An alternative to using AI cloud services for image classification is to load deep learning vision models into the browser. Some of the blocks in the next chapter can be used to classify images, detect objects, segment semantically images of people, sense body and hand poses, and produce 3D maps of faces. Some can be further trained by users which is especially useful when one wants to distinguish between images for idiosyncratic reasons (e.g. different facial expressions). One has been trained to recognize one thousand different kinds of things (but not people). While the models that run in the browser are not as capable or accurate as AI cloud services they have the advantage that no data leaves your device, maintaining privacy. Furthermore, they work even without a network connection.
You can import the blocks presented here as a project or download them as a library to import into your projects.
This chapter of the guide includes many interactive elements that are known to run well in the Chrome or Edge browser. This chapter relies upon there being a camera that the browser can access. There is a troubleshooting guide that should be consulted if problems are encountered.
A camera connected to a computer can report the color of each pixel in an image and not much else. A description of what is in front of the camera is returned when those pixels are sent to an image recognition service. There are many kinds of things an image description may contain. With speech recognition the description is what was spoken, how confident the system is, and possible alternatives. With image recognition there are many possible descriptions: descriptive tags, possible captions, dominant colors, location of faces and parts of the face if there are any, and the presence of landmarks, celebrities, well-known entities, and logos. Hand-written and scanned text can also be recognized.
A challenge in providing student-friendly programming blocks for image recognition is that different AI cloud services report different descriptions in different ways. We currently provide interfaces to image recognition services provided by Google and Microsoft. A further challenge is how to provide simple interfaces for simple tasks while still supporting more sophisticated uses and projects.
In the last few years there has been tremendous progress in computer vision. There are high performance systems for identifying objects, recognising faces, interpreting sketches, and using medical images to aid diagnosis. Driverless cars rely heavily upon computer vision.
Chrome has built-in support for speech synthesis and recognition. No browser, however, currently supports image recognition. To access vision recognition services from companies such as Google or Microsoft one needs to open an account. Accounts are free and provide some degree of free usage. Microsoft permits 5000 queries per month, and Google 1000 per month. To try the vision blocks described here you need at least one account. Comparing and contrasting the results from different services are interesting ways to gain some insight into how these services work.
In projects there is a different way of providing keys to Snap!
(as described here) but in this chapter you can paste your key or keys below
and they'll only be passed to the services as you use example blocks.
Otherwise they remain your browser tab.
This block takes a picture, sends it to the AI vision cloud server provider, waits for a response, and then reports a list of labels of the photo. The list is ordered by how confident the vision provider is that the label matches the image.
The show current photo block will display the most recent image sent to the specified AI vision services as the background of the Snap! stage. To try it first obtain some labels from a vision service. You can also use the use camera to create costume block to take a new picture and add it as a costume to the current sprite.
Here is a program that can contact any of Google or Microsoft and displays the tags returned.
This program is similar to the previous one except it is listening for the words "Google"or "Microsoft". When it hears one of these words it contacts that service. When the response is received it turns it into speech. As a demo it is impressive if you say things like "Tell me Google what do you see?" and "What do you see Microsoft?". This will work in every language. It is a nice illustration of how software can appear more intelligent than it is. Do you think this program seems intelligent? If so, why? Is it intelligent?
Image recognizers can do more than label images. Some can detect and locate faces. Some of those will estimate the age of the person and their gender. Some recognize landmarks and logos. Many can recognize characters in hand-written or scanned text. A problem creating Snap! blocks that provide this functionality is that different services have different capabilities and different structures to capture their responses.
Click to read an advanced topic
The Recognize new photo block addresses this problem by reporting a data structure capturing the entire response from the vision service. It is a Snap! list that contains lists that contains more data that might be text for labels, numbers for confidence scores, and even more lists for complex data. You can double click on the icons to drill down the structure.
The Current image property block takes an argument that describes what piece of the response structure should be reported. For example, Microsoft offers possible captions that can be found by following the path "description captions text". Common useful blocks are defined that use the Current image property block internally to get the labels and the confidence scores of the labels for each of the supported vision service providers. Note that after a response is received from any of the AI services it is stored so that calls to Current image property use the most recent response rather than ask for a new one. This is because a project may need to access multiple pieces of a response.
After having run Recognize new photo block you should select the same AI cloud provider (currently Google or Microsoft) when you run the Current image property block. The second argument can be a string or a list of strings specifying what information is desired from the image recognition. Each AI cloud provider supports different image properties:
Common calls to the get property reporter are pre-defined. They are Microsoft labels, Microsoft first caption, Google labels, and Google label scores.
Exercise. Recognize a picture and then use the Current image property block to see what kinds of descriptions the service provides. How might each one be useful?
Advanced Exercise. Compare the documentation for the vision services from Google and Microsoft.
Click to read another advanced topic
The Recognize new photo reporter block is implemented using the Ask <provider> to say what it sees block. This block does not wait for a response, instead it runs the user's blocks when a response is received. It isn't as convenient as 'Recognize new photo' but it can support more complex usage.
This chapter uses the API keys provided in the text areas above. When constructing a project there are two ways to provide keys:
Because all the AI cloud services are commercial services heavy use can be costly. Consequently, it is best if each student has their own account and minimizes the sharing of their keys. This conflicts with one of the great things about tools like Scratch and Snap! -- that it is so easy to share one's projects with a wider community. Adding the keys to the URL solves this problem if one is careful to keep the URL with keys private and share the version without the keys.
This is like asking what is vision good for. According to Wikipedia eyes have independently evolved between 50 and 100 times since animals first appeared. Computer vision enables robots to see, self-driving cars (which really are just a kind of robot), new kinds of user interfaces, support for doctors, police, sports coaches, farmers, the military, and more. It can help blind people to navigate their surroundings and to use devices.
Like much of AI technology it may take away many jobs. It may reduce our privacy since it makes it much easier to track people's movements and activities. It can enable autonomous weapons that may kill many.
Another danger is that either intentionally or unintentionally the data the machine learning system is trained on may contain biases. Here is a nice short video on bias and machine learning published by Google.
Image recognition begins with pixels.
Each pixel is a number (for the gray level) or three numbers (for red, green, and blue components)
that corresponds to a tiny piece of an image.
There are two main approaches to processing images:
1. A sequence of programmed processing steps to find edges, determine textures, identify objects, etc.
2. A machine learning system
Most of the recent progress has been with machine learning systems that for many tasks are near or better than humans. For some tasks such as detecting lung cancer from x-rays machine learning has exceeded the ability of experts.
A neural net is a program that is inspired by what little is known about how neurons in animal and human brains work. It can be trained to identify images. In the most common case it is trained on thousands or millions of images that have already been labeled. When given a new image, it computes the most likely labels. Some easy visual discrimination tasks can be handled by training only a hundred or less images. You can train a neural net to do this kind of vision recognition in the next chapter.
Different levels or stages of the processing by the neural net recognize different elements of an image. Researchers have built software that figures out what images produce the greatest response from the different layers. A very nice interactive tool for exploring what different pieces of a neural network respond to is the OpenAI Microscope.
Some AI cloud providers such as Google can accept a video stream and produce labels. It can also detect scene changes. This service can be expensive, though Google will analyze 1000 minutes of video per month at no cost. There currently are no Snap! blocks for supporting video input.
Adding image recognition to a robotics project can make the robot behave much more intelligently. It can head towards goals and avoid specified objects. A simple example would be a robot told to move to X (where X can be "the red ball", "the toy truck", "a person", or whatever). It begins by sending the current camera image for recognition. If the description returned matches X then go forward a few steps (assuming the camera is mounted pointing forward). If not, then turn a little and try again. Repeat until X is reached.
For projects that use a camera that can't move there are many possibilities:
Click to read an advanced topic
Google and Microsoft document their AI vision services. There are many more vision services, see for example this image recognition services comparison page. The Wikipedia page on computer vision is very thorough. There are many MOOCS on computer vision and machine learning for image recognition. Distill is a scientific journal that strives to explain clearly complex machine learning topics. A Distill article is an in-depth description of how a neural net does image recognition. A general audience summary of the article was written by the New York Times. Codecademy has a good interview with a data scientist about machine learning.
You can import the blocks presented here as a project or download them as a library to import into your projects.
The next chapter is about using machine learning models.
Return to the previous chapter on speech recognition.


Event Trace
Selection Change
Size Change
Connector Collection Change
Node Collection Change
Text Change
Segment Change
Rotation Change
Connector Target Change
Connector Source Change
Scroll Change
Double Click
Drag Enter
Drag Over
Drag Leave
Drag
Drop
Group Change
History Change
Click
Connection Change
Context Menu Before Open
Context Menu Click
Property Change
Mouse Over
Mouse Enter
Mouse Leave


Post-parsing
config.php
gera_thumbs.php
imagem.php
index.php
mediaserver.php
processa_imagem.php
redis_server.php
util.php
cache.php
Warnings:
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Warning on line 49 - File "/Library/WebServer/Documents/projects/media-server-PHP/inc/cache.php" has no page-level DocBlock, use @package in the first DocBlock to create one
config.php
Warnings:
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gera_thumbs.php
Warnings:
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imagem.php
Warnings:
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index.php
Warnings:
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mediaserver.php
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processa_imagem.php
Warnings:
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redis_server.php
Warnings:
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Warning on line 30 - @method magic tag does not have name, illegal. Ignoring tag "@method mixed OBJECT($subcommand) Inspect the internals of Redis objects"
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util.php
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Documentation generated on Sat, 04 Aug 2012 15:14:48 -0300 by phpDocumentor 1.4.4


