
| 64 |
1.1 |
|
| 69 |
1.1 |
|
| 74 |
1.1 2.2 |
|
| 81 |
1.1 |


--- layout: default title: Home description: The personal blog of designer and developer Bradley Taunt ---


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|
F´ Flight Software - C/C++ Documentation
NASA-v1.5.0
A framework for building embedded system applications to NASA flight quality standards.
|
#include <Os/IPCQueue.hpp>
Public Member Functions | |
| IPCQueue () | |
| ~IPCQueue () | |
| QueueStatus | create (const Fw::StringBase &name, NATIVE_INT_TYPE depth, NATIVE_INT_TYPE msgSize) |
| create a message queue More... | |
| QueueStatus | send (const Fw::SerializeBufferBase &buffer, NATIVE_INT_TYPE priority, QueueBlocking block) |
| send a message More... | |
| QueueStatus | receive (Fw::SerializeBufferBase &buffer, NATIVE_INT_TYPE &priority, QueueBlocking block) |
| receive a message More... | |
| QueueStatus | send (const U8 *buffer, NATIVE_INT_TYPE size, NATIVE_INT_TYPE priority, QueueBlocking block) |
| send a message More... | |
| QueueStatus | receive (U8 *buffer, NATIVE_INT_TYPE capacity, NATIVE_INT_TYPE &actualSize, NATIVE_INT_TYPE &priority, QueueBlocking block) |
| receive a message More... | |
| NATIVE_INT_TYPE | getNumMsgs (void) const |
| get the number of messages in the queue More... | |
| NATIVE_INT_TYPE | getMaxMsgs (void) const |
| get the maximum number of messages (high watermark) More... | |
| NATIVE_INT_TYPE | getQueueSize (void) const |
| get the queue depth (maximum number of messages queue can hold) More... | |
| NATIVE_INT_TYPE | getMsgSize (void) const |
| get the message size (maximum message size queue can hold) More... | |
| Public Member Functions inherited from Os::Queue | |
| Queue () | |
| virtual | ~Queue () |
| QueueStatus | create (const Fw::StringBase &name, NATIVE_INT_TYPE depth, NATIVE_INT_TYPE msgSize) |
| create a message queue More... | |
| QueueStatus | send (const Fw::SerializeBufferBase &buffer, NATIVE_INT_TYPE priority, QueueBlocking block) |
| send a message More... | |
| QueueStatus | receive (Fw::SerializeBufferBase &buffer, NATIVE_INT_TYPE &priority, QueueBlocking block) |
| receive a message More... | |
| QueueStatus | send (const U8 *buffer, NATIVE_INT_TYPE size, NATIVE_INT_TYPE priority, QueueBlocking block) |
| send a message More... | |
| QueueStatus | receive (U8 *buffer, NATIVE_INT_TYPE capacity, NATIVE_INT_TYPE &actualSize, NATIVE_INT_TYPE &priority, QueueBlocking block) |
| receive a message More... | |
| NATIVE_INT_TYPE | getNumMsgs (void) const |
| get the number of messages in the queue More... | |
| NATIVE_INT_TYPE | getMaxMsgs (void) const |
| get the maximum number of messages (high watermark) More... | |
| NATIVE_INT_TYPE | getQueueSize (void) const |
| get the queue depth (maximum number of messages queue can hold) More... | |
| NATIVE_INT_TYPE | getMsgSize (void) const |
| get the message size (maximum message size queue can hold) More... | |
| const QueueString & | getName (void) |
| get the queue name More... | |
Additional Inherited Members | |
| Public Types inherited from Os::Queue | |
| enum | QueueStatus { QUEUE_OK, QUEUE_NO_MORE_MSGS, QUEUE_UNINITIALIZED, QUEUE_SIZE_MISMATCH, QUEUE_SEND_ERROR, QUEUE_RECEIVE_ERROR, QUEUE_INVALID_PRIORITY, QUEUE_EMPTY_BUFFER, QUEUE_FULL, QUEUE_UNKNOWN_ERROR } |
| enum | QueueBlocking { QUEUE_BLOCKING, QUEUE_NONBLOCKING } |
| Static Public Member Functions inherited from Os::Queue | |
| static NATIVE_INT_TYPE | getNumQueues (void) |
| get the number of queues in the system More... | |
| Protected Member Functions inherited from Os::Queue | |
| QueueStatus | createInternal (const Fw::StringBase &name, NATIVE_INT_TYPE depth, NATIVE_INT_TYPE msgSize) |
| create a message queue More... | |
| Protected Attributes inherited from Os::Queue | |
| POINTER_CAST | m_handle |
| handle for implementation specific queue More... | |
| QueueString | m_name |
| queue name More... | |
| Static Protected Attributes inherited from Os::Queue | |
| static NATIVE_INT_TYPE | s_numQueues = 0 |
| tracks number of queues in the system More... | |
Definition at line 7 of file IPCQueue.hpp.
| Os::IPCQueue::IPCQueue | ( | ) |
Definition at line 53 of file IPCQueueStub.cpp.
| Os::IPCQueue::~IPCQueue | ( | ) |
Definition at line 84 of file IPCQueueStub.cpp.
| Queue::QueueStatus Os::IPCQueue::create | ( | const Fw::StringBase & | name, |
| NATIVE_INT_TYPE | depth, | ||
| NATIVE_INT_TYPE | msgSize | ||
| ) |
create a message queue
Definition at line 56 of file IPCQueueStub.cpp.
| NATIVE_INT_TYPE Os::IPCQueue::getMaxMsgs | ( | void | ) | const |
get the maximum number of messages (high watermark)
Definition at line 351 of file IPCQueueStub.cpp.
| NATIVE_INT_TYPE Os::IPCQueue::getMsgSize | ( | void | ) | const |
get the message size (maximum message size queue can hold)
Definition at line 369 of file IPCQueueStub.cpp.
| NATIVE_INT_TYPE Os::IPCQueue::getNumMsgs | ( | void | ) | const |
get the number of messages in the queue
Definition at line 342 of file IPCQueueStub.cpp.
| NATIVE_INT_TYPE Os::IPCQueue::getQueueSize | ( | void | ) | const |
get the queue depth (maximum number of messages queue can hold)
Definition at line 360 of file IPCQueueStub.cpp.
| Queue::QueueStatus Os::IPCQueue::receive | ( | Fw::SerializeBufferBase & | buffer, |
| NATIVE_INT_TYPE & | priority, | ||
| QueueBlocking | block | ||
| ) |
receive a message
Definition at line 15 of file IPCQueueCommon.cpp.
| Queue::QueueStatus Os::IPCQueue::receive | ( | U8 * | buffer, |
| NATIVE_INT_TYPE | capacity, | ||
| NATIVE_INT_TYPE & | actualSize, | ||
| NATIVE_INT_TYPE & | priority, | ||
| QueueBlocking | block | ||
| ) |
receive a message
Definition at line 317 of file IPCQueueStub.cpp.
| Queue::QueueStatus Os::IPCQueue::send | ( | const Fw::SerializeBufferBase & | buffer, |
| NATIVE_INT_TYPE | priority, | ||
| QueueBlocking | block | ||
| ) |
send a message
Definition at line 6 of file IPCQueueCommon.cpp.
| Queue::QueueStatus Os::IPCQueue::send | ( | const U8 * | buffer, |
| NATIVE_INT_TYPE | size, | ||
| NATIVE_INT_TYPE | priority, | ||
| QueueBlocking | block | ||
| ) |
send a message
Definition at line 176 of file IPCQueueStub.cpp.


