REASONING TEST PAPER 3
Study the statements and the two conclusions and state if:
(A) Only conclusion I follows
(B) Only conclusion II follows
(C) Both conclusions I and II follow
(D)Neither I or II follow
1. Statements: Due to contamination of water a large number of people were admitted to the hospital. The symptoms denoted Malaria.
Conclusion:
(I) Contamination of water may lead to Malaria
(II)Malaria is a disease 3
A B C D
2. Statements: To own a personal imported motor bike one requires an import license
Conclusions:
(I) Motor bikes are manufactured in India
(II) They can be imported easily 4
A B C D
3. Statements: The average number of students in cities is 40 per teacher, whereas in the villages it is 50. The combined average is 45.
Conclusions:
(I) The student-teacher ratio in the cities is not satisfactory
(II) Student-teacher ratio in cities is higher than that in the villages
A B C D
Study the statements and the two inferences that follow and state if:
(A) Only inference I follows
(B) Only inference II follows
(C) Both inferences I and II follow
(D)Neither I or II follow
4.  Some monkeys are rabbits
(I) Some rabbits are donkeys
(II) Some donkeys are rabbits
A B C D
5. Statements: No petal is a plant. No plant is a thorn
(I) No thorn is a petal
(II) No Petal is thorn
A B C D
6. No bird is an animal. All birds are insects
(I)No insect is animal
(II)No animal is insect
A B C D
7. All expectations are liars. All fears are dupes. So
(I)All expectations are fears
(II)All liars are dupes
A B C D
8. Every ink is blue. Flowers are blue. So
(I)Flower is ink
(II)Ink is flower 1
A B C D
9. No cat is rat. No rat is dog. So
(I)No cat is dog
(II)No dog is cat 4
A B C D
10. All slaves are masters. All masters are harsh
(I)All slaves are harsh
(II)All harsh are slaves
A B C D
Directions for questions 11 to15
Each question below is followed by four arguments. Classify them into strong and weak
arguments.
Strong arguments must be both important and directly related to the question.
Weak arguments may not be directly related or may be of minor importance
11.Movies should not be censored.
I No: Movies can contain obscenity and violence
II Yes: Censorship boards are overworked anyway.
III Yes: Censorship implies that a few people know what is good for the rest.
IV Yes. Movies are facing competition from television.
All strong
Only II weak
II and IV weak
All are weak
12. Honesty is the best policy
I Yes: To be honest pays in the long run
II No: Honesty is often taken advantage of
III No: Honesty is rarely appreciated
IV Yes: An honest person has a clear conscience, and is a happier person
I strong
All are strong
II is strong
III is strong
13. The dowry system has to be legalized
I Yes: All the dowry payers will be happy
II No: Legislation will encourage the practice
III Yes: Legalization will help institute checks and controls
IV No : A shameful practice is best conducted secretly
II and III strong
All are weak
IV is strong
II is weak
14. Money is the root of all evil
I Yes : Money can drive men to murder and robbery
II No : Money is mechanical
III No : Only trees have roots
IV Money signifies greed, which is the root of all evil
II strong
All are weak
All are strong
I and IV are strong
15. One day you have to quit working. Invest wisely today, and you won't stop spending
I Yes : A wise investment today can give one an assured future income
II No : It is not necessary that an investment today, however wise, can guarantee
'continued spending' in the future
III Yes : Someday or the other, everybody stops working
IV No : Investment is not an area that everybody is comfortable with.
I and II are strong
I II and III are strong
All are strong
All are weak
Directions for questions 16 to 20
Classify the statements as Fact(F), Inference(I), or Judgement(J), based on the
definitions given below
FACT: Something that can be seen or heard, and is capable of being verified.
INFERENCE: The statement that is drawn or concluded from a fact
JUDGEMENT: Is an opinion, and implies approval or disapproval
16. a] This is a red book
b] All red books are unlucky
c] Red books are more attractive than blue ones
FFF
IJJ
JFJ
FJJ
17. a] The goat is sitting on the grass
b] The grass is green
c] Plastic is not a bio-degradable substance
JJJ
FFF
FIJ
FFJ
18. a] It is unimaginable
b] God is great
c] Politics is the last refuge of the criminal
FFF
JIJ
JFF
JJJ
19. a] Catches win matches
b] Its just not cricket
c] Spectators find one-day matches more interesting than test matches
FFJ
JIJ
IJI
IIJ
20. a] She was writing, seated on a table
b] I love his mild nature
c] The secretary gave a friendly smile
JJJ
FJJ
FII
FFI
Directions for questions 21 to 25
An argument is a statement meant to convince another person about your point of
view
An assertion is a point of view
A counter-argument contains logic opposing the assertion
Based on the above definitions, classify each of the given set of statements into
I Assertion II Supporting reason
III Counter argument IV Irrelevant argument
21. a] They sold 850 chairs thereby getting a net profit of Rs 100,000
b] The IT department did the right thing when it acquired undervalued property nearly
10 years ago
c] In the last 2 auctions, the department failed to sell any of the 53 chairs on view
d] Even in an advertising blitz in the Gulf did not generate much revenue
IV,IV,III,III
I, II,III,III
II, I, III, III
I, II, II, II
22. a] It is difficult to be happy
b] The symptoms of happiness are a source of happiness
c] Happiness comes from a lack of want
d] Unhappiness creates a lack of want
I, II, II, II
IV, I, II, II
I, II, I, II
I, II, IV, IV
23. a] A cheat
b] A liar
c] He is a twisted man
d] A friend to be relied on
II, II, I, III
IV, IV, II, III
II, III, I, IV
I, I, II, II
24. a] The difference between good and evil is a fine dividing line
b] For example, is abortion right or wrong
c] Nature has not devised simplicity
d] Even a small leaf is of infinite complexity
I, II, I, II
II, I, II, I
II, I, I, III
I, II, III, III
25. a] "Yes, Iam keen to get married to her. It will be soon"
b] He says he plans to get married to a girl he professes to have fallen in love with,
while in jail
c] He is a renowned flirt and has broken hearts and promises down the line
d] Debonair and suave, his dress-sense is not to be faulted
II, I, I, II
IV, II, I, IV
IV, I, II, III
II, I, III, IV
Explanation to Paper III
1[3]
2[4]
3[4]
4[3]
5[4]
6[4]
7[4]
8[1]
9[4]
10[1]
11[3]
12[2]
13[1]
14[4]
15[1]
16[4]
17[2]
18[4]
19[3]
20[2]
21[3]
22[2]
23[1]
24[1]
25[4]


Like
extend
but only copies missing properties/values to the target object.
Install with npm:
$ npm install --save object.defaults
Install with bower
$ bower install object.defaults --save
var defaults = require('object.defaults');
var obj = {a: 'c'};
defaults(obj, {a: 'bbb', d: 'c'});
console.log(obj);
//=> {a: 'c', d: 'c'}
Or immutable defaulting:
var defaults = require('object.defaults/immutable');
var obj = {a: 'c'};
var defaulted = defaults(obj, {a: 'bbb', d: 'c'});
console.log(obj !== defaulted);
//=> true
Pull requests and stars are always welcome. For bugs and feature requests, please create an issue.
| Commits | Contributor |
|---|---|
| 16 | jonschlinkert |
| 1 | phated |
| 1 | sobolevn |
(This project's readme.md is generated by verb, please don't edit the readme directly. Any changes to the readme must be made in the .verb.md readme template.)
To generate the readme, run the following command:
$ npm install -g verbose/verb#dev verb-generate-readme && verb
Running and reviewing unit tests is a great way to get familiarized with a library and its API. You can install dependencies and run tests with the following command:
$ npm install && npm test
Jon Schlinkert
Copyright © 2017, Jon Schlinkert. Released under the MIT License.
This file was generated by verb-generate-readme, v0.5.0, on April 26, 2017.


The super-class for learning algorithms that treat the problem as a black box. At each step they change the policy, and get a fitness value by invoking the FitnessEvaluator (provided as first argument upon initialization).
Evaluable objects can be lists or arrays of continuous values (also wrapped in ParameterContainer) or subclasses of Evolvable (that define its methods).
Bases: pybrain.optimization.optimizer.BlackBoxOptimizer
A more restricted class of black-box optimization algorithms that assume the parameters to be necessarily an array of continuous values (which can be wrapped in a ParameterContainer).
Stochastic hill-climbing always moves to a better point, but may also go to a worse point with a probability that decreases with increasing drop in fitness (and depends on a temperature parameter).
A new version of NES, using the exact instead of the approximate Fisher Information Matrix, as well as a number of other improvements. (GECCO 2009).
Basic finite difference method.
Bases: pybrain.optimization.finitedifference.fd.FiniteDifferences
Policy Gradients with Parameter Exploration (ICANN 2008).
Bases: pybrain.optimization.finitedifference.fd.FiniteDifferences
Simultaneous Perturbation Stochastic Approximation.
This class uses SPSA in general, but uses the likelihood gradient and a simpler exploration decay.
Particle Swarm Optimization
size determines the number of particles.
boundaries should be a list of (min, max) pairs with the length of the dimensionality of the vector to be optimized (default: +-10). Particles will be initialized with a position drawn uniformly in that interval.
memory indicates how much the velocity of a particle is affected by its previous best position.
sociality indicates how much the velocity of a particle is affected by its neighbours best position.
inertia is a damping factor.
Standard Genetic Algorithm.
select some of the individuals of the population, taking into account their fitnesses
| Returns: | list of selected parents |
|---|


