---
layout: post
title: Syllabus for The Trade of Fitter Under CTS
subtitle: Duration - 2 Years
---
- Trade introduction, safety precautions and first aid
- Measurement and measuring tools
- Marking and marking tools
- Cutting tools
- Vice
- Striking tools
- File and its use
- Scrappers
- Drilling machine and drilling
- Tap and tapping
- Reamer
- Die
- Precision measuring instrument(micro meter)
- Precision measuring instruments (vernier calliper)
- Screw thread
- Gauge and jig fixture
- Surface finish
- Unit fits or tolerance
- Metallurgy
- Heat treatment and forging
- Fasteners
- Lathe machine
- Welding
- Pipe fitting
- Transmission of power
- Lubricant and coolant
- Soldering and brazing
- Arithmetic and its use
- Conversion
- Average and units
- Percentage
- Area
- Volume
- Logarithm
- Trigonometry
- Algebra
- Properties of matter
- Ferrous and non ferrous metal with insulated material of electricity
- Work energy and power
- Rest and motion
- Moment
- Composition and resolution of velocity and forces
- Density and specific gravity
- Lever and machines
- Friction and lubrications
- Transmission of mechanical power
- Cutting speed and feed
- Taper turning
- Measurement with a steel rule
- Surface filling
- Use of hermaphrodite clipper
- Use of centred dot
- Marking out
- Marking out for diagonal L
- Marking in inches
- Marking and punching hexagon
- Marking and punching pentagon
- Marking and punching inclined lines
- Fitting 900 angles and sawing angles
- Inclined hack sawing
- Hack sawing for L pieces
- Filling and grooving .
- Gap fitting
- V fitting
- Drilling hole
- Radius and square fitting
- H fitting
- T fitting
- Sawing, filling, champhering
- Marking out, sawing, filling and bending
- Chipping and edge
- Fitting 600 and 900 angle
- Double radius fitting
- Radius fitting


About
About
Janice Mason Steeves is represented by galleries in Canada and the U.S., with work in public, corporate, and private collections in Canada and internationally. As well as an active one-on-one Art Mentoring program, Janice teaches painting workshops in Canada, the U.S., and Europe. She has also been awarded artist residencies in Spain, Ireland, Sweden, and Iceland.
01
Work
Work
Mason Steeves’ abstract work centers on calm, compositionally minimal subject matter, with a priority on light and energy. Her current work includes cold wax and oil colour-field paintings on panel and small gestural works on paper.
02
Teaching
Teaching
“My main focus in teaching is on creating an atmosphere of mystery, imagination and discovery.”
Janice brings 35 years of painting experience to her teaching. She offers abstract painting workshops in cold wax and water-media internationally, in addition to one-on-one coaching through her Art Mentoring program. Composition, design, mindset, and self-expression are all important in her teaching.
Janice's current focus is leading workshops in wild places, where she facilitates ways to experience the land and translate that into the creation of abstract paintings. Find out more at workshopsinwildplaces.com.
03
News
News
Sign up for the JMS newsletter to get workshop dates, special offers, news, and more.
Hear Rebecca Crowell interview Janice on The Messy Studio.
Check out the latest JMS blog post.


Lectures on Active Targets
ECT summer school on Machine Learning and Data Analysis for Nuclear Physics
Day 8: July 1st, 2020
General introduction to active targets
Link: https://mediaspace.msu.edu/media/t/1_oknxp3a2
This video covers the issues associated with using reactions in inverse kinematics in low energy nuclear physics.
Link: https://mediaspace.msu.edu/media/t/1_wrivccrk
This lecture presents general concepts and technologies used in time projection chamber detectors, with an emphasis on using them as active targets.
Link: https://mediaspace.msu.edu/media/t/1_22gwszv0
This video describes in more details the Active Target Time Projection Chamber (AT-TPC).
Link: https://mediaspace.msu.edu/media/t/1_vvuz6srp
This video describes the data analysis process and prospects for the type of data taken by the AT-TPC.
Link: https://mediaspace.msu.edu/media/t/1_1uit6nvx
This lecture showcases some of the physics results obtained with active targets of different designs and purposes.
Link (part 1): https://mediaspace.msu.edu/media/t/1_f7hau3j9
Link (part 2): https://mediaspace.msu.edu/media/t/1_z9kd1y51


The asset_api class allows query of info about asset holders.
More...
#include <api.hpp>
The asset_api class allows query of info about asset holders.
Definition at line 548 of file api.hpp.
◆ asset_api()
◆ get_all_asset_holders()
Get all asset holders.
- Returns
- A list of all asset holders
Definition at line 944 of file api.cpp.
◆ get_asset_holders()
| vector< asset_api::account_asset_balance > graphene::app::asset_api::get_asset_holders |
( |
const std::string & |
asset_symbol_or_id, |
|
|
uint32_t |
start, |
|
|
uint32_t |
limit |
|
) |
| const |
Get asset holders for a specific asset.
- Parameters
-
| asset_symbol_or_id | The specific asset symbol or ID |
| start | The start index |
| limit | Maximum number of accounts to retrieve, must not exceed the configured value of api_limit_get_asset_holders |
- Returns
- A list of asset holders for the specified asset
Definition at line 893 of file api.cpp.
◆ get_asset_holders_count()
| int64_t graphene::app::asset_api::get_asset_holders_count |
( |
const std::string & |
asset_symbol_or_id | ) |
const |
Get asset holders count for a specific asset.
- Parameters
-
| asset_symbol_or_id | The specific asset symbol or id |
- Returns
- Holders count for the specified asset
Definition at line 933 of file api.cpp.
The documentation for this class was generated from the following files:


{% extends 'start/base.html'%} {% load bootstrap4%} {% block content %}
Get to see work done buy various programmers and give them a review.
You also get to rate them if they are to your liking
{{project.details}}
Creator:{{project.createdby}} on {{project.date_created}}


Managed cloud services
Management of server glitches and downtime is over. Keitaro provides solutions that help small and large organizations to complete all tasks with one click. The premium managed cloud services enable development of a secure cloud strategy, improve and extend the capabilities and implement and utilize clients plan.
Our cloud professionals design a strategy and assess the most secure environment specific to detailed business needs. The managed cloud services enable implementation of opportunities for the business as improved agility and cost reduction.
The premium managed cloud services boost the operations and provide low level of complexity of managing IT infrastructure. The enriched quality of the services improves the core business operations and provides support for innovation and growth.
Managed cloud services improve processes and operations by reducing the time spent on management of the data, continuous monitoring of data sources and its standardization.
With our extensive knowledge and expertise of Docker orchestration systems such as Kubernetes, Mesos DC/OS and others, the team successfully manages complex IT environments by building cloud solutions for unique business models.


MyMap
This App was created using the Google Map Places API. The user can see his current location on the map and the locations of the nearby buildings (hospitals, banks, supermarkets, restaurants) and their information. Another function is, if the user types a city name in the search field and then presses the search button, he can see the city's location on the map. In the video, this app is displayed on the emulator, so the user initial location (the Google's headquarters) is showed on the map.


Paper in this research
We are publish many research. This below is link for download paper.
- A Multi-Aspect Comparison and Evaluation on Thai Word Segmentation Programs
Chaluemwut Noyunsan, Choochart Haruechaiyasak, Seksan Poltree, Kanda Runapongsa Saikaew
ประกาศรายชื่อผู้โขคดี สำหรับการประเมินระบบ FB credibility
Bank Poommipat Tenoppakaw
และขอขอบคุณทุกท่านที่ร่วมประเมินครับ
Researcher
-
Kanda Runapongsa Saikaew
was born in Chiang Rai, Thailand, in 1975. He received the B.S. degree in electrical and computer engineering from Carnegie Mellon University, Pennsylvania, USA, in 1997, and the M.S. and Ph.D. degrees in computer science and engineering from the University of Michigan at Ann Arbor, in 1999 and 2003, respectively. In 2003, she joined the Department of Computer Engineering, Khon Kaen University, as a Lecturer, and became an Assistant Professor in 2006. Her current research interests include social network analysis.
(Home page) -
Chaluemwut Noyunsan
was born in Khon Kaen, Thailand, in 1981. He received the B.S. degree in computer engineering from Khon kaen University, Thailand, in 2003, and the M.S. degrees in computer science from Chulalongkorn University, Thailand, in 2009, respectively. Currently, he is studying PhD degree in the Department of Computer Engineering, Khon Kaen University. He current research interests include social network analysis and information credibility measurement
(Home page)


Click a location to update the widget.
Search for a location to update the widget.
Use the slider to update the widget.
This widget showcases a styled google map.