{% extends "article.html" %} {% load cache cms_tags menu_tags fix_menu_tags %} {% block title %} Demand Side - What is the relationship between placement numbers and diversity ratio? ({{ analytics_year }}) {% endblock %} {% block heading %} Demand Side - What is the relationship between placement numbers and diversity ratio? ({{ analytics_year }}) {% endblock %} {% block breadcrumb %}
{% endblock %} {% block article %}


Resources
Check out some useful open web resources to help you get started! Tweet out your own at #mozgames
BrowserQuest
BrowserQuest is a massively multiplayer adventure game created by Little Workshop (@glecollinet & @whatthefranck) and Mozilla.
BrowserQuest is a tribute to classic video-games with a multiplayer twist. You play as a young warrior driven by the thrill of adventure. No princess to save here, just a dangerous world filled with treasures to discover. And it’s all done in glorious HTML5 and JavaScript.
Even better, it’s open-source so be sure to check out the source code on GitHub. (some folks, including our friends at CERN, have forked the game. Check out ParticleQuest!)
Fork BrowserQuest yourself on github
Build New Games
Build New Games is a blog that teaches you game development for the Open Web. The techniques covered are cutting edge, cross-browser, and cross-device, and address topics like which engines to use engines, how to approach multiplayer, and how to design games using progressive enhancement.
Visit the Build New Games site
901 Resources
Jster has an amazing collection of open libraries and tools that you can use to start building your game.
Bananabread
BananaBread is a 3D first person shooter that runs on the web. It takes the Cube 2: Sauerbraten engine, which is written in C++ and OpenGL, and compiles it using Emscripten into JavaScript and WebGL so that it can run in modern browsers using standards-based web APIs and without the need for plugins.
The project has several goals. First, to serve as a testcase for running a demanding 3D game in browsers: Having a working testcase lets us try out new browser features and to profile performance in order to make browsers faster. Another goal is to prove that games of this nature can run in JavaScript and WebGL, which many people are skeptical about. Finally, all the code in this project is open (and practically all the art assets), so others can learn from this effort and use this code to create their own browser games.
Firefox OS
Firefox OS enables the Open Web as a platform for mobile devices. We’re making innovation possible by driving the development of new Web standards. Using HTML5 and the new Mozilla-proposed standard APIs, developers everywhere will be able to create amazing experiences and apps. Check out the Firefox OS resources and start building a game that is no longer tied to platform-specific native APIs.


--- layout: data_model title: IPFIXMessageHeaderType this_version: 1.1.1 ---
This type represents the message header for the IPFIX format. For more information about each of the fields, please refer to RFC 5101 (http://tools.ietf.org/html/rfc5101) under the heading, "Message Header Field Descriptions." Note that common elements are included in the Network_Flow_Label.
| Field Name | Type | Description |
|---|---|---|
| Version0..1 | HexBinaryObjectPropertyType |
Indicates the version number of Flow Record format exported in this message. The value of this field is 0x000a for the current version, incrementing by one the version used in the NetFlow services export version 9 [see RFC3954]. |
| Byte_Length0..1 | HexBinaryObjectPropertyType |
Indicates the total byte length of the IPFIX Message, measured in octets, including Message Header and Set(s). |
| Export_Timestamp0..1 | IntegerObjectPropertyType |
Indicates the time, in seconds, since 0000 UTC Jan 1, 1970, at which the IPFIX message header leaves the Exporter. |
| Sequence_Number0..1 | IntegerObjectPropertyType |
Indicates the incremental sequence counter modulo 2^32 of all IPFIX Data Records sent on this PR-SCTP stream from the current Observation Domain by the Exporting Process. This value SHOULD be used by the Collecting Process to identify whether any IPFIX Data Records have been missed. Template and Options Template Records do not increase the Sequence Number. |
| Observation_Domain_ID0..1 | IntegerObjectPropertyType |
Indicates a 32-bit identifier of the Observation Domain that is locally unique to the Exporting Process. See RFC 5101 under Observation Domain ID for more information. |