While learning the Nix ecosystem and trying to use it, I found it a bit more harder than I thought to achieve what I wanted. In this post and the next one, I’m documenting what I learned, partly for myself, partly to share with other people that would like to follow the same path.
The prerequesite to follow along this post is to install Nix and NixOps.
The basics of Nix are actually very well documented elsewhere; in particular:
As as a starting point, here I give a do.nix
file suitable for the nixops
executable. I will use it in the rest of the post as a running example:
$ cat do.nix
{
network.description = "Some machines (actually just one)";
resources.sshKeyPairs.ssh-key = {};
machine-1 = { config, pkgs, ... }: {
deployment.targetEnv = "digitalOcean";
deployment.digitalOcean.region = "ams2";
deployment.digitalOcean.size = "512mb";
}; # machine-1
}
That file contains a single Nix expression and is enough to get a machine up and running on Digital Ocean. (It seems the support for the AWS environment is much more mature but for what this post is about, Digital Ocean support is fine.)
Here is how you instruct NixOps to use that file to spin up a machine and provision it (you can create API tokens at https://cloud.digitalocean.com/settings/api/tokens):
$ nixops create -d do do.nix
$ nixops list
+----------------+------+------------------------+------------+--------------+
| UUID | Name | Description | # Machines | Type |
+----------------+------+------------------------+------------+--------------+
| c94aa2ee-7954- | do | Unnamed NixOps network | 0 | |
+----------------+------+------------------------+------------+--------------+
$ nixops info -d do
Network name: do
Network UUID: c94aa2ee-7954-11e7-8947-0242c5c1eaab
Network description: Some machines (actually just one)
Nix expressions: /home/thu/projects/web/nixops/do.nix
+-----------+---------------+---------------------+-------------+------------+
| Name | Status | Type | Resource Id | IP address |
+-----------+---------------+---------------------+-------------+------------+
| machine-1 | Missing / New | digitalOcean [ams2] | | |
+-----------+---------------+---------------------+-------------+------------+
$ DIGITAL_OCEAN_AUTH_TOKEN=xxxx nixops deploy -d do
machine-1> creating droplet ...
...
This creates a “deployment” using our expression: a mean for NixOps to track the state associated with our machines and name that deployment “do” (which can be used instead of its UUID) then actually deploy it to Digital Ocean. (For good measure I call also nixops list
and nixops info
to demonstrate those two commands.)
After a while (a long while when using the Digital Ocean target), you should be able to SSH into the machine:
...
machine-1> activation finished successfully
do> deployment finished successfully
$ nixops ssh -d do machine-1
[root@machine-1:~]#
For good measure again:
$ nixops list
+----------------+------+-----------------------------------+------------+--------------+
| UUID | Name | Description | # Machines | Type |
+----------------+------+-----------------------------------+------------+--------------+
| c94aa2ee-7954- | do | Some machines (actually just one) | 1 | digitalOcean |
+----------------+------+-----------------------------------+------------+--------------+
$ nixops info -d do
Network name: do
Network UUID: c94aa2ee-7954-11e7-8947-0242c5c1eaab
Network description: Some machines (actually just one)
Nix expressions: /home/thu/projects/web/nixops/do.nix
+-----------+-----------------+--------------+-------------+----------------+
| Name | Status | Type | Resource Id | IP address |
+-----------+-----------------+--------------+-------------+----------------+
| machine-1 | Up / Up-to-date | digitalOcean | | <IP_ADDRESS> |
| ssh-key | Up / Up-to-date | ssh-keypair | | |
+-----------+-----------------+--------------+-------------+----------------+
When you want to update the machine, following changes to the Nix expression describing it, the same deploy
command is used again. (You can already do it again with no changes and it should complete much more quickly.)
You can destroy the droplet with the following command:
$ DIGITAL_OCEAN_AUTH_TOKEN=xxxx nixops destroy -d do
machine-1> destroying droplet 57418645


{% extends "form-wrapper.html" %} {% block head_title %}Verify your company address - Business profile - GREAT.gov.uk{% endblock %} {% block form_title %}Your company address{% endblock%} {% block button_next_label %}Next{% endblock%} {% block button_finish_label %}Send{% endblock%} {% block formarea %}
Enter your name. We’ll then send a confirmation letter to your company’s registered address within 5 working days.
{{ company_name }}
Company number {{ company_number }}
{{ company_address }}


function setup() {
createCanvas(400,400);
textSize(48);
//tokenize splits a string of text
//into an array of words and tokens (. , !, ?, etc.)
var words = RiTa.tokenize("Got me looking so crazy right now.");
//to pull out each word,
//we need to loop over the words array
for(var i=0; i < words.length; i++){
text(words[i], 50, i*48 + 48);
}
}
function setup() {
createCanvas(400,400);
textSize(20);
// gets the phonemes from a string of text and returns a phonetically spelled string
var words = RiTa.getPhonemes("Got me looking so crazy right now.");
text(words, 50, 50,300,100);
}
function setup() {
createCanvas(400,400);
textSize(20);
var word = "huge";
var wordWidth = textWidth(word);
text(word, 50, 50,wordWidth,100);
rect(50,75, wordWidth, wordWidth);
var wordTwo = "huuuuge";
var wordTwoWidth = textWidth(wordTwo);
text(wordTwo, wordWidth + 75, 50,wordTwoWidth,100);
rect(wordWidth + 75, 75, wordTwoWidth, wordTwoWidth);
var wordThree = "huuuuuuuuge";
var wordThreeWidth = textWidth(wordThree);
text(wordThree, 220, 50,wordThreeWidth,100);
rect(220, 75, wordThreeWidth, wordThreeWidth);
}
var lyricLine = "Thank you very much Mister Robot.";
var words = [];
var stresses;
function setup() {
createCanvas(400,400);
textSize(20);
//get all of the stresses as an array of 1's and 0's where 1 means stressed
//and 0 means unstressed
stresses = RiTa.getStresses(lyricLine);
words = RiTa.tokenize(lyricLine);
}
function draw(){
background(255);
var wordPos = 0;
for(var i=0; i < words.length; i++){
text(words[i], wordPos, 0, textWidth(words[i]),20);
var wordStresses = RiTa.getStresses(words[i]);
text(wordStresses, wordPos, 20, textWidth(words[i]), 20);
wordPos += textWidth(words[i])+ 20;
}
}
//Couplet by Ezra Pound
var couplet = "Drawing a sword, cut into water, water again flow: Raise cup, quench sorrow, sorrow again sorry.";
var words = [];
var partsOfSpeech = [];
function setup() {
createCanvas(400,400);
textSize(12);
words = RiTa.tokenize(couplet);
partsOfSpeech = RiTa.getPosTags(words);
}
function draw(){
background(255);
var wordPosX = 0;
var wordPosY = width/4;
for(var i=0; i < words.length; i++){
text(words[i], wordPosX, wordPosY,
textWidth(words[i]),20);
//we check whether each parts of speech exists
//in our array
if( partsOfSpeech[i] != null){
text(partsOfSpeech[i], wordPosX, wordPosY+20, textWidth(words[i]), 20);
}
wordPosX += textWidth(words[i])+ 20;
//if wordPosX goes beyond our width,
//move the text down to a new line
if(wordPosX > width){
wordPosX = 0;
wordPosY += 100;
}
}
}
var lyricLine = "Thank you very much Mister Robot";
function setup() {
createCanvas(400,400);
textSize(20);
textAlign(CENTER);
}
function draw(){
background(255);
text(lyricLine, 0,height/2, width, 20);
text("number of words: "+ RiTa.getWordCount(lyricLine),
0,height/2 + 20,width,20
);
}
var lexicon;
function setup() {
createCanvas(400, 400);
//first create a new RiLexicon
lexicon = new RiLexicon();
textSize(20);
textAlign(CENTER);
text("Click for random word", width/2, height/2);
}
function mousePressed() {
background(255);
//get a random word from our lexicon object
text(lexicon.randomWord(), width/2, height/2);
}
/**
* by Allison Parrish
*/
var lexicon;
function setup() {
createCanvas(400, 400);
lexicon = new RiLexicon();
background(50);
fill(255);
noStroke();
textSize(32);
textAlign(CENTER, CENTER);
text("Click for fun", width/2, height/2);
}
function draw() {
}
function mousePressed() {
background(50);
textAlign(LEFT, TOP);
var output = "April is the " +
lexicon.randomWord("jj") + " " +
lexicon.randomWord("nn") + ", " +
lexicon.randomWord("vbg") + " " +
lexicon.randomWord("nns") +
" out of the " +
lexicon.randomWord("jj") + " " +
lexicon.randomWord("nn");
text(output, 10, 10, width-20, height-20);
}
var lexicon;
function setup() {
createCanvas(400, 400);
lexicon = new RiLexicon();
textSize(20);
textAlign(CENTER, CENTER);
textAlign(LEFT, TOP);
generateHaiku();
}
function mousePressed(){
generateHaiku();
}
function generateHaiku(){
var output =
//first line, 5 syllables
lexicon.randomWord("vb", 1) + " " +
lexicon.randomWord("jj", 2) + " " +
lexicon.randomWord("nn", 2) + "\n" +
//second line, 7 syllables
lexicon.randomWord("vbg", 2) + " " +
lexicon.randomWord("nn", 2) + " " +
lexicon.randomWord("rb", 3) + "\n" +
//third line, 5 syllables
lexicon.randomWord("nns", 1) + " " +
lexicon.randomWord("vbg", 2) + " " +
lexicon.randomWord("nn", 2);
background(255);
text(output, 10, 10, width-20, height-20);
}
var lines = [];//we declare lines as an empty array
//preload gets called BEFORE setup
function preload(){
//loadStrings returns an array of Strings from our text file
//you must have a local file in order for this function to work
lines = loadStrings('assets/The Problems of Philosophy.txt');
}
function setup() {
createCanvas(400,400);
//renders the contents of our lines array
text(lines, 0,0,width,height);
}
var lines = [];//we declare lines as an empty array
//preload gets called BEFORE setup
function preload(){
//loadStrings returns an array of Strings from our text file
//you must have a local file in order for this function to work
lines = loadStrings('assets/The Problems of Philosophy.txt');
}
function setup() {
createCanvas(400,400);
//joins an array into a long string
//and puts empty space between array elements
var linesAsString = lines.join(' ');
//in order to split by sentence,
//we need to have our lines as a single long string
var sentences = RiTa.splitSentences(linesAsString);
text('there are ' + sentences.length + ' sentences in this book', 0,10);
//loop through each of the sentences
//and do something with each of the sentences
for(var i=0; i < sentences.length;i++){
//your code here
}
}
Assignment: Generative Madlib poetry: Find a prose poetry text from Wikisource and replace all nouns and adjectives with random nouns and adjectives. Try adding a rhymescheme.
Reading: Concerning the Visual in Poetry


Allows to retrieve data from the 'CPTEC/INPE' weather forecast API. 'CPTEC' stands for 'Centro de Previsão de Tempo e Estudos Climáticos' and 'INPE' for 'Instituto Nacional de Pesquisas Espaciais'. 'CPTEC' is the most advanced numerical weather and climate forecasting center in Latin America, with high-precision short and medium-term weather forecasting since the beginning of 1995. See <http://www.cptec.inpe.br/> for more information.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.2.0) |
| Imports: | magrittr, rvest, xml2 |
| Suggests: | knitr |
| Published: | 2017-09-20 |
| Author: | Renato Prado Siqueira [aut, cre] |
| Maintainer: | Renato Prado Siqueira <rpradosiqueira at gmail.com> |
| BugReports: | http://github.com/rpradosiqueira/cptec/issues |
| License: | GPL-3 |
| URL: | http://github.com/rpradosiqueira/cptec |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | cptec results |
| Reference manual: | cptec.pdf |
| Package source: | cptec_0.1.0.tar.gz |
| Windows binaries: | r-devel: cptec_0.1.0.zip, r-release: cptec_0.1.0.zip, r-oldrel: cptec_0.1.0.zip |
| OS X binaries: | r-release: cptec_0.1.0.tgz, r-oldrel: cptec_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=cptec to link to this page.