We had our second general meeting last week for UPE where we went through the Bias Busters workshop that came out of Google, where we discussed unconscious bias, stereotype bias, as well as many other topics and how to deal with these scenarios when they arrive. I’d like to talk about a topic that really resonated with me: imposter syndrome. This is not to say that unconscious bias and stereotype bias are less important or any less “real”, but to me, imposter syndrome is something I think everyone feels at one time or another and I have more experience in this area than perhaps the other areas.
Imposter syndrome is this thought that comes into your head that tells you that you aren’t good enough. That you don’t belong where you are today and that one day, people will realize you’re just “faking” everything, even though you’re not.
And how can you not feel this way sometimes when you go to college? Especially to you Berkeley peeps? There are so many genius students, everyone you talk to seems to be taking an unbelievable course load and doing five different clubs/extracurriculars and have hundreds of internship offers from every company you can name, and you just feel out of place.
I remember during my orientation before freshman year, I went to this workshop on “exceeding in STEM”. And the professor talked about how difficult classes are, even the easy ones, and how you’ll really struggle through them. And then he asked the workshop, “Who has taken AP Calc?”, and literally everyone’s hand except mine shot up in the air. “Who’s taken Calc BC?”, and everyone’s hand remained in the air and people were now kind of glancing at me. “What about multivariable? Differential equations? Linear algebra? Real analysis?”, and on and on and then people’s hands went down a couple at a time. And I distinctly remember feeling like I was definitely going to drop out of Berkeley after my first semester. And maybe I should have not played pingpong as much and focused on high school more. Looking back, I did not drop out after first semester, and I definitely do not wish I played less pingpong.
I remember my first CS61A discussion. I remember filling out a form that asked you about your experience in computer science and I checked off nothing. Glancing over to other student’s laptops, I saw many other students filling out that they knew multiple languages and frameworks and all of this other stuff that I still have no clue about today. And I felt like I shouldn’t be here.
I remember last semester taking my first exam in Convex Optimization and getting below two standard deviations (i.e. 16% when the average was 60%) on my first quiz. I remember going to every office hours trying to figure out what was happening and getting looks from other students who were machine learning experts when I asked questions on simpler topics that I did not grasp as easily. Sometimes, I felt like the GSIs didn’t even understand my questions because they just operated on a higher level. I thought many times about dropping the class and just doing something else. Looking back, I am very grateful I took the course and I ended up doing pretty well and learning a lot.
These are just a few examples. And I’m sure everyone has their own stories of feeling inadequate. But how do we deal with imposter syndrome? Where does it come from?
I think it comes from the fact that every student at Berkeley is already at a high level, but then you feel like you aren’t because everyone else is. I like to say that one of my favorite things about Berkeley is that I’m always surrounded by amazing people. There’s so much to learn from others and being around amazing people is how you become more amazing. And my least favorite part about Berkeley is being surrounded by amazing people. It’s hard to stand out or to feel like you matter when it feels like everyone can do you what you do or more.
But if we shift our thinking from the second way to the first way more often, we might have a different perspective. We’re here to learn. And “if you’re the smartest person in the room, you’re in the wrong room”. Surround yourself with people smarter, better, more experienced than you. And if you can think about it in terms of learning potential rather than a discouraging factor, then you will get so much out of it.
Another important thing is to realize that your achievements are not a mistake or by luck.  UC Berkeley is a prestigious school, and you don’t keep a high level of intelligent students by randomly picking students or making mistakes. They wanted you here. And for any other achievement, sure, maybe some luck factored into it, but I’m sure your hard work and dedication and talent also did a lot, and fortune favors the brave.
And I think the last piece of advice that you should spend some time with yourself and learning what makes you special. This is something that I realized through recruiting. That although most companies never ask you “Why are you the best person for the job? What makes you different than everyone else?”, you should know. You should know who you are and why you are unique. Because if you don’t know or don’t believe it, no one else will. And knowing that what makes you special and unique will give you character that gives you a strong comeback to whenever these thoughts of inadequacy come into your mind.
Everyone feels imposter syndrome sometime. It’s a never ending battle. But being in this battle means you have a lot to potential to learn, a lot of potential to grow as a person, and a lot of potential to prove these thoughts wrong.


Coverage Summary for Class: LedgerEntry (org.apache.bookkeeper.client)
| Class | Class, % | Method, % | Line, % |
|---|---|---|---|
| LedgerEntry | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 21) |
1 /*
2 *
3 * Licensed to the Apache Software Foundation (ASF) under one
4 * or more contributor license agreements. See the NOTICE file
5 * distributed with this work for additional information
6 * regarding copyright ownership. The ASF licenses this file
7 * to you under the Apache License, Version 2.0 (the
8 * "License"); you may not use this file except in compliance
9 * with the License. You may obtain a copy of the License at
10 *
11 * http://www.apache.org/licenses/LICENSE-2.0
12 *
13 * Unless required by applicable law or agreed to in writing,
14 * software distributed under the License is distributed on an
15 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
16 * KIND, either express or implied. See the License for the
17 * specific language governing permissions and limitations
18 * under the License.
19 *
20 */
21 package org.apache.bookkeeper.client;
22
23 import static com.google.common.base.Preconditions.checkState;
24
25 import io.netty.buffer.ByteBuf;
26 import io.netty.buffer.ByteBufInputStream;
27
28 import java.io.InputStream;
29
30 import org.apache.bookkeeper.client.impl.LedgerEntryImpl;
31 import org.apache.bookkeeper.conf.ClientConfiguration;
32
33 /**
34 * Ledger entry. Its a simple tuple containing the ledger id, the entry-id, and
35 * the entry content.
36 *
37 */
38 public class LedgerEntry {
39
40 final long ledgerId;
41 final long entryId;
42 final long length;
43 ByteBuf data;
44
45 LedgerEntry(LedgerEntryImpl entry) {
46 this.ledgerId = entry.getLedgerId();
47 this.entryId = entry.getEntryId();
48 this.length = entry.getLength();
49 this.data = entry.getEntryBuffer().retain();
50 }
51
52 public long getLedgerId() {
53 return ledgerId;
54 }
55
56 public long getEntryId() {
57 return entryId;
58 }
59
60 public long getLength() {
61 return length;
62 }
63
64 /**
65 * Returns the content of the entry.
66 * This method can be called only once. While using v2 wire protocol this method will automatically release
67 * the internal ByteBuf
68 *
69 * @return the content of the entry
70 * @throws IllegalStateException if this method is called twice
71 */
72 public byte[] getEntry() {
73 checkState(null != data, "entry content can be accessed only once");
74 byte[] entry = new byte[data.readableBytes()];
75 data.readBytes(entry);
76 data.release();
77 data = null;
78 return entry;
79 }
80
81 /**
82 * Returns the content of the entry.
83 * This method can be called only once. While using v2 wire protocol this method will automatically release
84 * the internal ByteBuf when calling the close
85 * method of the returned InputStream
86 *
87 * @return an InputStream which gives access to the content of the entry
88 * @throws IllegalStateException if this method is called twice
89 */
90 public InputStream getEntryInputStream() {
91 checkState(null != data, "entry content can be accessed only once");
92 ByteBufInputStream res = new ByteBufInputStream(data);
93 data = null;
94 return res;
95 }
96
97 /**
98 * Return the internal buffer that contains the entry payload.
99 *
100 * <p>Note: Using v2 wire protocol it is responsibility of the caller to ensure to release the buffer after usage.
101 *
102 * @return a ByteBuf which contains the data
103 *
104 * @see ClientConfiguration#setNettyUsePooledBuffers(boolean)
105 * @throws IllegalStateException if the entry has been retrieved by {@link #getEntry()}
106 * or {@link #getEntryInputStream()}.
107 */
108 public ByteBuf getEntryBuffer() {
109 checkState(null != data, "entry content has been retrieved by #getEntry or #getEntryInputStream");
110 return data;
111 }
112 }


Personal Financial Information: All deposited files must be separated from any information with nonpublic personal, financial, or confidential content, including names, addresses, and phone numbers; bank and credit card account numbers; income and credit histories; and Social Security number. For more information, see the Federal Trade Commission’s Safeguard Rule and the Financial Services Modernization Act of 1999, also known as the Gramm-Leach-Bliley Act (GLBA).
Personal Medical Information: Deposited files must comply with all applicable Federal and State regulations concerning the privacy and security of personal medical information, including but not limited to the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH).
Personal and Sensitive Socioeconomic information: Deposited files must adhere to Federal OMB Circular A-110 guidelines on not recording personal and sensitive socioeconomic information, when such disclosure would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.
Sensitive Biological and Natural Heritage information: Deposited files must adhere to federal and state wildlife laws and policies concerning release of sensitive information such as the precise location species have been observed. For example, locations of endangered species or other data protected by the Endangered Species Act will not be shared and similar restrictions may apply to data maintained by states in natural heritage databases.


--- layout: default title: LATEST BY ETHAN THAMES permalink: /columnists/ethanthames author: ethan_thames --- {% assign author = site.data.authors[page.author] %}
{{ author.bio }}
{{ post.content | truncatewords: 50 | strip_html }}


Tristan Rome Delos Santos
STUDENT
Bachelor of Science in Computer Science
Cavite State University - Silang Campus
Birthday:
September 23, 1998
Caloocan City
Skills:
Adobe Photoshop , Lightroom , Adobe After Effects
Programming Languages:
C++ , C# , JAVA
Other Skills:
Microsoft Office
Computer Repairing
Encoding Data
Sampaloc 4 Cityhomes Dasmarinas City, Cavite
0906-502-0967


Files
B
C
D
E
F
G
- Game.php
- GameContainer.php
- GameExporter.php
- GameSorter.php
- GamesExporter.php
- Generator.php
- Group.php
H
I
- ImportValidator.php
- Importer.php
- InvalidImportDataException.php
- index.php
- index.php
- index.php
- index.php
- index.php
- index.php
- index.php
- index.php
M
P
R
S
T
- Team.php
- TeamContainer.php
- TeamExporter.php
- TeamFilter.php
- TeamSorter.php
- TeamsExporter.php
- Tournament.php
W
- WithCategories.php
- WithCategories.php
- WithGames.php
- WithGames.php
- WithGeneratorSetters.php
- WithGroups.php
- WithGroups.php
- WithId.php
- WithRounds.php
- WithRounds.php
- WithScoresModifier.php
- WithSkipSetters.php
- WithSkipSetters.php
- WithTeams.php
- WithTeams.php


---
layout: post
title: For Great Justice!
date: '2007-02-28T10:32:00.000-06:00'
author: Don Seiler
tags:
- oracle
- ayb
- zero wing
modified_time: '2010-11-19T22:48:47.466-06:00'
blogger_id: tag:blogger.com,1999:blog-7032512792942232766.post-6193508897694548552
blogger_orig_url: http://www.seiler.us/2007/02/for-great-justice.html
---
This brought a wry smile to my face. Although Ujang is royally screwed, Mladen still had the presence of mind to set him up the bomb.
I hope you know what I'm talking about.


MetaSkills.net
Coding things under other things!
Home
About Me
Projects
Resume
Twitter
Archives
{{ content }}
Society for coding things on top of other things!
Categories
Heuristics
Workflow
Apple/OS
Database
Lifestyle
Miscellaneous
Ruby/Rails
JavaScript
Tools


|
Push one of seven commonly used constants (in double extended-precision floating-point format) onto the FPU register stack. The constants that can be loaded with these instructions include +1.0, +0.0, log_10(2), log_e(2), pi, log_2(10), and log_2(e). For each constant, an internal 66-bit constant is rounded (as specified by the RC field in the FPU control word) to double extendedprecision floating-point format. The inexact-result exception (#P) is not generated as a result of the rounding, nor is the C1 flag set in the x87 FPU status word if the value is rounded up.
FLD1/FLDL2T/FLDL2E/FLDPI/FLDLG2/FLDLN2/FLDZ: Load Constant (x86 Instruction Set Reference)
|


Based on your location
Track another location
This Week — Last Week
Change Date RangeFilter by Location
Sentimeter


{% load i18n %} {% load inlinecss %} {% inlinecss "account/Css/Email-Styles.css" %}
Please Reset your password using the below link. If you did not request for password do not click and report to the support team immidately.
Best, Team YourWebsiteName
{% endinlinecss %}


Goodness-of-fit test with multiplier or parametric bootstrap.
| Version: | 0.2-1 |
| Depends: | mvtnorm, numDeriv |
| Published: | 2012-05-25 |
| Author: | Ivan Kojadinovic and Jun Yan |
| Maintainer: | Jun Yan <jun.yan at uconn.edu> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | fgof results |
| Reference manual: | fgof.pdf |
| Package source: | fgof_0.2-1.tar.gz |
| Windows binaries: | r-devel: fgof_0.2-1.zip, r-release: fgof_0.2-1.zip, r-oldrel: fgof_0.2-1.zip |
| OS X binaries: | r-release: fgof_0.2-1.tgz, r-oldrel: fgof_0.2-1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=fgof to link to this page.


