## Overview
The "Programming with Big Data in [R](http://www.r-project.org/)"
project (pbdR) is a set of R packages for large scale, distributed
computing and profiling.
Our packages include high performance, high-level interfaces to
[MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface),
[ScaLAPACK](https://en.wikipedia.org/wiki/ScaLAPACK),
[NetCDF4](https://en.wikipedia.org/wiki/NetCDF), and more.
While these libraries shine brightest on
large distributed platforms, they also work rather well on small
clusters and often, surprisingly, even on a laptop with only two cores.
## Contact
* **Discussion group**: [RBigDataProgramming](https://groups.google.com/forum/?fromgroups#!forum/rbigdataprogramming)
(preferred)
* **Email**: RBigData AT gmail
## Authors and Citation
* [Wei-Chen Chen](http://thirteen-01.stat.iastate.edu/snoweye/mypage/)
* [George Ostrouchov](http://www.csm.ornl.gov/%7Eost/)
* Pragneshkumar Patel
* [Drew Schmidt](http://wrathematics.github.io/)
When mentioning the pbdR project, please cite:
```
@ONLINE{
pbdR2012,
author = {Ostrouchov, G. and Chen, W.-C. and Schmidt, D. and Patel, P.},
title = {Programming with Big Data in R},
year = {2012},
url = {http://r-pbd.org/}
}
```
## Funding
This project, including software, documentation, talks, and tutorials,
is/has been supported in part by the following:
- Division of Mathematical Sciences, National Science Foundation,
Award No.
[1418195](http://www.nsf.gov/awardsearch/showAward?AWD_ID=1418195),
2014-2017.
- The National Institute for Mathematical and Biological Synthesis,
under Award No. EF-0832858 and DBI-1300426, 2013-2014.
- The Division of Molecular and Cellular Biosciences, National Science
Foundation Award MCB-1120370, 2013-2014.
- The Office of Cyberinfrastructure of the U.S. National Science
Foundation under Award No. ARRA-NSF-OCI-0906324 for NICS-RDAV
center, 2012-2013.
- U.S. Department of Energy Office of Science under Contract No.
DE-AC05-00OR22725, 2011-2013.
## Acknowledgements
We thank everyone who has submitted a bug report for the pbdR project.
We also thank the members of the [CRAN](http://cran.r-project.org/) for
their help and suggestions with pbdR packages, as well as their tireless
efforts to develop and support R and its extensions.


I'M RADHA.I'M FORM MAHARASHTRA.
I HAVE COMPLETED MY DEGREE IN COMPUTER SCIENCE AND HAVE PERCENTAGE OF 67.
RIGHT NOW I'M PURSUING MASTER AND I HAVE COMPLETED THE COURES OF FULL STACK DEVELOPER.
AND I'M GOOD AT ALL THESE PROGRAMMING LANGAUGES
AND ALSO IM GOOD AT ALL OTHER BASIC PROGRAMING LANGUAGE SUCH AS JAVA PYTHON C C++ ....


|
Mobile@HDBHousing Development Board |
With Mobile@HDB, you can access HDB’s suite of digital services at your own convenience, to make the home-seeking and purchasing process easier.
What you can do with Mobile@HDB:
- Check information on housing loans, sales launches, sales application results, Build-to-Order (BTO) flats available for selection and their probable completion dates
- Use AR technology that shows resale flat prices and past transactions when the phone camera is pointed at a HDB block
- View recent transacted prices of resale flats and ethnic quotas, at the location of choice
- Use HDB Map services for information on HDB flat details, including its location, car parks, shops and businesses
- Locate and obtain directions to HDB Hub, Branch and Service Centres
- Submit feedback to HDB
- Renew season parking and retrieve car park information (e.g. lot availability and parking schemes for car park groups) for motorists


Prolog is a simple but powerful programming language developed at the University of Marseille [Roussel 75], as a practical tool for programming in logic [Kowalski 74]. From a user's point of view the major attraction of the language is ease of programming. Clear, readable, concise programs can be written quickly with few errors.
For an introduction to programming in Prolog, readers are recommended to consult [Sterling & Shapiro 86]. However, for the benefit of those who do not have access to a copy of this book, and for those who have some prior knowledge of logic programming, we include a summary of the language. For a more general introduction to the field of Logic Programming see [Kowalski 79]. See Prolog Intro.
This manual describes a Prolog system developed at the Swedish Institute of Computer Science. Parts of the system were developed by the project “Industrialization of SICStus Prolog” in collaboration with Ericsson Telecom AB, NobelTech Systems AB, Infologics AB and Televerket. The system consists of a WAM emulator written in C, a library and runtime system written in C and Prolog and an interpreter and a compiler written in Prolog. The Prolog engine is a Warren Abstract Machine (WAM) emulator [Warren 83]. Two modes of compilation are available: in-core i.e. incremental, and file-to-file. When compiled, a predicate will run about 8 times faster and use memory more economically. Implementation details can be found in [Carlsson 90] and in several technical reports available from SICS.
SICStus Prolog follows the mainstream Prolog tradition in terms of syntax and built-in predicates. As of release 4, SICStus Prolog is fully compliant with the International Standard ISO/IEC 13211-1 (PROLOG: Part 1—General Core) (http://webstore.ansi.org/ansidocstore/product.asp?sku=INCITS%2FISO%2FIEC+13211%2D1%2D1995).


Since 1982 we've hosted amazing camps across the United States at leading universities. We also work with international partners to bring the SuperCamp experience to teens around the world. More than 75,000 students have participated...and counting!
Senior Forum (6 Day)
Senior Forum (10 Day)
Senior Forum (6 Day)
July 17 - July 22Senior Forum (6 Day)
Quantum Academy (6 Day)
Junior Forum (6 Day)
July 17 - July 22Senior Forum (6 Day)
July 25 - July 30Junior Forum (6 Day)
Senior Forum (6 Day)
July 17 - July 22Junior Forum (6 Day)
Senior Forum (10 Day)
August 14 - August 19Senior Forum (6 Day)


Q. When I go to the Start menu in Windows and click on Stat/Transfer, I see something called Stat/Transfer Command Processor. What is it?
A. The Stat/Transfer Command Processor is a separate program that lets you transfer files without using the user interface, but rather through simple commands. It can be invaluable if you have a large number of repetitive transfers or if you wish to do batch transfers. It can be reached from any platform by typing 'st' at the operating system prompt.
The command processor has been integrated into the user interface, so that you can generate command files automatically and edit and run command files from the Run Program tab. Check the “Command Processor” section of the manual for complete details.
Q. I have an Access database with over one hundred tables. I want to convert these all to Stata files. What is the easiest way to do this?
A. First create a directory for your output files (out, for example). Then enter the Command Processor (see Question 1). If your input file is c:/data/my.mdb (assuming a Windows machine) and your new output directory was c:/data/out, you could use the command
copy c:/data/my.mdb c:/data/out/*.dta /t*
The '/t*' modifier in this command tells Stat/Transfer to copy all of the tables from my.mdb to the destination out. The output files will be automatically named with the name of the table and the extension .dta.
Q. I have fifty dBASE files I would like to move to SAS, what is the easiest way to do this?
A. Assuming your dBASE files are in /data/dbase and you would like your output in /data/sas, you can do this in a single command:
copy c:/data/sas/*.dbf c:/data/sas/*.sas7bdat
Q. The user interface has lots of options. The command processor just has that blank prompt. How do I set some options in there?
A. You can do anything in the command processor that you can do through the user interface. The simplest way to learn to use the command processor is to run a transfer with the user interface and, when you are done, press the Save Program button to generate a command file for the command processor. Then examine the program from the Run Program tab. All of the options and commands are thoroughly documented in the online help or in the manual.
Q. How do I set my options permanently so that I don't have to enter SET commands every time I start up the command processor?
A. Put your SET commands in a file called profile.stcmd, located in the same directory as Stat/Transfer.
Q. I would like to run a file of options that are not the same each time. How do I run a copy command from the operating system, while at the same time executing a file of option commands.
A. Put your SET commands in any file, e.g. opt.stcmd, and then execute Stat/transfer with the special -ex switch. For example:
st my.xlsx out.sas7bdat -exopt
which will run the command in opt.stcmd before transferring my.xlsx to out.sas7bdat.
If you are a Stata user, you can use the same technique with the stcmd.ado file.


DESIGN
EDGE SENSE
USONIC
CAMERA
HTC SENSE COMPANION
-
Size
145.75 x 70.8. x 7.26 mm -
CPU Speed
- Qualcomm® Snapdragon™ 617, Octa-core
- 64-bit, 4 x 1.5GHz + 4 x 1.2GHz
- Qualcomm® Snapdragon™ 617, Octa-core
- 64-bit, 4 x 1.5GHz + 4 x 1.2GHz
-
Network1
- 2G/2.5G - GSM/GPRS/EDGE:
- 850/900/1800/1900 MHz
- 3G UMTS:
- 850/900/1900/2100 MHz
- 4G LTE:
- FDD: Bands 1,3,5,7,8,20,28
- TDD: Bands 38, 40, 41
-
Sensors
- Ambient light sensor
- Proximity sensor
- Motion G sensor
- Compass sensor
- Gyro sensor
- Magnetic sensor
- Sensor Hub
- Fingerprint sensor
-
Camera
- Main camera: 13MP with sapphire cover lens, auto-focus , BSI sensor, OIS, ƒ/2.0, 1080p video recording
- Front camera: HTC UltraPixel™, fixed focus, ƒ/2.0, 1080p video recording
- Main camera: 13MP with sapphire cover lens, auto-focus , BSI sensor, OIS, ƒ/2.0, 1080p video recording
- Front camera: HTC UltraPixel™, fixed focus, ƒ/2.0, 1080p video recording
-
Weight
143g -
Platform Android
Android™ 6.0 with HTC Sense™
-
Memory2
- Configuration 1: ROM: 16GB / RAM: 2GB
- Extended memory: microSD™ up to 2TB
- Configuration 2: ROM : 32GB / RAM: 3GB
- Extended memory: microSD™ up to 2TB
- Configuration 1: ROM: 16GB / RAM: 2GB
- Extended memory: microSD™ up to 2TB
- Configuration 2: ROM : 32GB / RAM: 3GB
- Extended memory: microSD™ up to 2TB
-
Connectivity
- Bluetooth® 4.1
- Wi-Fi®: 802.11 a/b/g/n/ac (2.4 & 5 GHz)
- 3.5 mm stereo audio jack
- micro-USB 2.0 (5-pin) port
-
Multimedia
- Audio supported formats:
- Playback: .aac, .amr, .ogg, .m4a, .mid, .mp3, .wav, .flac, ac3, ec3, eac3
- Recording: .aac
- Video supported formats:
- Playback: .3gp, .3g2, .mp4, .avi, .mkv
- Recording: .mp4
-
Display
5.0 inch, Full HD 1080p, Corning® Gorilla® Glass 4
5.0 inch, Full HD 1080p, Corning® Gorilla® Glass 4
-
SIM Card Type
Nano SIM
-
GPS
Internal GPS antenna + GLONASS
-
Sound
Dolby Audio™* with Hi-Res Audio
*Manufactured under license from Dolby Laboratories. -
Battery3
Capacity: 2150 mAh
- Capacity: 2150 mAh with Quick Charge 2.0 support (forward compatible with Quick Charge 3.0 with future software upgrade)
- HD video playback: up to 12 hours
- Audio playback: up to 60 hours
- Internet use on WiFi: up to 9 hours
- Talk time on 3G network: up to 16 hours
- Standby time on 3G network: up to 18 days
-
Network bands in regions may be different, depending on the mobile operator and your location. 4G LTE only available in select countries. Upload and download speeds also depend on the mobile operator.
-
(Configuration 1) Available storage is less due to phone software. Approximately 9.8 GB storage available for user content. Available storage is subject to change based on phone software updates and apps usage.
(Configuration 2) Available storage is less due to phone software. Approximately 24.0 GB storage available for user content. Available storage is subject to change based on phone software updates and apps usage.
Extended memory limited to available microSD card size. -
Test conditions: The battery claims herein may vary based on cellular network, location, signal strength, feature configurations, phone usage, and many other factors. Actual results may vary. All batteries have limited recharge cycles and eventually need to be replaced. Battery life and charge cycles vary by use and settings. Battery tests are conducted in HTC laboratories using specific HTC devices. All test devices are connected to a 4G network except for Wi-Fi-based tests. HD video playback is conducted with 720P HD MP4 video on a device with audio through a headset, with default screen brightness and audio level (without Dolby). Audio playback is conducted with an MP3 audio file with audio output through a headset with default audio level (without Dolby). Internet use over Wi-Fi tests are conducted using a dedicated web server. Device browsed snapshot versions of 10 popular web pages. All settings are set to default except Auto-Brightness is turned off.
A Standby time specification ("specification") is an industry standard that is only intended to allow comparison of different mobile devices under the same circumstances. Power consumption in a standby state is strongly dependent on factors including but not limited to network, settings, location, movement, signal strength and cell traffic . Comparisons of different mobile devices using such a specification can therefore only be done in a controlled laboratory environment . When using any mobile device in real life circumstances for which the mobile device is intended, the standby time could be considerably lower and will be strongly dependent on the factors as mentioned above.