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---
title: 07/07/2018
category: meetings
layout: minutes
present: Kif Keck (SFH, Board President), Keenan Gregory (Community, Board Treasurer), Brian Seveland (Olive), Muriel Ardis (SFH), Zak Grant (SFH), Maeve Hilgers (SFH), Jason Hering (Community), Gallal Obeid (Community), Lev Smyth (Community)
start: 16:00
end: 07:16
---
Agenda
| Topic |
Description/Details |
Outcome |
| Agenda Review |
|
Approved |
| Minutes Review |
|
Approved |
Reports
| Topic |
Description/Details |
| Staff Report |
Staff stuff |
| House Reports |
| Ad Astra |
Gayyy |
| Olive |
>:( |
| Sunflower |
dead squirrels |
|
| NASCO Properties Report |
NASCO!! |
| Committee Reports |
|
| Education |
Education stuff |
| Personnel |
Personnel stuff |
| Community Engagement |
CE stuff |
| Strategic Planning |
SP stuff |
| Policy |
Policy stuff |
Discussion Items
| Topic |
Description/Details |
Outcome |
| squirrel funeral |
this will be big! |
approved |
| caulking SFH |
yes |
yes |
| |
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| |
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| |
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Announcements: squirrel funeral next saturday!
Next Meeting Date: 07/29/2018
Next Meeting Location: SFH


 Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Fanatics have their dreams, wherewith they weave A paradise for a sect; the savage too From forth the loftiest fashion of his sleep Guesses at Heaven; pity these have not Trac'd upon vellum or wild Indian leaf The shadows of melodious utterance. But bare of laurel they live, dream, and die; For Poesy alone can tell her dreams, With the fine spell of words alone can save Imagination from the sable charm And dumb enchantment. Who alive can say, 'Thou art no Poet may'st not tell thy dreams?' Since every man whose soul is not a clod Hath visions, and would speak, if he had loved And been well nurtured in his mother tongue. Whether the dream now purpos'd to rehearse Be poet's or fanatic's will be known When this warm scribe my hand is in the grave.
(defun eval. (e a) (cond ((atom e) (assoc. e a)) ((atom (car e)) (cond ((eq (car e) 'quote) (cadr e)) ((eq (car e) 'atom) (atom (eval. (cadr e) a))) ((eq (car e) 'eq) (eq (eval. (cadr e) a) (eval. (caddr e) a))) ((eq (car e) 'car) (car (eval. (cadr e) a))) ((eq (car e) 'cdr) (cdr (eval. (cadr e) a))) ((eq (car e) 'cons) (cons (eval. (cadr e) a) (eval. (caddr e) a))) ((eq (car e) 'cond) (evcon. (cdr e) a)) ('t (eval. (cons (assoc. (car e) a) (cdr e)) a)))) ((eq (caar e) 'label) (eval. (cons (caddar e) (cdr e)) (cons (list. (cadar e) (car e)) a))) ((eq (caar e) 'lambda) (eval. (caddar e) (append. (pair. (cadar e) (evlis. (cdr e) a)) a)))))


--- layout: single author_profile: true ---
You have landed on my website! I created this website to provided an orgonized formate to all of the subjects that I want to and have learned. I personally have found that I tend to learn more and develope a deeper understanding of topics if I am able to explain topics in my own words.


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FEATURED PRODUCTS
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My Feature Name 1
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The feature's description. |
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My Feature Name 2
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The feature's description. |
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Page size and layout can be
adjusted according to your information needs.
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Copyright2000 Company
Name.All rights reserved.
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Why use us?
Incarcerated adults sometimes have a hard time re-entering society. Unemployment due to lack of skills or finding a willing employer is difficult. Unfortunately, this leads many to re-offend and return to the correctional system.
Prisoner Skills is preventing those situations for many offendors. We found lower levels of education and a lack of vocational skills adds to the repeat offender problem.
Inmates recieving training and the opportunity to earn while in-house or on furlough find their outlook on life change for the better. That's why Prisoner Skills helps match institutions & inmates with employers.
If you are a penal instition with a work training program, sign up and truly help reform your inmates.
If you are an employer, sign up and browse through our list of eager workers.


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pf
|
All non Rao-Blackwellized particle filters without covariates inherit from this. More...
#include <map>
#include <string>
#include <vector>
#include <Eigen/Dense>
Go to the source code of this file.
All non Rao-Blackwellized particle filters without covariates inherit from this.
inherit from this if, in addition to filtering, you want to simulate future trajectories from the current filtering distribution. This simulates two future trajectories for each particle you have–one for the state path, and one for the observation path. Unlike the above class, this one
inherit from this if, in addition to filtering, you want to simulate future trajectories from the current filtering distribution. This simulates two future trajectories for each particle you have–one for the state path, and one for the observation path.
inherit from this if you want to simulate from a homogeneous forward/generative model. This class is more general than the above because it can simulate future states using past observations.
inherit from this if you want to simulate from a homogeneous forward/generative model.
All Rao-Blackwellized particle filters inherit from this.
All non Rao-Blackwellized particle filters with covariates inherit from this.
| float_t | (e.g. double, float, etc.) |
| dimobs | the dimension of each observation |
| dimstate | the dimension of each state |
| float_t | (e.g. double, float, etc.) |
| dimobs | the dimension of each observation |
| dimstate | the dimension of each state |
| dimcov |
| float_t | (e.g. double, float, etc.) |
| dim_s_state | the dimension of the state vector that's sampled |
| dim_ns_state | the dimension of the state vector that isn't sampled |
| dimobs | the dimension of each observation vector |


Batu Kawan net profit falls to RM363.49m
KUALA LUMPUR, Nov 19 — Batu Kawan Bhd’s net profit for the financial year ended September 30, 2019 fell to RM363.49 million from RM365.68 million a year ago.
Revenue declined 15.3 per cent to RM16.05 billion from RM18.95 billion previously, due to weaker performance from the plantation, manufacturing and industrial chemical division, it said in a filing with Bursa Malaysia today.
The group said the plantation segment posted a 19.3 per cent lower revenue of RM6.39 million impacted by weaker crude palm oil (CPO) and palm kernel (PK) prices, while manufacturing reported a 13.2 per cent lower revenue of RM9.19 million.
Meanwhile, the industrial chemical division posted a 27.6 per cent lower profit of RM101.70 million from lower selling prices and higher energy costs, it said.
On prospects, the group expects its plantation profit to improve for financial year 2020 with CPO and PK prices recovering from their low levels.
“The performance of the group’s oleochemical division should be sustained with some additional capacities coming on stream, while the industrial chemical segment, profits from both chlor-alkali and sulphuric acid businesses are projected to be satisfactory.
“Overall, the group expects a higher profit for financial year 2020,” it said. — Bernama


Optimal policies from SDP. a Threshold for initial decision between large static and flexible design as a function of T and P during the first time period (2000–2020). b Thresholds for exercising the option to increase height of flexible dam as a function of T and P during the latter time periods as indicated on the legend. Results shown for planning scenario A


ttyrecv(1) Qualcomm Atheros Open Powerline Toolkit ttyrecv(1) NAME ttyrecv - Serial Line Rate Tester (Receive) SYNOPSIS ttyrecv [options] [>stdout] DESCRIPTION Receive data from a serial device. The primary purpose of this program is to receive data over a serial port for a spec‐ ified time duration. The ttyrecv program exits after the receive time (see -t) has elapsed. This program is part of the Qualcomm Atheros Powerline Toolkit. See the AMP man page for an overview and installation instructions. COMMENTS This program does not yet support Windows platforms. OPTIONS -c Consume all data received (do not print to standard output). -l file The serial port file to read data from. The default is /dev/ttyS0. -r Print the receive rate to standard output after the receive time has elapsed. -s speed The serial baud rate. Supported baud rates are 0, 50, 75, 110, 134, 150, 200, 300, 600, 1200, 1800, 2400, 4800, 9600, 19200, 38400, 57600, 115200, 230400, 460800, 500000 and 921600. -t time Receive for time seconds. The default is 10 seconds. -?,--help Print program help summary on stdout. This option takes precedence over other options on the command line. -?,--version Print program version information on stdout. This option takes precedence over other options on the command line. Use this option when sending screen dumps to Atheros Technical Support so that they know exactly which version of the Linux Toolkit you are using. EXAMPLES None. SEE ALSO amp(1), int6kuart(1), ttysend(1), CREDITS Charles Maier<EMAIL_ADDRESS>Nathaniel Houghton<EMAIL_ADDRESS>open-plc-utils-0.0.3 November 2013 ttyrecv(1)


firstName is too short.
firstName is too long.
Last Name is too short.
Last Name is too long.
Enter a valid email.
Enter a valid Phone Number.


Puzzle Pieces
Variation 3 | UV Offset
Our final example of this puzzle piece challenge looks at how to use the UV offset parameters for instancing. Here rather than actually slicing up our image, we instead provide a set of offset coordinates for where to sample our texture from. This kind of slicing is highly efficient, but difficult to “read” visually when looking at TouchDesigner. In order to understand what’s really happing in this example it’s important to have a handle on the relationship between texture coordinates and geometry.
Tested in TouchDesigner099 2020.23680
Updated 06.03.20
Matthew Ragan
Zoe Sandoval


We intend to enable pupils to think, design and (if they win) build an application with the help of our expert engineers.
Our goal is to reach all pupils from P1-P7 and let them start their digital design thinking.
We are working with 15 pilot primary schools to allow us to quickly trial the concept and make it happen.
We will include all pupils from P1-P7 in the pilot schools.