Simon Grayson challenges Blackpool players to upset promotion-chasing Ipswich Town
Blackpool boss Simon Grayson believes his players are more than capable of returning with a positive result when they travel to Ipswich Town.
by Gavin BrowneThe Seasiders make the long journey to face an Ipswich side currently second in League One as they seek an instant return to the Championship following their relegation last season.
Fifteen games into the season and their tally of 33 points is bettered only by Wycombe Wanderers (36), though Paul Lambert’s players have two games in hand on the current pacesetters.
Their home form has been good so far, posting three wins and two draws in six league games, with Rotherham United the only team to have won at Portman Road in League One thus far.
“Ipswich are one of the favourites,” Grayson acknowledged of tomorrow’s hosts.
“They have a really strong squad and have the threats of (Kayden) Jackson and (James) Norwood up front.
“We will go there in good spirits, make sure we’re hard to beat and be ruthless in both boxes, and there’s no reason why we can’t go there and get a good result.”
While Blackpool have had a free week, Ipswich were in midweek FA Cup action as they saw off Lincoln City in a replay.
Victory was their first in the competition for almost a decade but the Blackpool camp could not draw too many conclusions from their performance.
A number of senior players had been left behind by Lambert, whose decision to rotate his squad did not come as a surprise to his opposite number.
Grayson explained: “When you see their team, I think there were only two or three who played the previous league game.
“I was pretty sure that’s what Paul would do – play his fringe players – and keep a different team up his sleeve for Saturday.
“We’ve had a good week on the training pitch, and though it’s a long trip we’ll go in a positive mindset and hopefully give our supporters something to shout about.”


Lets you use HTTP verbs such as PUT or DELETE in places where the client doesn’t support it.
This is a Node.js module available
through the npm registry.
Installation is done using the
npm install
command:
NOTE It is very important that this module is used
before any module that needs to know the method of the
request (for example, it must be used prior to the
csurf
module).
Create a new middleware function to override the
req.method
property with a new value. This value will be
pulled from the provided getter
.
getter
- The getter to use to look up the overridden
request method for the request. (default:
X-HTTP-Method-Override
)
options.methods
- The allowed methods the original request
must be in to check for a method override value. (default:
['POST']
)
If the found method is supported by node.js core, then
req.method
will be set to this value, as if it has originally
been that value. The previous req.method
value will be stored
in req.originalMethod
.
This is the method of getting the override value from the request. If a
function is provided, the req
is passed as the first
argument, the res
as the second argument and the method is
expected to be returned. If a string is provided, the string is used to
look up the method with the following rules:
X-
, then it is treated as the
name of a header and that header is used for the method override. If the
request contains the same header multiple times, the first occurrence is
used.
This allows the specification of what methods(s) the request
MUST be in in order to check for the method override value. This
defaults to only POST
methods, which is the only method the
override should arrive in. More methods may be specified here, but it may
introduce security issues and cause weird behavior when requests travel
through caches. This value is an array of methods in upper-case.
null
can be specified to allow all methods.
To use a header to override the method, specify the header name as a
string argument to the methodOverride
function. To then make
the call, send a POST
request to a URL with the overridden
method as the value of that header. This method of using a header would
typically be used in conjunction with XMLHttpRequest
on
implementations that do not support the method you are trying to use.
var express = require('express')
var methodOverride = require('method-override')
var app = express()
// override with the X-HTTP-Method-Override header in the request
app.use(methodOverride('X-HTTP-Method-Override'))
Example call with header override using XMLHttpRequest
:
var xhr = new XMLHttpRequest()
xhr.onload = onload
xhr.open('post', '/resource', true)
xhr.setRequestHeader('X-HTTP-Method-Override', 'DELETE')
xhr.send()
function onload () {
alert('got response: ' + this.responseText)
}
To use a query string value to override the method, specify the query
string key as a string argument to the
methodOverride
function. To then make the call, send a
POST
request to a URL with the overridden method as the value
of that query string key. This method of using a query value would
typically be used in conjunction with plain HTML
<form>
elements when trying to support legacy browsers
but still use newer methods.
var express = require('express')
var methodOverride = require('method-override')
var app = express()
// override with POST having ?_method=DELETE
app.use(methodOverride('_method'))
Example call with query override using HTML <form>
:
<form method="POST" action="/resource?_method=DELETE">
<button type="submit">Delete resource</button>
</form>
var express = require('express')
var methodOverride = require('method-override')
var app = express()
// override with different headers; last one takes precedence
app.use(methodOverride('X-HTTP-Method')) // Microsoft
app.use(methodOverride('X-HTTP-Method-Override')) // Google/GData
app.use(methodOverride('X-Method-Override')) // IBM
You can implement any kind of custom logic with a function for the
getter
. The following implements the logic for looking in
req.body
that was in method-override@1
:
var bodyParser = require('body-parser')
var express = require('express')
var methodOverride = require('method-override')
var app = express()
// NOTE: when using req.body, you must fully parse the request body
// before you call methodOverride() in your middleware stack,
// otherwise req.body will not be populated.
app.use(bodyParser.urlencoded())
app.use(methodOverride(function (req, res) {
if (req.body && typeof req.body === 'object' && '_method' in req.body) {
// look in urlencoded POST bodies and delete it
var method = req.body._method
delete req.body._method
return method
}
}))
Example call with query override using HTML <form>
:
<!-- enctype must be set to the type you will parse before methodOverride() -->
<form method="POST" action="/resource" enctype="application/x-www-form-urlencoded">
<input type="hidden" name="_method" value="DELETE">
<button type="submit">Delete resource</button>
</form>