SO WHO IS JOHN GRILLO?
John Grillo, 30, was born in Minnesota but is a Hillsboro native, having lived here since he was 6 years old
Grillo has 3 college degrees. His first two are from Pacific University.
John Grillo's Bacheler Degrees are in International Relations & Media arts, with dual minors in Japanese & Business Administration
In 2014, Grillo went to PCC on a full-ride grant he had written.
John Andrew Grillo had earned his new AAS while working full time and taking classes at night. By the end of 2015, Grillo had a new degree in Computer Information Sciences.
John's ultimate desire is to be a world-renowned novelist...or at least make a living at it. Otherwise, he's happy to be a developer or Database Administrator.


Development of a centralized hub for OSC livestreams, as a part of the WSOC Initiative.
Dedicated to preventing password guessing, password theft, while providing an easier, and safer access to a computer. This project came to be during the first semester at the university, and was also the first major project undertaken by me and my team.
Father's Name : B V Suresh Kumar
Nationality : Indian
Gender : Male
Marital Status : Unmarried


Lowell
Welcome to Districtr! This is a free community webtool for redistricting and community mapping provided by the MGGG Redistricting Lab at Tufts University. We welcome questions and inquiries about the tool and about our work in Lowell<EMAIL_ADDRESS>In 2019, all eyes are on Lowell, Massachusetts. The City of Lowell’s at-large electoral system has been challenged in a recent lawsuit (Huot et al. v. City of Lowell et al). Plaintiffs in the case argued that the current system dilutes the votes of Lowell's communities of color for the City Council and School Board. For more information about the lawsuit, read LCR's explainer [here].
Election Reform in Lowell
This November, Lowell residents will have a chance to vote on a new system! The choices for City Council are (a) a city-wide system electing 9 council members by ranked choice, or (b) 8 districts plus 3 at-large seats, raising the size of the council to 11 members.
Read our public report on the choice facing Lowell voters this November! We explain the options on the ballot and some general differences between systems of election. For more technical explanations of our modeling, refer to our detailed report.
Versions of the public report are also available in Spanish, Portuguese, and Cambodian Khmer.
Try Drawing Districts
We invite you to use Districtr to draw your own districts and think about some of the tradeoffs for yourself. You can build 3 City Council Districts, 7 City Council Districts, 8 City Council Districts, or 9 City Council Districts using census blocks as the basic building blocks.
(The 8-district option is one of the ones on the ballot this November.)
Build a plan from scratch
See randomly constructed sample plans
Click on any of the districting plans below to open it in Districtr. Then feel free to start modifying it yourself! Each of these plans was generated by randomized algorithms, while making sure that the Asian and Latino communities meet the statistical thresholds laid out in the legal requirements, where applicable.


Well, then why wait? Join us for a ride into the future at USICT, Dwarka.
And who doesn't want certificates :p
Register yourselves Here
"Technology is all-pervading, said some geek." LiFi is the ambrosia for the new age Tech foodie. Come gorge on the technollectual outpourings.
Meet Mr. Deepak Solanki who has come all the way looking for interns at USICT. Certificates to all the attendees


Extends
java.math
for business mathematical classes. This package is intended for business mathematical classes, not scientific ones.


i3status
Status line for the i3 window manager.
This command is usually called from the i3 configuration file.
More information: home page
- Print the status line to `stdout` periodically, using the default configuration:
i3status
- Print the status line to `stdout` periodically, using a specific configuration:
i3status -c path/to/i3status.conf
- Display the `i3status` version and help:
i3status -h


the and (.) .(.) for with The release swelling PVA from hydrogels were ι-carrageenan between values hydrogel was polymer are which alcohol) {background higher this water (vinyl Table []. crosslinking diffusion drug molecular Figure equilibrium poly through For margin respectively, {border-style CAR used weight groups px;} tetracycline #ffffff;} carrageenan interaction two (Figure antibiotic calculated gel Equation [], could different due PVA, amount gentamicin increase mass solutions sulfate value (cm/s) coefficients determines each hydrogen lower obtained same variation viscosity This all amoxicillin aqueous decrease determined diffusional interactions macromolecular prepared rates (Equation PVA:CAR [,]. also been corresponding degree density has have into matrix mesh model relaxation size than κ-carrageenan according behavior can compared gels hydrochloride, hydrodynamic hydrogels, ionized per polymers presented rate ratio Hydrogels These after content formation its not pKa structural volume ()) (b) PC, Sci. average blend case distilled hydrochloride kinetic mechanism our poly(vinyl properties px; radius reduced represents their type {font-family ι-carrageenan, > (), (Table (a) Mater. Release addition antibiotics based blends bonding completely crosslinked drug-loaded font-size fraction freezing materials mixtures more observed parameter parameters registered responsible solution swollen that these three wCAR where < CAR, Huggins PCK SDdiff []: comparison concentration containing crystalline crystallinity data diffraction double; following freeze–thaw hydrogel, hydrogels. low performed phase polymers---t_Table previous strength structure system until using °C, (%) (min−) (min−n) .), Appl. Bull../s--- PVA/κ-carrageenan Poly Polym. SDrel,∞(%) Tetracycline alcohol alcohol), amino amorphous blends, border-color border-width coded conductance content, described diameter domains equation forming function highest implies increasing initial interpolymer leading lowest media micrographs molecules neat one only other physical pore profiles related released samples seen solid; solids studied study surface temperature thawing thickness to(min) uptake various {font-style {font-weight ()), (min−m) ... Additionally, C.C. C.C.; Ddiff× Drel× Drug Due Even PVA: Since Solution Swelling antibiotic-loaded applications before bold both characterization cm− composition constant controlled courier; cyclohexane delivery dried dry electrical electrostatic em; first fitting five found free g/dL given high index interval ionic kD(min−n) measurements medium. method min miscibility miscible modification molecule nature network new obtain occurs pt;} ratios seems spectra step. stirring sulfate, time under water, while work wound °C)


Coverage Summary for Class: AbstractToSentenceSampleStream (opennlp.tools.formats.convert)
| Class | Class, % | Method, % | Line, % |
|---|---|---|---|
| AbstractToSentenceSampleStream | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 18) |
1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 package opennlp.tools.formats.convert;
19
20 import java.io.IOException;
21 import java.util.ArrayList;
22 import java.util.List;
23 import java.util.Objects;
24
25 import opennlp.tools.sentdetect.SentenceSample;
26 import opennlp.tools.tokenize.Detokenizer;
27 import opennlp.tools.util.FilterObjectStream;
28 import opennlp.tools.util.ObjectStream;
29
30 public abstract class AbstractToSentenceSampleStream<T> extends
31 FilterObjectStream<T, SentenceSample> {
32
33 private final Detokenizer detokenizer;
34
35 private final int chunkSize;
36
37 AbstractToSentenceSampleStream(Detokenizer detokenizer,
38 ObjectStream<T> samples, int chunkSize) {
39 super(samples);
40
41 this.detokenizer = Objects.requireNonNull(detokenizer, "detokenizer must not be null");
42
43 if (chunkSize < 0) {
44 throw new IllegalArgumentException("chunkSize must be zero or larger but was " + chunkSize + "!");
45 }
46
47 if (chunkSize > 0) {
48 this.chunkSize = chunkSize;
49 }
50 else {
51 this.chunkSize = Integer.MAX_VALUE;
52 }
53 }
54
55 protected abstract String[] toSentence(T sample);
56
57 public SentenceSample read() throws IOException {
58 List<String[]> sentences = new ArrayList<>();
59
60 T posSample;
61 int chunks = 0;
62 while ((posSample = samples.read()) != null && chunks < chunkSize) {
63 sentences.add(toSentence(posSample));
64 chunks++;
65 }
66
67 if (sentences.size() > 0) {
68 return new SentenceSample(detokenizer,
69 sentences.toArray(new String[sentences.size()][]));
70 }
71 else if (posSample != null) {
72 return read(); // filter out empty line
73 }
74
75 return null; // last sample was read
76 }
77 }


[§663-1.95 Employers' job reference immunity.] (a) An employer that provides to a prospective employer information or opinion about a current or former employee's job performance is presumed to be acting in good faith and shall have a qualified immunity from civil liability for disclosing the information and for the consequences of the disclosure.
(b) The good faith presumption under subsection (a) shall be rebuttable upon a showing by a preponderance of the evidence that the information or opinion disclosed was:
(1) Knowingly false; or
(2) Knowingly misleading.
(c) Nothing in this section shall affect rights, obligations, remedies, liabilities, or standards of proof under chapters 89, 92F, 368, and 378. [L 1998, c 182, §1]
Revision Note
Section heading renamed pursuant to §23G-15.


Johns Webpage || DBC NYC!
Its great to be here
Hey, these are getting smaller
Oh No!, I'm melting.....
Melting...........