What does the quick brown fox say?
a literary pangram generator
Here are a bunch of Google free fonts which you can be sure to see every letter of due to these beautiful pangrams generated in the style of my favorite authors and texts
thin slimy creatures in order right back of brown bread. with deep inside every detail, the size of six months, ahead. and jumped up to enquire if I saw it you to amazing
- Author: Roald Dahl
- Book: Matilda
- Font: Quicksand
- Weight: 400 Normal
- Style by: John Hudson
moment the stars on weekday afternoons, with blue porcelain water-jug and even be delinquents and magazines that size." "The Trunchbull exploded. the size
- Author: Roald Dahl
- Book: Matilda
- Font: Slabo
- Weight: 400 Normal
- Style by: John Hudson
parent! over Matilda stared round chocolate cake, carefully writing. end-of-term reports for six o'clock, on boiling up just been here, on grinning. the squirming newt was gazing
- Author: Roald Dahl
- Book: Matilda
- Font: Open Sans
- Weight: 400 Italics
- Style by: John Hudson
dealer in the grey-black writing surface of my opinion", the five and quiet and seized a fixed it joins the blazes
- Author: Roald Dahl
- Book: Matilda
- Font: Open Sans
- Weight: 400 Italics
- Style by: John Hudson
Tom Robbins - Still Life with Woodpecker
special friend; she hugged for its armor glistening and bring every word, spoken. the expulsion of squeaky leather, accessories. the judiciary had vaporized
terminally drab necktie, the forgotten guards were conspiring. the experts to serving organized the antique lanterns. the junta permitted to gaze
Chuck Palahniuk - Fight Club
ticking suitcase. and watch another old people from behind, you have Project Mayhem, headquarters and tuxedos on my schizophrenic


|
KlusterKite
0.0.0
A framework to create scalable and redundant services based on awesome Akka.Net project.
|
The TestObject connection provider More...
Public Member Functions | |
| TestObjectConnection (Dictionary< Guid, TestObject > objects) | |
| Initializes a new instance of the TestObjectConnection class. More... | |
| Task< MutationResult< TestObject > > | Create (TestObject newNode) |
| Task< MutationResult< TestObject > > | Delete (object id) |
| Task< QueryResult< TestObject > > | Query (Expression< Func< TestObject, bool >> filter, IEnumerable< SortingCondition > sort, int? limit, int? offset, ApiRequest apiRequest) |
| Task< MutationResult< TestObject > > | Update (object id, TestObject newNode, ApiRequest request) |
| Task< MutationResult< TestObject > > | TypedMutation (Guid uid) |
| The typed mutation. More... | |
| Task< bool > | UntypedMutation (Guid uid) |
| The example of additional connection typed mutation More... | |
| Public Member Functions inherited from KlusterKite.API.Client.INodeConnection< TestObject > | |
| Task< QueryResult< T > > | Query (Expression< Func< T, bool >> filter, IEnumerable< SortingCondition > sort, int? limit, int? offset, ApiRequest apiRequest) |
| Query the datasource for nodes More... | |
| Task< MutationResult< T > > | Create (T newNode) |
| Creates a new node More... | |
| Task< MutationResult< T > > | Update (object id, T newNode, ApiRequest request) |
| Updates a node More... | |
| Task< MutationResult< T > > | Delete (object id) |
| Removes a node from the data store More... | |
The TestObject connection provider
Definition at line 28 of file TestObjectConnection.cs.
| KlusterKite.API.Tests.Mock.TestObjectConnection.TestObjectConnection | ( | Dictionary< Guid, TestObject > | objects | ) |
Initializes a new instance of the TestObjectConnection class.
| objects | The initial objects state. |
Definition at line 41 of file TestObjectConnection.cs.
| Task<MutationResult<TestObject> > KlusterKite.API.Tests.Mock.TestObjectConnection.Create | ( | TestObject | newNode | ) |
Definition at line 47 of file TestObjectConnection.cs.
| Task<MutationResult<TestObject> > KlusterKite.API.Tests.Mock.TestObjectConnection.Delete | ( | object | id | ) |
Definition at line 81 of file TestObjectConnection.cs.
| Task<QueryResult<TestObject> > KlusterKite.API.Tests.Mock.TestObjectConnection.Query | ( | Expression< Func< TestObject, bool >> | filter, |
| IEnumerable< SortingCondition > | sort, | ||
| int? | limit, | ||
| int? | offset, | ||
| ApiRequest | apiRequest | ||
| ) |
Definition at line 100 of file TestObjectConnection.cs.
| Task<MutationResult<TestObject> > KlusterKite.API.Tests.Mock.TestObjectConnection.TypedMutation | ( | Guid | uid | ) |
The typed mutation.
| uid | The uid. |
Definition at line 204 of file TestObjectConnection.cs.
| Task<bool> KlusterKite.API.Tests.Mock.TestObjectConnection.UntypedMutation | ( | Guid | uid | ) |
The example of additional connection typed mutation
| uid | The node id |
Definition at line 228 of file TestObjectConnection.cs.
| Task<MutationResult<TestObject> > KlusterKite.API.Tests.Mock.TestObjectConnection.Update | ( | object | id, |
| TestObject | newNode, | ||
| ApiRequest | request | ||
| ) |
Definition at line 134 of file TestObjectConnection.cs.


public interface Resolver
Methods such as PropertyUtilsBean's setNestedProperty()
method
use a Resolver
to process a property name
expression and resolve nested, indexed and mapped
property names. The following code provides an example usage
demonstrating all the methods:
// Iterate through a nested property expression while (resolver.hasNested(name)) { // isolate a single property from a nested expresion String next = resolver.next(name); // Process... String property = resolver.getProperty(next); if (resolver.isIndexed(next)) { int index = resolver.getIndex(next); bean = getIndexedProperty(bean, property, index); } else if (resolver.isMapped(next)) { String key = resolver.getKey(next); bean = getMappedProperty(bean, property, key); } else { bean = getSimpleProperty(bean, property); } // remove the processed property from the expression name = resolver.remove(name); }In order to create an implementation, it is important to understand how BeanUtils/PropertyUtils uses the
resolver
. The following are
the main methods that use it:
PropertyUtilsBean
PropertyUtilsBean.getIndexedProperty(Object, String)
PropertyUtilsBean.getMappedProperty(Object, String)
PropertyUtilsBean.getNestedProperty(Object, String)
PropertyUtilsBean.getPropertyDescriptor(Object, String)
PropertyUtilsBean.getSimpleProperty(Object, String)
PropertyUtilsBean.setIndexedProperty(Object, String, Object)
PropertyUtilsBean.setMappedProperty(Object, String, Object)
PropertyUtilsBean.setNestedProperty(Object, String, Object)
PropertyUtilsBean.setSimpleProperty(Object, String, Object)
BeanUtilsBean
BeanUtilsBean.copyProperty(Object, String, Object)
BeanUtilsBean.setProperty(Object, String, Object)
LocaleBeanUtilsBean
PropertyUtilsBean.setResolver(Resolver)
| Modifier and Type | Method and Description |
|---|---|
int |
getIndex(String expression)
Extract the index value from the property expression or -1.
|
String |
getKey(String expression)
Extract the map key from the property expression or
null . |
String |
getProperty(String expression)
Return the property name from the property expression.
|
boolean |
hasNested(String expression)
Indicates whether or not the expression
contains nested property expressions or not.
|
boolean |
isIndexed(String expression)
Indicate whether the expression is for an indexed property or not.
|
boolean |
isMapped(String expression)
Indicate whether the expression is for a mapped property or not.
|
String |
next(String expression)
Extract the next property expression from the
current expression.
|
String |
remove(String expression)
Remove the last property expresson from the
current expression.
|
int getIndex(String expression)
expression
- The property expressionIllegalArgumentException
- If the indexed property is illegally
formed or has an invalid (non-numeric) valueString getKey(String expression)
null
.expression
- The property expressionIllegalArgumentException
- If the mapped property is illegally formedString getProperty(String expression)
expression
- The property expressionboolean hasNested(String expression)
expression
- The property expressionboolean isIndexed(String expression)
expression
- The property expressiontrue
if the expresion is indexed,
otherwise false
boolean isMapped(String expression)
expression
- The property expressiontrue
if the expresion is mapped,
otherwise false
String next(String expression)
expression
- The property expressionCopyright © 2000–2019 The Apache Software Foundation. All rights reserved.


/usr/bin/site_perl /usr/bin/vendor_perl /usr/bin/core_perl
--- layout: default date: 2019-12-06 title: perl ---
Home/ Computing/ Languages/ Perl/
[1]
As of Oct 2019, Perl 6 is called Raku, 'Perl' now means Perl 5.
Perl is ubiquitious in Linux systems. In the path:
/usr/bin/site_perl /usr/bin/vendor_perl /usr/bin/core_perl


Good Will Hunting is a great movie about Matt Damon. He starts off as a janitor at MIT. He anonymously solves all the very difficultproblems put on the board. A professor decides to help him take his life in the right direction and along the way he meets a girl.
This movie is a classic for me. I watched it for the first time in my psychology class 3 years ago. Since then I've probably watched it 10 times and it never gets old. I consider this movie one of my all time favorite movies.