|
|
#include <pantheios/inserters/xi.hpp>
Public Types | |
| typedef integer | parent_class_type |
| The parent class type. | |
| typedef xi | class_type |
| This type. | |
Public Member Functions | |
| xi (int i, int width=get_default_width_(sizeof(int))) | |
| Construct from an int. | |
| xi (unsigned int i, int width=get_default_width_(sizeof(unsigned int))) | |
| Construct from an unsigned int. | |
| xi (long i, int width=get_default_width_(sizeof(long))) | |
| Construct from a long. | |
| xi (unsigned long i, int width=get_default_width_(sizeof(unsigned long))) | |
| Construct from an unsigned long. |
| typedef integer parent_class_type |
The parent class type.
| typedef xi class_type |
| xi | ( | int | i, | |
| int | width = get_default_width_(sizeof(int)) | |||
| ) | [inline, explicit] |
Construct from an int.
| xi | ( | unsigned int | i, | |
| int | width = get_default_width_(sizeof(unsigned int)) | |||
| ) | [inline, explicit] |
Construct from an unsigned int.
| xi | ( | long | i, | |
| int | width = get_default_width_(sizeof(long)) | |||
| ) | [inline, explicit] |
Construct from a long.
| xi | ( | unsigned long | i, | |
| int | width = get_default_width_(sizeof(unsigned long)) | |||
| ) | [inline, explicit] |
Construct from an unsigned long.
|
|
|
| pantheios Library documentation © Matthew Wilson & Synesis Software, 2006-2011 |


Here you'll find additional text utility classes, altering the color of any given text. These classes can be used similarly to the contextual color classes found in Bootstrap's documentation.
A dark background has been applied to the .text-white
code example so you can see the text and the class' impact on said text.
Lorem ipsum dolor sit amet, consectetur ...
Lorem ipsum dolor sit amet, consectetur ...
Lorem ipsum dolor sit amet, consectetur ...
Lorem ipsum dolor sit amet, consectetur ...
Lorem ipsum dolor sit amet, consectetur ...


{{company_name}}{{company_address}}, {{company_address2}}
{{company_city}} - {{company_postcode}}
{{company_contact_phone}}, {{company_contact_email}}
|
Daily Cash Sheet |
|
|
| Date | Received Through Cash & Cheque | LF | Amount |
|---|---|---|---|
| {{receipt_date}} | {{receipt_description}} | {{receipt_reference_number}} | {{receipt_amount}} |
|
|
Total Receipt |
|
{{total_receipt}} |
| Date | Particulars | Payment Type | LF | Amount |
|---|---|---|---|---|
| {{payment_date}} | {{payment_description}} | {{payment_type}} | {{payment_reference_number}} | {{payment_amount}} |
|
|
Total |
|
{{total_payment}} |
|
| AUTHORIZED SIGNATORY |
|
|---|


Computes the sum along sparse segments of a tensor.
tf.compat.v2.sparse.segment_sum
tf.sparse.segment_sum( data, indices, segment_ids, num_segments=None, name=None )
Read the section on segmentation for an explanation of segments.
Like tf.math.segment_sum
, but segment_ids
can have rank less than data
's
first dimension, selecting a subset of dimension 0, specified by indices
.
segment_ids
is allowed to have missing ids, in which case the output will
be zeros at those indices. In those cases num_segments
is used to determine
the size of the output.
c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) # Select two rows, one segment. tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0])) # => [[0 0 0 0]] # Select two rows, two segment. tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1])) # => [[ 1 2 3 4] # [-1 -2 -3 -4]] # With missing segment ids. tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 2]), num_segments=4) # => [[ 1 2 3 4] # [ 0 0 0 0] # [-1 -2 -3 -4] # [ 0 0 0 0]] # Select all rows, two segments. tf.sparse.segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1])) # => [[0 0 0 0] # [5 6 7 8]] # Which is equivalent to: tf.math.segment_sum(c, tf.constant([0, 0, 1]))
data
: A Tensor
with data that will be assembled in the output.indices
: A 1-D Tensor
with indices into data
. Has same rank as
segment_ids
.segment_ids
: A 1-D Tensor
with indices into the output Tensor
. Values
should be sorted and can be repeated.num_segments
: An optional int32 scalar. Indicates the size of the output
Tensor
.name
: A name for the operation (optional).A tensor
of the shape as data, except for dimension 0 which
has size k
, the number of segments specified via num_segments
or
inferred for the last element in segments_ids
.


Paddy McGuinness confirms Peter Kay and Phoenix Nights cast want a reboot
Paddy said that Peter and the rest of the cast are all on board for a reboot of the hugely popular comedy that went from our screens in 2002
by Will Twigger, https://www.mirror.co.uk/authors/will-twigger/Paddy McGuinness has driven Phoenix Nights fans wild with speculation that the show could be making a comeback.
The hit comedy about a working mens club hasn't been on telly screens since 2002, but Paddy's confirmed that everyone's on board to bring it back nearly twenty years down the line.
The show, which also starred legendary comedian Peter Kay, only ran for two series but instantly became a cult classic among a hugely devoted fan base.
Funnyman Paddy told The Daily Star: "It was great. There's always talk about doing another one."
The Take Me Out host added: "It's just time, it's just getting everyone together.
"But everyone is up for it."
The cast did make a brief return in 2015 for live shows to raise money for Comic Relief.
"We did do it live," Paddy continued, "And that went really well.
There was also a 2004 spin-off, Max and Paddy, based on Peter and Paddy's characters.
Peter previously revealed that while a third series has never materialised, the scripts have all been penned.
For the time being, Paddy's back on presenting duties for hit dating show, Take Me Out.
He's also landed the Top Gear presenting gig alongside cricketer Freddie Flintoff.
Wife Christine recently revealed that Paddy's role on the motoring show has helped bring him closer to his autistic son, thanks to their shared passion for cars.
"Top Gear has created a bond between my son and my husband," Christine said.
She added: "Leo is passionate about cars. He loves things with wheels, anything that spins. He doesn't understand what a celebrity is or what a TV show is, but he knows that his daddy works with cars."


This a travel website with several different package options. It uses HTML and CSS.
This is a website used to adopt exotic animals. It uses HTML and CSS.
This is a website about feline friends. It uses HTML and CSS.
This is a website intended for an interior designer. It use HTML and CSS.
My name is Ben, I am a budding programmer. I used to work in immigration
and previously studied social work. Life has changed for me and I aspire to
be a brogrammer. My hobbies include exploring nature and learning new things.
I deceided to learn coding because I like languages.


Frye leads Bowling Green past Cincinnati 91-84 in OT
by The Associated PressST. THOMAS (AP) — Dylan Frye matched his career high with 28 points, including seven in overtime, as Bowling Green beat Cincinnati 91-84 at the Paradise Jam on Sunday night for its fourth consecutive win.
Frye hit three 3-pointers, made 9 of 10 free throws, grabbed nine rebounds and had six assists. Daeqwon Plowden scored 18 points and Justin Turner, Caleb Fields and Trey Diggs each added 10 for Bowling Green (5-1).
Frye made two foul shots, assisted on a layup by Dylan Swingle and then hit a 3-pointer during a 7-0 spurt that gave the Falcons a five-point lead halfway through the extra period. Chris Vogt made the first of two free throws and Jarron Cumberland putback the second to trim Cincinnati’s deficit to 81-79 but Plowden made two foul shots with 1:55 to play and the Bearcats got no closer.
Jaevin Cumberland had 21 points for Cincinnati (3-2). Chris McNeal added 17 points and Jarron Cumberland scored 11.
The Bearcats led by nine with three minutes left in regulation, but Frye scored eight points in a 13-4 run capped by Trey Diggs’ 3-pointer that forced overtime.
Four Falcons players fouled out and another had four of Bowling Green’s 35 fouls as Cincinnati attempted 55 free throws — third most in program history and second most in a Division I game this season (Western Michigan, 57). The Bearcats, however, shot just 56.4% from the stripe, including 7 of 14 in OT.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
Elements of this story were generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com
The Associated Press


Authors
Quynh and Peter
Having a driver and navigator and keeping eachother from getting fatigued
Keep eachother in check and catch eachothers mistakes, it also fosters team enviroment.


Class: Aws::ForecastService::Types::DescribeDatasetResponse
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb |
Overview
Constant Summary
Instance Attribute Summary
-
#creation_time ⇒ Time
rw
When the dataset was created.
-
#data_frequency ⇒ String
rw
The frequency of data collection.
-
#dataset_arn ⇒ String
rw
The Amazon
::Aws::ForecastService::Resource
Name (ARN) of the dataset. -
#dataset_name ⇒ String
rw
The name of the dataset.
-
#dataset_type ⇒ String
rw
The dataset type.
-
#domain ⇒ String
rw
The domain associated with the dataset.
-
#encryption_config ⇒ Types::EncryptionConfig
rw
The Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
-
#last_modification_time ⇒ Time
rw
When you create a dataset,
LastModificationTime
is the same asCreationTime
. -
#schema ⇒ Types::Schema
rw
An array of
SchemaAttribute
objects that specify the dataset fields. -
#status ⇒ String
rw
The status of the dataset.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#creation_time ⇒ Time
(rw)
When the dataset was created.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#data_frequency ⇒ String
(rw)
The frequency of data collection.
Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "M" indicates every month and "30min" indicates every 30 minutes.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#dataset_arn ⇒ String
(rw)
The Amazon ::Aws::ForecastService::Resource
Name (ARN) of the dataset.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#dataset_name ⇒ String
(rw)
The name of the dataset.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#dataset_type ⇒ String
(rw)
The dataset type.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#domain ⇒ String
(rw)
The domain associated with the dataset.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#encryption_config ⇒ Types::EncryptionConfig (rw)
The Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#last_modification_time ⇒ Time
(rw)
When you create a dataset, LastModificationTime
is the same as
CreationTime
. While data is being imported to the dataset,
LastModificationTime
is the current time of the DescribeDataset
call. After a CreateDatasetImportJob operation has finished,
LastModificationTime
is when the import job completed or failed.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#schema ⇒ Types::Schema (rw)
An array of SchemaAttribute
objects that specify the dataset
fields. Each SchemaAttribute
specifies the name and data type of a
field.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end
#status ⇒ String
(rw)
The status of the dataset. ::Aws::States
include:
ACTIVE
CREATE_PENDING
,CREATE_IN_PROGRESS
,CREATE_FAILED
DELETE_PENDING
,DELETE_IN_PROGRESS
,DELETE_FAILED
UPDATE_PENDING
,UPDATE_IN_PROGRESS
,UPDATE_FAILED
The UPDATE
states apply while data is imported to the dataset from
a call to the CreateDatasetImportJob operation and reflect the
status of the dataset import job. For example, when the import job
status is CREATE_IN_PROGRESS
, the status of the dataset is
UPDATE_IN_PROGRESS
.
Status
of the dataset must be ACTIVE
before you can import
training data.
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2723
class DescribeDatasetResponse < Struct.new( :dataset_arn, :dataset_name, :domain, :dataset_type, :data_frequency, :schema, :encryption_config, :status, :creation_time, :last_modification_time) SENSITIVE = [] include Aws::Structure end