Version Control to Major Tom:
All the Projects
Back to Home
About Me
My GitHub: Fork It!
Latest Project
THIS WILL BE MY PROJECT, HAHAHA
Whatever Code Passes Rspec Comes Here
Bacon ipsum dolor amet swine ground round turducken, sausage shankle picanha salami pork turkey spare ribs ham hock andouille tail pork loin shoulder. Prosciutto bresaola doner spare ribs pork loin. Turkey kevin landjaeger, tongue pancetta spare ribs beef tail boudin meatloaf ground round short ribs strip steak andouille.
Capicola ball tip tri-tip picanha biltong leberkas pork chop beef strip steak chuck. Venison drumstick fatback leberkas pork belly shoulder boudin beef ribs.
Shall We Have a Cup of JavaScript?
This Will Be My Summary of DBC's Phase 0


--- layout: post title: Linear neuron tagline: " weights learning" permalink: /machine-learning/neural-network/linear-neuron-learning.html categories: [machine learning, neural networks] tags: [linear neuron, neuron's weights] ---
• The perceptron convergence procedure works by ensuring that every time the weights change, they get closer to every “generously feasible” set of weights.
– This type of guarantee cannot be extended to more complex networks in which the average of two good solutions may be a bad solution.
• So “multi-layer” neural networks do not use the perceptron learning procedure.
– They should never have been called multi-layer perceptrons.
• Instead of showing the weights get closer to a good set of weights, show that the actual output values get closer the target values.
– This can be true even for non-convex problems in which there are many quite different sets of weights that work well and averaging two good sets of weights may give a bad set of weights. – It is not true for perceptron learning.
• The simplest example is a linear neuron with a squared error measure.
• The neuron has a real- valued output which is a weighted sum of its inputs
• The aim of learning is to minimize the error summed over all training cases.
– The error is the squared difference between the desired output and the actual output. weight vector y = ∑ wixi = wTx
• It is straight-forward to write down a set of equations, one per training case, and to solve for the best set of weights.
– This is the standard engineering approach so why don’t we use it?
• Scientific answer: We want a method that real neurons could use. • Engineering answer: We want a method that can be generalized to multi-layer, non-linear neural networks.
• Each day you get lunch at the cafeteria.
– Your diet consists of fish, chips, and ketchup. You get several – portions of each.
• The cashier only tells you the total price of the meal
– After several days, you should be able to figure out the price of each portion.
• The iterative approach: Start with random guesses for the prices and then adjust them to get a better fit to the observed prices of whole meals.
• Each meal price gives a linear constraint on the prices of the portions:
price = xfishwfish + xchipswchips + xketchupwketchup
• The prices of the portions are like the weights in of a linear neuron.
w = (wfish , wchips , wketchup)
• We will start with guesses for the weights and then adjust the guesses slightly to give a better fit to the prices given by the cashier.
Figure 1: Graphical representation
Figure 2: First iteration
• Residual error = 350 • The “delta-rule” for learning is: price of meal = 500 Δw i = ε x i (t − y) 50 2 portions of fish 50 5 portions of chips 50 3 portions of ketchup • With a learning rate ε of 1/35, the weight changes are +20, +50, +30 • This gives new weights of 70, 100, 80. – Notice that the weight for chips got worse!Deriving the delta rule • Define the error as the squared residuals summed over all training cases: • Now differentiate to get error derivatives for weights • The batch delta rule changes the weights in proportion to their error derivatives summed over all training cases E = 1 2 ∑ (t n − y n ) 2 n∈training ∂E 1 ∂y n dE n = 2 ∑ n ∂w i ∂w dy i n = − ∑ x in (t n − y n ) n Δw i = − ε ∂E = ∑ ε x in (t n − y n ) ∂w i nBehaviour of the iterative learning procedure • Does the learning procedure eventually get the right answer? – There may be no perfect answer. – By making the learning rate small enough we can get as close as we desire to the best answer. • How quickly do the weights converge to their correct values? – It can be very slow if two input dimensions are highly correlated. If you almost always have the same number of portions of ketchup and chips, it is hard to decide how to divide the price between ketchup and chips.The relationship between the online delta-rule and the learning rule for perceptrons • In perceptron learning, we increment or decrement the weight vector by the input vector. – But we only change the weights when we make an error. • In the online version of the delta-rule we increment or decrement the weight vector by the input vector scaled by the residual error and the learning rate. – So we have to choose a learning rate. This is annoying.Neural Networks for Machine Learning Lecture 3b
Geoffrey Hinton with Nitish Srivastava Kevin SwerskyThe error surface in extended weight space • The error surface lies in a space with a horizontal axis for each weight and one vertical axis for the error. – For a linear neuron with a squared error, it is a quadratic bowl. – Vertical cross-sections are parabolas. – Horizontal cross-sections are ellipses. • For multi-layer, non-linear nets the error surface is much more complicated. E w1 w2Online versus batch learning • The simplest kind of batch learning does steepest descent on the error surface. • The simplest kind of online learning zig-zags around the direction of steepest descent: – This travels perpendicular to constraint from the contour lines. training case 1 w1 w1 w2 constraint from training case 2 w2Why learning can be slow • If the ellipse is very elongated, the direction of steepest descent is almost perpendicular to the direction towards the minimum! – The red gradient vector has a large component along the short axis of the ellipse and a small component along the long axis of the ellipse. – This is just the opposite of what we want. w1 w2Neural Networks for Machine Learning Lecture 3c
Geoffrey Hinton with Nitish Srivastava Kevin SwerskyLogistic neurons • These give a real-valued output that is a smooth and bounded function of their total input. – They have nice derivatives which make learning easy. y = z = b + ∑ x i w i 1 + e i 1 y 0.5 0 1 0 z −zThe derivatives of a logistic neuron • The derivatives of the logit, z, with respect to the inputs and the weights are very simple: z = b + ∑ x i w i i ∂z = x i ∂w i • The derivative of the output with respect to the logit is simple if you express it in terms of the output: 1 y = 1 + e ∂z = w i ∂x i −z dy = y ( 1 − y) dzThe derivatives of a logistic neuron y = 1 1 + e −z = ( 1 + e −z ) −1 " 1 % " e −z % dy − 1 (−e −z ) ' = y(1− y) = = $ ' $$ ' −z 2 −z −z dz
& ( 1 + e ) 1 + e