Centeur
问题描述 :
The aliens in Centaurus a star reach the earth on Dec. 21th in 2012, and they are fairly smart. Before creating a friendly relationship with the human beings, they make a request that the intelligence of human must be tested. Upon their will, an earth citizen is asked to play the testing game with their boss, and rules of the game are as follows:
Given a row of bulbs whose total number is n, and the bulbs are numbered 1 to n from left to right, and each bulb has two states, named turned-on and turned-off. With the citizen from earth starts first, two players make their operations by turns. Every operation they make must meet the conditions:
Choose a bulb which is now on, then turn it off, and we assume this bulb is numbered p;
Choose a bulb and change its state(if on, then turn off, else, turn on), and suppose it is numbered q, which should meet the request of q <= p/2.
Given a row of bulbs whose total number is n, and the bulbs are numbered 1 to n from left to right, and each bulb has two states, named turned-on and turned-off. With the citizen from earth starts first, two players make their operations by turns. Every operation they make must meet the conditions:
Choose a bulb which is now on, then turn it off, and we assume this bulb is numbered p;
Choose a bulb and change its state(if on, then turn off, else, turn on), and suppose it is numbered q, which should meet the request of q <= p/2.
In addition, the step 2 is not a must.
And the one cannot move loses.
Eventually, the smart earth citizen wins this test soon.
However, another question is asked by an evil alien: Supposing there are turned-on bulbs in some sections at the beginning, and the other states can be decided by the citizen from earth. With this fixed rules, and the alien goes first, then how many original states will make the citizen from earth win the game?
输入:
Line 1: a single integer t(1<=t<=30), which refers to the number of cases;
And for each case, the first line contains two integers n m(1<=n<=263-1,0<=m<=1000), and in the following m lines, each line has two integers:s t(1<=s<=t<=n), which means that the bulbs numbered from s to t (include s and t)are turned-on originally;
Attention, these sections may be overlapped.
And for each case, the first line contains two integers n m(1<=n<=263-1,0<=m<=1000), and in the following m lines, each line has two integers:s t(1<=s<=t<=n), which means that the bulbs numbered from s to t (include s and t)are turned-on originally;
Attention, these sections may be overlapped.
输出:
Line 1: a single integer t(1<=t<=30), which refers to the number of cases;
And for each case, the first line contains two integers n m(1<=n<=263-1,0<=m<=1000), and in the following m lines, each line has two integers:s t(1<=s<=t<=n), which means that the bulbs numbered from s to t (include s and t)are turned-on originally;
Attention, these sections may be overlapped.
And for each case, the first line contains two integers n m(1<=n<=263-1,0<=m<=1000), and in the following m lines, each line has two integers:s t(1<=s<=t<=n), which means that the bulbs numbered from s to t (include s and t)are turned-on originally;
Attention, these sections may be overlapped.
样例输入:
2 1 1 1 1 3 0
样例输出:
0 2HintWhen n = 3 , m = 0, there are two kinds of original states guaranteeing the winner of the earth citizen (From left to right, the bulbs are numbered 1 to n, and o refers to the turned-on bulbs, And * refers to the turned-off): *** ooo
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话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
话说新闻报他横渡计划失败，在加拿大降下来了……
在方法1里面：
//遍历所有的边，计算入度
for(int i=0; i<V; i++)
{
degree = 0;
for (j = adj .begin(); j != adj .end(); ++j)
{
degree[*j]++;
}
}
为什么每遍历一条链表，要首先将每个链表头的顶点的入度置为0呢？
比如顶点5，若在顶点1、2、3、4的链表中出现过顶点5，那么要增加顶点5的入度，但是在遍历顶点5的链表时，又将顶点5的入度置为0了，那之前的从顶点1234到顶点5的边不是都没了吗？
简洁的代码
太好了，收藏
这是研发类的题还是算法类？
具体实现上有所差别，但思路是一样的
已修改。一些字符在编辑的时候转义了
已修正
哎，path[0]应该是从零开始吧，为什么是赋值1呢？
厉害啊，我遇见数学问题就头疼！
L（X [0 .. M-1]，Y [0 .. N-1]）= 1 + L（X [0 .. M-2]，Y [0 .. N-1]）这个地方也也有笔误
应改为L（X [0 .. M-1]，Y [0 .. N-1]）= 1 + L（X [0 .. M-2]，Y [0 .. 此时的数据量还是很小的，耗时却不短。这种方法确实可以，当然或许还有其他的优化方案，但是优化只能针对某些数据，不太可能在所有情况下都能在可接受的时间内求解出答案。
看似简单，还是不少坑的
嗯，尽量吧。题目太多了，很多解题报告是用程序自动发布的，所以就只有代码了


Boarding Pass
Passenger Details
Passenger Name
Gender
Male
Female
Other
Age
Flight Details
Name of Class
Flight Number
Flight Date:
From
To
Gate No:
Seat No:
Boarding Time
Remarks
Observation
Fill the DETAILS


Value Type:
an object.
Initial Value:
Description:
The variables +, ++, and +++ are maintained by the Lisp read-eval-print loop to save forms that were recently evaluated.
The value of + is the last form that was evaluated, the value of ++ is the previous value of +, and the value of +++ is the previous value of ++.
Examples:
(+ 0 1) => 1 (- 4 2) => 2 (/ 9 3) => 3 (list + ++ +++) => ((/ 9 3) (- 4 2) (+ 0 1)) (setq a 1 b 2 c 3 d (list a b c)) => (1 2 3) (setq a 4 b 5 c 6 d (list a b c)) => (4 5 6) (list a b c) => (4 5 6) (eval +++) => (1 2 3) #.`(,@++ d) => (1 2 3 (1 2 3))
Affected By:
See Also:
- (variable), * (variable), / (variable), Section 25.1.1 (Top level loop)
Notes: None.


§560:3-413 Formal testacy proceedings; vacation of order for other cause. For good cause shown, an order in a formal testacy proceeding may be modified or vacated within the time allowed for appeal. [L 1996, c 288, pt of §1]


{% extends 'base.html'%} {% block css %} {% endblock %} {% block body %}
“Culinary is one of the most significant needs of our society as culinary is not just about the food, but also about a community lifestyle. The norms and values of a place can be significantly related to varieties of food available there. This page allows user to choose their meal according to various cuisines available in market around them. User can easily pick up a cuisine of their choice and get access to recommendations accordingly. ”


Primary
Secondary
Success
Danger
Warning
Info
Light
Dark
Primary
Secondary
Success
Danger
Warning
Info
Light
Dark


| rational {MASS} | R Documentation |
Find rational approximations to the components of a real numeric object using a standard continued fraction method.
rational(x, cycles = 10, max.denominator = 2000, ...)
x |
Any object of mode numeric. Missing values are now allowed. |
cycles |
The maximum number of steps to be used in the continued fraction approximation process. |
max.denominator |
An early termination criterion. If any partial denominator
exceeds |
... |
arguments passed to or from other methods. |
Each component is first expanded in a continued fraction of the form
x = floor(x) + 1/(p1 + 1/(p2 + ...)))
where p1
, p2
, ... are positive integers, terminating either
at cycles
terms or when a pj > max.denominator
. The
continued fraction is then re-arranged to retrieve the numerator
and denominator as integers and the ratio returned as the value.
A numeric object with the same attributes as x
but with entries
rational approximations to the values. This effectively rounds
relative to the size of the object and replaces very small
entries by zero.
X <- matrix(runif(25), 5, 5) zapsmall(solve(X, X/5)) # print near-zeroes as zero rational(solve(X, X/5))


Matthew Lee White
Democrat
Managing Director, Public Finance, Stifel Inc. 2019-2020, Executive Vice President, George K. Baum & Co. 1991-2019
Bachelor's of Science Mechanical Engineering, Rensselaer Polytechnic Institute, 1968 Masters of Business Administration, Harvard Business School, 1975 Masters of City Planning, MIT, 1977
80218
I have been active in many civic organizations for over 45 years. For example, I served as Chairman of Denver Civic Ventures downtown Denver organization in the mid-1980s and was on Denver Water Board Citizens Advisory Committee. I also was elected twice to the Denver School Board as an At-Large member beginning in 1975. I was an elder at my Church, Montview Presbyterian Church. I was Co-Chair of the Raise the Bar Yes on Amendment 71 committee in 2016 when that Amendment was adopted. I am on the Executive Committee of Building a Better Colorado, a non-partisan business group promoting state-wide community by community analysis of major civic issues like voting, campaign finance, state budget issues like TABOR and Gallagher.
Michael Bennet for US Senate, Michael Bennet for President, Jason Crow for Congress, Ed Perlmutter for Congress, Joe Neguse for Congress, Diane Mitch Bush for Congress, Michael Johnston for Governor, Hickenlooper for US Senate, Hickenlooper for President, Yes on Amendment 71
Member Colorado Forum, Rocky Mountain Prep Charter School Board Chairman, Denver Public School Foundation Board member, volunteer Colo Democratic Party 2020, Co-Chair Amendment 71 (Raise the Bar) in 2016
I strongly believe the new way to redistrict on a non-political basis is extremely healthy for Colorado voting and belief in the democratic process. Peoples faith in the validity of the democratic process is enhanced if federal and state legislative boundaries are fairly drawn rather than to favor any political party. In particular, the growth of independent voters strongly suggests disapproval of party affiliation. The Commission will have an interesting difficult task in balancing the desire to maintain districts with common geographical communities of interest against the desire to see more politically competitive districts.
I have an excellent grasp of state-wide issues beginning when I was in the Cabinet and State Budget Director for Governor Lamm 1976-81. I have traveled extensively through the state from eastern plains to western slope over my 44 years spent in Colorado. I also spend a great deal of time at my second home in Routt County. I have a good appreciation of the views of rural Colorado. I was Co-Chair of Amendment 71 in 2016 which built a strong coalition of groups for the passage of Amendment 71 in 60 of the State's 64 counties. I am an excellent listener and actively study human decision thinking and political reasoning.
I have a technical undergraduate degree (Mechanical Engineering), an MBA and have been in finance for the last 40 years. I possess strong analytical skills and am highly confident with handling data sets.
male


AngularJS Santander Cycles
Enter your address or postcode to find Santander cycles docking stations around you.
Error:
Sorry, there are no docking stations around you! Try to enter a different address.
| Location | Distance | Available bikes | Empty Docks |
|---|---|---|---|
| {{bike.name}} | {{bike.distance}} miles | {{bike.availableBikes}} | {{bike.emptyDocks}} |


Icons
The icon set is carefully designed for simplicity to convey a clear and concise user experience. Having a simple but effective icon set is crucial for ease of use within sophisticated software environments.
Some icons are saved as pre-rendered images and can be found in the resource folder (.../src/main/webapp/img
). Other
icons are based on Font Awesome which gives us scalable vector icons that can be customized by
size, color, drop shadow, and anything that is provided by CSS.
For more links and license information refer to References - Font Awesome.
Note: Font Awesome contains over 630 icons, the cheat sheet of icons lists the icons and the corresponding CSS class.
fa-rotate-90
fa-rotate-180
fa-rotate-270
fa-flip-horizontal
fa-flip-vertical
fa-flag on fa-circle
fa-terminal on fa-square
fa-ban on fa-camera
Section Navigation Tab Icons
These icons are to represent different sections within the UI and should only be represented in a “flat” graphic style. Simplicity is key in the design of section icons. The user must easily identify the icons. Extension of these icons must represent a similar style.
/* [ Inactive ] */
color
:
#C6C6C6
;
/* [ Active ] */
color
:
#A1A1A1
;
Country Icons
Country icons are located at the top-right of the interface beside the user dropdown to indicate language that the UI is represented in.
Each flag should:
- be public domain
- be an svg image
- have an aspect ratio of 3:2
- be named according to the Crowdin languages
- be without additional decoration (official flags)
A good source for these flags are the national flag articles of Wikipedia.
E.g.
https://en.wikipedia.org/wiki/Flag_of_Germany
.nav-dd-container .lang img {
border: 1px solid gray;
border-radius: 1px;
height: 18px;
vertical-align: middle;
}


JavaScript Denial of Service tool
I CLAIM NO RESPONSIBILITY!!
FOR EDUCATIONAL PURPOSES ONLY!!
URL:
How it works: It sends as many iframe requests as the client can.
This is why you should be cautious about JavaScript...
I could of made it run in the background and attack localhost (<IP_ADDRESS>) without the user knowing.


# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017-2020, Oasis Loss Modelling Framework
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
 IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
__all__ = [
'OasisExposuresManagerInterface'
]
from interface import Interface
[docs]class OasisExposuresManagerInterface(Interface):
"""
An interface for defining the behaviour of an Oasis exposures manager.
"""
def __init__(self, oasis_models=None):
"""
Class constructor.
"""
pass
[docs] def add_model(self, oasis_model):
"""
Adds Oasis model object to the manager and sets up its resources.
"""
pass
[docs] def update_model(self, oasis_model):
"""
Updates an existing Oasis model object in the manager.
"""
pass
[docs] def delete_model(self, oasis_model):
"""
Deletes an existing Oasis model object in the manager.
"""
pass
[docs] def start_files_pipeline(self, oasis_model, **kwargs):
"""
Starts the exposure transforms pipeline for the given ``oasis_model``,
i.e. the generation of the canonical exposures files, keys file
and finally the Oasis files.
All the required resources must be provided either in the model object
resources dict or the ``kwargs`` dict.
It is up to the specific implementation of a manager of whether to
use the model object resources dict or additional optional arguments
in ``kwargs`` for this process.
In a standard implementation of the manager the call to
`start_files_pipeline` should trigger calls to the individual methods for
performing file transformations in a normal sequence, e.g.
`transform_source_to_canonical`
`transform_canonical_to_model`
`transform_model_to_keys`
`load_canonical_profile`
`generate_oasis_files`
and the generated files should be stored as attributes in the given
model object's transforms files pipeline.
"""
pass
[docs] def clear_files_pipeline(self, oasis_model, **kwargs):
"""
Clears the exposure transforms files pipeline for the given
``oasis_model`` optionally using additional arguments in the ``kwargs``
dict.
All the required resources must be provided either in the model object
resources dict or the ``kwargs`` dict.
In the design of the exposure transform framework a model's files
pipeline is an object value in its resources dict with the key
``transforms_files_pipeline`` and is thereby accessible with
`oasis_model.resources['transforms_files_pipeline']`
This returns an object of type
`exposure_transforms.OasisExposureTransformsFilesPipeline`
which stores the different files in the transformation stages for the
model as property attributes, e.g.
`oasis_model.resources['transforms_files_pipeline'].source_exposures_file`
A standard implementation could either assign a new object of this
type in the call to ``clear_files_pipeline``, or set some subset of the
file attributes of this pipelines object to null.
"""
pass
[docs] def get_keys(self, oasis_model, **kwargs):
"""
Generates the Oasis keys CSV file for a given model object, with
headers
``LocID,PerilID,CoverageID,AreaPerilID,VulnerabilityID``
All the required resources must be provided either in the model object
resources dict or the ``kwargs`` dict.
It is up to the specific implementation of this class of how these
resources will be named in ``kwargs`` and how they will be used to
effect the transformation.
A "standard" implementation should use the lookup service factory
class in ``oasis_utils`` (a submodule of `omdk`) namely
``oasis_utils.oasis_keys_lookup_service_utils.KeysLookupServiceFactory``
"""
pass
[docs] def load_canonical_profile(self, oasis_model, **kwargs):
"""
Loads a JSON file representing the canonical exposures profile for a
given ``oasis_model``.
All the required resources must be provided either in the model object
resources dict or the ``kwargs`` dict.
It is up to the specific implementation of this class of how these
resources will be named in ``kwargs`` and how they will be used to
effect the transformation.
"""
pass
[docs] def generate_items_file(self, oasis_model, **kwargs):
"""
Generates an items file for the given ``oasis_model``.
"""
pass
[docs] def generate_coverages_file(self, oasis_model, **kwargs):
"""
Generates a coverages file for the given ``oasis_model``.
"""
pass
[docs] def generate_gulsummaryxref_file(self, oasis_model, **kwargs):
"""
Generates a gulsummaryxref file for the given ``oasis_model``.
"""
pass
[docs] def generate_oasis_files(self, oasis_model, **kwargs):
"""
For a given ``oasis_model`` generates the standard Oasis files, namely
``items.csv``
``coverages.csv``
``gulsummaryxref.csv``
All the required resources must be provided either in the model object
resources dict or the ``kwargs`` dict.
It is up to the specific implementation of this class of how these
resources will be named in ``kwargs`` and how they will be used to
effect the transformation.
"""
pass


The dotLua project:
dotLua is a C# class library which allows C# 2.0 to interact with the LUA scripting engine. It bases on the original, but
slightly modified, LUA C API. LUA is a leightweight, fast, structual scripting language.
Core features:
- Easy to use interface. But it retains the power of the native C LUA API.
- C# callbacks for LUA: LUA calls C# and reverse.
- You can also write LUA libraries in form of C# static classes.
- Benefit from a small and lightweight .NET class library.


Appeal court sacks two PDP lawmakers
by NANThe Court of Appeal sitting in Kaduna, on Tuesday sacked two Kaduna State House of Assembly members.
The appellate court ordered that a rerun be conducted by INEC to determine who will represent Kagarko and Sanga constituencies in the state assembly.
In Kagarko, the appellate court ordered for a rerun in 22 polling units located in two wards of the constituency.
In the judgement read by Justice Daniel Kaliop, the appellate court affirmed the earlier ruling of the state assembly election Tribunal in respect of Kagarko constituency.
News Agency of Nigeria recalled that Mr. Nuhu Shadalafiya of the All Progressives Congress (APC) had challenged the victory of Mr Morondia Tanko of the Peoples Democratic Party (PDP) in the March 9 election at the Tribunal.
The Tribunal had ordered for fresh election in the 22 polling units, but Tanko of the PDP run to the Appeal Court, asking it to set aside the tribunal judgement and affirm his election as declared by INEC.
The Appeal Court in a unanimous decision dismissed the case for lacking in merit and ordered the Independent National Electoral Commission (INEC) to conduct a fresh election in the 22 polling units.
Counsel to Shadalafiya, Mr Ussy Edikhiola said they were happy with the appeal court’s judgement, describing it as the triumph of the rule of law.
Similarly, the Appeal Court has ordered for re-run in Sanga constituency after it upturned the earlier ruling of the Tribunal, which affirmed the election of Mrs Confort Amwe of the PDP.
Amwe is the only female lawmaker in the state assembly.
Gambo Danga of APC had challenged the tribunal decision at the appeal court.


--- layout: post title: Headspring Sponsoring Open Source Hackathon categories: - Small Talk & Big News status: publish type: post published: true meta: _edit_last: '4' dsq_thread_id: '845958115' ---
In conjunction with Pablo’s Fiesta, an open-space development conference being held in Austin September 30-October 2, Headspring will be sponsoring an Open Source Hackathon at our offices on Sunday October 2nd from 1 PM to 6 PM. Over 150 developers from all over the United States and beyond are coming to Austin for 3 days of ‘sharpening the saw’, and the Los Techies Crew realized that with all that brainpower in one place, we ought to be able to do some concentrated work on the open source tools we all use so much.
If you’re attending the Fiesta, or even if you aren’t, feel free to come by the Headspring offices on Sunday to learn more about how open source software is written, ask questions about tools you’re using or would like to use, or even kick-start a new open-source project! Please bring a laptop if you can!
Get more details by going to the Google Group for Pablo’s Fiesta Hackathon.


VLBenchmarks is a MATLAB framework for testing image feature detectors and descriptors. The latest version can be downloaded here.
If you use this project in your work, please cite it as:
@misc{lenc12vlbenchmarks, Author = {K. Lenc and V. Gulshan and A. Vedaldi}, Title = {VLBenchmkars}, Year = {2011}, Howpublished = {\url{http://www.vlfeat.org/benchmarks/}xsxs} }
This project is sponsored by the PASCAL Harvest programme as part of this project. VLBenchmkar is a sibling of the VLFeat Library, which it uses as suppot, but is otherwise independent of it.
The authors would like to thank Andrew Zisserman, Jiri Matas, Krystian Mikolajczyk, Tinne Tuytelaars, and Cordelia Schmid for helpful discussion and supports.
VLBechmarks is a MATLAB framework to evaluate feature detector and descriptors automatically. Benchmarking your own features is as simple as writing a single wrapper class. Then VLBenchmarks takes care of downloading the required benchmarking data from the Internet and running the evaluation(s). The framework ships with wrappers for a number of publicly available features to enable comparing to them easily. VLBenchmarks has a number of functionalities, such as caching of intermediate results, that allow running benchmarks efficiently.
The current version of VLBechmarks implements:
The code is distributed under the permissive MIT license.
The archive linked above contains the complete implementation of the VLBenchmarks suite, including a copy of this documentation. The minimal required version of MATLAB is R2010a (7.10) or later (due to extensive use of the new class type introduced in that release). To install simple unpack the archive, start MATLAB, change thea directory just created, and run:
This script downloads and installs a copy of VLFeat as the only dependency, and it compiles a number of MEX files. To successfully compile the code, you will need to be able to compile MEX files in your MATLAB environment, for detail see MATLAB documentation.
To test the benchmark installation run:
Note that this script will download and install the VGG Affine Dataset.
Here is a script for computing image feature extractor repeatability [1]. This script gets pair of images from the 'Graffiti' dataset, run the feature extractor and compute there repeatability:
With a result:
Running this script once again you will get the results immediately as
they are being loaded from cache. Now let's play with the descriptor
parameters. For example we can test what would happen if we compute features
of up-sampled images (by setting vl_sift
parameter 'FirstOctave'
to -1):
Few moments later we get the results:
Now let's run your custom feature extractor instead. Suppose you have
a command line utility called "DETECTORX" that implements it. Start by copying
a template wrapper located in 'localFeatures/TemplateWrapper.m'
and edit it according to your feature extractor:
Let's compute matching score of your feature extractor [1]. Because created feature extractor is not able to compute descriptors we will use SIFT descriptors instead.
Retrieval benchmark puts the feature extractor into a simple image retrieval system and calculates its mean Average Precision. Interface of the retrieval benchmark is similar to the repeatability benchmark. This minimal example shows how to calculate the Mean Average Precision of an image retrieval system which uses VLFeat SIFT feature extractor.