Please input the number of bills or coins for each denomination. This page will automatically calculate the total.
Total
Photo by Mathieu Turle on Unsplash


| DOSE: |
|
oral |
MDMA |
(pill / tablet) |
Absolutely incredible. I had two pills over the course of one weekend. The second experience was far superior to the first, mostly because of my discoveries from the first. Briefly, I will describe the first, only to put the second in perspective.
So, my buddy and I took the first one's around 5:45. We then smoked a bowl, and chose to go for a walk, since it was a nice day outside. Picked up some Gatorade and cigarettes on the way, since we'd known from our previous experiences, that we would likely be chain smoking, and sweating. After about half an hour, we were at campus. We chose to walk around till around 6:15, when we rented a piano room.
By now we had begun talking our heads off, mostly about ideas for songs. I then looked in the mirror and began to notice my pupils were huge. I briefly felt a paper towel that was in the room, trying to become familiar with my surroundings, and realizing how incredibly awesome everything was in this otherwise mundane little room.
It was around 6:30 by now, and it was getting cold, so we grabbed a bus back to my buddy's place. We both felt an incredible anxiousness as we waited on the bus, and tapped our feet to the ticking of the bus noises. Unfortunately, both his roommates were home, and neither could be told of this experience. So we quietly waited in his room, being careful not to attract attention. I felt an incredible anxiousness, and constantly tapped my feet or tried to move around.
By around 8:00, I looked at a comforter I was sitting on. I rubbed it with my hand and finger tips. I kept rubbing it. Pure pleasure!! I looked at my friend,'Dude,' I said, 'Feel something.' He gave me a strange look, but still rubbed his hands on his rough textured chair. His eyes widened, 'Oh my,' he said. We had both discovered something incredible. From all our research, the past few hours of our experience, and even our one previous experience, we had only just now discovered the pure pleasure resulting from touch. We knew of things that affected virtually all other senses, but this one affected touch; made it incredible.
Later in the night, we had picked up a few girls. We had mostly come down, although our pupils were still huge. We decided to play a simple, mind-reading card game. One person would pick a card off the top of the deck and look at it. The other person, would look into the other's eyes, and attempt to read what color the card is. My friend picked up the deck and I began. I looked at his face, concentrated, and tried to read the color from his face each time.
I felt as if his face projected red or black, and I responded according to what I felt his face projected. Six in a row correct! We tried this game once more with one of the girls holding the deck, and I guessed again. Eight in a row correct! You can interpret these results any way you like. But, I am beginning to believe that in my state, I had some kind of enhanced capabilities, whether it be some form of telepathy, or an enhanced ability to read faces. In a completely sober state, the most I've gotten correct is four.
So, the following night, we felt we had to more fully experience this new discovery of touch, so we got two more. We took them around 11:15. Since his roommates would all be asleep, I told him I was going to be crazy. I just wanted to listen to music, dance, feel the rug, or all of the above. As it came on, we both began to describe how incredible everything around us was. We became children. I searched for some sort of toys, I wanted G.I. Joe's, Ninja Turtles, legos, or just some toy cars. I didn't care, but unfortunately, we had almost none of these. The feeling was very similar to a child's anxiousness as he waits for an exciting weekend his parents has planned for him. We were giddy little kids, and I still wanted toys. I tried to build a tent, but, we did not have the proper materials.
My friend had pretty much been sitting down the whole time. I wanted him to have fun, I wanted everyone to have fun and be happy. I told him to get up and dance or something.  I continued being crazy on my own, as he sat on his bed and watched. I knew I was looking and acting crazy, and I didn't care. I felt great anywayz.
Instead of playing with toys, I danced, and I had a great time. Every song was awesome, and ingeniously written, no matter how many times we both heard it.  I saw incredibly vivid visuals. My friend had already had his eyes closed and watching hallucinations for about the past hour. He described various trips through the universe as he passed by heavenly bodies and planets. I saw similar things as well, but I also saw other things. The music seemed to be dictating what I saw.
After this we finally went to sleep. It was the most incredible experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


This repository
Pull Requests
Issues
Marketplace
Explore
Knowre
/
WebDevCurriculum
Unwatch
19
Star
113
Fork
106
Code
Issues
0
Pull Requests
1
Projects
0
Insights
KnowRe web development curriculum for noobs
44
commits
1
branch
0
releases
2
contributors
MIT
Branch:
master
New pull request
Create new file
Upload files
Find file
Clone or download
KnowRe
Quest 02 added constraint
Latest commit
8a3080e
on 7 Aug 2016
Part 0. git /
Quest 00. Hello, git
Part 2 finished
2 years ago
Part 1. HTML & CSS
Quest 02 added constraint
2 years ago
README.md
Fix: Removed advertisement
2 years ago
README.md
KnowRe web development curriculum 2018


Worked on apps in OCaml related to trading.
Worked on DealFinder as a capstone project, a webapp that allows users to upload and share deals, hosted on Google's app engine. Project uses Java Servlets for the backend, and bootstrap for the frontend.
Worked on frontend and backend on a chart sharing web app using React and Django.
Worked on the traintraintrain project, which allows commuters to track MRT train locations in real-time. Automated anomaly detection in train timings and set up a notification system using AWS.
Tutor for CS2040C, Data Structures and Algorithms. Conducted tutorials and consultations.
August 2020 - November 2020Tutor for CS3230, Design and Analysis of Algorithms. Conducted tutorials, as well as set programming assignments.


Home
Shop
Checkout
Log In/Sign Up
Log Out
Car
$1.00
Add to Cart
Notepad
$36.00
Add to Cart
Pen
$200.00
Add to Cart
Pillow
$15.00
Add to Cart
Chair
$8.00
Add to Cart
Bike
$42.00
Add to Cart


UTC -5.
Voltage: 110 V
Frequency: 60 Hz
Gourde (Gde) of 100 centimes. US currency also circulates.
Credit cards are widely accepted.
At least one local bank has ATMs in Port au Prince, but take care as there have been reports of accounts being over charged..
International Dialing Code for Haiti is +509.
There are no roaming agreements with other cell phone networks.
Digicel update December 2014:
Anyone cruising using Digicel should be aware that if you purchase a SIM card in one country, it cannot be topped up in different one, as previously possible. So if you wish to use the sim bought here while cruising in other countries, you should either put on a large enough sum to cover your needs, or set up an account and do it online.
You can top-up Digicel at https://online-top-up.digicelgroup.com/
Internet cafes are available in towns.
Post office hours: Mon-Fri 08:00-20:00, Sat 08:30-12:00. Letters posted after 09:00 will not be despatched until the following working day.
Public transport in Haiti consists mainly of “tap-taps” that run regular routes within urban areas and between towns in the countryside. A handful of public buses exist in the capital. Neither is considered reliable nor safe.
Regular marked taxis are nonexistent.
Carnival, three days before Ash Wednesday
Emergencies:
Ambulance - Dial 118
Police - Dial 114
IMRAY AND ADLARD COLES PILOT BOOKS are available at a discounted price for Noonsite.com users via World Cruising Club
The Haiti Cruising Guide - 3.0
www.freecruisingguides.com (free download)
Free Cruising Guide produced September 2010 and last updated July 2014 by Frank Virgintino (author of the Jamaica and DR Free Cruising Guides). The only complete guide to Haiti.
A Thinking Mans Guide to Voyages South - the many facets of Caribbean Cruising
Author: Frank Virgintino
Published: 3rd Edition January 2013
ISBN<PHONE_NUMBER>847
Available as a Kindle version at www.amazon.com as well as other EPub bookstores.
The latest edition of this route planner is larger than ever. For more details see this report
The Gentleman's Guide to Passages South
By Bruce Van Sant
Publisher: Bruce Van Sant and Cruising Guide Publications; 9 edition (November 15, 2006)
ISBN-13:<PHONE_NUMBER>795
More information available from www.ThornlessPath.com/
The orphanage of St. Francis (L’Oeuvre St Francois D’Assises)
http://www.iaeh.fr/soeur-flora.htm
This orphanage has been run for over 30 years by a Canadian nun, Sister Flora. Her clinic, orphanage and school are an institution on the island and she is loved by everyone. Many cruisers visit the orphanage and offer their assistance during their time on the island.
Friends of Ile A Vache Haiti
A non-profit organization dedicated to sustainable economic development, reaching out to the public to raise funds for its many projects currently under operation on Ile a Vache.
Good Samaritans of Haiti
A non-profit organization based in Ile a Vache with projects countrywide for schools, microcredit, agriculture etc. Cruisers can arrange to deliver donated goods and work with charitable projects as volunteers thus ensuring goodwill and local contacts. They welcome volunteers with experience in English teaching, music, mechanical repairs etc. for short or long visits.
Travel to Haiti - First Impressions
A great insight into the character of Haiti.
Noonsite welcomes information and updates especially regarding clearance, customs and immigration procedures from cruisers visiting this country.
Please E-mail noonsite with any new information, updates or corrections. Even just a short email confirming that the current data is accurate would be most helpful.