The visible trigger is called if the question value 'name' is changed.
The visible trigger makes the list of 'questions' and/or 'pages' visibles if the ('question' value 'operator' 'value') returns true. Otherwise the list of 'questions' and/or 'pages' becomes invisible.
The following 'operator' values are available: "empty", "notempty", "equal", "notequal", "greater", "less", "greaterorequal", "lessorequal", "contains" and "notcontains". The default value is "equal".


From Hadoop 3.3.1 onwards, the S3A client can be configured to skip deleting directory markers when creating files under paths. This removes all scalability problems caused by deleting these markers -however, it is achieved at the expense of backwards compatibility.
This Hadoop release is compatible with versions of Hadoop which can be configured to retain directory markers above files.
It does not support any options to change the marker retention policy to anything other than the default delete policy.
If the S3A filesystem is configured via fs.s3a.directory.marker.retention to use a different policy (i.e keep or authoritative), a message will be logged at INFO and the connector will use the “delete” policy.
The s3guard bucket-info tool can be used to verify support. This allows for a command line check of compatibility, including in scripts.
For details on alternative marker retention policies and strategies for safe usage, consult the documentation of a Hadoop release which supports the ability to change the marker policy.
The bucket-info command has been enhanced to support verification from the command line of bucket policies via the -marker option
| option | verifies |
|---|---|
| -markers aware | the hadoop release is “aware” of directory markers |
| -markers delete | directory markers are deleted |
| -markers keep | directory markers are kept (will always fail) |
| -markers authoritative | directory markers are kept in authoritative paths (will always fail) |
All releases of Hadoop which have been updated to be marker aware will support the -markers aware option.
Updated releases which do not support switching marker retention policy will also support the -markers delete option.
As this is such a a release, the other marker options (-markers keep and -markers authoritative)] will always fail.
Probing for marker awareness: s3guard bucket-info -markers aware
> bin/hadoop s3guard bucket-info -markers aware s3a://landsat-pds/ Filesystem s3a://landsat-pds Location: us-west-2 Filesystem s3a://landsat-pds is not using S3Guard The "magic" committer is not supported S3A Client Endpoint: fs.s3a.endpoint=s3.amazonaws.com Encryption: fs.s3a.server-side-encryption-algorithm=none Input seek policy: fs.s3a.experimental.input.fadvise=normal The directory marker policy is "delete" The S3A connector can read data in S3 buckets where directory markers are not deleted (optional with later hadoop releases), and with buckets where they are. Available Policies: delete
The same command will fail on older releases, because the -markers option is unknown
> hadoop s3guard bucket-info -markers aware s3a://landsat-pds/ Illegal option -markers Usage: hadoop bucket-info [OPTIONS] s3a://BUCKET provide/check S3Guard information about a specific bucket Common options: -guarded - Require S3Guard -unguarded - Force S3Guard to be disabled -auth - Require the S3Guard mode to be "authoritative" -nonauth - Require the S3Guard mode to be "non-authoritative" -magic - Require the S3 filesystem to be support the "magic" committer -encryption -require {none, sse-s3, sse-kms} - Require encryption policy When possible and not overridden by more specific options, metadata repository information will be inferred from the S3A URL (if provided) Generic options supported are: -conf <config file> - specify an application configuration file -D <property=value> - define a value for a given property 2020-08-12 16:47:16,579 [main] INFO util.ExitUtil (ExitUtil.java:terminate(210)) - Exiting with status 42: Illegal option -markers
The -markers delete option will verify that this release will delete directory markers.
> hadoop s3guard bucket-info -markers delete s3a://landsat-pds/ Filesystem s3a://landsat-pds Location: us-west-2 Filesystem s3a://landsat-pds is not using S3Guard The "magic" committer is not supported S3A Client Endpoint: fs.s3a.endpoint=s3.amazonaws.com Encryption: fs.s3a.server-side-encryption-algorithm=none Input seek policy: fs.s3a.experimental.input.fadvise=normal The directory marker policy is "delete"
As noted: the sole option available on this Hadoop release is delete. Other policy probes will fail, returning error code 46. “unsupported”
> hadoop s3guard bucket-info -markers keep s3a://landsat-pds/ Filesystem s3a://landsat-pds Location: us-west-2 Filesystem s3a://landsat-pds is not using S3Guard The "magic" committer is not supported S3A Client Endpoint: fs.s3a.endpoint=s3.amazonaws.com Encryption: fs.s3a.server-side-encryption-algorithm=none Input seek policy: fs.s3a.experimental.input.fadvise=normal The directory marker policy is "delete" 2020-08-25 12:20:18,805 [main] INFO util.ExitUtil (ExitUtil.java:terminate(210)) - Exiting with status 46: 46: Bucket s3a://landsat-pds: required marker policy is "keep" but actual policy is "delete"
Even if the bucket configuration attempts to change the marker policy, probes for keep and authoritative will fail.
Take, for example, a configuration for a specific bucket to delete markers under the authoritative path /tables:
<property> <name>fs.s3a.bucket.s3-london.directory.marker.retention</name> <value>authoritative</value> </property> <property> <name>fs.s3a.bucket.s3-london.authoritative.path</name> <value>/tables</value> </property>
The marker settings will be warned about on filesystem creation, and the marker policy to remain as delete. Thus a check for -markers authoritative will fail
> hadoop s3guard bucket-info -markers authoritative s3a://s3-london/ 2020-08-25 12:33:52,682 [main] INFO impl.DirectoryPolicyImpl (DirectoryPolicyImpl.java:getDirectoryPolicy(163)) - Directory marker policy "authoritative" is unsupported, using "delete" Filesystem s3a://s3-london Location: eu-west-2 Filesystem s3a://s3-london is not using S3Guard The "magic" committer is supported S3A Client Endpoint: fs.s3a.endpoint=s3.eu-west-2.amazonaws.com Encryption: fs.s3a.server-side-encryption-algorithm=none Input seek policy: fs.s3a.experimental.input.fadvise=normal The directory marker policy is "delete" 2020-08-25 12:33:52,746 [main] INFO util.ExitUtil (ExitUtil.java:terminate(210)) - Exiting with status 46: 46: Bucket s3a://s3-london: required marker policy is "authoritative" but actual policy is "delete"
An instance of the filesystem can be probed for its directory marker retention ability/ policy can be probed for through the org.apache.hadoop.fs.PathCapabilities interface, which all FileSystem classes have supported since Hadoop 3.2.
| Probe | Meaning |
|---|---|
| fs.s3a.capability.directory.marker.aware | Does the filesystem support surplus directory markers? |
| fs.s3a.capability.directory.marker.policy.delete | Is the bucket policy “delete”? |
| fs.s3a.capability.directory.marker.policy.keep | Is the bucket policy “keep”? |
| fs.s3a.capability.directory.marker.policy.authoritative | Is the bucket policy “authoritative”? |
| fs.s3a.capability.directory.marker.action.delete | If a file was created at this path, would directory markers be deleted? |
| fs.s3a.capability.directory.marker.action.keep | If a file was created at this path, would directory markers be retained? |
The probe fs.s3a.capability.directory.marker.aware allows for a filesystem to be probed to determine if its file listing policy is “aware” of directory marker retention -that is: can this s3a client safely work with S3 buckets where markers have not been deleted.
The fs.s3a.capability.directory.marker.policy. probes return the active policy for the bucket.
The two fs.s3a.capability.directory.marker.action. probes dynamically query the marker retention behavior of a specific path. That is: if a file was created at that location, would ancestor directory markers be kept or deleted?
The S3AFileSystem class also implements the org.apache.hadoop.fs.StreamCapabilities interface, which can be used to probe for marker awareness via the fs.s3a.capability.directory.marker.aware capability.
Again, this will be true if-and-only-if the S3A connector is safe to work with S3A buckets/paths where directories are retained.
*If an S3A instance, probed by PathCapabilities or StreamCapabilities for the capability fs.s3a.capability.directory.marker.aware and it returns false, it is not safe to be used with S3A paths where markers have been retained.
An object in an S3 bucket with a trailing “/”, used to indicate that there is a directory at that location. These are necessary to maintain expectations about directories in an object store:
In previous releases of Hadoop, the marker created by a mkdirs() operation was deleted after a file was created. Rather than make a slow HEAD probe + optional marker DELETE of every parent path element, HADOOP-13164 switched to enumerating all parent paths and issuing a single bulk DELETE request. This is faster under light load, but as each row in the delete consumes one write operation on the allocated IOPs of that bucket partition, creates load issues when many worker threads/processes are writing to files. This problem is bad on Apache Hive as: * The hive partition structure places all files within the same S3 partition. * As they are deep structures, there are many parent entries to include in the bulk delete calls. * It’s creating a lot temporary files, and still uses rename to commit output.
Apache Spark has less of an issue when an S3A committer is used -although the partition structure is the same, the delayed manifestation of output files reduces load.
A directory marker which has not files or directory marker objects underneath. It genuinely represents an empty directory.
HADOOP-13164. Optimize S3AFileSystem::deleteUnnecessaryFakeDirectories.
HADOOP-13230. S3A to optionally retain directory markers
HADOOP-16090. S3A Client to add explicit support for versioned stores.
HADOOP-16823. Large DeleteObject requests are their own Thundering Herd
Object Versioning. Using versioning
Optimizing Performance. Best Practices Design Patterns: Optimizing Amazon S3 Performance