Articles
-
Time-Series Forecasting using Prophet- part2
May 30, 2020 - Shoaib Khan
In the last post, we learned that we can build a simple Prophet model by combining trend and seasonality to forecast the future. We saw, that in the model equation not only can we add those factors but we can also include holidays effects and extra regressors, which makes it extremely powerful.
-
Time-Series Forecasting using Prophet- part1
May 20, 2020 - Shoaib Khan
Prophet is a time-series forecasting library by facebook and it’s available in Python and in R. It is simple to use and requires very little classical time-series experience, for e.g. ARIMA, SARIMA etc. Thus, you can start building models quickly, get good results and become effective in no time. Considering this, prophet has become a popular library for time-series applications.
-
Adaptive models during COVID-19
April 17, 2020 - Shoaib Khan
COVID-19 has derailed many aspects of life, and it’s hard to adapt. However, to survive we must. The story is hardly different when we look at our mathematical models, many good models have come crashing down as they failed to adjust to sudden fluctuations in the data.
-
Why ECDF is better than a Histogram
January 20, 2019 - Shoaib Khan
If you have never heard of binning bias then you could be using histograms and drawing incorrect conclusions. Please read my original post on Medium and it will help you avoid those mistakes.
-
Conundrums of the Confusion Matrix
January 12, 2019 - Shoaib Khan
Its extremely important to understand your model’s performance. In this post, I explain fundamental metrics such as precision, recall, accuracy, error rate etc.. these metrics help us evaluate all types of classification models e.g. logistic regression, random forest, neural network etc. I had written this post originally on Medium and its been appreciated by many for its simplicity and visuals.


We recently spoke at one of f8's breakout session about Flux, a data flow architecture that works well with React. Check out the video here:
To summarize, Flux works well for us because the single directional data flow makes it easy to understand and modify an application as it becomes more complicated. We found that two-way data bindings lead to cascading updates, where changing one data model led to another data model updating, making it very difficult to predict what would change as the result of a single user interaction.
In Flux, the Dispatcher is a singleton that directs the flow of data and ensures that updates do not cascade. As an application grows, the Dispatcher becomes more vital, as it can also manage dependencies between stores by invoking the registered callbacks in a specific order.
When a user interacts with a React view, the view sends an action (usually represented as a JavaScript object with some fields) through the dispatcher, which notifies the various stores that hold the application's data and business logic. When the stores change state, they notify the views that something has updated. This works especially well with React's declarative model, which allows the stores to send updates without specifying how to transition views between states.
Flux is more of a pattern than a formal framework, so you can start using Flux immediately without a lot of new code. An example of this architecture is available, along with more detailed documentation and a tutorial. Look for more examples to come in the future.


FIX PROBLEM where content below is blocked from view. I cannot solve in CSS. More lines of text here. And OnTheLine.trincoll.edu. And even more here. And even more here.And even more here. And even more here.And even more here. And even more here.
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{{ partial "head.html" . }} {{ partial "logo.html" . }} Oops! The page you're looking for isn't here. Homepage {{ partial "footer.html" . }}


--- author: display_name: JP email<EMAIL_ADDRESS>tags: - railroads - transportation categories: [] date: 2011-11-23 13:15:10 -0700 Railroads: downloads: - url: https://drive.google.com/drive/folders/0ByStJjVZ7c7mb05pQjd6WFJzWXc label: Railroads updates: - July, 2017 endpoints: - url: https://utah.maps.arcgis.com/home/item.html?id=fe20e5c6bb4240919d0cd5ba4116d106 label: ArcGIS Online Feature Service RailroadMps: downloads: - url: https://drive.google.com/a/utah.gov/uc?id=0ByStJjVZ7c7meE5ndEZtbU4zRzQ&export=download label: Railroad mile posts as FGDB - url: https://drive.google.com/a/utah.gov/uc?id=0ByStJjVZ7c7mU3RQNHR3SmkwdkE&export=download label: Railroad mile posts as Shapefile updates: - Created October 1 2017 --- {% include abstract.html name="Railroads" stewards="AGRC, U.S. Geological Survey, Federal Railroad Administration (FRA)" abstract="Transportation.Railroads portrays the location of railroads in the state of Utah derived from the most recent high resolution imagery available, including 1 foot resolution color imagery, NAIP, 1 meter resolution imagery, and 1 meter USGS DOQs. The dataset has been updated to include cartographic attribution for fine scale representations and also a differentiation between light, heavy, transit, and electrified tracks." %}
This dataset was developed for many uses, including but not limited to, emergency planning. There are no constraints or warranties with regard to the use of this data.
Railroad mileposts are used to reference locations along railroads by the closest quarter milepost number and railroad division name. The points are intended to improve dispatch of emergency services along mainline railroads.
The statewide roads dataset is maintained by AGRC and created from distances along SGID10.TRANSPORTATION.Railroads in combination with railroad mileposts provided by the Federal Railway Administration.


Bulk exclusion performed, as required in 273(13), with the following identified as under 273(13)
Candidate A:
Candidate B:
Candidate C:
Nearest candidates (with margin of {{ round.exclusion.margin | numberfmt }} votes):
Papers will be distributed from the excluded candidates in order of transfer value:


On the Create marketplace page, you can create a marketplace to be operated on your platform.
Creating a marketplace includes defining the marketplace features and settings and assigning an organization as the owner. The organization you assign as the owner is automatically assigned the marketplace owner role. All administrators automatically receive the marketplace manager role. The marketplace manager role enables them to administrate and customize the marketplace.
As a prerequisite for creating a marketplace, the organization to be assigned as the marketplace owner must already exist.
If the platform is operated in OIDC authentication mode, you can select the tenant to be associated with the new marketplace. Users logging in to the marketplace will be authenticated by the OIDC provider system represented by the tenant.
To create a marketplace:
The administrators of the owner organization are assigned the marketplace manager role. They are notified by email about the creation of the marketplace. The email contains two URLs. One can be used by the marketplace managers for administrating and customizing the marketplace. The other can be used for public access to the marketplace.
Once you have created a marketplace, your role as a platform operator allows you to change the marketplace owner, to delete the marketplace, if required.


@(assignment: Assignment, problems: List[models.Problem], preFix: String)
Assignment is successfully created and the URL of created assignment is:


Bug 1588154 R[Core:DOM: Service Workers]Turn on/enable service worker parent interception ("dom.serviceWorkers.parent_intercept") by default
Bug 1589669 R[Core:Graphics: WebRender]Picture caching doesn't work well when zoomed in on android
Bug 1605412 R[Core:Widget: Cocoa]Remove the NS_PAPER_ORIENTATION_* defines
Bug 1603272 R[Firefox:General]List installed site specific browsers in the app menu
###########
Changes to 4 bugs - 4 fixed.


Home
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Site Code is: UA-153594637-1
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My name is Siddhant, and I am currently pursuing my Master's in Computer Science at the University of Illinois at Chicago (UIC).
Prior to my graduate studies, I worked at Qualcomm, India as an XR [eXtended Reality] software Developer for 3 years, after completing my bachelor's degree in Computer Science from RVCE Bangalore, India. During my undergrad, I have interned at Boeing, Indian Institute of Science and Krishiyog. With experience in both industry and academia, I bring a unique blend and a broad range of skills to every project I work on.
As a software engineer, I have a wide range of interests, including software development, machine learning, computer vision, and more. I am always looking for new opportunities to expand my skills and gain experience in different software domains.
Currently, I am seeking full-time opportunities in software development and machine learning roles. I am passionate about leveraging technology to solve real-world problems and create innovative solutions that have a positive impact on people's lives. I am excited to bring my skills, experience, and creativity to a new challenge, and to work with like-minded individuals who share my vision.
Thank you for visiting my website, and please feel free to reach out to me if you would like to learn more about my work or discuss potential opportunities. (or just wanna hang out :) )
Modeling 2D/3D assets, AR/VR gameplay design, custom shaders, UI/UX, shot composition, sound design, and end-to-end publishing. Published content to Google Play Store, Jio Tesseract XR, Instagram Filters
Brainstormed several innovations in XR space resulting in 4 patents filed as lead contributer. Lead Incharge of the XR 3DR pipeline framework for profiling and emulation. The framework presents insightful data about the 3DR pipeline performance via a visual web app. It also provides canned data functionality for consistent developement, integration and testing.
Developed USB Driver functionality for PC-HMD setup which is the foundation software for Lenovo ThinkReality A3 Smart Glasses. Worked on software redesigns to upgrade from Android-R to Android-S platform for AR/VR headsets. Built camera frames transfer mechanisms and profiling tools to bring down the M2R2P (motion-to-render-to-photon) numbers as much as possible.
Implemented a novel way of virtualizing the ’Mobile Display Processor’ pipeline using the Writeback hardware module, resulting in a 15% to 26% improvement in data commit time across different configurations.
Developed android simulation app for training personnel about onboard equipment and its procedures for classified aircraft, positively impacting training routines for 4-5 aircraft
◦ Conducted Computer Vision experiments for mapping and classifyng the distribution of natural resources using multispectral satellite images as part of the ISRO Respond project.
◦ Neural models and image preprocessing techniques were employed for detecting Powerlines by high winger UAV. Mainly worked on preprocessing the UAV RedEdge camera feed and porting the neural model output to android.
In charge of end-to-end development of the Crop Health Rating module.This involved maintaining a Google Firebase database for crop sensor data, and developing an android app for displaying insightful data to the end-users. Aided estimated 120 marginalized farmers to get a respectable price for their quality produce.
Pursuing my Master's in Computer Science with depth focus on Artificial Intelligence & Systems programming undertaking various courses in software developement and machine learning and gaining a deep understanding of the latest research, tools, and techniques in this exciting and rapidly evolving field.
During my undergraduate studies at RVCE, I majored in Computer Science and pursued my passion of designing and developing Videogames
My interest in computer vision stems from its ability to uncover insights and help computer and systems "see" the world like we do!. Its applications in AR/VR and Defence sectors are exciting!
I have a strong passion for web/mobile development and UI, driven by my desire to create intuitive and visually appealing user experiences that combine functionality and aesthetics to meet user needs.
I am fascinated by game development and AR/VR because it combines my passion for technology and creativity, allowing me to create immersive and interactive experiences that transport users to different worlds and challenge them in new ways.
I may spend most of my time coding, but when I'm not, you can find me hiking, doing yoga, or reading a good book - all while trying not to trip over my own two feet.
Stuff that I really care about! Surrounded myself with so many like-minded individuals that I started to feel like a cluster algorithm.
"I'm a skilled and dedicated computer science professional with a passion for innovation and problem-solving. With a proven track record of success in software developement, I bring a broad range of expertise to any project. Whether working independently or as part of a team, I am committed to delivering high-quality results that exceed expectations." - Siddhant