PopWi is building a global radio network for low-power and low-cost internet connectivity. The Internet-of-Things has an exciting future with an expected 1 trillion connected devices by 2025. PopWi's new low bandwidth radio service is designed to service Machine-to-Machine (M2M), embedded devices, and sensor networks which otherwise could not connect to the internet due to cost or battery life.
Talking about technology comparison here.
table goes here


Sphinx 3.3 vs. Sphinx 4 Algorithm
Differences
There are some differences in performance between Sphinx 3.3 and
Sphinx 4. Currently for large vocabulary tasks such as HUB4,
Sphinx 3.3 runs almost twice as fast as Sphinx 4 for the same
accuracy. This paper describes some of the major algorithmic
differences between Sphinx 3.3 and Sphinx 4. The goal is to help us
understand the performance gap and to give us some ideas of how to
improve the performance of Sphinx 4.
Major Differences
- Token Passing vs. Multiple Lex
Trees - This is a fundamental difference between Sphinx
3.3 and Sphinx 4. In large vocabulary recognition there are
potentially many active search paths. Managing the active paths
efficiently is central to the decoding process. Sphinx 3.3 and
Sphinx 4 use different techniques in dealing with this issue. In
Sphinx 3.3 the search space is managed as a set of lex trees with
active paths managed directly in the lex tree. Only the best path
through a particular node is retained (this is known as Viterbi
pruning). Since there can be many simultaneous active paths through a
particular node of the lex tree (due to different word histories)
these paths would incorrectly compete against each other. To
avoid this, a set of lex trees are used (typically 3 in Sphinx3.3 plus
one for filler words). As paths reach the end of a lex tree they
a propagated into the next lex tree. This reduces, but does not
eliminate, the chance of unfair Viterbi pruning. For a good
discussion of multiple lex trees see Spoken
Language Processing (Huang et al.) page 648.
Sphinx-4 takes a very different approach to manging the active search
paths. Sphinx-4 maintains a single lex tree. However, no active search
information is retained in this tree. Instead a token tree is used to
manage the active paths during the search. Paths only complete
against each other if their lex tree state, HMM state and word
histories are identical. This approach allows Sphinx-4 to
perform true n-gram decoding and is theoretically correct and results
in higher accuracy. This generality has a price however.
For instance, it is straightforward in S3.3 to determine if two
paths have reached the same state in the lex tree since the two paths
are maintained in the same lex tree structure. In S4, however, there
isn't this explicit structure, instead the context of the paths
(lextree state, HMM state and word history) have to be be explicitly
compared to determine if the two paths collide. Similarly, to
propagate a path into a new state, S3.3 merely has to set the active flag in the new lex tree
node to true and then add the node to the active list, while S4
has to generate a new search graph state (containing the lex tree
state, HMM state and word history) as well as generating a new token to
add to the path and and adding the token to the active list.
- Composites - Sphinx
3.3 uses composite senones to represent the word ending nodes.
Composite senones are a method of dealing with the large fanout in
states that occur at word boundaries. This fanout typically
accounts for 40 to 50% of all active states in the search. Since
we are typically dealing with context dependent units where the context
is a single left and right unit, the right context for a word ending
unit must include all possible initial units for the next set of
words. Typically there are 20 or 30 separate CD units needed to
represent the final phone in any word. Composites deal reduce
this fanout by collecting all of the possible word ending CD units into
a single bucket. This bucket of senones is scored by taking the maximum
(or the average) of the scores of the individual senones inthe
bucket. The composite doesn't reduce the number of HMM states
that are scored since all of the underlying senones are scored, but it
does reduce the overall processing time by reducing the number of
states that need to be managed and propagated. The imprecision of
the composites can potentially reduce the accuracy of the recognizer.
Differences in Constraints
- Arbitrary HMM Topologies -
Sphinx 4 allows HMMs to consist of an arbitrary number of
states with no constraints on the transitions between these states.
Sphinx 3.3 only allows 3 or 5 state HMMs with left-to-right
transitions. Sphinx-4 currently cannot take advantage of this
flexibility since we are relying on Sphinx-3 acoustic models which are
3 or 5 state left-to-right HMMs.
- Arbitrary Context Sizes - Sphinx-4,
in general, allows for arbitrary context sizes, while Sphinx 3.3 has a
fixed context of a single left and right unit. Note however
that the current Sphinx-4 linguists also constrain the context size to
the single left and right unit since there are some performance
optimizations to be gained with this constraint.
- Arbitrary language model depth
- The Sphinx-4 decoder supports any depth of n-gram.
Currently, we are constrained to 3-grams by the n-gram model loader
since we only have access to 3-gram language models. Sphinx 3.3 is
restricted to use 3-grams.
- Support for rule grammars
- Sphinx-4 can use rule based grammars such as those
specified by JSGF or FSTs. Sphinx 3.3 supports only stochastic grammars.
- Arbitrary search graph
topologies - The Sphinx-4 representation of the search graph is
independent of the topology of the graph. For instance, a
flat topology is used for small vocabulary recognition, while a
lex tree is used for medium and large vocabulary recognition. Sphinx
3.3 only supports a lex tree topology.
- Tuning Parameters - Sphinx-4
is more configurable and tunable, with about 300 separate configurable
properties compared to S3.3 with about 25.
Scoring / Pruning Differences
There are a number of differences in how pruning is performed.
- Sub-vector quantization - Sphinx
3.3 uses sub-vector quantization to reduce the amount of time needed to
score the active states. Sphinx 4 scores all the active states fully,
while Sphinx 3.3 will perform a quick pass using sub-vq to select a
subset of active states to fully score.
- HMMs vs. HMM States - In
Sphinx 3.3, the lowest level scoreable entity is an HMM, while in
Sphinx-4 it is an HMM State. Sphinx 3.3 treats the HMM as a
whole. Whenever an HMM is active, S3.3 scores all of the states of the
HMM against the incoming feature and generates two values, a 'best
score' and an 'exit score'. The 'best score' is the best score
among all of the states of the HMM. This score is used to determine if
the HMM will remain active in the succeeding frames. The 'exit
score' is used to determine if the subsequent HMMs in the lex tree will
become active (that is, should we propagate the path into the next
HMM). In Sphinx-4, each individual state of the HMM is scored
separately and is subject to pruning and propagating. This method
used by Sphinx-4 allows for arbitrary HMM topologies and allows for
consistent pruning. It does however require handling state transitions
much more frequently. Sphinx-4 will generate state transitions
four times more often than Sphinx 3.3 because of this method of
handling finer grained states. This most likely has a peformance
impact. For instance, the test that determines if a path should be
dropped because of a competing higher scoring path (aka Viterbi
pruning) is done four times as often in S4 because of this.
Although this is a fairly quick test it is done quite often (50,000
times per 10ms audio frame), so this can add significantly to the
processing time for S4.
- Folding of common senone
sequences - Due to state tying, it is often the case that
several distinct context dependent phones will share a common senone
sequence. Both Sphinx 3.3 and Sphinx 4 will identify states
that have identical seneone sequences and allow these to be shared,
greatly reducing the number of states processed. Sphinx 3.3 also
performs this folding on the entry into the lex tree. That is, the
initial set of states into the lex tree are also compressed based upon
common senone sequences. It is unclear how much this optimization
saves in terms of state creation.
- Absolute Beam - Sphinx
4 applies the absolute beam by applying a 'randomizing partitioning
algorithm', while Sphinx 3.3 uses a simple binning algorithm.
Both of these algorithms are O(n), however they have different overhead
constants. The Sphinx-4 algorithm is precise in that it
guarantees to produce the top scorers in the surviving beam, whereas
the Sphinx 3.3 binning algorithm is only approximate.
- LexTree Pruning - In
S3.3, at the end of a frame, only the best word exit for each distinct
word final CI phone per lex tree is allowed to continue. In S4
there is no pruning based on the final phone.
- Phone Beam - In
S3.3 there is relative beam that is applied on the transition out of an
HMM.  S4 has no such beam.
- Narrow Phone Beam - In
S3.3 there is an option to narrow the phone beam every Nth frame.
- Word History Pruning - In
S3.3 the word beam only ever contains a single path for a particular
word. For instance if there are three path candidates for the
beam:
- "the dog" score 100
- "my dog" score 75
- "my cat", score 50
Only paths (1) and (3) would be retained, whereas in S4 both paths (1)
and (2) could survive.
- More Word History Pruning
- In S3.3 there is a constraint placed upon the total
number of word histories retained, in S4 there is no such constraint.
- Filler Pruning - In
S3.3 only the best scoring filler word is allowed into the beam. All
others are dropped. In S4 fillers compete for space in the beam
with all other words.
- Integer vs. Float Scores -
In S4 all path scores are maintained as single precision
floating point values. In S3.3, path scores are maintained as
32-bit integer values.
- Single vs. Double Precision
scoring - internally, S3.3 uses double precision math to
score HMMs whereas S4 uses single precision math. Note that we have
done some detaileed analysis on the scoring code and have determined
that the differences in scoring code do not significantly affect the
results.
- AcousticLookAhead - In
S4 there is an option to prune based upon an acoustic score that
has been extrapolated into the future by N frames. S3.3 has no such
option.
- Grow Skipping - In S4
there is an option to skip the propagation of states every Nth frame.
S3.3 has no such option.
- Frame Dropping In
S4 there is an option to drop every Nth frame. S3.3 has no such option.
- Frame Replacement - In
S4 there is an option to replace every Nth frame with the preceding
frame. S3.3 has no such option.
Miscellaneous Differences
- S3.3 has an optional filler penalty for each type of filler,
whereas S4 has only a single filler penalty for all all non-silence
fillers.
- S3.3 filler words are maintained in their own lex tree. Since the
actual identity of fillers (cough, breath, silence, smack) are not
usually important, s3.3 treats filler words separately, giving them
their own lex tree where they compete only against each other and not
against other words.
- S4 has a threaded scorer that can be used to improve the time
spent scoring states by spreading the computation over multiple CPUs if
available.
- S3.3 seems to have a memory leak. It seems to slowly grow
its foot print over time (estimate of 1MB for every 3 minutes of audio
processed).