e −z (1+ e −z ) −1 (1+ e −z ) −1 = = = 1− y because −z −z −z 1 + e −z 1 + e 1 + e 1 + eUsing the chain rule to get the derivatives needed for learning the weights of a logistic unit • To learn the weights we need the derivative of the output with respect to each weight: ∂y ∂z dy = = x i y (1− y) ∂w i ∂w i dz delta-rule ∂E = ∂w i ∑ n ∂y n ∂E n n n n n = − x y (1− y ) (t − y ) ∑ i n ∂w i ∂y n extra term = slope of logisticNeural Networks for Machine Learning Lecture 3d
Geoffrey Hinton with Nitish Srivastava Kevin SwerskyLearning with hidden units (again) • Networks without hidden units are very limited in the input-output mappings they can model. • Adding a layer of hand-coded features (as in a perceptron) makes them much more powerful but the hard bit is designing the features. – We would like to find good features without requiring insights into the task or repeated trial and error where we guess some features and see how well they work. • We need to automate the loop of designing features for a particular task and seeing how well they work.Learning by perturbing weights (this idea occurs to everyone who knows about evolution) • Randomly perturb one weight and see if it improves performance. If so, save the change. – This is a form of reinforcement learning. – Very inefficient. We need to do multiple forward passes on a representative set of training cases just to change one weight. Backpropagation is much better. – Towards the end of learning, large weight perturbations will nearly always make things worse, because the weights need to have the right relative values. output units hidden units input unitsLearning by using perturbations • We could randomly perturb all the weights in parallel and correlate the performance gain with the weight changes. – Not any better because we need lots of trials on each training case to “see” the effect of changing one weight through the noise created by all the changes to other weights. • A better idea: Randomly perturb the activities of the hidden units. – Once we know how we want a hidden activity to change on a given training case, we can compute how to change the weights. – There are fewer activities than weights, but backpropagation still wins by a factor of the number of neurons.The idea behind backpropagation • We don’t know what the hidden units ought to do, but we can compute how fast the error changes as we change a hidden activity. – Instead of using desired activities to train the hidden units, use error derivatives w.r.t. hidden activities. – Each hidden activity can affect many output units and can therefore have many separate effects on the error. These effects must be combined. • We can compute error derivatives for all the hidden units efficiently at the same time. – Once we have the error derivatives for the hidden activities, its easy to get the error derivatives for the weights going into a hidden unit.Sketch of the backpropagation algorithm on a single case • First convert the discrepancy between each output and its target value into an error derivative. • Then compute error derivatives in each hidden layer from error derivatives in the layer above. • Then use error derivatives w.r.t. activities to get error derivatives w.r.t. the incoming weights. E = 1 2 (t j − y j ) 2 ∑ j∈output ∂E = −(t j − y j ) ∂y j ∂E ∂y j ∂E ∂y iBackpropagating dE/dy y j j z j y i i dy j ∂E ∂E ∂E = = y j (1− y j ) ∂z j dz j ∂y j ∂y j ∂E = ∂y i dz j ∂E ∑ dy ∂z = i j j ∑ w ij j ∂z j ∂E ∂E ∂E = = y i ∂w ij ∂w ij ∂z j ∂z j ∂E ∂z jNeural Networks for Machine Learning Lecture 3e
Geoffrey Hinton with Nitish Srivastava Kevin SwerskyConverting error derivatives into a learning procedure • The backpropagation algorithm is an efficient way of computing the error derivative dE/dw for every weight on a single training case. • To get a fully specified learning procedure, we still need to make a lot of other decisions about how to use these error derivatives: – Optimization issues: How do we use the error derivatives on individual cases to discover a good set of weights? (lecture 6) – Generalization issues: How do we ensure that the learned weights work well for cases we did not see during training? (lecture 7) • We now have a very brief overview of these two sets of issues.Optimization issues in using the weight derivatives • How often to update the weights – Online: after each training case. – Full batch: after a full sweep through the training data. – Mini-batch: after a small sample of training cases. • How much to update (discussed further in lecture 6) – Use a fixed learning rate? – Adapt the global learning rate? – Adapt the learning rate on each connection separately? – Don’t use steepest descent? w1 w2Overfitting: The downside of using powerful models • The training data contains information about the regularities in the mapping from input to output. But it also contains two types of noise. – The target values may be unreliable (usually only a minor worry). – There is sampling error. There will be accidental regularities just because of the particular training cases that were chosen. • When we fit the model, it cannot tell which regularities are real and which are caused by sampling error. – So it fits both kinds of regularity. – If the model is very flexible it can model the sampling error really well. This is a disaster .A simple example of overfitting input = x Which output value should you predict for this test input? • Which model do you trust? – The complicated model fits the data better. – But it is not economical. • A model is convincing when it fits a lot of data surprisingly well. – It is not surprising that a complicated model can fit a small amount of data well.Ways to reduce overfitting • A large number of different methods have been developed. – Weight-decay – Weight-sharing – Early stopping – Model averaging – Bayesian fitting of neural nets – Dropout – Generative pre-training • Many of these methods will be described in lecture 7.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/.60.5Ba.2Ca.2B1.2Ca.2B2.2C.2E.2E.2E.2Cb-1.5D.60.3F.html ---
Is there a name in Lean for the list (or multiset, or finset)[a,a+1,a+2,...,b-1]
?
python range(a,b)
def range : nat → nat → list nat | a 0 := [] | 0 1 := [0] | a 1 := [] | a (b+2) := if b+1 < a then [] else range a (b+1) ++ [nat.succ b]
You will notice that most of the properties of list.range
are proven by using an auxiliary definition list.range'
, which is very similar to the list you have described. list.range' s n
is the list [s, s+1, ..., s+n-1]
, so your range
function is list.range' a (b-a)
. The definition of range' using start and length instead of start and end is of course because it has better properties wrt induction - it's not possible to have a range that goes to a value less than it starts with, and it's best to encode that directly in the definition
def range : nat → nat → list nat | a 0 := [] | a (b+1) := if b < a then [] else range a b ++ [b]
Last updated: Apr 24 2020 at 19:02 UTC