{% extends 'base.html' %} {% import 'macros.html' as macro %} {% block content %}
{{business.pitch}}
Posted On:{{business.posted}}
Author:{{business.user.username}}


[gecode-users] Porting attributed variables
Maria Garcia de la Banda
Maria.GarciadelaBanda at infotech.monash.edu.au
Wed Dec 17 14:25:36 CET 2008
Guido Tack wrote:
> Do you want to do the extra pruning in the branching? E.g., collect
> some attributes for particular variables, and then perform the
> branching based on these attributes? This reminds me of branchings
> for scheduling, where propagators determine precedences between
> activities, and the branching uses this precedence information to
> order activities. Is that a similar scenario?
What we are considering is a restricted kind of symmetry breaking during
search (i.e., at each search node after a failed assignment we try to
prune extra values based on the known symmetries). While some symetries
are easy to break efficiently, others require (for efficiency) direct
access from the variable to a data structure that indicates in which
variables symmetries it appears (kind of reverse indexing). We could
always lookup every element of the data structure to find where the
variable appears, but that is too slow.
> With reflection you can only get information about the variable
> domain, but you can't add extra information.
Aha. So that is off.
> But you can simply
> introduce an additional data structure that collects the attributes,
> and that is shared between the branching and whoever is responsible
> for maintaining the attributes (e.g. some propagators).
The datastructure must be maintained through the entire search, mmmh,
actually, this is not true, it only needs to be maintained down in the
search tree , all other branches are kind of independent of siblings.
> You'd only have to make sure that a) the data structure is copied
> properly, and b) that the branching is still compatible with
> recomputation. For a), you could abuse a SharedArray and always set
> the bool share flag to false during update. For b), you have to
> record at each branching step which prunings your branching does, and
> encode them in the BranchingDesc, so that later recomputation steps
> can perform the exact same prunings again.
>
> That's just the overall idea, if you need more concrete hints how to
> implement this, just ask.
So, I could use a Shared structure (like the array) to keep track of the
info of all variables not yet labeled. Could I somehow maintain pointers
to where exactly each variable appears in the structure? (mmh, I think I
am mixing paradigms).
Cheers, Maria
More information about the gecode-users
mailing list


The git plugin provides fundamental git operations for Jenkins projects. It can poll, fetch, checkout, and merge contents of git repositories.
The git plugin provides an SCM implementation to be used with the Pipeline SCM checkout
step.
The Pipeline Syntax Snippet Generator guides the user to select git plugin checkout options and provides online help for each of the options.
Use the Pipeline Snippet Generator to generate a sample pipeline script for the checkout
step.
Examples of the checkout
step include:
The checkout
step provides access to all the Pipeline capabilities provided by the git plugin:
checkout scmGit(userRemoteConfigs: [ [ url: 'https://github.com/jenkinsci/git-plugin' ] ])
NOTE: The checkout
step is the preferred SCM checkout method.
For simpler cases that do not require all the capabilities of the git plugin, the git
step can also be used.
Use the Pipeline Snippet Generator to generate a sample pipeline script for the checkout step.
The checkout
step can be used in many cases where the git
step cannot be used.
Refer to the git plugin documentation for detailed descriptions of options available to the checkout step.
For example, the checkout
step supports:
Checkout from the git plugin source repository using https protocol, no credentials, and the master branch.
The Pipeline Snippet Generator generates this example:
checkout scmGit(userRemoteConfigs: [ [ url: 'https://github.com/jenkinsci/git-plugin' ] ])Example: Checkout step with https and a specific branch
Checkout from the Jenkins source repository using https protocol, no credentials, and a specific branch (stable-2.289).
The Pipeline Snippet Generator generates this example:
checkout scmGit(branches: [[name: 'stable-2.289']], userRemoteConfigs: [ [ url: 'https://github.com/jenkinsci/jenkins.git' ] ])Example: Checkout step with ssh and a private key credential
Checkout from the git client plugin source repository using ssh protocol, private key credentials, and the master branch. The credential must be a private key credential if the remote git repository is accessed with the ssh protocol. The credential must be a username / password credential if the remote git repository is accessed with http or https protocol.
The Pipeline Snippet Generator generates this example:
checkout changelog: false, scm: scmGit(userRemoteConfigs: [ [ credentialsId: 'my-private-key-credential-id', url: 'git@github.com:jenkinsci/git-client-plugin.git' ] ])Example: Checkout step with https and changelog disabled
Checkout from the Jenkins source repository using https protocol, no credentials, the master branch, and changelog calculation disabled.
If changelog is false
, then the changelog will not be computed for this job.
If changelog is true
or is not set, then the changelog will be computed.
See the workflow scm step documentation for more changelog details.
The Pipeline Snippet Generator generates this example:
checkout changelog: false, scmGit(userRemoteConfigs: [ [ url: 'https://github.com/jenkinsci/credentials-plugin' ] ])Example: Checkout step with git protocol and polling disabled
Checkout from the Jenkins platform labeler repository using git protocol, no credentials, the master branch, and no polling for changes.
If poll is false
, then the remote repository will not be polled for changes.
If poll is true
or is not set, then the remote repository will be polled for changes.
See the workflow scm step documentation for more polling details.
The Pipeline Snippet Generator generates this example:
checkout poll: false, scmGit(userRemoteConfigs: [ [ url: 'git://github.com/jenkinsci/platformlabeler-plugin' ] ])


Would it be possible, when a new message is posted on a section we are a member of, to indicate in battlelog updates (the little flag top right) that there actually is a new msg in some of my sections .
I'm really bored having to check each of my sections to follow discussions ...
Thanks
dapil
Plugin Developer
Do you mean platoons?
Papsforever
Section = platoon. This is a really good idea
ORFK
Global Moderator
if YOU write a post or a comment to a platoon wall you already get notifications about these conversations. if you didn't wrote a comment to a post on the platoon wall and/or the post on the platoon wall is not originally from you you won't get notifications. that make also no sense, because that would be the same as you would get notifications about each new feed entries.
Papsforever
the first post is not notified and I think this is one. I agree with L'ASTIC would most practical
Topicstarter
L-Astic
As said paps, the point is that when you join a section in the battlelog, you are NOT noticed about other members posting messages unless you have already posted in his message. So that if you do not already know that someone has posted, you are never aware of it.

That's a Huge issue with battlelog you know :)
Topicstarter
L-Astic
ORFK wrote:
that make also no sense, because that would be the same as you would get notifications about each new feed entries.
Yes of course, but only from the sections you are in. Isn't that what you want when you are member of a section ?? Actually that's what I would like. To be able to be notified of any new message in those sections.
BrainFooLong
Administrator
That must be integrated by DICE. We cannot do this without a whole overhead of requests.