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Read this article
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This work explores the possibilities of a future internet built on distributed technologies. The work is participatory and is an experiment in what a network of distributed individuals can create. Participants follow a set of instructions to record a video message that when combined with other participants realizes a score. This video message is then played back abstractly, as a series of 3d meshes, that vary in shape depending on how many participants are on the network, and how many messages have been contributed to the work. The work will evolve drastically over time. Because of distributed internet technologies, copies of the work will exist on each participant's computer, the work will exist as long as people keep contributing to it and need not be maintained by the artist.
Studying new ways of structuring the internet is important because our current one is broken. Jenny Odell critiques the current internet as being the attention economy and sees art as a remedy because it allows us to perceive at different scales and tempos. Predominant social media apps profit off our attention and encourage shorter and shorter attention spans that make it more difficult to connect with nature and each other. Alexander R. Galloway looks to distributed technologies as a "diagram for our current social formation." A distributed network is a network of stronger social connections and a rejection of centralized, undemocratic power. Where a centralized internet reflects centralized political authority, a distributed internet redistributes this authority among its users. This work will explore these ideas through the generative process allowed by this new technology.
Simply add the data attribute "data-modal-dismiss" in the parent in order to do so


A series of messages from the comparison algorithm. Errors indicate that solutions cannot be interoperable across both implementation guides (or that there are structural flaws in the definition of at least one).
[%messages%]The intersection of the 2 constraint statements. This is what resource authors (either client or server) would need to conform to produce content valid against both implementation guides.
[%subset%]The union of the 2 constraint statements. This is what resource authors (either client or server) would need to be able to handle to accept content valid against either implementation guides.
[%superset%]


|
Fig. 1
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Current day Chl-a and its interannual variability. Composite ... |
|
Fig. 2
|
Remotely sensed reflectance. Current day composite (1998–201 ... |
|
Fig. 3
|
Relative magnitude of interannual variability. Global median ... |
|
Fig. 4
|
Change between current day and end of the 21st century. a Di ... |
|
Fig. 5
|
Hue angle. a Mean for 1998–2015, b difference in model 2085– ... |
|
Fig. 6
|
Absorption and backscatter. Components of total absorption ( ... |
|
Fig. 7
|
Change in contribution of optically important constituents. ... |
|
Fig. 8
|
Time series of changes in two locations. a, b are in the Nor ... |
|
Fig. 9
|
Trends and time of emergence. Model linear trend (%/year) fo ... |
|
Fig. 10
|
Percent ocean area with significant trend. The amount of ope ... |


Aurelia Kawaii is a fork of React Kawaii: cute SVG illustrations as custom elements. Ideal if you want to give some cuteness and personality to your Aurelia application.


{{>head}}
{{>nav}}
Lookup by Airport
Enter an airport and click "submit" to see a list of matching records
Airport Code:
{{>footer}}


{% extends 'base.html' %} {% block title_block %} Sorry we couldn't find this page! {% endblock %} {% block content %}
Sorry we couldn't find this page!


What is Redux state managment ? What it is all about ?
By using Redux state managment we have central data store in our application. The store contains the state of application and is single source of truth for our components. By using that, we don't need to synchronize state between components manually. Instead we can fully rely on Redux store, our single source of truth at any time.
Actions are used to send information from app to the store. We are using them to change some part of application state.
Actions are just JavaScript object like that:
{
type: LOGIN_USER,
payload: {username: ‘sebastian’, password: ‘123456’}
}
Long story short, we say that we want to LOGIN_USER and that we want to pass also some payload information. Actions can be also without payload information
dispatch({type: LOGIN_USER});
...then it goes to...
Reducers are the most important blocks of whole idea of Redux.
Reducers are pure functions that take current state application and action type, then returns the new state of the application.
They will never mutate the state, but the new state will be created.
It is important to remember that Pure functions never do any side effects.
The store is just JavaScript object, central object that hold whole state of application.
What is RxJS ?
RxJS is implementation of Reactive Extensions in JavaScript.
Reactive extensions is a library for composing asynchronous and event-based programs by using observables sequences.
Very short description will be that Reactive Extensions are:
... are sequences of items. We can think of Observable as an array of things in time.
We can create observables:
... are special functions which are doing something with our sequence. We can filter, map, and for example merge two observables together.
public getData(): Observable {
return this.http.get('apiUrl/something')
.map(res => res.json())
.filter(res => res.score > 5)
.catch(error => Observable.throw("An error has occured"));
}
public getData(): Observable {
return this.http.get('apiUrl/something')
.pipe(
map(res => res.json()),
filter(res => res.score > 5),
catch(error => Observable.throw("An error has occured")),
);
}
First, we want to define our actions as const variables like that:
export const ADD_TODO = '[Todos] Add Todo';
export const REMOVE_TODO = '[Todos] Remove Todo';
export const EDIT_TODO = '[Todos] Edit Todo';
export const ARCHIVE_TODO = '[Todos] Archive Todo';
export const UNARCHIVE_TODO = '[Todos] Unarchive Todo';
We could just use strings in our application when we dispatch action, but with that approach we have 2 benefit:
In NGRX we are creating something called Action Creators which are Classes which implements Action interface from @ngrx/Store
export class AddTodoAction implements Action {
readonly type = ADD_TODO;
constructor(public payload: Todo) { }
}
Then in file with actions we are exporting it like that (in this example we have some more actions like that).
// This union makes it easier for reducers to use only certain action types
export type Actions
= AddTodoAction
| RemoveTodoAction
| EditTodoAction
| ArchiveTodoAction
| UnarchiveTodoAction;
To dispatch (run) our action in component we write something like that:
delete(id: number) {
this.store.dispatch(new todo.RemoveTodoAction(id));
}
<button (click)="delete(1)">delete</button>
Reducers in NGRX looks like that, just a function which wait for certain action and returns new state.
export function reducer(state: State = initialState, action: Action): State {
switch(action.type) {
case todo.ADD_TODO:
let id = Math.max(...state.ids) + 1;
return {
ids: [...state.ids, id],
entities: {
...state.entities,
[id]: action.payload
}
};
case todo.REMOVE_TODO:
return {
ids: state.ids.filter(id => id !== action.payload),
// Lodash omit function
entities: omit(state.entities, action.payload)
}
default:
return state;
}
}
We have store, we have actions and we have reducers which are changing our state through special action. Now, we want take some chunk of state and display it in components.
this.todos$ = this.store.select<fromTodos.State>('todos')
.map( todos => {
return {
ids: todos.ids.filter(id => !todos.entities[id].archived),
entities: todos.entities
}
}
In NGRX, store is always represented as Observable, and here comes RxJs. We can use whatever operator we want.
side effects for ngrx
Here is boilerplate for you, to start playing with NGRX:
Getting started with NGRXand repo for this reveal js presentation:
slides for workshopIn both cases, it will stay on my github for a long time. It will be updated and enchance also soon.
todos.actions.ts
import { Action } from '@ngrx/store';
export const ADD_TODO = '[Todos] Add Todo';
export const SOME_OTHER_ACTION = 'SOME_OTHER_ACTION';
export class AddTodoAction implements Action {
readonly type = ADD_TODO;
constructor(public payload: any) {}
}
export class SomeOtherAction implements Action {
readonly type = SOME_OTHER_ACTION;
constructor(public payload: any) {}
}
export type Actions = AddTodoAction | SomeOtherAction;
todos.reducer.ts
import { Action } from '@ngrx/store';
import * as fromTodoActions from '../actions/todo.actions';
export interface Todos {
id: number;
task: string;
year: number;
month: number;
day: number;
hour: number;
min: number;
archived: boolean;
}
export interface TodosState {
todos: Todos[];
}
export const initialState: TodosState = {
todos: [
{
id: 1,
task: 'Test 1',
year: 2017,
month: 5,
day: 6,
hour: 19,
min: 17,
archived: false,
},
{
id: 2,
task: 'Test 1',
year: 2018,
month: 7,
day: 2,
hour: 23,
min: 21,
archived: false,
}
]
};
export function reducer(state: TodosState = initialState, action: Action): TodosState {
switch (action.type) {
case fromTodoActions.ADD_TODO:
return {
...state,
};
default:
return state;
}
}
app.module.ts
StoreModule.forRoot({todos: reducer}),
app.component.ts
import { Component } from '@angular/core';
import { Store } from '@ngrx/store';
import * as fromRootReducer from './reducers/app.reducer';
import * as fromTodoActions from './actions/todo.actions';
@Component({
selector: 'app-root',
templateUrl: './app.component.html',
styleUrls: ['./app.component.css']
})
export class AppComponent {
constructor( private store: Store ) {}
public addTodoAction() {
console.log('addTodoAction function from component');
this.store.dispatch(new fromTodoActions.AddTodoAction('whatever'));
}
}