There are countless ingredients that make up the human body and mind, like all the components that make up me as an individual with my own personality. Sure, I have a face and voice to distinguish myself from others, but my thoughts and memories are unique only to me, and I carry a sense of my own destiny. Each of those things are just a small part of it. I collect information to use in my own way. All of that blends to create a mixture that forms me and gives rise to my conscience.
- Ghost In The Shell -


| 004:161 Khan | : |
And their taking of Riba (usury) though they were forbidden from taking it and their devouring of men's substance wrongfully (bribery, etc.). And We have prepared for the disbelievers among them a painful torment. |
| 004:161 Maulana | : |
And for their taking usury -- though indeed they were forbidden it -- and their devouring the property of people falsely. And We have prepared for the disbelievers from among them a painful chastisement. |
| 004:161 Pickthal | : |
And of their taking usury when they were forbidden it, and of their devouring people's wealth by false pretences, We have prepared for those of them who disbelieve a painful doom. |
| 004:161 Rashad | : |
And for practicing usury, which was forbidden, and for consuming the people's money illicitly. We have prepared for the disbelievers among them painful retribution. |
| 004:161 Sarwar | : |
their taking usury which was prohibited for them, and their consuming people's property unjustly. For the unbelievers among them, We have prepared a painful torment. |
| 004:161 Shakir | : |
And their taking usury though indeed they were forbidden it and their devouring the property of people falsely, and We have prepared for the unbelievers from among them a painful chastisement. |
| 004:161 Sherali | : |
And because of their taking interest although they had been forbidden it, and because of their devouring peoples wealth wrongfully. And WE have prepared for those of them, who disbelieve, a painful punishment. |
| 004:161 Yusufali | : |
That they took usury, though they were forbidden; and that they devoured men's substance wrongfully;- we have prepared for those among them who reject faith a grievous punishment. |
Concepts/Themes Covered: الإيمان > الغيب > النار > أصحاب النار Faith> The Unseen World> Hell-Fire>Hell Fellows العمل > العمل الطالح > في المال > أكل الأموال بالباطل Action(Work)> Bad Deeds>In Earning Money> Eating up One another's Property Unjustly العمل > العمل الطالح > في المال > الربا Action(Work)> Bad Deeds>In Earning Money>Usury الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > الربا Man and The Moral Relations>The Dispraised Morals>Usury تنظيم العلاقات المالية > أكل الأموال بالباطل Organizing Financial Relationships>Eating up One Another's Property Unjustly تنظيم العلاقات المالية > أموال الناس Organizing Financial Relationships>People's Property
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Welcome to the Disting mk4 Quick Guide. This guide is a cheat sheet for the 97 algorithms included with the Expert Sleepers Disting mk4 (as of firmware update 4.21). Since the bulk of the text of the manual is not repeated here, the Quick Guide is not designed to be your primary source for getting to know your Disting. Once you have a basic understanding of how the algorithms work, however, I hope that you’ll find this page a handy map to the ins, outs and parameter settings of each algorithm.
In addition to distilling the info to the basics, this guide is designed to be mobile-friendly, so you can view it without being tied to your computer. You can save the guide to the homescreen of your mobile device for easy mobile access.
If you find any errors or have any suggestions, please don’t hesitate to send them to<EMAIL_ADDRESS>The code is open sourced under the MIT License. If you’d like to clone it for your own purposes, you can find the source at Github.
Happy Bleeping!
Damon Holzborn
rustleworks.com
damonholzborn.com
@damonholzborn (Twitter)
@damonholzborn (Instagram)