This class implements a simple perceptron (i.e., a single layer neural network).
More...
|
| | Perceptron (const MatType &data, const arma::Row< size_t > &labels, int iterations) |
| | Constructor - constructs the perceptron by building the weightVectors matrix, which is later used in Classification. More...
|
| |
| | Perceptron (const Perceptron<> &other, MatType &data, const arma::rowvec &D, const arma::Row< size_t > &labels) |
| | Alternate constructor which copies parameters from an already initiated perceptron. More...
|
| |
| void | Classify (const MatType &test, arma::Row< size_t > &predictedLabels) |
| | Classification function. More...
|
| |
|
| void | Train (const arma::rowvec &D) |
| | Training Function. More...
|
| |
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
class mlpack::perceptron::Perceptron< LearnPolicy, WeightInitializationPolicy, MatType >
This class implements a simple perceptron (i.e., a single layer neural network).
It converges if the supplied training dataset is linearly separable.
- Template Parameters
-
Definition at line 38 of file perceptron.hpp.
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
Constructor - constructs the perceptron by building the weightVectors matrix, which is later used in Classification.
It adds a bias input vector of 1 to the input data to take care of the bias weights.
- Parameters
-
| data | Input, training data. |
| labels | Labels of dataset. |
| iterations | Maximum number of iterations for the perceptron learning algorithm. |
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
Alternate constructor which copies parameters from an already initiated perceptron.
- Parameters
-
| other | The other initiated Perceptron object from which we copy the values from. |
| data | The data on which to train this Perceptron object on. |
| D | Weight vector to use while training. For boosting purposes. |
| labels | The labels of data. |
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
| void mlpack::perceptron::Perceptron< LearnPolicy, WeightInitializationPolicy, MatType >::Classify |
( |
const MatType & |
test, |
|
|
arma::Row< size_t > & |
predictedLabels |
|
) |
| |
Classification function.
After training, use the weightVectors matrix to classify test, and put the predicted classes in predictedLabels.
- Parameters
-
| test | Testing data or data to classify. |
| predictedLabels | Vector to store the predicted classes after classifying test. |
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
Training Function.
It trains on trainData using the cost matrix D
- Parameters
-
| D | Cost matrix. Stores the cost of mispredicting instances |
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
Stores the class labels for the input data.
Definition at line 80 of file perceptron.hpp.
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
To store the number of iterations.
Definition at line 77 of file perceptron.hpp.
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
Stores the training data to be used later on in UpdateWeights.
Definition at line 86 of file perceptron.hpp.
template<typename LearnPolicy = SimpleWeightUpdate, typename WeightInitializationPolicy = ZeroInitialization, typename MatType = arma::mat>
Stores the weight vectors for each of the input class labels.
Definition at line 83 of file perceptron.hpp.
The documentation for this class was generated from the following file:


Request for Security Deposit
Date:
Tenant's Name: %tnameprint%
Tenant's Address:
%taddressprint%
%tcityprint%, BC
%tpostalprint%
Landlord's Name: %lnameprint%
Landlord's Address:
%laddressprint%
%lcityprint%, BC
%lpostalprint%
Dear %lnameprint%,
This is a formal request for a return of the $%secdepamountprint% security deposit paid on %idateprint%.
According to B.C's Residential Tenancy Policy Guideline #17(http://www2.gov.bc.ca/assets/gov/housing-and-tenancy/residential-tenancies/policy-guidelines/gl17.pdf):
"The right of a landlord to obtain the tenant’s consent to retain or file a claim against a security deposit for damage to the rental unit is extinguished if:
- the landlord does not offer the tenant at least two opportunities for inspection as required (the landlord must use Notice of Final Opportunity to Schedule a Condition Inspection (form RTB-22) to propose a second opportunity); and/or
- having made an inspection does not complete the condition inspection report, in the form required by the Regulation, or provide the tenant with a copy of it."
I am hoping that we can resolve the above issue on or before %rdateprint% without having to apply for dispute resolution with the Residential Tenancy Branch (RTB). If not completed by the requested date, I have the right to take legal action through the RTB. If the deposit is not returned wihtin 15 days the RTB can order you to repay up to twice the amount of the damage deposit.
Thank you,
_______________
%tnameprint%


vignettes/time-series-clustering.Rmd
time-series-clustering.Rmd
Clustering is all about finding groups of similar observations based on their features. It’s used for
exploratory data analysis. Especially useful for time series where you can cluster time series of similar “shape”.
labeling groups of time series.
feature engineering for time series classification, using the cluster as a feature. Can also be thought of as a dimensionality reduction technique.
In this guide, I’ll show how you can use tsrecipes to cluster time series with dynamic time warping using the dtwclust R package1.
If you’d like to learn more about dynamic time warping, check out my dynamic time warping article.
For an introduction to clustering in general, UC Business Analytics Programming Guide has an excellent series on clustering, introducing distance metrics and clustering techniques.
With clustering, I think it’s important to evaluate the clusters using objective and subjective criteria.
Subjective criteria include
visualizing the “shape” of time series within clusters to see if there is a pattern. If the shape isn’t obvious, you can try alternative methods or increase the number of clusters. Visualizations of noisy, high-frequency time series may not be useful. In this case, you may want to visualize smoothed trends of the cluster, rather than raw time series.
inspecting clusters for clutter: elements within the cluster that don’t seem to belong. This may indicate you need to increase the number of clusters.
Objective criteria include
checking the number of elements per cluster. Especially with hierarchical clustering, occasionally a cluster will have 90% of the data, which isn’t very useful.
calculating cluster statistics2.
evaluation against known classes, if available. This can even be helpful if only a small amount of labeled data is available.
These principles will serve as a guide while clustering the Ethanol data.
The ethanol
dataset has four classes, based on the levels of alcohol in the fuel.
library(tsrecipes) library(tidyverse) #> ── Attaching packages ─────────────────────────────────────────────────── tidyverse 1.2.1 ── #> ✓ ggplot2 3.3.2 ✓ purrr 0.3.4 #> ✓ tibble 3.0.3 ✓ dplyr 1.0.0 #> ✓ tidyr 1.1.0 ✓ stringr 1.4.0 #> ✓ readr 1.3.1 ✓ forcats 0.4.0 #> ── Conflicts ────────────────────────────────────────────────────── tidyverse_conflicts() ── #> x dplyr::filter() masks stats::filter() #> x dplyr::lag() masks stats::lag() library(dtwclust) #> Loading required package: proxy #> #> Attaching package: 'proxy' #> The following objects are masked from 'package:stats': #> #> as.dist, dist #> The following object is masked from 'package:base': #> #> as.matrix #> Loading required package: dtw #> Loaded dtw v1.21-3. See ?dtw for help, citation("dtw") for use in publication. #> dtwclust: #> Setting random number generator to L'Ecuyer-CMRG (see RNGkind()). #> To read the included vignettes type: browseVignettes("dtwclust"). #> See news(package = "dtwclust") after package updates. library(tidymodels) #> ── Attaching packages ────────────────────────────────────────────────── tidymodels 0.1.1 ── #> ✓ broom 0.7.0 ✓ recipes 0.1.13 #> ✓ dials 0.0.8 ✓ rsample 0.0.7 #> ✓ infer 0.5.3 ✓ tune 0.1.1 #> ✓ modeldata 0.0.2 ✓ workflows 0.1.3 #> ✓ parsnip 0.1.3 ✓ yardstick 0.0.7 #> ── Conflicts ───────────────────────────────────────────────────── tidymodels_conflicts() ── #> x scales::discard() masks purrr::discard() #> x dplyr::filter() masks stats::filter() #> x recipes::fixed() masks stringr::fixed() #> x dplyr::lag() masks stats::lag() #> x yardstick::spec() masks readr::spec() #> x recipes::step() masks stats::step()
ethanol %>% mutate(n = list(1:1751)) %>% unnest(c(ts, n)) %>% ggplot(aes(n, ts)) +#, color = as.factor(id))) + geom_line(aes(group = id), alpha = 0.2, show.legend = FALSE) + facet_wrap(~class)
Visually, it’s hard for me to distinguish between classes. Maybe this indicates it will be hard to classify, or cluster into meaningful groups.
Four classes are known ahead of time, so it seems reasonable to start with four clusters.
step_dtw
clusters time series using the dynamic time warping similarity metric. Behind the scenes, step_dtw
uses dtwclust. All it’s options are available, but we’ll stick with the defaults.
prepped <- recipe(ethanol) %>% step_dtw(ts, k = 4) %>% prep() ethanol_clusters <- bake(prepped, ethanol)
I always start with visualizing the time series within each cluster.
ethanol_clusters %>% mutate(n = list(1:1751)) %>% unnest(c(ts, n)) %>% ggplot() + geom_line(aes(n, ts, group = id), alpha = 0.2, show.legend = FALSE) + facet_wrap(~dtwclust_ts)
I see distinct shapes within each cluster. Clusters 1 and 4 both have a clear middle dip, with different wiggles leading the the middle. Cluster 2 has very few wiggles on the right, and cluster 3 is sort of its mirror image, with few wiggles on the left.
All clusters seem a little cluttered: especially 2 and 3 with all the wiggles on the left and right respectively. Cluttered clusters mean we might get more logical groups if we increase the number of clusters.
Comparing the “shape” of the clusters to the shape of the individual classes from the previous section, there doesn’t seem to be a lot of obvious similarity. There’s a lot more variation and a lot less structure to the class plots.
The clusters appear logically grouped, but they don’t necessarily look like the classes. Fortunately the ethanol
dataset is labeled. We can test to see if the groups are predictive of the outcome.
mm_model <- ethanol_clusters %>% mutate(dtwclust_ts = as.factor(dtwclust_ts)) %>% nnet::multinom(class ~ dtwclust_ts, data = .) #> # weights: 20 (12 variable) #> initial value 698.692358 #> iter 10 value 692.557559 #> final value 692.467993 #> converged
pred_eth <- ethanol_clusters %>% mutate(dtwclust_ts = as.factor(dtwclust_ts)) %>% mutate(pred = predict(mm_model, ., type = "class")) pred_eth %>% group_by(class, pred) %>% summarise(n = n()) %>% group_by(pred_correct = class == pred) %>% summarise(n = sum(n)) %>% mutate(percent = n / sum(n)) #> `summarise()` regrouping output by 'class' (override with `.groups` argument) #> `summarise()` ungrouping output (override with `.groups` argument) #> # A tibble: 2 x 3 #> pred_correct n percent #> <lgl> <int> <dbl> #> 1 FALSE 354 0.702 #> 2 TRUE 150 0.298
Predicting the class based on the cluster is only 31% accurate. Better than random chance (25%), but still not great.
Based on this analysis, I bet the clustering will be more predictive of the class if there are more clusters.
If you are following along, you may have noticed that clustering takes a long time.
One expensive operation is calculating the distance matrix3. This must be done for every pair of observations. For 500 observations, that’s 124,750 unique pairs4.
For each pair, you also need to calculate the dynamic time warping distance, which has a computational complexity of \(O(N^2)\)5.
Fortunately, the dtwclust::tsclust
lets you precompute the the similarity matrix and supply that to the cluster algorithms. Care must be taken here to avoid data leakage; make sure the distance matrix is only computed on the training data.
Using the precomputed distance matrix, it’s possible to tune the number of clusters much faster than otherwise (although not necessarily recommended: see the next section).
distmat <- prepped$steps[[1]]$dtwclust$ts@distmat
dtw_options = list(control = dtwclust::partitional_control(distmat = distmat)) rec <- recipe(ethanol, var = names(ethanol), roles = c("id", "outcome", "input")) %>% step_dtw(ts, k = tune(), options = dtw_options) %>% step_mutate_at(all_predictors(), fn = factor)
validation_set <- tibble( splits = list(make_splits( list(analysis = 1:504, assessment = 1:504), data = ethanol )) ) %>% new_rset(c(id = "validation"), subclass = "rset")
tune_results <- workflow() %>% add_model(multinom_reg() %>% set_engine("nnet")) %>% add_recipe(rec) %>% tune_grid( resamples = validation_set, grid = expand_grid(k = c(4, 8, 16, 32, 64)) )
tune_results %>% collect_metrics() %>% filter(.metric == "accuracy") %>% select(k, mean) #> # A tibble: 5 x 2 #> k mean #> <dbl> <dbl> #> 1 4 0.335 #> 2 8 0.333 #> 3 16 0.379 #> 4 32 0.409 #> 5 64 0.472
Increasing the number of clusters does make the feature more useful in predicting the outcome. With too many clusters, the usefulness starts to decrease. There may be more correlation with the class, but you can no longer visualize and understand the characteristics of certain clusters.
If the goal is to simplify increase the classification accuracy, rather than understanding similar groups, there are better and faster ways.
Tuning clusters is not necessarily something I’d recommend. Clustering takes a long time, even if we are using the precomputed distance matrix.
And there are much better options for dimensionality reduction for time series. Predicting the class using 8 discrete cosine transform coefficients is almost as accurate as predicting the class with 64 clusters. 32 coefficients brings the accuracy to 92%. Using step_dct
is also significantly faster.
rec <- recipe(ethanol, var = names(ethanol), roles = c("id", "outcome", "input")) %>% step_dct(ts, k = tune()) tune_results <- workflow() %>% add_model(multinom_reg() %>% set_engine("nnet")) %>% add_recipe(rec) %>% tune_grid( resamples = validation_set, grid = expand_grid(k = c(4, 8, 16, 32, 64)) ) #> Warning in `[.tbl_df`(x, is.finite(x <- as.numeric(x))): NAs introduced by #> coercion tune_results %>% collect_metrics() %>% filter(.metric == "accuracy") %>% select(k, mean) #> # A tibble: 5 x 2 #> k mean #> <dbl> <dbl> #> 1 4 0.312 #> 2 8 0.417 #> 3 16 0.792 #> 4 32 0.919 #> 5 64 0.925
Many clustering techniques and options are available in available in dtwclust::tsclust
. I recommend reading the documentation if you aren’t satisified with the defaults.
Additionally, step_dtw
only allows tuning the number the number of clusters, but if you want more flexibility look into dtwclust::compare_clusterings
.
You can also cluster time series using the discrete cosine transform coefficients found using step_dct
. These features are uncorrelated, so you are free to use traditional distance metrics and clustering techniques.↩
I won’t cover clustering statistics here, but the UC Business Analytics Programming Guide series has many examples.↩
Also called the dissimilarity matrix.↩
500 choose 2↩
Some improvements can be made. dtwclust
offers dtw_basic
by default, which is significantly faster, with fewer features. And the theoretical computational complexity is \(O(n^2/\log\log(n))\), although I don’t know if this has been implemented anywhere, or if its technically feasible to do so.↩