Date of Table Update:201110141048
| GRIB2 Id | FNMOC id | Name | FNMOC Status1 | WMO Status2 |
|---|---|---|---|---|
| 0 | 0 | none | current | operational |
| 1 | 1 | coordinate circles | current | operational |
| 2 | 2 | coordinate lines | current | operational |
| 3 | 3 | grid | current | operational |
| 4-254 | 4 | reserved | current | operational |
| 255 | 255 | missing | current | operational |
Date of Table Creation:200902061512
Date of Table Update:201110141048
References:
| title: | Product Name Standard Document |
|---|---|
| version: | 2.2 |
| docId: | P-3146-2.2 |
| dtg: | May 2012 |
| organization: | Fleet Numerical Meteorology and Oceanography Center |
| infoUsed: | Interpretation of List of Numbers Defining Number of Points |
|
|
|
| title: | GRIB 2 |
| version: | 11.0.0 |
| docId: | FM 92-XII |
| organization: | World Meteorological Organization |
| infoUsed: | GRIB2 Interpretation of List of Numbers Defining Number of Points, table 3.11 |
|
|
Footnotes:
| value | definition |
|---|---|
| available | Entry was used in the past, is not in production now, and could be used again. |
| current | Entry is currently in production and available to customers. |
| deprecated | Entry name is no longer to be used. Parameter may or may not be in production. |
| listed | Entry is listed in WMO documentation. Not in production with no plans to be produced. |
| obsolete | Entry is not to be used and is not in production, if it ever was. |
| pending | Entry is being prepared for production. |
| planned | Entry is being looked at for possible production. |
| value | definition |
|---|---|
| deprecated | Entry is no longer used. |
| experimental | Entry is not recommended for use outside of bi-lateral previously agreed tests. |
| operational | Entry is available for use. |
| validation | Entry is in the approval process, not recommended for use. |


DevFestDC will be held on September 23-24, 2016 at the Capital One Conference Center (1680 Capital One Dr, McLean VA).
We expect nearly 600 attendees to these action packed sessions by leading technology experts from Google and other companies representing an exciting array of technologies including:
Great Sessions and Code Labs on DevOps, Android, Machine Learning, Wearables, Firebase, Polymer, AngularJS, Google Cloud Platform, Startups and others.
Get tickets


A subcategory is a detailed specific search category. For example, if you
have a speed boat that could be used as a cabin boat then your
main category would be "speed" and your subcategory would be
"cabin". In a search, this boat would come up under both listings.


The OnCustomDrawRow and OnExtendedCustomDrawRow events allow you to set row/cell classes and/or styles depending on the row data.
In this example rows are highlighted according to the movie rating using custom CSS classes. See the code.


BTS Jimin was featured in two different international shows on the same day!
by parkhaneul1013On November 6th, BTS Jimin was seen being featured in two different international show which is Argentina edition of "Who Wants to Be a Millionaire?" and TNT Latin America for their BTS special segment.
Who Wants to Be a Millionaire? is a famous international television game show where they test their contestant with multiple-choice questions to win large cash prizes. BTS Jimin along with BTS SUGA were spotted being featured in one of the question in Argentina edition of "Who Wants to Be a Millionaire?".
TNT or Turner Network Television is a well known American TV network for broadcasting all of the high-profile award shows including the Oscars and Grammys. TNT Latin America is one of TNT international versions and BTS Jimin was seen being featured for their BTS special segment. TNT Latin America uploaded an introduction video about BTS Jimin along with all of his achievements as an idol on their official YouTube channel.
Sources
http://www.polinews.co.kr/news...
http://www.fintechpost.co.kr/n...
http://www.apsk.co.kr/news/art...


Getting started
Welcome to SODA, your one-stop tool for curating SPARC datasets!
Watch the following tutorial videos to familiarize yourself with the user interface and learn how you can prepare and submit your SPARC dataset easily with SODA.
Familiarize yourself with the user interface of SODA
Click on the image below:
Follow our tutorial for easily organizing and submitting SPARC datasets
Click on the image below:


OpenROV fully assembled.
I know this problem can be solved. I just haven’t figured it out yet, and I need help from the OpenROV community to do so.
Update: The OpenROV community delivered. Solution: ESC Programming and Calibration for OpenROV.
Parts That Work
Software image updates on the Beaglebone Black work flawlessly thanks to scripts written by Brian Adams and Dominick Fretz. The cockpit software shows a real-time image from the camera, the LED lights are super-bright, and the lasers hold steady at 10cm apart. My fully assembled OpenROV passes the water submersion test. Yes, it’s waterproof, thanks to O-rings and silicon-based lubricant!
What about the motors? The motors work, almost. Could it be the
electronic speed control (ESC) settings, calibration, or less than
perfect soldering? That’s where I need help.


| 1 |
Kusano_2007 |
Immunochromatographic assay for simple and rapid detection of Satsuma dwarf virus and related viruses using monoclonal antibodies |
Kusano, Nario; Hirashima, Keita; Kuwahara, Minoru; Narahara, Kenji; Imamura, Tadashi; Mimori, Tomohiro; Nakahira, Ken; Torii, Kuniaki |
2007 |
2007-02-16 |
PMC |
N |
PMC7087811 |
|
10.1007/s10327-006-0316-6 |
z4fpy607 |
0.782156 |
|
Thuy_Tien_2018 |
| 2 |
Takano_2014 |
Serological Diagnosis of Feline Coronavirus Infection by Immunochromatographic Test |
Takano, Tomomi; Hohdatsu, Tsutomu |
2014 |
2014-12-18 |
PMC |
N |
PMC7122596 |
25720468.0 |
10.1007/978-1-4939-2438-7_3 |
wfy5kz63 |
0.751293 |
|
|
| 3 |
Takano_2014 |
Use of recombinant nucleocapsid proteins for serological diagnosis of feline coronavirus infection by three immunochromatographic tests |
Takano, Tomomi; Ishihara, Yuka; Matsuoka, Masafumi; Yokota, Shoko; Matsuoka-Kobayashi, Yukie; Doki, Tomoyoshi; Hohdatsu, Tsutomu |
2014 |
2014-02-28 |
PMC |
N |
PMC7113643 |
24516876.0 |
10.1016/j.jviromet.2013.10.014 |
k6wkpmpn |
0.727878 |
|
|
| 4 |
Tao_C_2014 |
A rapid one‐step immunochromatographic test strip for rabies detection using canine serum samples |
Tao, C.; Li, G. |
2014 |
2014-06-02 |
PMC |
N |
PMC7165885 |
24820246.0 |
10.1111/lam.12282 |
q3pn9e9c |
0.683895 |
|
|
| 5 |
Liu_J_2020 |
Development of a rapid immunochromatographic strip test for the detection of porcine epidemic diarrhea virus specific SIgA in colostrum |
Liu, Jianbo; Gao, Ran; Shi, Hongyan; Cong, Guangyi; Chen, Jianfei; Zhang, Xin; Shi, Da; Cao, Liyan; Wang, Xiaobo; Zhang, Jialin; Ji, Zhaoyang; Jing, Zhaoyang; Feng, Li |
2020 |
2020-05-31 |
PMC |
N |
PMC7151449 |
32173373.0 |
10.1016/j.jviromet.2020.113855 |
441aclcc |
0.683856 |
|
Chen_2012 |
| 6 |
XIANG_2012 |
A novel double antibody sandwich-lateral flow immunoassay for the rapid and simple detection of hepatitis C virus |
XIANG, TINGXIU; JIANG, ZHENG; ZHENG, JIAN; LO, CHAOYU; TSOU, HARRY; REN, GUOSHENG; ZHANG, JUN; HUANG, AILONG; LAI, GUOQI |
2012 |
2012-09-06 |
COMM-USE |
N |
PMC3573733 |
22960954.0 |
10.3892/ijmm.2012.1121 |
2586thss |
0.625323 |
Vemulapalli_2009 |
|
| 7 |
Wang_2019 |
Development of an Immunochromatographic Strip for Rapid Detection of Canine Adenovirus |
Wang, Shujie; Wen, Yongjun; An, Tongqing; Duan, Guixin; Sun, MingXia; Ge, Jinying; Li, Xi; Yang, Kongbin; Cai, Xuehui |
2019 |
2019-12-11 |
COMM-USE |
N |
PMC6917642 |
31921060.0 |
10.3389/fmicb.2019.02882 |
yfmng12f |
0.622542 |
|
Chen_2012, Thuy_Tien_2018, An_D_2008 |
| 8 |
Ehrnhoefer_2011 |
A Quantitative Method for the Specific Assessment of Caspase-6 Activity in Cell Culture |
Ehrnhoefer, Dagmar E.; Skotte, Niels H.; Savill, Jane; Nguyen, Yen T. N.; Ladha, Safia; Cao, Li-Ping; Dullaghan, Edie; Hayden, Michael R. |
2011 |
2011-11-29 |
COMM-USE |
N |
PMC3226564 |
22140457.0 |
10.1371/journal.pone.0027680 |
73hw8922 |
0.581089 |
|
|
| 9 |
Chen_2012 |
Development and evaluation of an immunochromatographic strip for rapid detection of porcine hemagglutinating encephalomyelitis virus |
Chen, Keyan; Zhao, Kui; Song, Deguang; He, Wenqi; Gao, Wei; Zhao, Chuanbo; Wang, Chengli; Gao, Feng |
2012 |
2012-08-24 |
COMM-USE |
N |
PMC3508965 |
22920192.0 |
10.1186/1743-422x-9-172 |
u28y1ttw |
0.560939 |
Vemulapalli_2009 |
Thuy_Tien_2018, An_D_2008, Wang_2019 |
| 10 |
Yan_Z_2006 |
Rapid quantitative detection of Yersinia pestis by lateral-flow immunoassay and up-converting phosphor technology-based biosensor |
Yan, Zhongqiang; Zhou, Lei; Zhao, Yongkai; Wang, Jing; Huang, Lihua; Hu, Kongxin; Liu, Haihong; Wang, Hong; Guo, Zhaobiao; Song, Yajun; Huang, Huijie; Yang, Ruifu |
2006 |
2006-12-07 |
PMC |
N |
PMC7125792 |
|
10.1016/j.snb.2006.01.029 |
mh5rkhsg |
0.558180 |
Jiang_2011, Liu_H_2006 |
Wu_M_2018, Chen_2012 |
| 11 |
Jochmans_2012 |
A novel method for high-throughput screening to quantify antiviral activity against viruses that induce limited CPE |
Jochmans, Dirk; Leyssen, Pieter; Neyts, Johan |
2012 |
2012-08-31 |
PMC |
N |
PMC7112866 |
22575574.0 |
10.1016/j.jviromet.2012.04.011 |
17oiye59 |
0.540754 |
|
|
| 12 |
Wang_2011 |
Phages Bearing Affinity Peptides to Bovine Rotavirus Differentiate the Virus from Other Viruses |
Wang, Xin; Li, Guangxing; Ren, Yudong; Ren, Xiaofeng |
2011 |
2011-12-06 |
COMM-USE |
N |
PMC3232237 |
22163050.0 |
10.1371/journal.pone.0028667 |
j0v0psyq |
0.508140 |
Dinh_2011, Benson_2008 |
|
| 13 |
Hoffmann_2004 |
Proteomic analyser with applications to diagnostics and vaccines |
Hoffmann, Geoffrey W. |
2004 |
2004-06-21 |
PMC |
N |
PMC7134612 |
15178195.0 |
10.1016/j.jtbi.2004.02.011 |
kaxcxynk |
0.487691 |
|
|
| 14 |
Zhang_2010 |
A double antibody sandwich enzyme-linked immunosorbent assay for detection of soft-shelled turtle iridovirus antigens |
Zhang, M.; Yang, J.X.; Lin, X.M.; Zhu, C.H.; He, J.Q.; Liu, H.; Lin, T.L. |
2010 |
2010-08-31 |
PMC |
N |
PMC7112847 |
20399233.0 |
10.1016/j.jviromet.2010.04.004 |
6j4jb87s |
0.471207 |
Borja_1992, Fan_Q_2012 |
|
| 15 |
Jiang_2018 |
A simple and safe antibody neutralization assay based on polio pseudoviruses |
Jiang, Zheng; Liu, Guixiu; Guo-yang, Liao; Sun, Mingbo; Xu, Kangwei; Ying, Zhifang; Wang, Jianfeng; Li, Xuguang; Li, Changgui |
2018 |
2018-10-16 |
NONCOMM |
N |
PMC6422504 |
30273512.0 |
10.1080/21645515.2018.1526553 |
8jnrc01n |
0.432596 |
|
|
| 16 |
Wu_M_2019 |
High-throughput Luminex xMAP assay for simultaneous detection of antibodies against rabbit hemorrhagic disease virus, Sendai virus and rabbit rotavirus |
Wu, Miaoli; Ma, Lei; Cong, Feng; Zhu, Yujun; Xu, Fengjiao; Lian, Yuexiao; Huang, Bihong; Xiao, Li; Chen, Meili; Zhang, Yu; Huang, Ren; Guo, Pengju |
2019 |
2019-04-15 |
PMC |
N |
PMC7087182 |
30982935.0 |
10.1007/s00705-019-04226-9 |
ob9t82y5 |
0.430638 |
Benson_2008 |
Wu_M_2018 |
| 17 |
Kogaki_2005 |
Novel rapid immunochromatographic test based on an enzyme immunoassay for detecting nucleocapsid antigen in SARS‐associated coronavirus |
Kogaki, Hiroyuki; Uchida, Yoshiaki; Fujii, Nobuyuki; Kurano, Yoshihiro; Miyake, Kazushige; Kido, Yasuji; Kariwa, Hiroaki; Takashima, Ikuo; Tamashiro, Hiko; Ling, Ai‐Ee; Okada, Masahisa |
2005 |
2005-01-01 |
PMC |
N |
PMC6807888 |
16025480.0 |
10.1002/jcla.20070 |
vf93zmtp |
0.416344 |
|
|
| 18 |
Domínguez_2019 |
Quantitation of monoclonal antibody by capture ELISA based on initial enzyme activity rate |
Domínguez, Mercedes; Moreno, Inmaculada; Toraño, Alfredo |
2019 |
2019-11-30 |
PMC |
N |
PMC7094287 |
31404550.0 |
10.1016/j.jim.2019.112645 |
axthsvvj |
0.384148 |
Benson_2008, Li_H_2019 |
|
| 19 |
Bian_2019 |
A new immunochromatographic assay for on-site detection of porcine epidemic diarrhea virus based on monoclonal antibodies prepared by using cell surface fluorescence immunosorbent assay |
Bian, Hongfen; Xu, Fei; Jia, Yumin; Wang, Lei; Deng, Shengchao; Jia, Aiqing; Tang, Yong |
2019 |
2019-01-18 |
COMM-USE |
N |
PMC6339306 |
30658643.0 |
10.1186/s12917-019-1773-4 |
u0r7oh9z |
0.367446 |
Dinh_2011, Vemulapalli_2009, Benson_2008 |
Xu_F_2020, Chen_2012 |
| 20 |
Papadopoulos_1999 |
Rhinovirus identification by BglI digestion of picornavirus RT-PCR amplicons |
Papadopoulos, N.G.; Hunter, J.; Sanderson, G.; Meyer, J.; Johnston, S.L. |
1999 |
1999-07-31 |
PMC |
N |
|
10471027.0 |
10.1016/s0166-0934(99)00045-2 |
xacgssrc |
0.365009 |
|
|
| 21 |
Thuy_Tien_2018 |
Development of a Rapid Fluorescent Immunochromatographic Test to Detect Respiratory Syncytial Virus |
Thuy Tien, Trinh Thi; Park, Hyun; Tuong, Hien Thi; Yu, Seung-Taek; Choi, Du-Young; Yeo, Seon-Ju |
2018 |
2018-10-02 |
COMM-USE |
N |
PMC6212954 |
30279406.0 |
10.3390/ijms19103013 |
x0o0q5sz |
0.349345 |
Dinh_2011, Kang_2010, Escutenaire_2006, Chen_2010 |
Shirato_2007, Chen_2012 |
| 22 |
Wang_2012 |
Yeast surface display-based microfluidic immunoassay |
Wang, Jing; Cheng, Danhui; Chan, Jay Kwok-Lun; Luo, Xiaoteng; Wu, Hongkai; Hsing, I-Ming |
2012 |
2012-05-20 |
PMC |
N |
PMC7127699 |
|
10.1016/j.snb.2012.02.048 |
5lz5cajv |
0.344190 |
Liu_H_2006 |
Yeung_2006 |
| 23 |
Wang_2018 |
Development of a sensitive and specific xMAP assay for detection of antibodies against infectious laryngotracheitis and bronchitis viruses |
Wang, Huanan; Cong, Feng; Guan, Jianchi; Xiao, Li; Zhu, Yujun; Lian, Yuexiao; Huang, Ren; Chen, Meili; Guo, Pengju |
2018 |
2018-09-21 |
COMM-USE |
N |
PMC6151043 |
30241540.0 |
10.1186/s12985-018-1048-x |
eqqvwwh6 |
0.322361 |
|
|