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|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.hp.hpl.jena.rdf.model.ResourceFactory
public class ResourceFactory
A Factory class for creating resources.
This class creates resources and properties and things of that ilk. These resources are not associated with a user-modifiable model: doing getModel() on them will return null.
It is designed as a singleton. There are static convenience methods on this class itself, so the easy way to create resource is for example to do something like:
Resource r = ResourceFactory.createResource();
If a factory object is needed, then this can be obtained using
the getInstance
method on the class. The factory
object used may be changed using the setInstance
method.
| Nested Class Summary | |
|---|---|
static interface |
ResourceFactory.Interface
the interface to resource factory objects. |
| Method Summary | |
|---|---|
static Literal |
createPlainLiteral(java.lang.String string)
|
static Property |
createProperty(java.lang.String uriref)
create a new property. |
static Property |
createProperty(java.lang.String namespace,
java.lang.String localName)
create a new property. |
static Resource |
createResource()
create a new anonymous resource. |
static Resource |
createResource(java.lang.String uriref)
create a new resource. |
static Statement |
createStatement(Resource subject,
Property predicate,
RDFNode object)
create a new statement. |
static Literal |
createTypedLiteral(java.lang.Object value)
Answer a typed literal. |
static Literal |
createTypedLiteral(java.lang.String string,
RDFDatatype dType)
|
static ResourceFactory.Interface |
getInstance()
get the current factory object. |
static ResourceFactory.Interface |
setInstance(ResourceFactory.Interface newInstance)
set the current factory object. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Method Detail |
|---|
public static ResourceFactory.Interface getInstance()
public static ResourceFactory.Interface setInstance(ResourceFactory.Interface newInstance)
newInstance
- the new factory object
public static Resource createResource()
Uses the current factory object to create a new anonymous resource.
public static Resource createResource(java.lang.String uriref)
Uses the current factory object to create a new resource.
uriref
- URIREF of the resource
public static Literal createPlainLiteral(java.lang.String string)
public static Literal createTypedLiteral(java.lang.String string, RDFDatatype dType)
public static Literal createTypedLiteral(java.lang.Object value)
value
- a java Object, the default RDFDatatype for that object will be used
public static Property createProperty(java.lang.String uriref)
Uses the current factory object to create a new resource.
uriref
- URIREF of the property
public static Property createProperty(java.lang.String namespace, java.lang.String localName)
Uses the current factory object to create a new property.
namespace
- URIREF of the namespace of the propertylocalName
- localname of the property
public static Statement createStatement(Resource subject, Property predicate, RDFNode object)
subject
- the subject of the new statementpredicate
- the predicate of the new statementobject
- the objectof the new statement
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


It’s very important to keep PhotoSwipe updated, especially during the beta period.
Each time PhotoSwipe gets an update - GitHub releases page is updated with details about release. Releases page has an Atom feed, you may setup email notifications when feed is updated using IFTTT:
Altervatively, you can follow @PhotoSwipe on Twitter, or join my tiny email newsletter:


The key management procedures should require secure storage.
Check the information security policy to identify the key management procedures. Make sure that they include a provision for secure storage. Inspect the key management procedures to verify that they provide secure storage.
Design and document key management procedures to require secure storage. Implement the key management procedures.


Genesis 36:18
And these are the sons of Aholibamah Esau's wife; duke Jeush, duke Jaalam, duke Korah: these were the dukes that came of Aholibamah the daughter of Anah, Esau's wife.


pyfor.gisexport module¶
-
pyfor.gisexport.
array_to_raster
(array, affine, crs, path)¶ Writes a GeoTIFF raster from a numpy array.
- Parameters
array – 2D numpy array of cell values
affine – The affine transformation.
crs – A rasterio-compatible coordinate reference (e.g. a proj4 string)
path – The output bath of the GeoTIFF
-
pyfor.gisexport.
project_indices
(indices, raster)¶ Converts indices of an array (for example, those indices that describe the location of a local maxima) to the same space as the input cloud object.
- Parameters
indices – The indices to project, an Nx2 matrix of indices where the first column are the rows (Y) and
the second column is the columns (X) :param raster: An object of type pyfor.rasterizer.Raster :return:


Privacy Policy
All data collected during the hackathon is for test purposes only and discarded. The architecture is such that Penguin servers don't retain content -- they simply orchestrate between users end clients and APIs for cognitive processing. Our site is not intended to collect any personal information from Under 13s rather just provide a closed community for safe communications.