QUESTION #1
Suppose that I have a Todo model struct with a foreign key of Task:
type Todo struct {
uadmin.Model
Task Task
TaskID uint
}
Fill in the blanks that corresponds to the inline based on the given structure above.
uadmin.RegisterInlines(models.___, map[string]string){
"___": "___",
}
QUESTION #2
Suppose that I have a Dog model struct with a foreign key of Cat:
type Dog struct {
uadmin.Model
Cat Cat
CatID uint
}
Fill in the blanks that corresponds to the inline based on the given structure above.
uadmin.RegisterInlines(models.___, map[string]string){
"___": "___",
}
QUESTION #3
Suppose that I have a Family model struct with a foreign key of Mother:
type Family struct {
uadmin.Model
Mother Mother
MotherID uint
}
Fill in the blanks that corresponds to the inline based on the given structure above.
uadmin.RegisterInlines(models.___, map[string]string){
"___": "___",
}
QUESTION #4
Suppose that I have a Restaurant model struct with a foreign key of Jollibee:
type Restaurant struct {
uadmin.Model
Jollibee Jollibee
JollibeeID uint
}
Fill in the blanks that corresponds to the inline based on the given structure above.
uadmin.RegisterInlines(models.___, map[string]string){
"___": "___",
}
QUESTION #5
Suppose that I have an A model struct with a foreign key of B:
type A struct {
uadmin.Model
B B
BID uint
}
Fill in the blanks that corresponds to the inline based on the given structure above.
uadmin.RegisterInlines(models.___, map[string]string){
"___": "___",
}
QUESTION #6
Given a register inline function that has a parent model of House and a Roof model inside it:
uadmin.RegisterInlines(models.House, map[string]string){
"Roof": "HouseID",
}
Fill in the blanks that corresponds to the model struct based on the given structure above.
type ___ struct {
uadmin.Model
___ ___
___ ___
}
QUESTION #7
Given a register inline function that has a parent model of Dog and a Cat model inside it:
uadmin.RegisterInlines(models.Dog, map[string]string){
"Cat": "DogID",
}
Fill in the blanks that corresponds to the model struct based on the given structure above.
type ___ struct {
uadmin.Model
___ ___
___ ___
}
QUESTION #8
Given a register inline function that has a parent model of Food and a Rice model inside it:
uadmin.RegisterInlines(models.Food, map[string]string){
"Rice": "FoodID",
}
Fill in the blanks that corresponds to the model struct based on the given structure above.
type ___ struct {
uadmin.Model
___ ___
___ ___
}
QUESTION #9
Given a register inline function that has a parent model of Bread and a Caramel model inside it:
uadmin.RegisterInlines(models.Bread, map[string]string){
"Caramel": "BreadID",
}
Fill in the blanks that corresponds to the model struct based on the given structure above.
type ___ struct {
uadmin.Model
___ ___
___ ___
}
QUESTION #10
Given a register inline function that has a parent model of Bread and a Caramel model inside it:
uadmin.RegisterInlines(models.NewYork, map[string]string){
"USA": "NewYorkID",
}
Fill in the blanks that corresponds to the model struct based on the given structure above.
type ___ struct {
uadmin.Model
___ ___
___ ___
}


South Korea tells Japan it will reverse decision to end intel-sharing pact: NHK
TOKYO (Reuters) - South Korea on Friday told the Japanese government it will continue to share real-time intelligence on North Korea, reversing an earlier decision to end a cooperation pact amid a spat over trade and history, Japanese public broadcaster NHK reported.
South Korea had come under pressure from the United States to continue its General Security of Military Information Agreement (GSOMIA) with Japan. Washington was concerned that a return to passing information through the U.S. military would have undermined efforts to respond to any threat posed by North Korean missiles.
South Korea in August said it would end the GSOMIA after Japan slapped restrictions on exports to South Korea of materials necessary for semiconductor and display production.


Please fill out the form to register.
You do not need to login to pay the registration.
Please Finish Waiver Form and Return
Thank you!


Description
The Multi-Line Entry Field Sample is a simple editor. This editor opens existing files via the file dialog, saves data to a file via the file dialog, and changes fonts via the font dialog. It also performs cut, copy, and paste functions to the clipboard.
Concept
Sample program demonstrates IMultiLineEdit, IFileDialog, and IFontDialog. Loading a file via a thread is also demonstrated (OS/2 and Windows only).
Task
Open files from the file dialog, change fonts, and cut/copy/paste text.
Supported
Supported
Supported (except multithread)


This is to show some type of back ground stuff. This would normally be your website.
This is to show some type of back ground stuff. This would normally be your website.
This is to show some type of back ground stuff. This would normally be your website.
This is to show some type of back ground stuff. This would normally be your website.
This is to show some type of back ground stuff. This would normally be your website.
This is to show some type of back ground stuff. This would normally be your website.


accumulate
sliding window
hash table
]
Leetcode 1658. Minimum Operations to Reduce X to Zero
https://leetcode.com/problems/minimum-operations-to-reduce-x-to-zero
We can reformulate this problem: we need to choose several values from the beginning and several values from end, such that sum of this numbers is equal to x
. It is equivalent to finding some contiguous subarray, such that it has sum of elements equal to sum(nums) - x
, which has the biggest length. In this way problem becomes quite classical and I prefer to solve it using cumulative sums.
Imagine, that we have nums = [1,1,4,2,3], x = 5
. Then we need to find contiguous subarray, such that its sum is equal to sum(nums) - x = 6
. It means, that we need to find two cumulative sums, one of them equal to goal
plus another one. Note also, that all nums
are positive, so all cumulative sums will be different.
We keep in dic
indexes for each cumulative sum, so, when we iterate num in dic
and check if num + goal in dic
, then we can get length of window: dic[num + goal] - dic[num]
and update ans
.
Complexity: time complexity is O(n)
, space complexity as well.
Remark: this idea will work also if we can have negative numbers in our nums
. We need to keep in dic
smallest and biggest indexes for each value of cumulative sum and then find maximum between ends of two segments. If we asked to find window of minimum length, and we have negative numbers, it is also possible, but we need to keep defaultdict of all indexes and then use idea of merge sort to find closest pair. Complexities will be also O(n)
. I think I saw these problems on leetcode, but I do not remember they numbers, if you recognize them, please let me know!
class Solution:
def minOperations(self, nums, x) :
cumsum = [0] + list(accumulate(nums))
dic = {c:i for i,c in enumerate(cumsum)}
goal = cumsum[-1] - x
ans = -float("inf")
if goal < 0: return -1
for num in dic:
if num + goal in dic:
ans = max(ans, dic[num + goal] - dic[num])
return len(nums) - ans if ans != -float("inf") else -1
If you like the solution, you can upvote it on leetcode discussion section: Problem 1658


To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
Without this supply the grass will not perform, re-growth will be slow and the roots will be weakened leading to disease and bare patches.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.
To keep your lawn healthy and in tip top condition a steady supply of quality nutrients is a must.