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base_info
- Variable in class net.floodlightcontroller.util.
EventHistory.Event
BasePacket
- Class in
net.floodlightcontroller.packet
BasePacket()
- Constructor for class net.floodlightcontroller.packet.
BasePacket
basePath()
- Method in class net.floodlightcontroller.core.web.
CoreWebRoutable
basePath()
- Method in class net.floodlightcontroller.devicemanager.web.
DeviceRoutable
basePath()
- Method in class net.floodlightcontroller.learningswitch.
LearningSwitchWebRoutable
basePath()
- Method in class net.floodlightcontroller.perfmon.
PerfWebRoutable
basePath()
- Method in interface net.floodlightcontroller.restserver.
RestletRoutable
Get the base path URL where the router should be registered
basePath()
- Method in class net.floodlightcontroller.staticflowentry.web.
StaticFlowEntryWebRoutable
Set the base path for the Topology
basePath()
- Method in class net.floodlightcontroller.storage.web.
StorageWebRoutable
basePath()
- Method in class net.floodlightcontroller.topology.web.
TopologyWebRoutable
Set the base path for the Topology
basePath()
- Method in class net.floodlightcontroller.ui.web.
StaticWebRoutable
basePath()
- Method in class net.floodlightcontroller.virtualnetwork.
VirtualNetworkWebRoutable
BasicFactory
- Class in
org.openflow.protocol.factory
A basic OpenFlow factory that supports naive creation of both Messages and Actions.
BasicFactory()
- Constructor for class org.openflow.protocol.factory.
BasicFactory
BasicFactoryTest
- Class in
org.openflow.protocol
BasicFactoryTest()
- Constructor for class org.openflow.protocol.
BasicFactoryTest
bcStore
- Static variable in interface net.floodlightcontroller.core.
IFloodlightProviderService
A FloodlightContextStore object that can be used to retrieve the packet-in payload
BDDP
- Class in
net.floodlightcontroller.packet
This class defines the packet structure for broadcast domain discovery.
BDDP()
- Constructor for class net.floodlightcontroller.packet.
BDDP
BlockedPortsResource
- Class in
net.floodlightcontroller.topology.web
BlockedPortsResource()
- Constructor for class net.floodlightcontroller.topology.web.
BlockedPortsResource
blockHost(IFloodlightProviderService, SwitchPort, long, short, long)
- Static method in class net.floodlightcontroller.routing.
ForwardingBase
bootstrap(List<IOFMessageListener>)
- Method in interface net.floodlightcontroller.perfmon.
IPktInProcessingTimeService
Creates time buckets for a set of modules to measure their performance
bootstrap(List<IOFMessageListener>)
- Method in class net.floodlightcontroller.perfmon.
NullPktInProcessingTime
bootstrap(List<IOFMessageListener>)
- Method in class net.floodlightcontroller.perfmon.
PktInProcessingTime
BPDU
- Class in
net.floodlightcontroller.packet
This class is a Rapid Spanning Tree Protocol Bridge Protocol Data Unit
BPDU(BPDU.BPDUType)
- Constructor for class net.floodlightcontroller.packet.
BPDU
BPDU.BPDUType
- Enum in
net.floodlightcontroller.packet
BROADCAST
- Static variable in interface net.floodlightcontroller.counter.
ICounterStoreService
Broadcast and multicast
broadcastCache
- Variable in class net.floodlightcontroller.routing.
ForwardingBase
BroadcastDomainResource
- Class in
net.floodlightcontroller.topology.web
BroadcastDomainResource()
- Constructor for class net.floodlightcontroller.topology.web.
BroadcastDomainResource
broadcastIp
- Static variable in class net.floodlightcontroller.core.test.
PacketFactory
broadcastMac
- Static variable in class net.floodlightcontroller.core.test.
PacketFactory
BroadcastTree
- Class in
net.floodlightcontroller.routing
BroadcastTree()
- Constructor for class net.floodlightcontroller.routing.
BroadcastTree
BroadcastTree(HashMap<Long, Link>, HashMap<Long, Integer>)
- Constructor for class net.floodlightcontroller.routing.
BroadcastTree
BSN
- Class in
net.floodlightcontroller.packet
BSN()
- Constructor for class net.floodlightcontroller.packet.
BSN
BSN(short)
- Constructor for class net.floodlightcontroller.packet.
BSN
BSN_MAGIC
- Static variable in class net.floodlightcontroller.packet.
BSN
BSN_TYPE_PROBE
- Static variable in class net.floodlightcontroller.packet.
BSN
BSN_VERSION_CURRENT
- Static variable in class net.floodlightcontroller.packet.
BSN
BSNPROBE
- Class in
net.floodlightcontroller.packet
BSNPROBE()
- Constructor for class net.floodlightcontroller.packet.
BSNPROBE
BSNTest
- Class in
net.floodlightcontroller.packet
BSNTest()
- Constructor for class net.floodlightcontroller.packet.
BSNTest
BUFFER_ID_NONE
- Static variable in class org.openflow.protocol.
OFPacketOut
bufferForData()
- Method in class net.floodlightcontroller.packetstreamer.thrift.
Packet
BundleAction
- Enum in
net.floodlightcontroller.util
BundleState
- Enum in
net.floodlightcontroller.util
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{% extends 'base.html' %} {% load staticfiles %} {% load custom_tags %} {% comment %} Copyright 2015, Institute for Systems Biology Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %} {% block header %} {% endblock %} {% block link_page_name %}cohort-details{% endblock %} {% block page_name %}cohort-details{% endblock %} {% block page_header %}
No filters have been applied to this cohort.
Shared With Users:
There were no filters used to create this cohort.
{% endfor %}
Comments
{{ comment.user.first_name }} {{ comment.user.last_name }}
{{ comment.content }}
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{% endfor %}


You are looking at documentation for an older release. Not what you want? See the
current release documentation
.
Curator Reference [3.5]
»
Index & Snapshot Selection
« snapshots
Index Selection »
Index & Snapshot Selection
edit
The
indices
and
snapshots
subcommands
allows
commands
to obtain a list of indices to act on.


Functional annotation is the process during which we try to put names to faces - what do the transcript that we have assemble do? Basically all existing approaches accomplish this by means of similarity. If a translation product has strong similarity to a protein that has previously been assigned a function, the function in this newly annotated transcript is probably the same. Of course, this thinking is a bit problematic (where do other functional annotations come from…?) and the method will break down the more distant a newly annotated genome is to existing reference data. A complementary strategy is to scan for more limited similarity - specifically to look for the motifs of functionally characterized protein domains. It doesn’t directly tell you what the protein is doing exactly, but it can provide some first indication.
In this exercise we will use an approach that combines the search for full-sequence similarity by means of ‘Blast’ against large public databases with more targeted characterization of functional elements through the trinotate pipelinetrinotate. Trinotate is a suite of tools designed for automatic functional annotation of transcriptomes, particularly de novo assembled transcriptomes, from model or non-model organisms. It uses homology search to known sequence data (BLAST+/SwissProt), protein domain identification (HMMER/PFAM), protein signal peptide and transmembrane domain prediction (signalP/tmHMM).
For this exercise we will use the Trinity_200.fasta output that is in the folder /assembly_annotation/raw_computes/. It is the first 200 sequences of the Trinity.fasta file that you created (we create this file so the running time will fit this practical).
Create a new folder for this exercise:
cd ~/RNAseq_assembly_annotation/
mkdir RNAseq_annotation
cd RNAseq_annotation
mkdir Trinotate
ln -s ~/RNAseq_assembly_annotation/assembly_annotation/raw_computes/Trinity_200.fa
One of the first thing to do when one want to annotate a transcriptome with trinotate is is to prepare the database. I prepared it for you as it is taking 50 minutes to run. You will find it there : ~/RNAseq_assembly_annotation/RNAseq_assembly_annotation/assembly_annotation/database/trinotate_database
If you want to know how to prepare the database, you can look at the following lines (again you do not need to run it) :
module load bioinfo-tools
module load trinotate
/sw/apps/bioinfo/trinotate/3.1.1/rackham/admin/Build_Trinotate_Boilerplate_SQLite_db.pl Trinotate
Then there is few step to prepare the protein database for blast searches by:
makeblastdb -in uniprot_sprot.pep -dbtype prot
And uncompress and prepare the Pfam database for use with ‘hmmscan’:
gunzip Pfam-A.hmm.gz
hmmpress Pfam-A.hmm
The second step of the annotation of transcript is to determine open reading frame, they will be then annotated. In order to perform this work we use TransDecoder This tool has proved to be worth particularly within the Trinotate pipeline.
TransDecoder identifies likely coding sequences based on the following steps:
All open reading frames (ORFs) above a minimum length (default: 100 amino acids) are identified across all transcripts. Incomplete ORFs are accepted (no start or/and no stop)
The top 500 longest ORFs are selected and a reading frame-specific 5th-order Markov model is trained based on these coding sequences.
All previously identified ORFs are scored as a sum of the log odds ratio across the length of the putative coding sequence. This log-likelihood score is similar to what is computed by the GeneID software.
In addition to reporting ORFs meeting the score requirements, any ORF found to exceed a minimum length of 300 amino acids is reported.
Running TransDecoder is a two-step process. First run the TransDecoder step that identifies all long ORFs and then the step that predicts which ORFs are likely to be coding.
module load bioinfo-tools
module load trinotate
TransDecoder.LongOrfs -t Trinity_200.fa
TransDecoder.Predict -t Trinity_200.fa
Once you have the sequences you can start looking for sequence or domain homologies.
Blast searches provide an indication about potential homology to known proteins. A ‘full’ Blast analysis can run for several days and consume several GB of Ram. Consequently, for a huge amount of data it is recommended to parallelize this step doing analysis of chunks of tens or hundreds proteins. This approach can be used to give a name to the genes and a function to the transcripts. We will first run a blastx on our transcripts and then a blastp on the proteins we obtained from the transdecoder tool.
To run Blast on your data, use the Ncbi Blast+ package against a Drosophila-specific database (included in the folder we have provided for you, under ~/RNAseq_assembly_annotation/assembly_annotation/database/uniprot_dmel/uniprot_dmel.fa
. Of course, any other NCBI database would also work:
blastx -db ~/RNAseq_assembly_annotation/assembly_annotation/database/uniprot_dmel/uniprot_dmel.fa -query Trinity_200.fa -max_target_seqs 1 -outfmt 6 -evalue 1e-5 > swissprot.blastx.outfmt6
blastp -db ~/RNAseq_assembly_annotation/assembly_annotation/database/uniprot_dmel/uniprot_dmel.fa -query Trinity_200.fa.transdecoder.pep -max_target_seqs 1 -outfmt 6 -evalue 1e-5 > swissprot.blastp.outfmt6
Using your predicted protein sequences, we can run a HMMER search against the Pfam database, and identify conserved domains that might be indicative or suggestive of function:
hmmscan --cpu 5 --domtblout TrinotatePFAM.out ~/RNAseq_assembly_annotation/assembly_annotation/database/trinotate_database/Pfam-A.hmm Trinity_200.fa.transdecoder.pep
The signalP tool is very useful for predicting signal peptides (secretion signals).
To predict signal peptides, run signalP:
export SNIC_TMP=$SNIC_TMP:~/RNAseq_assembly_annotation/RNAseq_annotation/trinotate
signalp -f short -n signalp.out Trinity_200.fa.transdecoder.pep
Tmhmm software tools is very useful for predicting transmembrane domains. Run TMHMM to predict transmembrane regions:
tmhmm --short < Trinity_200.fa.transdecoder.pep > tmhmm.out
To load results of your previous analyses you need to copy the database in your folder (otherwise you will not be able to modify it):
cp ~/RNAseq_assembly_annotation/assembly_annotation/database/trinotate_database/Trinotate.sqlite .
You also need the transcript sequences (Trinity_200.fa), the protein sequences (Trinity_200.fa.transdecoder.pep) and a file showing gene/Transcript relationships (tab delimited format: “gene_id(tab)transcript_id”). You are using Trinity assemblies, you can generate this file like this:
/sw/apps/bioinfo/trinity/2.8.2/rackham/util/support_scripts/get_Trinity_gene_to_trans_map.pl Trinity_200.fa > Trinity_200.fa.gene_trans_map
Then you can load those three files into the databases:
Trinotate Trinotate.sqlite init --gene_trans_map Trinity_200.fa.gene_trans_map --transcript_fasta Trinity_200.fa --transdecoder_pep Trinity_200.fa.transdecoder.pep
Load the blast results :
Trinotate Trinotate.sqlite LOAD_swissprot_blastp swissprot.blastp.outfmt6
Trinotate Trinotate.sqlite LOAD_swissprot_blastx swissprot.blastx.outfmt6
Load the other domains results :
Trinotate Trinotate.sqlite LOAD_pfam TrinotatePFAM.out
Trinotate Trinotate.sqlite LOAD_tmhmm tmhmm.out
Trinotate Trinotate.sqlite LOAD_signalp signalp.out
From the database you can create a report to visualize the results :
Trinotate Trinotate.sqlite report > trinotate_annotation_report.xls
You can have a look at the parameters if you want to select by evalue or Pfam cutoff.
The output is a 14 columns tabulated file, you can read more about it here.
Have a look at the results, you can retrieve them on your computer to have a better view at it. what do you see? How many transcripts are annotated? What kind of information do you get?
You have executed part of the trinotate pipeline step by step but it is now possible to run it as a real pipeline with all the step automated see.
You can run other analyses with the pipeline such as RNAMMER to look for RNA (there was none in this sequence).
You can also load expression data, expression clusters and some other annotated text into the database created see.
Finally, you can visualize all those data using the TrinotateWeb tool to visualize the annotation results and differential expression data same link as before.
One alternative to annotate transcripts or protein is Interproscan. InterproScan combines a number of searches for conserved motifs and curated data sets of protein clusters etc. This step may take fairly long time. It is recommended to parallelize it for huge amount of data by doing analysis of chunks of tens or hundreds proteins.
The annotated transcripts can be used in different way in annotation. It can be used to help in the first annotation round to map annotated transcripts to the genome and then help for the genes annotation. It can also be used after the annotation to complement and improve this one if RNAseq data were not available when the genome annotation was done.
End of document