TABLE OF CONTENTS
A. GS1 Related Social Issues 1. NHRC notice to TN, AP over Devadasi-like system B. GS2 Related Polity 1. Government launches Saubhagya scheme for household electrification International Affairs/Bilateral Relations 1. Mattis may push for Indian troops in Afghanistan 2. Raja Mandala: New foray into the great game C. GS3 Related Economy 1. Govt’s 1st estimate sees kharif food output dropping 2.8% to 134.67 mt 2. Database of savings schemes linked with Aadhaar in offing D. GS4 Related E. Prelims Fact F. UPSC Prelims Practice Questions G. UPSC Mains Practice Questions
1. NHRC notice to TN, AP over Devadasi-like system
In news:
- The NHRC has issued notices to the governments and police chiefs of Tamil Nadu and Andhra Pradesh over the alleged inhuman treatment being meted out to girls and women who are forcibly taken to temples as part of an old ritual that resembles the banned Devadasi system.
- The girls and women are taken to temples of Goddess Mathamma for “offering” in Thiruvallur district of Tamil Nadu and adjoining places.
- Practice:
- As a part of the ritual, the girls are dressed up as a bride and once the ceremony is over, their dresses are removed by five boys, virtually leaving them naked.
- They are denied to live with their families and have education.
- They are forced to live in Mathamma temple deemed to be like a public property and face sexual exploitation
- This practice is allegedly “another form of Devadasi system”, which is still practiced in some parts of Tamil Nadu and Andhra Pradesh
- NHRC observations: The rights panel has observed that if the allegations are true, these amount to violation of human rights, including the right to education, rights to life and dignity besides the childrens rights.
- Key Fact:
- Devadasi system is a banned religious practice in some parts of India whereby parents marry a daughter to a deity or a temple.
- The marriage usually occurs before the girl reaches puberty.
- The Union Home Ministry in December 2015 had asked all states to take strong action against those involved in the heinous practice which is against the dignity of women.
1. Government launches Saubhagya scheme for household electrification
In news:
- Government has launched a Rs 16,320-crore scheme to supply electricity to all households by December 2018,
- This scheme will provide free connections to the poor and at very low cost to others
- Scheme: The Pradhan Mantri Sahaj Bijli Har Ghar Yojna, named ‘Saubhagya‘
- Rural Electrification Corp will be the nodal agency for the scheme
- While public institutions and panchayats will be authorised to collect applications forms, distribute bills and collect payments
Modalities of the scheme:
- It aims to improve the environment, public health, education and connectivity with the help of last-mile power connections across India
- It will help reduce the use of kerosene lamps in non-electrified households
- States that complete the scheme by December next year would be given an additional 15% grant by the Centre as incentive
- The scheme is being funded to the extent of 60% by central grants, 30% by bank loans and 10% by states
- Under the scheme, electricity connections will be given free to people identified by the Socio Economic and Caste Census of 2011
- The scheme promises on-the-spot registration of applications
Category: INTERNATIONAL AFFAIRS/BILATERAL RELATIONS
1. Mattis may push for Indian troops in Afghanistan
In news:
- S. Defence Secretary Jim Mattis landed in India, for his maiden three-day visit
- Key fact: the first visit by a Cabinet member of the Trump administration.
- Key areas of focus:
- Deepening the defence partnership and expand the high technology cooperation under the Defence Technology and Trade Initiative (DTTI)..
- Afghanistan situation discussions.
- Push for Indian troops in Afghanistan.
- The two sides could also discuss the new Chinese posturing in the region and terrorism originating from Pakistan.
- The two sides are expected to conclude the formal agreement for holding a bilateral maritime exercise, focused on providing humanitarian assistance and disaster relief.
Indian aid to Afghanistan:
- India has already extended a $3-billion aid to Afghanistan and also provides training to its military and other assistance.
2. Raja Mandala: New foray into the great game
Context:
- The question of a larger Indian role in securing Afghanistan is expected to figure in the talks between the US Defense Secretary James Mattis and the Indian leadership.
Background
- The developments in Afghanistan in the 1970s and 1980s saw the United States deepen its partnership with Pakistan to the dismay of India.
- Since the early 2000s, when the US warmed up to India, Afghanistan remained an area of disagreement.
New south Asian policy:
- In a major speech on the new administration’s policy towards South Asia, Donald Trump demanded that Pakistan end its destabilisation of Afghanistan by providing safe havens to the Taliban and other terror networks.
- Trump’s emphasis in the speech was indeed on India stepping up its economic and development assistance to Afghanistan.
- India and the US are fully aware that without stronger external military support to Kabul, the talk of development could become increasingly futile
- The 2011 Strategic Partnership Agreement between Delhi and Kabul explicitly called for security cooperation, including the transfer of military equipment.
Key points:
- Delhi did not want deeper security cooperation with Afghanistan that could cast a shadow over its ties with Pakistan.
- A second constraint has been geography. The lack of physical access has imposed substantial limitations on Delhi’s military role in Afghanistan
- US resistance to a larger Indian security footprint in Afghanistan
- The US was convinced that any Indian military role in Afghanistan would grate Pakistan’s political sensitivities.
- But Trump has now actively encouraging India to take greater responsibilities in Afghanistan.
- Trump’s Indo-Pak inversion may or may not be sustainable over the long-term.
- Many in Washington would want to go back to the default option of avoiding confrontation with Pakistan in Afghanistan.
1. Govt’s 1st estimate sees kharif food output dropping 2.8% to 134.67 mt
In news:
- The 2017 kharif foodgrain output may comes down to 134.67 million tonnes (mt) from a record harvest of 138.52 mt during the previous kharif season
- Reasons: Floods and erratic rainfall in different parts of the country
- The drop in output was seen across all major rain-fed crops except sugarcane
- Sugarcane registered a nearly 10 per cent increase in cropping area and a corresponding increase in output
Impacts:
- It may have an impact on farm incomes, which will be lower because of lower output
- This would mean that the demand for other goods in rural areas will not recover the way the government was hoping it would
- In other words, despite having two good monsoon in a row, private consumption expenditure will not go up and thus may have an impact on the overall economy
2. Database of savings schemes linked with Aadhaar in offing
In news:
- The Cabinet Secretariat is keen on a central database of government and private sector employees who have subscribed to the general provident fund (GPF), public provident fund (PPF) and employees’ provident fund (EPF) with Aadhaar as the primary identifier.
- It has directed the various departments monitoring such savings schemes to ensure that 100% Aadhaar seeding of all salaried accounts takes place by December 31.
- Key Fact:
- EPF is the savings scheme for private sector employees
- The GPF is meant for government employees and
- PPF can be subscribed to by all workers.
Nothing here for Today!!!
Nothing here for Today!!!
F. Practice Questions for UPSC Prelims Exam
Question 1. Identify the correct statement with reference to Khurki system.
- It is an irrigation system adopted in South Asia.
- It is a system of land revenue adopted by Mughals.
- It is a system used by the British planters to compel the farmers to sow indigo.
- None of the above
Question 2. Recently Health Ministry of Government of India, introduced new
vaccine - Pneumococcal conjugate vaccine. This particular vaccine is used to treat
- Hib pneumonia
- Pneumococcal pneumonia
- Diarrhoea
- Polio
Question 3. Which among the following vaccines come under Penatavalent vaccine?
- Diphtheria, Pertussis or whooping cough, tetanus, Hepatitis B and Hib vaccines.
- Diphtheria, Pertussis/whooping cough, polio), Hepatitis B and Hib vaccines.
- Diphtheria, Pertussis/whooping cough and tetanus), Hepatitis C and Hib vaccines.
- Diphtheria, Pertussis/whooping cough and tetanus), Hepatitis A and Hib vaccines.
Question 4. Consider the following statement with reference to Egyptian vultures,
which was in news recently.
- It is the largest among all the vultures.
- It is an endangered species as per IUCN’s Red data book.
- It is also called as white scavenger.
Which of the above statements is/are correct?
- 1 only
- 1 and 3 only
- 2 and 3 only
- All are correct
Question 5. ‘UDAN’, recently seen in the news, is a scheme launched by the
Government of India. This scheme is
- A scholarship instituted to award meritorious students belonging to Economically Weaker Sections among the upper castes in the state of Gujarat.
- The Railway Ministry’s efforts to increase the average train speed along the existing tracks.
- The Civil Aviation Ministry’s Regional Connectivity Scheme.
- A collaboration of the Railways Ministry and the Environment Ministry aimed at reducing road transport’s share in freight traffic and its concomitant increase in railway’s share.
G. UPSC Mains Practice Questions
GS Paper I
- Major cities of India are becoming vulnerable to flood conditions. Discuss.
GS Paper II
- Has the formation of linguistic states strengthened the cause of Indian unity?
GS Paper III
-
What is water-use efficiency? Describe the role of micro-irrigation in increasing the water-use efficiency.
Also, check previous Daily News Analysis
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Say Hello! Extending Thymeleaf in 5 minutes
Extending Thymeleaf is easy: you only have to create a dialect and add it to your template engine. Let's see how.
(All the code seen here comes from a working application. You can download the source code of such application from the documentation page)
Dialects
Thymeleaf Dialects are sets of features you can use in your templates. These features include:
- Processing logic specified via processors that apply on specific DOM nodes.
- DOCTYPE translations which allow the automatic conversion of the template's DOCTYPE declaration into a target one, thus allowing validation both before processing --against a DTD including the dialect's tags and attributes-- and after processing --against a DTD not including these, like e.g. a standard XHTML DTD.
- DOCTYPE resolution entries which are fed into Thymeleaf's template parsers and allow the resolution of DTD files for the validation of templates (if required) in an offline manner, this is, without the need to perform remote HTTP connections and/or download anything.
All of these features are optional, and a dialect can specify only some of them. For example, your dialect might not need to specify any DOCTYPE translations because you do not plan to allow the ability to validate templates against a DTD including your dialect's features.
If you've seen fragments of code written in the Standard Dialects, you should have noticed that the processable attributes start with th:. That "th" is called the dialect prefix, and it means that all tags and attributes processed by that dialect will start with such prefix.
It is important also to note that a Template Engine can be set several dialects at a time, thus allowing the processing of templates including features from all of the specified dialects (think of dialects as a sort of JSP taglibs in steroids). What's more, some of these dialects can share prefix, effectively acting as an aggregate dialect.
The simplest dialect ever: Say Hello!
Let's create a dialect for one of our applications. This will be a Spring MVC application, so we will be already using the SpringStandard dialect (have a look at the Thymeleaf + Spring 3 tutorial for more details). But we want to add a new attribute that allows us to say hello to whoever we want, like this:
The processor
First, we will have to create the attribute processor that will take care of displaying our salutation message.
All attribute processors implement the org.thymeleaf.processor.IProcessor interface —which marks them as processors— and specify a matcher (IProcessor.getMatcher()) that implements the org.thymeleaf.processor.IAttributeNameProcessorMatcher interface —which specifically marks them as attribute processors and allows the use of several engine optimizations—. However, several convenience abstract implementations exist that not only implement the right interfaces, but also ease the development of each type of processor.
Specifically, the AbstractTextChildModifierAttrProcessor class specializes in attributes that substitute the body of the tag they sit on by a text (which is exactly what we want). So we will extend this class for our attribute processor:
The dialect class
Creating our processor was very easy, but now we will need to create the dialect class, which will be in charge of telling Thymeleaf that our processor is available.
We could make our dialect implement the org.thymeleaf.dialect.IDialect interface (which is the one every dialect should implement), but instead we will extend a convenience abstract class called AbstractDialect that already implements all methods from that interface, returning empty values for all of them (so that we only have to override the ones we require).
As you can see, we didn't need to set any DOCTYPE resolution entries or translations. We didn't need any of those because our dialect is meant to be used together with the SpringStandard dialect, which already includes a useful set of all those things (although we could have defined them here also if for some reason we wanted to be more exhaustive). All our dialect does is specify a prefix (hello) and our "sayto" attribute processor.
Using the hello dialect
Using our new dialect is very easy. This being a Spring MVC application, we just have to set it at the additionalDialects property of the Template Engine bean, so that it is added to the default SpringStandard dialect:
And our new attribute would work seamlessly:
Want to know more?
Then have a look at "Say Hello Again! Extending Thymeleaf even more in another 5 minutes".