JEE-Adv Previous Year Solutions
HOME
VIDEO SOLUTIONS
PDF SOLUTIONS
ABOUT ME
CONTACT
Hello
I am Gaurav Singh
Undergrad Student @NIT-TRICHY
Upvote
Connect With Me


|
S2OPC OPCUA Toolkit
|
Tools for time management. More...
Go to the source code of this file.
Macros | |
| #define | SOPC_MONOTONIC_CLOCK true |
| the toolkit provide and use monotonic clock for time references (used for timers) Note: it is possible to set the clock as non monotonic defining variable on configuration. Otherwise default value is true. More... | |
Typedefs | |
| typedef uint64_t | SOPC_TimeReference |
Functions | |
| void | SOPC_Sleep (unsigned int milliseconds) |
| Suspend current thread execution for (at least) a millisecond interval. More... | |
| int64_t | SOPC_Time_GetCurrentTimeUTC (void) |
| return the current time in DateTime format which is 100 nanoseconds from 1601/01/01 00:00:00 UTC More... | |
| char * | SOPC_Time_GetStringOfCurrentLocalTime (bool compact) |
| return the current local time as a C String, e.g.: More... | |
| char * | SOPC_Time_GetStringOfCurrentTimeUTC (bool compact) |
| return the current UTC time as a C String, e.g.: More... | |
| SOPC_TimeReference | SOPC_TimeReference_GetCurrent (void) |
| return the current time reference More... | |
| SOPC_TimeReference | SOPC_TimeReference_AddMilliseconds (SOPC_TimeReference timeRef, uint64_t ms) |
| return the time reference corresponding to the given time reference incremented by the given duration in milliseconds More... | |
| int8_t | SOPC_TimeReference_Compare (SOPC_TimeReference left, SOPC_TimeReference right) |
| return the comparison of given time references More... | |
| SOPC_ReturnStatus | SOPC_Time_Breakdown_Local (time_t t, struct tm *tm) |
| Breaks down a timestamp to its structured representation in local time. More... | |
| SOPC_ReturnStatus | SOPC_Time_Breakdown_UTC (time_t t, struct tm *tm) |
| Breaks down a timestamp to its structured representation in UTC time. More... | |
| SOPC_ReturnStatus | SOPC_Time_FromTimeT (time_t time, int64_t *res) |
| Converts a UNIX timestamp to a time expressed in 100ns slices since 1601/01/01 00:00:00 UTC. More... | |
| SOPC_ReturnStatus | SOPC_Time_ToTimeT (int64_t dt, time_t *res) |
| Converts a time expressed in 100ns slices since 1601/01/01 00:00:00 UTC to a UNIX timestamp. More... | |
Tools for time management.
| #define SOPC_MONOTONIC_CLOCK true |
the toolkit provide and use monotonic clock for time references (used for timers) Note: it is possible to set the clock as non monotonic defining variable on configuration. Otherwise default value is true.
| typedef uint64_t SOPC_TimeReference |
Time reference type (milliseconds)
| void SOPC_Sleep | ( | unsigned int | milliseconds | ) |
Suspend current thread execution for (at least) a millisecond interval.
| milliseconds | The milliseconds interval value for which execution must be suspended |
| SOPC_ReturnStatus SOPC_Time_Breakdown_Local | ( | time_t | t, |
| struct tm * | tm | ||
| ) |
Breaks down a timestamp to its structured representation in local time.
| t | the timestamp. |
| tm | the structured representation of the timestamp in local time. |
| SOPC_ReturnStatus SOPC_Time_Breakdown_UTC | ( | time_t | t, |
| struct tm * | tm | ||
| ) |
Breaks down a timestamp to its structured representation in UTC time.
| t | the timestamp. |
| tm | the structured representation of the timestamp in UTC time. |
| SOPC_ReturnStatus SOPC_Time_FromTimeT | ( | time_t | time, |
| int64_t * | res | ||
| ) |
Converts a UNIX timestamp to a time expressed in 100ns slices since 1601/01/01 00:00:00 UTC.
| time | the UNIX timestamp |
| res | the resulting time |
| int64_t SOPC_Time_GetCurrentTimeUTC | ( | void | ) |
return the current time in DateTime format which is 100 nanoseconds from 1601/01/01 00:00:00 UTC
Note: since the clock is not monotonic, it should not be used to measure elapsed time
| char* SOPC_Time_GetStringOfCurrentLocalTime | ( | bool | compact | ) |
return the current local time as a C String, e.g.:
| compact | provides compact version when flag is set |
| char* SOPC_Time_GetStringOfCurrentTimeUTC | ( | bool | compact | ) |
return the current UTC time as a C String, e.g.:
| compact | provides compact version when flag is set |
| SOPC_ReturnStatus SOPC_Time_ToTimeT | ( | int64_t | dt, |
| time_t * | res | ||
| ) |
Converts a time expressed in 100ns slices since 1601/01/01 00:00:00 UTC to a UNIX timestamp.
| dt | the input time |
| res | the resulting time_t |
| SOPC_TimeReference SOPC_TimeReference_AddMilliseconds | ( | SOPC_TimeReference | timeRef, |
| uint64_t | ms | ||
| ) |
return the time reference corresponding to the given time reference incremented by the given duration in milliseconds
| timeRef | the time reference to be incremented |
| ms | the duration in milliseconds to use for increment |
| int8_t SOPC_TimeReference_Compare | ( | SOPC_TimeReference | left, |
| SOPC_TimeReference | right | ||
| ) |
return the comparison of given time references
| left | the left time reference operand (NULL pointer considered less than any other value) |
| right | the right time reference operand (NULL pointer considered less than any other value) |
left
< right
operand, 0 if left
= right
and 1 if left
> right | SOPC_TimeReference SOPC_TimeReference_GetCurrent | ( | void | ) |
return the current time reference
Note: clock is monotonic if SOPC_MONOTONIC_CLOCK is true


Looking for some trusted people to do a KMD for your usd paypal, looking at $500-$1000 to swap
Keppie02-Dec-19 01:25 PM
I think @TwilightPunkster K64#7533 is used to handling that. Very trustworthy guy. Hit him up!(edited)
Might be hard to reach tho. Only access to phone once a day
Markus02-Dec-19 01:33 PM
@Keppie Lets be more serious, this joke is not funny and disrespectful against ppl who actually got scammed by him.(edited)
Keppie02-Dec-19 01:36 PM
Then it is disrespectful to me. I lost quite some
This is how I handle this situation. With sarcasm.
Spoiler alert @hyperDEX . Don't contact the guy, you can't trust him
Markus02-Dec-19 01:38 PM
For some reason ppl tend to call it sarcasm everytime after reminding them that what they wrote is not so appropriate, even if it in this case was not everyone think the same.
Keppie02-Dec-19 01:40 PM
To who is it not appropriate ?  Hope justice serves him
grewalsatinder02-Dec-19 01:46 PM
would be much better if someone can help hyperDEX instead of leading convo to another direction.
Keppie02-Dec-19 02:17 PM
Im not looking to buy more for now
Neko02-Dec-19 03:23 PM
@Keppie I think its not him but a scammer impersonating
almost got scammed yesterday
Keppie02-Dec-19 03:28 PM
Only 1 hyperdex in the discord. And with the roles (or can those be faked ?)
You reported your nearly scam experience ?
Patrick Crypto02-Dec-19 03:33 PM
What is a typical price for CRYPTO(the coin) either with KMD or BTC
[ANN] SuperNET NXT asset<PHONE_NUMBER>4977229797, SUPERNET KMD assetchain in summer
How it started and what it was about
freeman9902-Dec-19 05:10 PM
Thanks @richardt I will read this
jspook1602-Dec-19 09:17 PM
Is cryptopia back yet?
phm8702-Dec-19 09:21 PM
no, the liquidation is ongoing, a new kyc will be done to get back assets later, an update should arrive in december
Neko02-Dec-19 09:46 PM
at least buy support is decent
grewalsatinder03-Dec-19 01:31 AM
agree, don't go all in in ANY cryptocurrency.
divide you portfolio in percentage wise and keep that percentage thoughtful and keep shuffling your risk exposure from one cryptocurrency to another based on your own risk measures and liking to risk money on some particular cryptocurrency project.(edited)
BoBot-BUY KMD03-Dec-19 05:44 AM
PAMP IT
any wallets working? waiting for new agama update
I guess not
wow pretty dead in here for this PAMP
Lennart03-Dec-19 05:55 AM
we're mooning
2
BoBot-BUY KMD03-Dec-19 06:01 AM
12k sats less gooo
Lennart03-Dec-19 06:02 AM
lol what is this?? what a candle... haven't seen such a candle in while
BoBot-BUY KMD03-Dec-19 06:03 AM
lol looks sweet
WOWZERS lol
13k sats
Lennart03-Dec-19 06:05 AM
13k sat
what a way to enjoy a morning coffee
alex134403-Dec-19 06:09 AM
Why is it still 9.3K sat on Bittrex??
Crabby Paddy03-Dec-19 06:10 AM
Of course
After I sell 15k
Not 12 hours later
alex134403-Dec-19 06:11 AM
It is a big difference... there is a nice arbitrage to play
Crabby Paddy03-Dec-19 06:11 AM
Every single time.
Bitfinger03-Dec-19 06:11 AM
Did binance just open wallets?
jspook1603-Dec-19 06:13 AM
Moon!!!
2
Oh no crashing. Rocket is out of control..
1
Alexfrog03-Dec-19 06:15 AM
so the moon is when youre only at a 50% loss?
jspook1603-Dec-19 06:16 AM
It's all relative I suppose. Someone's moon could be someone else's break even point
Alexfrog03-Dec-19 06:17 AM
and at my breakeven point others are still down 80% because they bought the top
but itll be 9k again shortly so it doesnt matter
Acura03-Dec-19 06:20 AM
Smedley03-Dec-19 06:24 AM
Binance deposits need to open up for anyone to actually understand the market at all
Smedley03-Dec-19 06:32 AM
It at least seems clear to me that they are opening up very soon and there are a few people making money off of it
jl777c03-Dec-19 06:35 AM
it looks like binance claimed rewards properly this month
Deltazulu03-Dec-19 06:45 AM
1.6 mio kmd's bought and only 135k sold in the last 1.5h on binance


Hello!
has added you You have been added to the organization domain at Knappsack! No further action is necessary.
has invited you You have been invited to join the organization domain at Knappsack! Please follow the link and sign up at Knappsack in order to access applications.
has added you You have been added to the group domain for the organization parentDomainName at Knappsack! No further action is necessary.
has invited you You have been invited to join the group domain for the organization parentDomainName at Knappsack! Please follow the link and sign up at Knappsack in order to access applications.
Regards,
The Knappsack Team