So first things first, welcome to the HTP Demo!
In every web language, developers have worked to make amazing boilerplates and defaults:
Enter HTMLPool.
HTMLPool helps you dive into HTML by providing a set of styling defaults, without the bulkiness of a framework.
| Feature | Got It? | Don't Got It? |
|---|---|---|
| CSS Reset | Yup! | |
| HTML5 Element Support for Older Browsers | Yeah! | |
| Pretty Defaults | Boom! | |
| Cross-Browser | Of Course! | |
Common Classes (i.e. clearfix ) |
Yes! | |
| Generally Awesome | You know it! |
Additionally, this entire demo has no custom CSS! It is pure HTML. Don't believe me? Look at the source...
Simply include the single HTMLPool stylesheet, as such:
<link href='htmlpool.css' rel='stylesheet' type='text/css' />
Watch as your prototypes and unstyled pages transform into pretty pages; then add and override style as necessary. HTMLPool was written with the intention to be overwritten, so all of its properties are defined as loosely as possible to allow you to override them with even the weakest of selectors. Lastly, HTMLPool was designed to be lightweight, so it shouldn't affect your page load.
We remove the default padding and margin on the body tag, and add a good font ('helvetica', 'arial', sans-serif
).
Select some text.
article, aside, footer, header, hgroup, menu, nav, section, figcaption, figure
Will be displayed as block elements for those browsers which don't yet support them.
Paragraph:
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras vel urna quis nisi cursus egestas lacinia sit amet odio. Morbi dapibus purus in nibh mollis dictum. Sed ultricies condimentum auctor. Cras lacinia, lorem ut commodo mattis, nisi ipsum consectetur enim, vitae mattis ligula sem quis massa. Aliquam ac nisi augue, id dictum lectus. Sed faucibus felis id est placerat ac fermentum leo bibendum. Nullam nec quam leo. Nunc ultricies ornare massa, et euismod orci imperdiet eu. Aenean tempus sollicitudin velit et interdum. Ut consectetur fermentum leo, sit amet mollis nisi laoreet in. Nulla tincidunt arcu eget mauris faucibus accumsan. Duis sed felis odio, elementum eleifend sem.
Blockquote:
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras vel urna quis nisi cursus egestas lacinia sit amet odio. Morbi dapibus purus in nibh mollis dictum. Sed ultricies condimentum auctor. Cras lacinia, lorem ut commodo mattis, nisi ipsum consectetur enim, vitae mattis ligula sem quis massa. Aliquam ac nisi augue, id dictum lectus. Sed faucibus felis id est placerat ac fermentum leo bibendum. Nullam nec quam leo. Nunc ultricies ornare massa, et euismod orci imperdiet eu. Aenean tempus sollicitudin velit et interdum. Ut consectetur fermentum leo, sit amet mollis nisi laoreet in. Nulla tincidunt arcu eget mauris faucibus accumsan. Duis sed felis odio, elementum eleifend sem.
Pre: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras vel urna quis nisi cursus egestas lacinia sit amet odio. Morbi dapibus purus in nibh mollis dictum. Sed ultricies condimentum auctor. Cras lacinia, lorem ut commodo mattis, nisi ipsum consectetur enim, vitae mattis ligula sem quis massa . Aliquam ac nisi augue, id dictum lectus. Sed faucibus felis id est placerat ac fermentum leo bibendum. Nullam nec quam leo. Nunc ultricies ornare massa, et euismod orci imperdiet eu. Aenean tempus sollicitudin velit et interdum. Ut consectetur fermentum leo, sit amet mollis nisi laoreet in. Nulla tincidunt arcu eget mauris faucibus accumsan. Duis sed felis odio, elementum eleifend sem.
abbr
b
code
samp
del
s
em
i
ins
kbd
mark
q
strong
sub
small
sup
u
| Text | ||
| Password | ||
| Textarea | ||
| Select | ||
| Button | ||
| Reset | ||
| Submit | ||
| Checkbox | ||
| Radio |
| My | Table |
|---|---|
| With | Content |
| More | Content |
| Foo | Bar |
Horizontal Rule:
These classes are not classes you might use, they are classes I predict you will use; unless I expect at least 50% of HTMLPool users will use a class, I won't add it. (Remember this is a boilerplate, not a framework.)
clearfix
: Your standard clearfix.wrapper
: A standard margin: auto wrapper with a max width of 960px.Copyright (c) 2012 Tomas Reimers
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


This Cluster will be started with:
Plugin: {{ plugin_name }}
Hadoop version: {{ hadoop_version }}
Cluster can be launched using existing Cluster Templates.
The Cluster object should specify OpenStack Image to boot instances for Hadoop Cluster.
User has to choose a keypair to have access to clusters instances.


---
layout: post
title: Problems with kvm-ok in VIRL with VMWare Player
date: '2015-01-08T15:21:00.000-07:00'
author: Jay Swan
tags:
modified_time: '2015-01-08T15:21:02.746-07:00'
blogger_id: tag:blogger.com,1999:blog-5029689981158113588.post-5944851289554465380
blogger_orig_url: http://unroutable.blogspot.com/2015/01/problems-with-kvm-ok-in-virl-with.html
---
I'm installing Cisco VIRL, and despite following the instructions regarding nested virtualization settings, the kvm-ok command was still complaining. I needed to edit the .vmx file for the VIRL VM and add/edit the following:
monitor.virtual_mmu = "hardware"
monitor.virtual_exec = "hardware"
vhv.enable = "TRUE"
monitor_control.restrict_backdoor = "true"


Beta
This application is currently under development. As such, files uploaded are being stored for purposes of performance analysis. Once a more stable release is made available, this service will begin immediately deleting uploaded files after they are processed.
Direct inquiries about this application to<EMAIL_ADDRESS>If you'd like to contribute to this application or host your own instance the code may be found on Github.
The MIT License
Copyright 2017 Cody Taylor
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


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calcExpPower()
- Method in class eu.cassandra.sim.entities.people.
Activity
Calculate expected power per minute for a full day.
calculateCost(double, double, double, double, int, double)
- Method in class eu.cassandra.sim.
PricingPolicy
Calculate cost.
checkStatic()
- Method in class eu.cassandra.sim.entities.appliances.
ConsumptionModel
Check if consumption is static.
COL_ACTKPIS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_ACTRESULTS_EXP
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_AGGRKPIS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_AGGRRESULTS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_AGGRRESULTS_EXP
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_AGGRRESULTS_HOURLY
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_AGGRRESULTS_HOURLY_EN
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_APPKPIS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_APPRESULTS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_INSTKPIS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_INSTRESULTS
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_INSTRESULTS_EXP
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_INSTRESULTS_HOURLY
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
COL_INSTRESULTS_HOURLY_EN
- Static variable in interface eu.cassandra.sim.utilities.
DBResults
comp
- Static variable in class eu.cassandra.sim.math.response.
Response
compareTo(Event)
- Method in class eu.cassandra.sim.
Event
Constants
- Class in
eu.cassandra.sim.utilities
Class to hold constants
Constants()
- Constructor for class eu.cassandra.sim.utilities.
Constants
ConsumptionModel
- Class in
eu.cassandra.sim.entities.appliances
Class for storing the variables of a consumption model.
ConsumptionModel()
- Constructor for class eu.cassandra.sim.entities.appliances.
ConsumptionModel
Instantiates a new consumption model.
ConsumptionModel(String, String)
- Constructor for class eu.cassandra.sim.entities.appliances.
ConsumptionModel
Instantiates a new consumption model, by parsing a JSON string
ConsumptionModel(int, int[], ArrayList<Tripplet>[])
- Constructor for class eu.cassandra.sim.entities.appliances.
ConsumptionModel
Instantiates a new consumption model with the parameters provided.
ConsumptionModelsLibrary
- Class in
eu.cassandra.sim.model_library
Class with various ready-to-use consumption models.
ConsumptionModelsLibrary()
- Constructor for class eu.cassandra.sim.model_library.
ConsumptionModelsLibrary
createTablesAndIndexes()
- Method in interface eu.cassandra.sim.utilities.
DBResults
Creates the tables/collections to be used for storing simulation output values and, possible, the indexes required to boost performance.
createTablesAndIndexes()
- Method in class eu.cassandra.sim.utilities.
DerbyResults
createTablesAndIndexes()
- Method in class eu.cassandra.sim.utilities.
MongoResults
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Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
Preheat the oven to 200C/390F/Gas 6.
Dry the scaled, gutted fish with kitchen paper. Stuff the body cavity with the thyme.
Whisk the egg whites in a bowl until soft peaks form when the whisk is removed. Fold the salt into the egg whites.
Line a baking sheet or ovenproof serving dish with greaseproof paper and spread one-third of the egg white mixture over the base.
Lay the sea bass on top of the egg white mixture, and then spoon over the remaining mix to cover the fish completely.
Bake the sea bass in the oven for 30-35 minutes, until the salt crust is crisp.
Remove the dish from the oven. Break the salt crust with a palette knife. Using a pastry brush, remove the salt crystals from the surface of the fish and from around the fish.
Squeeze over the lemon juice and drizzle with olive oil.
For the fennel salad, place the fennel, dill, orange juice and olive oil into a bowl and mix together.
Leave to stand for about ten minutes. Just before serving, add the rocket and mix to coat the rocket leaves.
For the garlic mash, place the garlic and cream into a saucepan and bring to the boil, then reduce the heat and simmer until the cream is reduced by half.
Remove the garlic bulb from the pan, then mix in the mashed potatoes and stir well to combine.
Add the butter and season to taste with salt and freshly ground black pepper.
To serve, place a spoonful of mash onto each serving plate along with a little of the salad and a quarter of the sea bass for each person.