Note: 1. I have explored various psychoactives in the past. 2. I am a very mellow individual with little stress. 3. I have taken no medication aside from Tylenol in the 2 weeks prior to this.
Substance: Salvia Divinorum 10x
Amount: 12 large inhalations (about 4 pipe bowls of medium size)
Method of ingestion: Smoked *My partner will hold the pipe, light the pipe, and refill the pipe as quickly and efficiently as she can so I will be able to consume a large about in a brief time period.
Mindset: I am in an unusually good mood before I proceeded to try this substance. The whole day was an all around good positive atmosphere. I am relaxed, comfortable, and greatly anticipating this experience.
 Also, there is a slight breeze, birds chirping, and the trees swaying. At this time, I am in the presence of my dog, my girlfriend, and an array of wild animals.
The Experience: As I sit comfortably on the ground, I prepare my mind through meditation for what is about to happen. My partner then raised the long-stem pipe to my mouth and begins to light it, all the while given me calm instruction. I took large hits from the pipe holding each lung-full for almost exactly 20 seconds. Upon exhaling, I immediately took another hit. Repeating this process through the help of my partner for 12 hits took around 4 minutes. After the 6 or 7th hit from the pipe the only thing I recall is my 'trip' and not smoking anymore, although my partner confirms I did indeed finish 12 hits.
I remember being overwhelmed by an extreme fit of laughter that put me in a state of delerium. I fell back from my sitting position and looked up at the trees. Suddenly I quit laughing and became very annoyed by my usually peaceful surroundings.  The sound of the bell on my dog's collar was beyond irritating. And suddenly, the comfortable ground I was rested upon became uncomfortable. I could feel every little detail of the dirt and debris below me.
I suddenly sat up and proceeded to laugh again, except this time more aware that I was under the influence of salvia.
After effects: I noticed the after effects tend to linger for about an hour. I was quite relaxed yet lethargic. I also later noticed feelings of sexual attraction were more intense. I felt rather turned on and more comfortable with my sexuality as with MDMA minus the empathy. Depression set in about 20 minutes after the initial trip and lasted another 20 minutes. Coming out of it was a quick - as if my mind was suddenly cured. I instantly was overcome with a sense of 'everything is fine. Nothing is wrong.' and the rest of my day was a loving, positive, atmosphere.
Physical effects: Sweating, rise in heartbeat, Skin irritation, difficulty focusing eyes, difficulty concentrating.
Mental/Emotional effects: Gravity seems somewhat distorted, as if on a mild roller coaster ride, taken over randomly with fits of laughter, slight confusion, and a sensitivity to your surroundings.
Note: I have at least 20 experiences with salvia; I just felt this symbolizes the normal trip. Several of mine have been spiritual where trees talked to me through telepathic energy and told me they were to heal all my problems. While this was a great thing, it only happened a few times, under some strict conditions of meditation and total silence.
Overall/Conclusion: Looking over my log notes, I can see the ability for salvia to be recreational, but I still believe from my personal experiences that salvia should remain for healing, insight, and enlightenment through the exploration of yourself, and your world. While I had fun, this is not something I would probably do to cure a boring night at home. Be careful of how you prepare your surroundings. Any slight noise or distraction that seems minor to you now, will multiply later in your journey.
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.


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Because of the inherent concurrency occurring within SDF dataflow graph models, there are two notions of time in DT -- global time and local time. Global time increases steadily as execution progresses. Moreover, global time increments by fixed discrete chunks of time based on the value of the period parameter. On the other hand, local time applies to each of the actors in the model. All the actors have distinct local times as an iteration proceeds. The local time of an actor during an iteration depends on the global time, period, firing count, port rates, and the schedule. These local times obey the following constraint:
The DT domain is an experimental domain, the code has not been reviewed, and the interfaces are likely to change.


[gecode-users] Memory problem
Guido Tack
tack at ps.uni-sb.de
Thu Aug 28 09:20:23 CEST 2008
I tried to find out more about the Mac OS malloc.
Apparently, Mac OS never frees blocks that are smaller than 15359
bytes, but rather tries to reuse them for further mallocs in the same
process.
http://kevin.sb.org/2007/07/05/malloc-free-space-not-reclaimed/
I tried replacing malloc with ptmalloc3 (as suggested in that
article), and it completely solves the problem.
Cheers,
Guido
More information about the gecode-users
mailing list


---
layout: post
status: publish
published: true
title: Silent Night
author: Leask
author_login: leask
author_email<EMAIL_ADDRESS>author_url: https://leaskh.com
wordpress_id: 320
wordpress_url: http://leaskh.wordpress.com/2007/12/24/silent-night
date: '2007-12-24 14:34:51 +0800'
date_gmt: '2007-12-24 14:34:51 +0800'
categories:
tags: []
comments: []
---
Silent Night
by Sarah McLachlan
Silent night, holy night
All is calm, all is bright
Round yon Virgin Mother and Child
Holy Infant so tender and mild
Sleep in heavenly peace
Sleep in heavenly peace
Silent night, holy night!
Shepherds quake at the sight
Glories stream from heaven afar
Heavenly hosts sing Alleluia!
Christ, the Saviour is born
Christ, the Saviour is born
Silent night, holy night
Son of God, love's pure light
Radiant beams from Thy holy face
With the dawn of redeeming grace
Jesus, Lord, at Thy birth
Jesus, Lord, at Thy birth


Man says he stabbed McDonald's employee for overpriced food
AKRON, Ohio. (WOIO/WVLT) -- Ohio investigators said a man reportedly stabbed a fast-food employee because he thought he was being overcharged for his meal.
According to WOIO, 63-year-old Darrell Roberts was charged with felonious assault and carrying a concealed weapon after an incident that reportedly happened on November 23.
Investigators were called out to a McDonald's on East Market Street in Akron for reports of an assault. Upon arrival, investigators spoke to an employee who said that Roberts argued with her over the cost of his food and refused to leave when asked.
Police said Roberts pulled a knife out and walked around the counter to confront the employee before swinging the knife and cutting her on the arm.
Roberts was eventually removed from the location by employees, and police said they found him a short time later down the road.
Police said Roberts admitted to getting angry about the alleged overcharges.
Roberts was booked into jail.