{% extends "base.html" %} {% set active_page = "index" %} {% block content %}
I have graduated and currently seeking full time positions in industry and academia.
As a PhD student I worked with the Architecture Research Group at Simon Fraser University advised by Arrvindh Shriraman. I was also supervised by Nick Sumner, Viji Srinivasan and Sasha Fedorova.
My research can be broadly described as generalized methods for application specific hardware specialization.
I have worked on cache memory systems, coherence protocols, workload characterization and application specific hardware
specialization.
Publications : HPCA'17, IISWC'16, MICRO'16, ICS'16, ISCA'15, ICS'15, ISCA'13, MICRO'12
Contact :
Resume:
The need for computational power has increased with emerging workloads such as
machine learning driving the information economy.
Yet data center and embedded computing alike
desire reduced power consumption.
With the breakdown of semiconductor technology scaling, we no longer attain improved
performance and reduced energy consumption by using smaller transistors.
The semiconductor industry has
resorted to hardware specialization as a means to provide the
performance and energy benefits consumers have experienced since the
invention of the microprocessor. However, specialized hardware units
introduce new challenges, namely -- what to specialize,
how to specialize and how to integrate specialized units. Current approaches require
manual effort to analyse, restructure and rewrite workloads to take advantage
of specialized hardware accelerators.
My research has focused on automated compiler techniques with a goal
of specialization.
It is the first to directly address the problems of
what to specialize and how to specialize program
regions in large, irregular workloads.
I recognized the utility of adapting program analysis
techniques to address the problem.
My work at IISWC'16 demonstrates that analyses at coarser granularities
smears characteristics critical to hardware specialization.
Using a methodology adapted from program analysis,
I have synthesized an accelerator workload suite to assist researchers.
I have also developed program abstractions to ease the mapping of irregular
programs onto specialized hardware accelerators (HPCA'17).
The tools I have built are available as free and
open source software. Built upon a modern compiler framework,
they allow researchers to easily reproduce and extend the work that I have done.
In addition to this line of work, I have also conducted research on mitigating
energy consumption due to extraneous data movement.
In our work presented at ISCA'15, we showed the utility of time-stamp based coherence
protocols to reduce the overhead of data movement in accelerator rich architectures.
It allows for close integration of multiple specialized units
while easing the programming burden of explicit data transfers.
Furthermore, I have designed and evaluated adaptive granularity caching mechanisms to
eliminate waste (MICRO'12). This technique also provides a convenient substrate
for subsequent optimizations such as cache compression.
Contact:
TASC1 8208,
Simon Fraser University, BC,
Canada V5A1S6


2 Police Officers Shot Dead In Dandora After Ambush By Gangsters
by WycliffeTwo police officers were on Friday morning shot dead by suspected gangsters near Saika Dandora bridge, Nairobi.
Reports indicate that the two police constables, attached to Obama Police post, were on patrol Mwenyenge when they were ambushed and fatally shot at around 4am.
Authorities revealed that PC Joel Chesire was shot in the head while PC Albert Abeere was shot in the chest and neck resulting in their deaths.
The killers stole the officers’ guns, radio calls and mobile phones.
The incident was confirmed by Kayole OCP Wilson Kosgey.
Read: Police Donned In Women Attire Gun Down Two Gangsters In Dandora
Nairobi Police boss Philip Ndolo arrived at the scene hours later and said police have launched investigations into the incident.
Kayole is notorious for criminal activities.
In April this year, policemen disguised as women from a worship service gunned down two gangsters.
Read Also: Police Gun Down Defiant Armed Gang Members In Mlango Kubwa
The incident followed reports by locals at Dandora Police station that they had been robbed at around 4.am.
The two are said to have been members of a five-man gang terrorizing locals at Stage ya Maji area.
The other three escaped unhurt.
Locals said the gang is notorious of waylaying and robbing worshipers from prayer vigils.


Our Mission
To inspire the next generation of scientist and engineers, and make an impact in the community by promoting public awareness of science. We would like to be a service for local teachers to bring good science and motivate young people by exposing them to exciting physical demonstrations.


Drought is a normal part of climate variability that occurs in every type of climate. Drought is caused by a lack of precipitation over an extended period of time, often resulting in a water shortage for some activity, sector, or the environment. In contrast to other environmental hazards, droughts develop slowly over a period of weeks, months or years.
Drought is the second most costly weather and climate disaster affecting the United States, preceded only by tropical cyclones. From 1980 to 2016, monetary losses caused by droughts equaled $226 billion, or 19% of total losses from natural disasters. Drought, in conjunction with associated heat waves, also contributed to 2,993 deaths during that time period.1
Although South Carolina typically receives adequate precipitation, droughts can occur at any time of the year and last for several months to several years. Recent droughts have impacted agriculture, forestry, tourism, power generation, public water supply, fisheries, and ecosystems. Drought conditions can also contribute to diminished water and air quality, increased public health and safety risks, and reduced quality of life and social well-being.
Meteorological drought is an extended period of departure from average precipitation for a specific location or region. The amount of deficit is determined using the normal amount of precipitation that would be expected over a given time period for that same location.
Agricultural drought is a lack of adequate moisture to sustain plant growth and development.
Hydrological drought is measured by effects on streamflow, reservoirs, lakes, and groundwater. As these effects may take longer to become noticeable, hydrological drought often lags behind meteorological and agricultural droughts.
Ecological drought is a prolonged and widespread deficit in available water or soil moisture that stresses aquatic and terrestrial ecosystems.
South Carolina normally experiences considerable variability in the rainfall it receives, so it can be difficult to know exactly when a drought is beginning, worsening, or ending. As a first step, understanding what is “normal” can help inform those who monitor conditions and make determinations about whether South Carolina is in drought, or not. Measuring droughts is difficult because they can span large regions, vary in terms of severity and duration, and affect different water uses and sectors at differing time scales. South Carolina uses multiple indicators and indices to monitor drought.
The long-term, statewide annual precipitation average is 47.66 inches.
The Upstate region receives the highest annual averages, ranging from 48 inches to between 70 and 80 inches of rainfall at the highest elevations. The central region is, on average, the State’s driest. Annual totals are less than 48 inches. The Coastal Plain receives annual precipitation amounts that range from 48 to 56 inches.
One year may have much greater than average precipitation, while the next is below average. Sometimes there are periods of multiple years that are drier, or wetter, than average. 10-year moving average graphs are used to show wet and dry periods. Wetter periods occurred during the 1960s, 1970s, and 1990s. Drier periods occurred from the 1920s to 1950s and since the early 2000s.
Average precipitation is around 3 to 5 inches per month. Summer precipitation is normally the greatest, but can also be the most variable since it is connected to localized showers and thunderstorms. Fall is historically the driest season. Tropical storms and hurricanes, many of which occur in the fall, can be “drought-busters.” Winter and spring precipitation occurs mostly through frontal systems.3
Drought in South Carolina can begin during any season. Seasonal variability is often associated with variations in weather patterns, such as changes in pressure, storm tracks, and the jet stream. Other factors, such as extreme heat, wind, and evapotranspiration rates, can also influence the development of droughts.
For the Southeast, the strength and placement of the Bermuda High, a semi-permanent subtropical area of high pressure in the Atlantic Ocean, influences rainfall conditions particularly in spring and summer. Intensification and shifts in this highpressure system has been linked to shifts in storm tracks, decreased summer precipitation, and drought conditions.5
The El Niño–Southern Oscillation (ENSO) is another climate phenomenon that influences dry and wet spells in the State. ENSO fluctuates between three phases: Neutral, cooling La Niña, and warming El Niño. Extremes of these oscillations cause extreme weather. In the southeastern United States winter precipitation tends to be enhanced during the warm phase (El Niño) and reduced during the cold phase (La Niña). There is a less consistent signal during fall and no evident connection between ENSO and spring and summer precipitation.6 The La Niña stage can be an aid for forecasting seasonal droughts in the region.7
The multi-year drought that began in 2007 was accompanied by high summer temperatures and affected the entire Southeast region. US Drought Monitor authors use data available from multiple sources to create the weekly map.
Spatial extent: All South Carolina counties can be affected by drought. Droughts can extend beyond single states into multi-state regions.
Duration: Short-term droughts last less than six months and bring agricultural impacts, especially when occurring during growing season. Long-term droughts last more than six months and can last for many years, affecting hydrology, ecology, and societal well-being.
Severity (or Intensity): Many different indicators and methods are used to measure and monitor drought severity. The choice of an indicator may depend on the type of drought being considered, the impacts of most interest, and the region or location in which drought is occurring. Due to drought’s complexity, multiple indicators are often used to depict severity.
Indicators are values used to describe drought conditions, using precipitation, stream flow, groundwater and reservoir levels, or soil moisture information.
Indices are computed numerical representations of drought severity, using climatic or hydrological data as inputs. For example, the South Carolina Drought Response Committee uses the Palmer Drought Severity Index (PDSI), Crop Moisture Index (CMI), Keetch-Byram Drought Index (KBDI), Standardized Precipitation Index (SPI), and US Drought Monitor (USDM).
An impact is an observable loss or change at a specific time due to drought. Examples include crop loss or damage, closed boat ramps due to low lake levels, or burning restrictions due to higher fire risks.
The South Carolina Drought Response Committee and the State Climatology Office (within the Land, Water and Conservation Division of the South Carolina Department of Natural Resources) address drought related issues and response in South Carolina. The Drought Response Committee is composed of statewide and local members.
The State Climatology Office routinely monitors climatic conditions in the State. The Drought Response Committee meets regularly when needed to evaluate conditions and impacts within Drought Management Areas. The committee votes county by county to issue drought status declarations in four drought severity categories: incipient, moderate, severe, and extreme.10 Each severity category initiates a certain level of drought response.
The Indicators and Indices used by the South Carolina Drought Response Committee and the Current Drought Status are outlined on the Current Drought Conditions page.


Read John 1:12-13, 1 John 3:1-2, and Romans 8:15-17. Describe what it means personally to know you’re part of this covenant of amazing promises.
The second thing God promised them was a place—a land that would hold all these future nations, a place they could call home for generations to come.
Reread Genesis 17:8 and fill in the blanks below:
“The whole of , where you are now an alien, I will give as an everlasting possession to you and your descendants after you.” (NIV)
It would be a land that would be amazingly beautiful and fruitful and belong to their family. Remember that permanent wealth wasn’t determined by currency in the bank but by many children and ownership of land to pass down through the generations. These two promises alone were the equivalent of winning the lottery many times over.
Third, and best of all, God promised them a presence—His presence.
Reread Genesis 17:7-8 and fill in the blanks below:
“I will establish my covenant as an everlasting covenant between me and you and your descendants after you for the generations to come, to be and the God of your descendants after you. . . . I will be .” (NIV)
He promised to be with them and to be their God—to love and take care of them.
God promised Abraham and Sarah three things: the two greatest desires of their hearts—people and place—and a third thing they hadn’t even imagined they could desire, because the presence of a God who loved and cared and guided them through life was unheard of. The greatest promise of all was that He would be with them, establishing a covenant to be their God and the God of their descendants. This third promise was worth more than the others combined—God’s presence with them. The greatest promise of all is God’s presence, His commitment to walk with us as His children.
What do you learn from the following verses about God’s presence?
Psalm 16:11