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2.5 The os ModuleAs mentioned, os contains all the usual operating-system calls you may have used in your C programs and shell scripts. Its calls deal with directories, processes, shell variables, and the like. Technically, this module provides POSIX tools -- a portable standard for operating-system calls -- along with platform-independent directory processing tools as nested module os.path. Operationally, os serves as a largely portable interface to your computer's system calls: scripts written with os and os.path can usually be run on any platform unchanged. In fact, if you read the os module's source code, you'll notice that it really just imports whatever platform-specific system module you have on your computer (e.g., nt, mac, posix). See the file os.py in the Python source library directory -- it simply runs a from* statement to copy all names out of a platform-specific module. By always importing os instead of platform-specific modules, though, your scripts are mostly immune to platform implementation differences. 2.5.1 The Big os ListsLet's take a quick look at the basic interfaces in os. If you inspect this module's attributes interactively, you get a huge list of names that will vary per Python release, will likely vary per platform, and isn't incredibly useful until you've learned what each name means: >>> import os >>> dir(os) ['F_OK', 'O_APPEND', 'O_BINARY', 'O_CREAT', 'O_EXCL', 'O_RDONLY', 'O_RDWR', 'O_TEXT', 'O_TRUNC', 'O_WRONLY', 'P_DETACH', 'P_NOWAIT', 'P_NOWAITO', 'P_OVERLAY', 'P_WAIT', 'R_OK', 'UserDict', 'W_OK', 'X_OK', '_Environ', '__builtins__', '__doc__', '__file__', '__name__', '_execvpe', '_exit', '_notfound', 'access', 'altsep', 'chdir', 'chmod', 'close', 'curdir', 'defpath', 'dup', 'dup2', 'environ', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'fdopen', 'fstat', 'getcwd', 'getpid', 'i', 'linesep', 'listdir', 'lseek', 'lstat', 'makedirs', 'mkdir', 'name', 'open', 'pardir', 'path', 'pathsep', 'pipe', 'popen', 'putenv', 'read', 'remove', 'removedirs', 'rename', 'renames', 'rmdir', 'sep', 'spawnv', 'spawnve', 'stat', 'strerror', 'string', 'sys', 'system', 'times', 'umask', 'unlink', 'utime', 'write'] Besides all of these, the nested os.path module exports even more tools, most of which are related to processing file and directory names portably: >>> dir(os.path) ['__builtins__', '__doc__', '__file__', '__name__', 'abspath', 'basename', 'commonprefix', 'dirname', 'exists', 'expanduser', 'expandvars', 'getatime', 'getmtime', 'getsize', 'isabs', 'isdir', 'isfile', 'islink', 'ismount', 'join', 'normcase', 'normpath', 'os', 'split', 'splitdrive', 'splitext', 'splitunc', 'stat', 'string', 'varchars', 'walk'] 2.5.2 Administrative ToolsJust in case those massive listings aren't quite enough to go on, let's experiment with some of the simpler os tools interactively. Like sys, the os module comes with a collection of informational and administrative tools: >>> os.getpid( ) -510737 >>> os.getcwd( ) 'C:\\PP2ndEd\\examples\\PP2E\\System' >>> os.chdir(r'c:\temp') >>> os.getcwd( ) 'c:\\temp' As shown here, the os.getpid function gives the calling process's process ID (a unique system-defined identifier for a running program), and os.getcwd returns the current working directory. The current working directory is where files opened by your script are assumed to live, unless their names include explicit directory paths. That's why I told you earlier to run the following command in the directory where more.py lives: C:\...\PP2E\System>python more.py more.py The input filename argument here is given without an explicit directory path (though you could add one to page files in another directory). If you need to run in a different working directory, call the os.chdir function to change to a new directory; your code will run relative to the new directory for the rest of the program (or until the next os.chdir call). This chapter has more to say about the notion of a current working directory, and its relation to module imports, when it explores script execution context later. 2.5.3 Portability ConstantsThe os module also exports a set of names designed to make cross-platform programming simpler. The set includes platform-specific settings for path and directory separator characters, parent and current directory indicators, and the characters used to terminate lines on the underlying computer:[4]
>>> os.pathsep, os.sep, os.pardir, os.curdir, os.linesep (';', '\\', '..', '.', '\015\012') Name os.sep whatever character is used to separate directory components on the platform Python is running on; it is automatically preset to "\" on Windows, "/" for POSIX machines, and ":" on the Mac. Similarly, os.pathsep provides the character that separates directories on directory lists -- ":" for POSIX and ";" for DOS and Windows. By using such attributes when composing and decomposing system-related strings in our scripts, they become fully portable. For instance, a call of the form string.split(dirpath,os.sep) will correctly split platform-specific directory names into components, even though dirpath may look like "dir\dir" on Windows, "dir/dir" on Linux, and "dir:dir" on Macintosh. 2.5.4 Basic os.path ToolsThe nested module os.path provides a large set of directory-related tools of its own. For example, it includes portable functions for tasks such as checking a file's type (isdir, isfile, and others), testing file existence (exists), and fetching the size of a file by name (getsize): >>> os.path.isdir(r'C:\temp'), os.path.isfile(r'C:\temp') (1, 0) >>> os.path.isdir(r'C:\config.sys'), os.path.isfile(r'C:\config.sys') (0, 1) >>> os.path.isdir('nonesuch'), os.path.isfile('nonesuch') (0, 0) >>> os.path.exists(r'c:\temp\data.txt') 0 >>> os.path.getsize(r'C:\autoexec.bat') 260 The os.path.isdir and os.path.isfile calls tell us whether a filename is a directory or a simple file; both return (false) if the named file does not exist. We also get calls for splitting and joining directory path strings, which automatically use the directory name conventions on the platform on which Python is running: >>> os.path.split(r'C:\temp\data.txt') ('C:\\temp', 'data.txt') >>> os.path.join(r'C:\temp', 'output.txt') 'C:\\temp\\output.txt' >>> name = r'C:\temp\data.txt' # Windows paths >>> os.path.basename(name), os.path.dirname(name) ('data.txt', 'C:\\temp') >>> name = '/home/lutz/temp/data.txt' # Unix-style paths >>> os.path.basename(name), os.path.dirname(name) ('data.txt', '/home/lutz/temp') >>> os.path.splitext(r'C:\PP2ndEd\examples\PP2E\PyDemos.pyw') ('C:\\PP2ndEd\\examples\\PP2E\\PyDemos', '.pyw') Call os.path.split separates a filename from its directory path, and os.path.join puts them back together -- all in entirely portable fashion, using the path conventions of the machine on which they are called. The basename and dirname calls here simply return the second and first items returned by a split as a convenience, and splitext strips the file extension (after the last "."). This module also has an abspath call that portably returns the absolute full directory pathname of a file; it accounts for adding the current directory, ".." parents, and more: >>> os.getcwd( ) 'C:\\PP2ndEd\\cdrom\\WindowsExt' >>> os.path.abspath('temp') # expand to full path name 'C:\\PP2ndEd\\cdrom\\WindowsExt\\temp' >>> os.path.abspath(r'..\examples') # relative paths expanded 'C:\\PP2ndEd\\examples' >>> os.path.abspath(r'C:\PP2ndEd\chapters') # absolute paths unchanged 'C:\\PP2ndEd\\chapters' >>> os.path.abspath(r'C:\temp\spam.txt') # ditto for file names 'C:\\temp\\spam.txt' >>> os.path.abspath('') # empty string means the cwd 'C:\\PP2ndEd\\cdrom\\WindowsExt' Because filenames are relative to the current working directory when they aren't fully specified paths, the os.path.abspath function helps if you want to show users what directory is truly being used to store a file. On Windows, for example, when GUI-based programs are launched by clicking on file explorer icons and desktop shortcuts, the execution directory of the program is the clicked file's home directory, but that is not always obvious to the person doing the clicking; printing a file's abspath can help. 2.5.5 Running Shell Commands from ScriptsThe os module is also the place where we run shell commands from within Python scripts. This concept is intertwined with others we won't cover until later in this chapter, but since this a key concept employed throughout this part of the book, let's take a quick first look at the basics here. Two os functions allow scripts to run any command line that you can type in a console window:
<IP_ADDRESS> What's a shell command?To understand the scope of these calls, we need to first define a few terms. In this text the term shell means the system that reads and runs command-line strings on your computer, and shell command means a command-line string that you would normally enter at your computer's shell prompt. For example, on Windows, you can start an MS-DOS console window and type DOS commands there -- things like dir to get a directory listing, type to view a file, names of programs you wish to start, and so on. DOS is the system shell, and commands like dir and type are shell commands. On Linux, you can start a new shell session by opening an xterm window and typing shell commands there too -- ls to list directories, cat to view files, and so on. There are a variety of shells available on Unix (e.g., csh, ksh), but they all read and run command lines. Here are two shell commands typed and run in an MS-DOS console box on Windows: C:\temp>dir /B ...type a shell command-line about-pp.html ...its output shows up here python1.5.tar.gz ...DOS is the shell on Windows about-pp2e.html about-ppr2e.html newdir C:\temp>type helloshell.py # a Python program print 'The Meaning of Life' <IP_ADDRESS> Running shell commandsNone of this is directly related to Python, of course (despite the fact that Python command-line scripts are sometimes confusingly called "shell tools"). But because the os module's system and popen calls let Python scripts run any sort of command that the underlying system shell understands, our scripts can make use of every command-line tool available on the computer, whether it's coded in Python or not. For example, here is some Python code that runs the two DOS shell commands typed at the shell prompt shown previously: C:\temp>python >>> import os >>> os.system('dir /B') about-pp.html python1.5.tar.gz about-pp2e.html about-ppr2e.html newdir 0 >>> os.system('type helloshell.py') # a Python program print 'The Meaning of Life' 0 The "0"s at the end here are just the return values of the system call itself. The system call can be used to run any command line that we could type at the shell's prompt (here, C:\temp>). The command's output normally shows up in the Python session's or program's standard output stream. <IP_ADDRESS> Communicating with shell commandsBut what if we want to grab a command's output within a script? The os.system call simply runs a shell command line, but os.popen also connects to the standard input or output streams of the command -- we get back a file-like object connected to the command's output by default (if we pass a "w" mode flag to popen, we connect to the command's input stream instead). By using this object to read the output of a command spawned with popen, we can intercept the text that would normally appear in the console window where a command line is typed: >>> open('helloshell.py').read( ) "# a Python program\012print 'The Meaning of Life'\012" >>> text = os.popen('type helloshell.py').read( ) >>> text "# a Python program\012print 'The Meaning of Life'\012" >>> listing = os.popen('dir /B').readlines( ) >>> listing ['about-pp.html\012', 'python1.5.tar.gz\012', 'helloshell.py\012', 'about-pp2e.html\012', 'about-ppr2e.html\012', 'newdir\012'] Here, we first fetch a file's content the usual way (using Python files), then as the output of a shell type command. Reading the output of a dir command lets us get a listing of files in a directory which we can then process in a loop (we'll meet other ways to obtain such a list later in this chapter). So far, we've run basic DOS commands; because these calls can run any command line that we can type at a shell prompt, they can also be used to launch other Python scripts: >>> os.system('python helloshell.py') # run a Python program The Meaning of Life 0 >>> output = os.popen('python helloshell.py').read( ) >>> output 'The Meaning of Life\012' In all of these examples, the command-line strings sent to system and popen are hardcoded, but there's no reason Python programs could not construct such strings at runtime using normal string operations (+, %, etc.). Given that commands can be dynamically built and run this way, system and popen turn Python scripts into flexible and portable tools for launching and orchestrating other programs. For example, a Python test "driver" script can be used to run programs coded in any language (e.g., C++, Java, Python) and analyze their outputs. We'll explore such a script in Section 4.4 in Chapter 4. <IP_ADDRESS> Shell command limitationsYou should keep in mind two limitations of system and popen. First, although these two functions themselves are fairly portable, their use is really only as portable as the commands that they run. The preceding examples that run DOS dir and type shell commands, for instance, work only on Windows, and would have to be changed to run ls and cat commands on Unix-like platforms. As I wrote this, the popen call on Windows worked for command-line programs only; it failed when called from a program running on Windows with any sort of user interface (e.g., under the IDLE Python development GUI). This has been improved in the Python 2.0 release -- popen now works much better on Windows -- but this fix naturally works only on machines with the latest version of Python installed. Second, it is important to remember that running Python files as programs this way is very different, and generally much slower, than importing program files and calling functions they define. When os.system and os.popen are called, they must start a brand-new independent program running on your operating system (on Unix-like platforms, they run the command in a newly forked process). When importing a program file as a module, the Python interpreter simply loads and runs the file's code in the same process, to generate a module object. No other program is spawned along the way.[5]
There are good reasons to build systems as separate programs too, and we'll later explore things like command-line arguments and streams that allow programs to pass information back and forth. But for most purposes, imported modules are a faster and more direct way to compose systems. If you plan to use these calls in earnest, you should also know that the os.system call normally blocks (that is, pauses) its caller until the spawned command line exits. On Linux and Unix-like platforms, the spawned command can generally be made to run independently and in parallel with the caller, by adding an & shell background operator at the end of the command line: os.system("python program.py arg arg &") On Windows, spawning with a DOS start command will usually launch the command in parallel too: os.system("start program.py arg arg") The os.popen call generally does not block its caller -- by definition, the caller must be able to read or write the file object returned -- but callers may still occasionally become blocked under both Windows and Linux if the pipe object is closed (e.g., when garbage is collected) before the spawned program exits, or the pipe is read exhaustively (e.g., with its read( ) method). As we will see in the next chapter, the Unix os.fork/exec and Windows os.spawnv calls can also be used to run parallel programs without blocking. Because the os system and popen calls also fall under the category of program launchers, stream redirectors, and cross-process communication devices, they will show up again in later parts of this and the following chapters, so we'll defer further details for the time being. 2.5.6 Other os Module ExportsSince most other os module tools are even more difficult to appreciate outside the context of larger application topics, we'll postpone a deeper look until later sections. But to let you sample the flavor of this module, here is a quick preview for reference. Among the os module's other weapons are these:
And so on. One caution up front: the os module provides a set of file open, read, and write calls, but these all deal with low-level file access and are entirely distinct from Python's built-in stdio file objects that we create with the built-in open function. You should normally use the built-in open function (not the os module) for all but very special file-processing needs. Throughout this chapter, we will apply sys and os tools such as these to implement common system-level tasks, but this book doesn't have space to provide an exhaustive list of the contents of the modules we meet along the way. If you have not already done so, you should become acquainted with the contents of modules like os and sys by consulting the Python library manual. For now, let's move on to explore additional system tools, in the context of broader system programming concepts. |
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"Dressed to Kill" - 1980. Review by Paul Xavier
Starring: Angie Dickinson, Michael Caine, Nancy Allen, and Keith Gordon. Rated R for graphic violence, nudity, language and sexual situations.
"Psycho" is a classic. Many people hold the work of Alfred Hitchcock in the highest regard. "Dressed to Kill", from Brian DePalma, is admittedly, a re-working of Hitchcock's classic thriller. A lot of people allege DePalma is a poor man's Hitchcock. That may be so, but there is no denying DePalma can make a good film.
"Dressed to Kill" first introduces us to Kate Miller (Angie Dickinson), a woman who is not happy in her marriage.  She is so unsatisfied from her husband, she even asks her psychiatrist if he would sleep with her. However, he is married.
She then visits an art museum, and sees a mysterious stranger that catches her eye. She obviously catches his eye as well, as they have a fling in a cab, and later back at his apartment.
This is the just the kind of excitement that Kate needed in her life, until she finds out that the tall, dark and handsome man has a letter from the Department of Health in his drawer stating he has a veneral disease. Flustered, she leaves his apartment while he still sleeps.
On the way down in the elevator, she notices her wedding ring is gone and remembers that she left it upstairs in loverboy's apartment. Going back to retrieve it, as the elevator door to the top floor opens, a tall blond woman with a shiny razor proceeds to slash Kate into mincemeat.
The aftermath of this event is discovered by Liz, a street-smart call girl who finds Kate, and the killer, in the elevator, holding the razor. Liz then goes to the police, to report the murder. Meanwhile, Dr. Elliot is getting a call from "Bobbi" on his answering machine. Bobbi is a woman inside a man's body, was a former patient of Dr. Elliot's, and is obviously disturbed, as she is the one who slashed Kate in the elevator using Elliot's razor.
All this happens in the first half hour. During and after this set-up, DePalma gives one a movie experience with rich style, which is his biggest strength, providing dazzling camera work and some wonderfully filmed sequences; the above mentioned elevator sequence and many others. Another plus is the wonderful score by Pino Donaggio, completely involving the viewer during crucial scenes. DePalma's direction and the music score work together wonderfully.
Still another plus is Nancy Allen as Liz, who the movie focuses on after Kate's fate. In spite of her not so respectable occupation, Liz is a very likable character that one can't help but root for.
Even if you've seen "Psycho" (and who hasn't?), don't sell "Dressed to Kill" short. In spite of the similarities in both films, "Dressed to Kill" easily stands on it's own as an exciting, suspenseful film and deserves a place in movie history as a classic just as much as "Psycho".
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I can't change the direction of the wind, but I can adjust my sails to always reach my destination.