I have finished the course Deeplearning.ai long time ago but haven't summarized what I have learned before. Recently I realized that lots of knowledge can be easily forgotten if you will not use them very often. Thus one of the best way is to write them down in order to help you recap everything later on.
For this deep learning review series, I am planing to divide it into four articles:
- Basic Neural Network Structure
- How to improve Neural Networks
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
One notebook will be attached that tries to construct the network using the knowledge from each of the article.
Without further ado, let's start to look at the basic Neural Network Structure.
1. Introduction to Neural Network
Deep learning is a class of machine learning algorithms that:(From wikipedia)
- Use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation.
- learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.
- learn multiple levels of representations that correspond to different levels of abstraction
From my point of view, one of the important breakthrough of deep learning is that it overcomes the challenge of the often tedious feature engineering task and helps with parameterizing traditional neural networks with many layers.
2. Neural Network Structure
2.1 Basic units
The basic unit of a neural network is the neuron, often called a node or unit. It receives input from some other nodes, or from an external source and computes an output. Each input has an associated weight \(\omega\), which is assigned on the basis of its relative importance to other inputs. The node applies a activation function \(g(z)\) to the weighted sum of its inputs as shown in Figure below:
In summary:
- \(Z\) controls the linear transformation of the inputs
- \(g(Z)\) controls the nonlinear transformation of the inputs
So we could see that even for a unit it tries to use both linear and non-linear transformation to grab the information from the input data. So no need to say why a deep neural network with hundreds of unit can have very good prediction performance.
2.2 Feedforward Neural Networks
Then let's see a complete feedforward neural networks. It contains multiple neurons (nodes) arranged in layers. Nodes from adjacent layers have connections or edges between them. All these connections have weights associated with them. Let me make a specific example to further explain it:
This is a feedforward NN with two layers (one hidden layer; one output layer). Input layer is not counted when calculating the total layers of one NN.
Since each NN will have lots of nodes and parameters. So it is very important to vectorize it. Here is the notation and dimension for each of the component of this NN:(suppose we have \(n\) sample data) \[X = [x^{(1)}, x^{(2)},x^{(3)}]_{(n,3)}\] Where \(x^{(i)}_{(n,1)}\) is the column vector contains all \(n\) samples' value for feature \(i\). \[Z^{[1]}_{(n,4)}=Z^{[0]}_{(n,3)}\omega^{[1]}_{(3,4)} + b^{[1]}_{(1,4)}\] \[g(Z^{[1]})_{(n,4)}=g(Z^{[0]}_{(n,3)}\omega^{[1]}_{(3,4)} + b^{[1]}_{(1,4)})\] Where \([i]\) tells us which layer does this variable belongs to; \((n,m)\) tells us the dimension for that variable
So the intuition to quickly come up with the dimension for the weights \(\omega\) is that weights are the connections to quantify last layer's influence to this new layer. So \(w_i^{[1]}\) should be a column vector with the number of rows equaling to the number of features from last layer. Then to stack all the \(\omega^{[1]}\) from this new layer, we get its dimension (#of nodes in last layer, # of nodes in this layer). For \(Z^{[i]}\) it is more easy to get its dimension. Since we can always think it as the input, its dimension will be like (# of samples, # of features(nodes) in this layer)
Finally: \[Z^{[2]}_{(n,1)}=Z^{[1]}_{(n,4)}\omega^{[2]}_{(4,1)}+b^{[2]}_{(1,1)}\] \[g(Z^{[2]}_{(n,1)})=g(Z^{[1]}_{(n,4)}\omega^{[2]}_{(4,1)}+b^{[2]}_{(1,1)})\]
2.3 Forward and Backward Propagation
- Forward propagation propagates the inputs forward through the network. The whole process above is just the example of forward propagation.
- Backward propagation propagates from the output to input. After forward propagation, we can choose one loss function to evaluate the output. Then each parameter can be updated by using gradient decent to minimize the loss.
3. Activation Functions
Activation functions are just functions that you use to get the output of a node. It calculates a “weighted sum” of its input, adds a bias and then decides whether it should be “fired” or not.
3.1 Why we need it
Activation functions normally have two properties:
- Non-linear
- Having activations bound in a range, say \((0,1)\) or \((-1,1)\)
The reason it has these two properties can help us understand why we need it:
- Each node is simply a weighted sum of inputs (linear), and activation function can add non-linearity to the model
- Mapping the bound to a range can make clear distinctions on prediction
3.2 Different Type of AF
3.2.1 Tanh Function
\[A = \frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}\]
- Pros: similar to sigmoid but derivatives are steeper, bound to range (-1,1) so won't blow up the activations. Centering data can make the learning of next layer simple.
- Cons: when z values are large, gradient is very small and then learning speed is very slow => gradient vanishing
3.2.2 Relu Function
\[A = max(0,z)\]
- Pros: Sparse activation: all inputs smaller than zero will be firing and the network is lighter. Also, relu is a good approximator. Any function can be approximated with combinations of ReLu
- Cons: It is not bound. The range of ReLu is \([0, inf)\). This means it can blow up the activation. Also, it faced with dying relu problem, which means for activation smaller than zero, gradient will be zero and the parameter will not be updated any more. To solve this problem, we introduce leaky Relu
3.2.3 Leaky Relu
\[A = max(0.01z, z)\]
Basically it just make it a slightly inclined line rather than horizontal line when z < 0.
4. Steps to construct a DNN
- Make sure the layers' dimension to initialize weights
- Forward propagation including linear forward and activation forward
- Define cost function
- Backward propagation
- Update parameter
- Make final prediction
Finally let's create a feed forward Neural Network by using Tensorflow.
Reference:
DeepLearning.ai
https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/
https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0


Polar Bear Population Distribution
Switch to different view
Increasing
Declining
Stable
Insufficient


Keeping references to PsiElement, PsiReference, or Project instances can result in memory leaks.
PsiElement
PsiReference
Project
Ideally, these should be stateless. For quick-fix, see LocalQuickFixOnPsiElement as a convenient base class.
LocalQuickFixOnPsiElement


{% from "macros/form_builder.html" import range_bulder, select_builder with context %} {% from "macros/whitespace.html" import span with context %} {% macro settings_form_mock(details) %}
{% endmacro %} {% macro settings_form_opencv(details) %} {% endmacro %} {% macro settings_form_picamera(details) %} {% endmacro %}


| Class | Rack::Sendfile |
| In: |
lib/rack/sendfile.rb
|
| Parent: | Object |
The Sendfile middleware intercepts responses whose body is being served from a file and replaces it with a server specific X-Sendfile header. The web server is then responsible for writing the file contents to the client. This can dramatically reduce the amount of work required by the Ruby backend and takes advantage of the web server‘s optimized file delivery code.
In order to take advantage of this middleware, the response body must respond to to_path and the request must include an X-Sendfile-Type header. Rack::File and other components implement to_path so there‘s rarely anything you need to do in your application. The X-Sendfile-Type header is typically set in your web servers configuration. The following sections attempt to document
Nginx supports the X-Accel-Redirect header. This is similar to X-Sendfile but requires parts of the filesystem to be mapped into a private URL hierarachy.
The following example shows the Nginx configuration required to create a private "/files/" area, enable X-Accel-Redirect, and pass the special X-Sendfile-Type and X-Accel-Mapping headers to the backend:
location ~ /files/(.*) { internal; alias /var/www/$1; } location / { proxy_redirect off; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Sendfile-Type X-Accel-Redirect; proxy_set_header X-Accel-Mapping /var/www/=/files/; proxy_pass http://<IP_ADDRESS>:8080/; }
Note that the X-Sendfile-Type header must be set exactly as shown above. The X-Accel-Mapping header should specify the location on the file system, followed by an equals sign (=), followed name of the private URL pattern that it maps to. The middleware performs a simple substitution on the resulting path.
See Also: wiki.codemongers.com/NginxXSendfile
Lighttpd has supported some variation of the X-Sendfile header for some time, although only recent version support X-Sendfile in a reverse proxy configuration.
$HTTP["host"] == "example.com" { proxy-core.protocol = "http" proxy-core.balancer = "round-robin" proxy-core.backends = ( "<IP_ADDRESS>:8000", "<IP_ADDRESS>:8001", ... ) proxy-core.allow-x-sendfile = "enable" proxy-core.rewrite-request = ( "X-Sendfile-Type" => (".*" => "X-Sendfile") ) }
See Also: redmine.lighttpd.net/wiki/lighttpd/Docs:ModProxyCore
X-Sendfile is supported under Apache 2.x using a separate module:
Once the module is compiled and installed, you can enable it using XSendFile config directive:
RequestHeader Set X-Sendfile-Type X-Sendfile ProxyPassReverse / http://localhost:8001/ XSendFile on
The third parameter allows for an overriding extension of the X-Accel-Mapping header. Mappings should be provided in tuples of internal to external. The internal values may contain regular expression syntax, they will be matched with case indifference.
| F | = | ::File |


Cells
Tempo implements two types of cells: MemoryCell<T>
and ListCell<T>
. MemoryCell<T>
stores a single
value of type T. ListCell<T>
represents a list of values of type T. Both cells can notify observers
when their contents change. The ListCell<T>
can also notify observers of exactly what has changed,
to minimize the amount of recomputation observers need to perform.
MemoryCell
MemoryCell<T>
implements two interfaces: ICellRead<T>
and ICellWrite<T>
. This separates the
authority to read from a cell from the authority to write to the cell. This means that since
the Whilst operator accepts an argument of type ICellRead<bool>
, we can pass a memory cell but
be guaranteed that the value of the cell will not be modified.
The CellBuilder class provides some methods for constructing read-only cells.
CellBuilder.Const constructs a cell that only ever has a single value for the duration of its
lifetime. This can be useful where a method requires an ICellRead<T>
argument, but the caller
knows the value cannot change. The Cellbuilder.Unit method constructs a constant cell which always
holds the unit value. This is useful for implementing functions that must return an ICellRead<T>
but the implementation does not need to return any information.
It can often be useful to generate a derived value by combining a number of values. The CellBuilder.Merge methods can construct a read-only cell which is derived from the value of two or more other cells.
ICellRead<T>
also has some extension methods. MemoryCellExtentions.Select applies a transformation
to a MemoryCell value. Use Select instead of WithEach when the transformation does not need to
allocate any resources, and can be simply derived from its argument.
MemoryCellExtensions.Flatten flattens a nested ICellRead<ICellRead<T>>
into a single ICellRead<T>
value which contains the concatenation of all of the innermost values.
ListCell
As with MemoryCell, ListCell implements separate interfaces for reading and writing (IListCellRead<T>
and IListCellWrite<T>
). The ListCell represents a time-varying list value. Any time the list is
modified, the cell notifies listeners of exactly what has changed.
There are some extension methods to ListCell for working with time-varying lists.
Select applies a transformation to each element of the list. Similar to Select for ICellRead<T>
,
use this method if there is no need for a temporal scope in the transformation, and it is a simple
derivation of the list element.
FirstOrDefault returns a read-only memory cell which is always equal to the first element of the list. If the list changes, the returned cell updates to reflect the change.
ElementAt combines a time-varying list with a time-varying integer index, the return value continuously updates to reflect the value at the index.
Flatten turns a list of observable values into a plain list of values. If one of the observable values in the source list changes, that is modelled in the output list as though that value was replaced by another. For example, if the source list is [cell{1}, cell{2}, cell{3}] then the output list will initially be [1, 2, 3]. If the second value of the source list changes to 4, so the source list is [cell{1}, cell{4}, cell{3}], then the output list will be [1, 4, 3]