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Utility for making OAuth 2.0 easier. Instantiate and then use with oauth_required or oauth_aware as decorators on webapp.RequestHandler methods. Example: decorator = OAuth2Decorator( client_id='837...ent.com', client_secret='Qh...wwI', scope='https://www.googleapis.com/auth/plus') class MainHandler(webapp.RequestHandler): @decorator.oauth_required def get(self): http = decorator.http() # http is authorized with the user's Credentials and can be used # in API calls
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Constructor for OAuth2Decorator Args: client_id: string, client identifier. client_secret: string client secret. scope: string or iterable of strings, scope(s) of the credentials being requested. auth_uri: string, URI for authorization endpoint. For convenience defaults to Google's endpoints but any OAuth 2.0 provider can be used. token_uri: string, URI for token endpoint. For convenience defaults to Google's endpoints but any OAuth 2.0 provider can be used. user_agent: string, User agent of your application, default to None. message: Message to display if there are problems with the OAuth 2.0 configuration. The message may contain HTML and will be presented on the web interface for any method that uses the decorator. callback_path: string, The absolute path to use as the callback URI. Note that this must match up with the URI given when registering the application in the APIs Console. **kwargs: dict, Keyword arguments are be passed along as kwargs to the OAuth2WebServerFlow constructor.
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Decorator that starts the OAuth 2.0 dance. Starts the OAuth dance for the logged in user if they haven't already granted access for this application. Args: method: callable, to be decorated method of a webapp.RequestHandler instance. |
Create the Flow object. The Flow is calculated lazily since we don't know where this app is running until it receives a request, at which point redirect_uri can be calculated and then the Flow object can be constructed. Args: request_handler: webapp.RequestHandler, the request handler. |
Decorator that sets up for OAuth 2.0 dance, but doesn't do it. Does all the setup for the OAuth dance, but doesn't initiate it. This decorator is useful if you want to create a page that knows whether or not the user has granted access to this application. From within a method decorated with @oauth_aware the has_credentials() and authorize_url() methods can be called. Args: method: callable, to be decorated method of a webapp.RequestHandler instance. |
True if for the logged in user there are valid access Credentials. Must only be called from with a webapp.RequestHandler subclassed method that had been decorated with either @oauth_required or @oauth_aware. |
Returns the URL to start the OAuth dance. Must only be called from with a webapp.RequestHandler subclassed method that had been decorated with either @oauth_required or @oauth_aware. |
Returns an authorized http instance. Must only be called from within an @oauth_required decorated method, or from within an @oauth_aware decorated method where has_credentials() returns True. |
The absolute path where the callback will occur. Note this is the absolute path, not the absolute URI, that will be calculated by the decorator at runtime. See callback_handler() for how this should be used. Returns: The callback path as a string.
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RequestHandler for the OAuth 2.0 redirect callback. Usage: app = webapp.WSGIApplication([ ('/index', MyIndexHandler), ..., (decorator.callback_path, decorator.callback_handler()) ]) Returns: A webapp.RequestHandler that handles the redirect back from the server during the OAuth 2.0 dance. |
WSGI application for handling the OAuth 2.0 redirect callback. If you need finer grained control use `callback_handler` which returns just the webapp.RequestHandler. Returns: A webapp.WSGIApplication that handles the redirect back from the server during the OAuth 2.0 dance. |
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| Generated by Epydoc 3.0.1 on Thu Jan 24 16:14:40 2013 | http://epydoc.sourceforge.net |