Module stick/middleware/mount
This module provides middleware for mounting other applications on a specific URI path or virtual host.
Applying this middleware adds a mount
method to the application. The mount
method takes a path or virtual host specification and an application as arguments.
If the spec is a string, it is interpreted as the URI path on which the app will be
mounted. If it is an object, it may contain path
or host
properties
that will be matched against the URI path and Host
header of incoming requests.
Note that virtual host based mounting has not been tested so far.
Mounting one application within another causes the scriptName
and pathInfo
properties in the request object to be adjusted so that the mounted application
receives the same pathInfo as if it was the main application. This means
that forward and reverse request routing will usually work as expected.
This middleware maintains an index mapping applications to mount points which can be accessed using the lookup function. The [stick/helpers] module provides higher level functions for this which include support for the route middleware.
Example
app.configure("mount"); app.mount("/wiki", module.resolve("vendor/ringowiki"));
Functions
- lookup(target)
- middleware(next, app)
lookup (target)
Return the URI path of a mounted application
Parameters
| target | a mounted JSGI application |
Returns
| the URI path of the application, or "" |
middleware (next, app)
Middleware to mount other application on specific URI paths or virtual hosts.
Parameters
| Function | next | the wrapped middleware chain |
| Object | app | the Stick Application object |
Returns
| Function | a JSGI middleware function |


Hirschsprung’s disease is a condition in which the nerve supply to the large bowel is abnormal. Boys are affected about five times more commonly than girls, and the disease is more frequent in children with Down’s syndrome. The symptoms depend on the length of bowel affected, which varies from a few centimeters near the anus to the entire length of the large bowel.
If only a short segment of the bowel is involved, diagnosis may be delayed. Any baby who does not pass a stool in the first twenty-four hours of his or her life and does not have another obvious bowel problem or an anal abnormality should be investigated for this condition. Children who have been constipated since birth should also be investigated. If a diagnosis is not made quickly, the bowel may become blocked, inflamed, and could even burst (perforate). Note: It is common for a healthy toddler who has previously had normal bowel movements to experience constipation while potty training.
Babies may get a condition in their bowels (called intussusception) wherein one portion of the bowel telescopes into the next and gets stuck. This blocks the bowel and cuts off the blood supply to that portion of bowel. If the condition is not reversed rapidly, the bowel will die, and the infant will become very ill.
It may be difficult to differentiate a baby with colic (spasmodic pain in the abdomen) from one with intussusception. A baby with intussusception may spontaneously cry in regular intervals when he or she is apparently content. This pattern can be likened to the pattern of labor pains. A sausage-shaped lump may be felt in the belly, and in rare cases the baby may pass jellylike stools. If intussusception is diagnosed early, it can often be cured by a special enema, avoiding the need for surgery. This should always be done by an experienced doctor. This condition is an emergency.
Older children may flush the toilet without observing the nature of a diarrhea stool. They should be asked to look at the stool and show it to an adult if it is abnormal. With the emergence of E. coli as a potentially very dangerous germ, it has become important that parents watch diarrhea carefully (see Tip 220 for more emergency information on diarrhea in babies and children). Large numbers of stools, severe cramping with diarrhea, and blood in the stools should cause parents to seek medical attention.
Remember, when observing the stool, that if the child drinks red liquids or eats beets, the stool may have a red color.
Diarrhea and vomiting are both common problems in babies and infants. Dehydration can occur rapidly, especially if the infant also has a fever.
If your child has diarrhea and/or vomiting and any of the following symptoms, you should get him or her to the emergency room.
In children over six months, dehydration may be prevented by giving oral solutions such as Pedialyte. Consult with the child’s physician before giving these solutions to babies younger than six months of age, since they do not have the important nourishment that a baby needs. You may be able to continue giving breast milk or formula. They are best when given in small volumes at frequent intervals, and they can be very effective.  Solutions that contain a high sodium content, such as Rehydralyte, should only be given to a child (of any age) on the advice of a physician.
Viruses are the most common cause of diarrhea in children. Babies who are exclusively breast-fed until they are six months of age very rarely get significant diarrhea. Eating undercooked ground beef and unwashed vegetables increases the risk for dangerous E. coli diarrhea.


"ALPHAEUS "(1.) The father of James the Less, the apostle and writer of the epistle (Matthew 10:3; Mark 3:18; Luke 6:15; Acts 1:13), and the husband of Mary (John 19:25). The Hebrew form of this name is Cleopas, or Clopas (q.v.). (2.) The father of Levi, or Matthew (Mark 2:14).
"ALPHAEUS "(1.) The father of James the Less, the apostle and writer of the epistle (Matthew 10:3; Mark 3:18; Luke 6:15; Acts 1:13), and the husband of Mary (John 19:25). The Hebrew form of this name is Cleopas, or Clopas (q.v.).
(2.) The father of Levi, or Matthew (Mark 2:14).


Marketplace overview
Latest offer: 05/02/2019 Latest trade: 05/02/2019
HIGHEST BID
$1750
WINES ON MARKET
1500
TOTAL QUANTITY
20467
Last 12 months
OFFERS
1768
BIDS
3478
QUANTITY
10347
Currency market
| EUR/USD: 1.14 | EUR/GBP: 1.02 | EUR/JPY: 0.94 | EUR/CHF: 0.75 |
Newest tokenized wine on the platform
| Brand | Wine | Units | Price | Location flag |
|---|---|---|---|---|
| Name of brand | Name of wine | 1 | $100 | |
| Name of brand | Name of wine | 6 | $1200 | |
| Name of brand | Name of wine | 12 | $1200 |
My cellar 500 Tokenized units Total token value: $10000
| Wine | Token ID | Units | Token value |
|---|---|---|---|
| Name of wine | 1 | 6 | $800 |
| Name of wine | 2 | 1 | $75 |
| Name of wine | 3 | 12 | $1200 |
Create
Tokenize
| Brand | Wine | Price | Bottle/Stock | Tokenized |
|---|
Buy
| Brand | Wine | Price | Type | Actions |
|---|---|---|---|---|
| Name of Brand | Name of Wine | $1200 | Red | View details |
| Name of Brand | Name of Wine | $150 | Red | View details |
| Name of Brand | Name of Wine | $80 | Red | View details |
| Name of Brand | Name of Wine | $100 | White | View details |
| Name of Brand | Name of Wine | $90 | Red | View details |
Offers
| Brand | Wine | Asking price | From | Offer |
|---|---|---|---|---|
| Name of Brand | Name of Wine | $1200 | Some name | $1250 |
| Brand | Wine | Asking price | From | Offer |
|---|---|---|---|---|
| Name of Brand | Name of Wine | $100 | Some name | $105 |
Analysis/reporting
Today's highest offer: $1200
Total wines on market: 8542
Average value (all wines): $120
Track & Trace
| Brand | Wine | Price | Type | Actions |
|---|---|---|---|---|
| Name of Brand | Name of Wine | $1200 | Red | Track details |
| Name of Brand | Name of Wine | $150 | Red | Track details |
| Name of Brand | Name of Wine | $80 | Red | Track details |
| Name of Brand | Name of Wine | $100 | White | Track details |
| Name of Brand | Name of Wine | $90 | Red | Track details |
Tracking
-
Trader
Expected: 21 April, 2019 -
Negociant
Expected: 5 April, 2019 -
Courtier
Sent: 18 March, 2019 -
Chateau
Sent: 10 March, 2019
Negociant
Status: Waiting, next in line
Expected: 5 April, 2019
Address: Street, City, Code


The Weekly Top of Utopian showcases Staff Picks from contributions made in the past week, hand-selected by our Community Managers from across all Utopian.io categories.
Staff Picks
This week we feature a valuable development update from a Steem dApp, informative tutorials and bug reports, among others. These are just some of the best contributions our Community Managers spotted on Utopian.io during the week.
My Logo proposal to Steemg [Task Request] by @ggabogarcia [graphics]
Excellent quality of both logo and its presentation. Author payed a lot of attention to make additional deliverables such as patter, safe zone, app icon, open and closed version etc. Inclusion of sketches and creation process further enhance the presentation.
Total payout: 72.90 STU Number of votes: 147
Steem Pay Update - Creating the First REAL ECONOMY on Steem Blockchain! by @asbear [development]
SteemPay is an application that aims to simplify the selling and buying process using SBD, with some people in Korea already actively using it in stores! In this update @asbear added support for 8 more currencies and other important features such as the ability to sign up and log in - all in all it provides some great value to the Steem community!
Total payout: 732.91 STU
Number of votes: 953
Tutorial: Building a web app with React, Redux, and the Steem Javascript API, PART 2 by @nicknyr [tutorials]
This is the second staff-picked tutorial by @nicknyr, providing some great insights into utilizing dsteem, a great alternative to steemJS, which the author substitutes now in his tutorial for building React app to communicate with the Steem blockchain.
Total payout: 62.80 STU
Number of votes: 55
Introducing HEDE: Wiki Platform & Knowledge Sharing Dictionary on Steem by @hede-io [blog]
Hede-io is a social networking platform inspired by a dictionary format popular in Turkey which is mostly used for sharing knowledge based on experiences in an informal way, unlike usual dictionaries on the web. The founder developer of the project @ercu published a spectacular introduction post containing every detail of the project including an introduction to the format, development progress, future plans and comparisons with the similar concepts and projects. While opening up the format to the rest of the world, the project has a great potential to create an active community on the Steem blockchain.
Total payout: 138.06 STU
Number of votes: 161
[eSteem Surfer] [Version 1.0.5] - Markdown Renderer Problem by @josephace135 [bug-hunting]
@josephace135 being new to the community, has shown great improvement and his work is superb. Though the severity of this bug is Minor, and despite this being his 2nd post, he made extra effort to make this a great contribution.
Total payout: 41.17 STU
Number of votes: 340
Beem: race condition slowing down multi-threaded blockchain stream by @stmdev [bug-hunting]
A major bug, reported with great insights into the project. The contributor had a productive conversation with the project owner, trying to figure out problem and in the end solved it. This relationship is what Utopian strives to build. The contribution was well-written and every use case was shown in proper snippets, which increases the readability of this bug report.
Total payout: 41.30 STU
Number of votes: 15
The Factors Influencing Steem’s Retention Rate by @algo.coder [analysis]
image source: LiveChatInc.com
A late staff pick for the previous week. In this contribution, @algo.coder looked into some of the quantifiable factors affecting STEEM’s user retention; factors such as used applications, used tags, powered up STEEM, and the amount of payouts. What made this contribution unique was its attempt to uncover user behavior that could help concerned readers decide on the course of action to improve the user retention in the platform.
Total payout: 187.78 STU
Number of votes: 97
Bug: Existing product link searching is not working in case of google play store product link by @sourovafrin [bug-hunting]
Extremely useful information was provided along with proper testing, these are all characteristics of a great bug-hunter. The bug has the potential to affect a majority of posts. Hopefully it is fixed by the dev’s in under one day. I expect more good work from @sourovafrin.
Total payout: 10.49 STU
Number of votes: 28
Mix Rust Code (WebAssembly) with Vue Component #optimization - analyze wasm call graph and shrink the size by @drsensor [tutorials]
Another remarkable tutorial by @drsensor about mixing Vue with Rust code, analyzing WebAssembly code, and minimizing wasm file size. Great illustrations, detailed, well-planned, and top of the line tutorial.
Total payout: 62.92 STU
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|Category|Reviewed|Rewarded|Total rewards|Top contributor| |:-|:-|:-|-:|:-| |graphics|76|56|2933.59 STU|@tobaloidee| |translations|21|19|953.27 STU|@susanli3769| |documentation|2|2|51.48 STU|@stmdev| |ideas|11|5|56.17 STU|@sourovafrin| |development|44|36|2577.25<EMAIL_ADDRESS>|bug-hunting|41|26|472.67 STU|@mightypanda| |analysis|5|5|285.39 STU|@petermail| |tutorials|15|9|382.31 STU|@pckurdu| |video-tutorials|1|1|68.69 STU|@buckydurddle| |blog|6|5|308.84 STU|@hede-io|
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Utopian Witness
This page is synchronized from the post: Weekly Top of Utopian.io: June 21 - 27


| User | Sessions | Time in App | Taps | Screens | Days Active |
Waiting for data from the SDK…
Once {{ app.names.short }} runs, our SDK will
automatically send data that will
fill in this list
with information about who’s using your app.
Does your debug app use a different bundle ID? Add that app instead


Helper directive that makes it possible to create control groups inside a
form
directive.
These "child forms" can be used, for example, to determine the validity of a sub-group of
controls.
ngForm
cannot be used as a replacement for <form>
, because it lacks its
built-in HTML functionality.
Specifically, you cannot submit ngForm
like a <form>
tag. That means,
you cannot send data to the server with ngForm
, or integrate it with
ngSubmit
.
<ng-form
[name="string"]>
...
</ng-form>
<ANY
[ng-form="string"]>
...
</ANY>
<ANY class="[ng-form: string;]"> ... </ANY>
| Param | Type | Details |
|---|---|---|
|
ngForm
| name
(optional)
|
string |
Name of the form. If specified, the form controller will be published into the related scope, under this name. |


This package provides methods and examples to support a method of Gene Set Analysis (GSA). DBGSA is a novel distance-based gene set enrichment analysis method. We consider that, the distance between 2 groups with different phenotype by focusing on the gene expression should be larger, if a certain gene functional set was significantly associated with a particular phenotype.
| Version: | 1.2 |
| Depends: | R (≥ 2.12.2), fdrtool (≥ 1.2.6) |
| Published: | 2012-01-16 |
| Author: | Li Jin, Huang Meilin |
| Maintainer: | Li Jin <lijin at hrbmu.edu.cn> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | DBGSA results |
| Reference manual: | DBGSA.pdf |
| Package source: | DBGSA_1.2.tar.gz |
| Windows binaries: | r-devel: DBGSA_1.2.zip, r-release: DBGSA_1.2.zip, r-oldrel: DBGSA_1.2.zip |
| OS X binaries: | r-release: DBGSA_1.2.tgz, r-oldrel: DBGSA_1.2.tgz |
| Old sources: | DBGSA archive |
Please use the canonical form https://CRAN.R-project.org/package=DBGSA to link to this page.


Editing Pilots
General Description:
The pilot roster window is for creating and managing your pilots. Keep in mind if you setup a new pilot
first adjust the pilot type/subtype and gender, cause both selections are locked after you saved and loaded your
pilot again.
Printing:
If you hit the print icon while the pilot window is open, your pilot is printed in separate page.
Not only his stats, like in the aircraft printout, but also his feats, appearance, etc.
Pilot Type/Subtype:
You may chooes between Pilot, Crew and NPC. This changes the possible choices for
your subtype. If you choose Pilot, the subtype can be Ace, Hero, Wingman, Zeppelin
Captain or Custom.
Hero or Wingman are the two possible pilots you know from the official rules.
The crew subtype was introduced with update 1.1.0, to add the many different crew
types from Zeppelin & Bombers.
Your choices affect the starting EPs and the validation rules for this pilot.
If you choose custom Pilot or Zeppelin Captain you can adjust the starting EP
in the "Start EP" Textfield.
Stats:
The Stats known from the official rules. It includes the optional rule
for Quickdraw leveling from "Behind the Crimson Veil".
Feats/Languages:
These are FF5 Housrules visit our website for further informations.
Adding EP and Mission Updates:
A click on the "Add EP" Button opens the Mission update page in the
Toolbook.
Add a Pilot Picture:
Just click on the empty picture frame to import a picture for your pilot.
Can Level and Linked-To:
Both inputs are in mosted cases fixed, but for Gunners and Co-Pilots
there are 2 different types. E.g. The Nose Gunner of a Bomber can level up
but all other Gunners are linked to the Nose Gunner.
If "Can Level" is selected the "Linked-To" pulldown is inactive. But the
The Pilot can adjust its stats and it changes cost EP.
If "Can Level" is not selected the "Linked-To" pulldown becomes active
and you can selected a Pilot who's stats are used and reduced by (-1).
Use For Aircrafts/Zepplins:
These checkboxes are just for filtering the pilot files if you want to
assign them on an aircraft or zeppelin. E.g. in the aircraft window
the pilot pulldown is only filled with pilot files where the
"use for Aircrafts" flag is checked.


|
libcpuid
|
This contains the recognized CPU features/info. More...
#include <libcpuid.h>
Data Fields | |
| char | vendor_str [VENDOR_STR_MAX] |
| char | brand_str [BRAND_STR_MAX] |
| cpu_vendor_t | vendor |
| uint8_t | flags [CPU_FLAGS_MAX] |
| int32_t | family |
| int32_t | model |
| int32_t | stepping |
| int32_t | ext_family |
| int32_t | ext_model |
| int32_t | num_cores |
| int32_t | num_logical_cpus |
| int32_t | total_logical_cpus |
| int32_t | l1_data_cache |
| int32_t | l1_instruction_cache |
| int32_t | l2_cache |
| int32_t | l3_cache |
| int32_t | l4_cache |
| int32_t | l1_assoc |
| int32_t | l2_assoc |
| int32_t | l3_assoc |
| int32_t | l4_assoc |
| int32_t | l1_cacheline |
| int32_t | l2_cacheline |
| int32_t | l3_cacheline |
| int32_t | l4_cacheline |
| char | cpu_codename [64] |
| int32_t | sse_size |
| uint8_t | detection_hints [CPU_HINTS_MAX] |
| struct cpu_sgx_t | sgx |
This contains the recognized CPU features/info.
| char cpu_id_t::brand_str[BRAND_STR_MAX] |
contains the brand string, e.g. "Intel(R) Xeon(TM) CPU 2.40GHz"
| char cpu_id_t::cpu_codename[64] |
The brief and human-friendly CPU codename, which was recognized.
Examples:
| uint8_t cpu_id_t::detection_hints[CPU_HINTS_MAX] |
contain miscellaneous detection information. Used to test about specifics of certain detected features. See CPU_HINT_* macros below.
| int32_t cpu_id_t::ext_family |
CPU extended family
| int32_t cpu_id_t::ext_model |
CPU extended model
| int32_t cpu_id_t::family |
CPU family
| uint8_t cpu_id_t::flags[CPU_FLAGS_MAX] |
contain CPU flags. Used to test for features. See the CPU_FEATURE_* macros below.
| int32_t cpu_id_t::l1_assoc |
Cache associativity for the L1 data cache. -1 if undetermined
| int32_t cpu_id_t::l1_cacheline |
Cache-line size for L1 data cache. -1 if undetermined
| int32_t cpu_id_t::l1_data_cache |
L1 data cache size in KB. Could be zero, if the CPU lacks cache. If the size cannot be determined, it will be -1.
| int32_t cpu_id_t::l1_instruction_cache |
L1 instruction cache size in KB. Could be zero, if the CPU lacks cache. If the size cannot be determined, it will be -1.
| int32_t cpu_id_t::l2_assoc |
Cache associativity for the L2 cache. -1 if undetermined
| int32_t cpu_id_t::l2_cache |
L2 cache size in KB. Could be zero, if the CPU lacks L2 cache. If the size of the cache could not be determined, it will be -1
| int32_t cpu_id_t::l2_cacheline |
Cache-line size for L2 cache. -1 if undetermined
| int32_t cpu_id_t::l3_assoc |
Cache associativity for the L3 cache. -1 if undetermined
| int32_t cpu_id_t::l3_cache |
L3 cache size in KB. Zero on most systems
| int32_t cpu_id_t::l3_cacheline |
Cache-line size for L3 cache. -1 if undetermined
| int32_t cpu_id_t::l4_assoc |
Cache associativity for the L4 cache. -1 if undetermined
| int32_t cpu_id_t::l4_cache |
L4 cache size in KB. Zero on most systems
| int32_t cpu_id_t::l4_cacheline |
Cache-line size for L4 cache. -1 if undetermined
| int32_t cpu_id_t::model |
CPU model
| int32_t cpu_id_t::num_cores |
Number of CPU cores on the current processor
| int32_t cpu_id_t::num_logical_cpus |
Number of logical processors on the current processor. Could be more than the number of physical cores, e.g. when the processor has HyperThreading.
| struct cpu_sgx_t cpu_id_t::sgx |
contains information about SGX features if the processor, if present
| int32_t cpu_id_t::sse_size |
SSE execution unit size (64 or 128; -1 if N/A)
| int32_t cpu_id_t::stepping |
CPU stepping
| int32_t cpu_id_t::total_logical_cpus |
The total number of logical processors. The same value is available through cpuid_get_total_cpus.
This is num_logical_cpus * {total physical processors in the system} (but only on a real system, under a VM this number may be lower).
If you're writing a multithreaded program and you want to run it on all CPUs, this is the number of threads you need.
| cpu_vendor_t cpu_id_t::vendor |
contains the recognized CPU vendor
| char cpu_id_t::vendor_str[VENDOR_STR_MAX] |
contains the CPU vendor string, e.g. "GenuineIntel"


abbr
element
abbr
element
with title
attribute
BBC
abbr
element without title
attribute
CBS
abbr
element with aria-label
attribute
WWW
abbr
element with aria-label
attribute and aria-describedby
attribute WWW
AKA the Internet
abbr
element with aria-label
and title
attributes
WWW
abbr
element with aria-label
and title
attributes with differing values
WWW


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