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| Create a new local repository: | git init |
| Clone an existing repository: | git clone<EMAIL_ADDRESS>|
| Check file status of directory: | git status |
| Add all changed files: | git add . |
| Add a specific file: | git add -p file |
| Commit changes: | git commit -m message |
| Amend the last commit: | git commit --amend |
| List all existing branches: | git branch |
| Create a new branch: | git branch new-branch |
| Delete a local branch: | git branch -d branch |
| List all currently configured remotes: | git remote -v |
| Download all changes from remote: | git fetch remote |
| Download changes and integrate into head: | git pull remote branch |
| Publish local changes to remote: | git push remote branch |
| Merge branch into head: | git merge branch |
| Discard all local changes in dir: | git reset --hard HEAD |
| Merge branch into head: | git merge branch |


trajectories.Rmd
VISION can be used as a downstream tool for analyzing the biological significance of trajectories. The tool interfaces with the objects output from Dynverse. Briefly, Dynverse objects are general wrappers for any trajectory method, and VISION specifically depends on two components in particular:
This amounts to a latent space, which describes relationships between cells, and can be used for calculating signature consistency scores in the default pipeline.
In this vignette, we’ll be analyzing a set of ~5,000 cells during haematopoiesis (Tusi et al, Nature 2018).
We begin by loading in the data and requisite libraries:
We’ll now follow the dyno vignette in the github README. You’ll need to make sure you have Docker installed. Although we use Monocle2 here, there is a wide variety of methods that can be used, as listed on the Dynmethods page.
Before running the model, we’ll need to perform some gene filtering. We first only select the genes used in the original Tusi et al study, then for further efficiency, we further filter using VISION’s Fano filtering.
k.genes = read.table("data/bBM.filtered_gene_list.paper.txt.gz", sep=',')[,1]
filt.counts = counts[k.genes,]
f.genes = VISION:::filterGenesFano(filt.counts)
filt.counts = filt.counts[f.genes,]
Now, resuming the workflow recommended on the dyno vignette,
scale.factor = median(colSums(counts))
expr = apply(counts, 2, function(x) (x * scale.factor) / (sum(x) + 1))
filt.expr = log(1 + expr[f.genes,])
dataset = wrap_expression(
counts = t(filt.counts),
expression = t(filt.expr)
)
model <- infer_trajectory(dataset, "projected_slingshot", verbose=T)
model <- model %>% add_dimred(dyndimred::dimred_mds, expression_source = dataset$expression)
plot_dimred(
model,
expression_source = dataset$expression
)
Now, with this model saved we can pass it to VISION via the latentTrajectory
parameter. The model will automatically be analzyed with the same signatures passed to the FastProjectR object, and with this model, the Trajectory
tab will be activated in the VISION output report.
covar = read.table("data/hemato_covariates.txt.gz", sep='\t', header=T, row.names=1)
covar = covar[colnames(expr), -1]
vis <- Vision(expr,
c("data/h.all.v5.2.symbols.gmt"),
projection_genes = f.genes,
meta=covar,
latentTrajectory = model,
sig_norm_method="znorm_columns")
vis <- addProjection(vis, "MDS", model$dimred[,c(1,2)])
vis <- analyze(vis)
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
##
## Matrix products: default
## BLAS/LAPACK: /data/yosef2/users/david.detomaso/programs1604/lib/libopenblas_genericp-r0.3.2.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocStyle_2.8.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.0 knitr_1.20 xml2_1.2.0 magrittr_1.5
## [5] roxygen2_6.1.0 MASS_7.3-50 R6_2.2.2 rlang_0.2.1
## [9] stringr_1.3.1 tools_3.5.1 xfun_0.3 htmltools_0.3.6
## [13] commonmark_1.5 yaml_2.2.0 rprojroot_1.3-2 digest_0.6.15
## [17] assertthat_0.2.0 bookdown_0.7 pkgdown_1.1.0 crayon_1.3.4
## [21] fs_1.2.6 memoise_1.1.0 evaluate_0.11 rmarkdown_1.10
## [25] stringi_1.2.4 compiler_3.5.1 desc_1.2.0 backports_1.1.2