--- title: Competitive Programming Library keywords: fastai sidebar: home_sidebar summary: "Things to be used by copy & paste. Also useful for other situtations as well!" ---
Find wether a number is prime or not
def is_prime(n: int) -> bool:
# Corner cases
if (n <= 1) :
return False
if (n <= 3) :
return True
# This is checked so that we can skip
# middle five numbers in below loop
if (n % 2 == 0 or n % 3 == 0) :
return False
i = 5
while(i * i <= n) :
if (n % i == 0 or n % (i + 2) == 0) :
return False
i = i + 6
return True
%timeit is_prime(10**9+7)
Gives all prime numbers less than or equal to n.
def sieve_of_eratosthenes(n):
prime = [True for i in range(n+1)]
p = 2
while p**2 <= n:
if prime[p] == True:
for i in range(p**2, n+1, p):
prime[i] = False
p += 1
return [p for p in range(2, n) if prime[p]]
%timeit sieve_of_eratosthenes(10**6)
Given an integer $n$ and a prime number $p$, find the largest $x$ such that $p^x$ divides $n!$
def largest_power(n, p):
x = 0
while n:
n = n // p
x += n
return x
%timeit largest_power(10**18, 27)
Resources:
How many ways can we choose k objects from n objects?
Binomial coefficient are usually written in the form:
$$\binom{n}{k}$$This form is pretty and is useful for proofs, but it is does not give intuition for efficient calculation. A better way to calculate binomials is with the following recurrence:
$$\binom{n}{k} = \frac{n}{k}\binom{n-1}{k-1}$$Examples:
1. How many ways can 5 digit numbers can we make by selecting from the set {1,2,3,4,5,6,7}?
There are $\binom{n}{k}$ ways to choose 5 numbers from a total of 7 different numbers. The actual combinations can be easily obtained using itertools.combinations()
.
with timing():
n, k = 7, 5
n_combs = binomial_coefficients(n, k)
combs = list(combinations(range(1,8), 5))
print(n_combs)
print('Examples:', combs[:5])
A multinomial coefficient is often written in the form
$$\binom{n}{k_1,k_2,\ldots,k_m}$$where $k_1+k_2+\cdots+k_m = n.$ Because of that last equation, the n is redundant. Binomial coefficients are technically multinomial coefficients as well, but instead of writing $\binom{n}{k_1,k_2}$ where $k1+k2=n$, we usually write either $\binom{n}{k_1}$ or $\binom{n}{k_2}$. These are all mean exactly the same thing:
$$\binom{k_1+k_2}{k_1,k_2}=\binom{k_1+k_2}{k_1}=\binom{k_1+k_2}{k_2} = \frac{(k_1+k_2)!}{k_1!\,k_2!}.$$They can also be expressed as a product of binomial coefficients:
$$\binom{n}{k_1,k_2,\ldots,k_m} = \binom{k_1}{k_1}\binom{k_1+k_2}{k_1}\dots\binom{k_1+k_2+\dots+k_m}{k1}$$A multinomial coefficient can arise in a counting problem as follows:
items = [2,2,3,4,4]
# multinomial_combinations(items)
tuple(items)
Examples:
The word coffee contains the letters C×1, O×1, F×2, E×2. Therefore the permutations will be,
$$\binom{6}{1, 1, 2, 2} = \frac{6!}{1!1!2!2!} = 180$$with timing():
n_combs = multinomial_coefficients([1,1,2,2])
combs = list(multinomial_combinations([1,1,2,2]))
print(n_combs)
print(combs[:5])
def multinomial_coefficients(K):
"""
Computes the multinomial coefficient
K = [k1, k2, k3, k4...]
src: https://stackoverflow.com/a/46378809/5034709
"""
res, i = 1, sum(K)
i0 = K.index(max(K))
for a in K[:i0] + K[i0+1:]:
for j in range(1,a+1):
res *= i
res //= j
i -= 1
return res
K = [3,3,3,3,3]
print(multinomial_coefficients(K))
K = ['MISSISSIPPI'.count(c) for c in set('MISSISSIPPI')]
print(multinomial_coefficients(K))
Very slow method of computing multinomial combinations using permutations and sets
from itertools import permutations
def multinomial_combinations_slow(items):
return set(permutations(items))
items = [c for c in 'MISSISSIPPI']
with timing():
combs = multinomial_combinations_slow(items)
print(next(iter(combs)))
print(len(combs))
Below is a faster dynamic programming solution.
from itertools import combinations
from itertools import chain,repeat,islice,count
from collections import Counter
def combinations_without_repetition(r, iterable=None, values=None, counts=None):
if iterable:
values, counts = zip(*Counter(iterable).items())
f = lambda i,c: chain.from_iterable(map(repeat, i, c))
n = len(counts)
indices = list(islice(f(count(),counts), r))
if len(indices) < r:
return
while True:
yield tuple(values[i] for i in indices)
for i,j in zip(reversed(range(r)), f(reversed(range(n)), reversed(counts))):
if indices[i] != j:
break
else:
return
j = indices[i]+1
for i,j in zip(range(i,r), f(count(j), islice(counts, j, None))):
indices[i] = j
with timing():
combs = combinations_without_repetition(len(items),items)
print(list(combs))
# print(next(iter(combs)))
# print(len(combs))
if len(K) == 1:
for c in combinations(items, K[0]):
yield (c,)
else:
for c_first in combinations(items, K[0]):
items_remaining = []
for c_other in m_way_ordered_combinations(items_remaining, K[1:]):
yield (c_first,) + c_other
import numpy, itertools
def multiset_combinations(n, k, max_power=None):
"""returns a list (2d numpy array) of all length k sequences of
non-negative integers n1, ..., nk such that n1 + ... + nk = n."""
bar_placements = itertools.combinations(range(1, n+k), k-1)
tmp = [(0,) + x + (n+k,) for x in bar_placements]
sequences = numpy.diff(tmp) - 1
if max_power:
return sequences[numpy.where((sequences<=max_power).all(axis=1))][::-1]
else:
return sequences[::-1]
K = 3
multinomial_combinations(5, K)
A multiset coefficient is often written in the form
$$\binom{n+k-1}{k}$$A multiset coefficient can arise as follows:
A multiset coefficient can alternatively arise this way:
Calculates all the factorials up to n, $O(n)$.
Max n is $10^4$. If theres a large prime look at the factorial_mod()
solution.
def init_factorial(n):
facs = [1]
for i in range(1, n+1):
facs.append(facs[i-1] * i)
return facs
facs = init_factorial(10**4)
Fast way to solve $nCr \ \ \% \ \ MOD $ when MOD is prime.
def binomial_mod(n, k, p):
numerator = 1
for i in range(k):
numerator = (numerator * (n-i)) % p
denominator = 1
for i in range(1, k+1):
denominator = (denominator * i) % p
return (numerator * pow(denominator, p-2, p)) % p
MOD = 10**9+7
%timeit binomial_mod(10**9, 10**6, MOD)
Fast way to solve $n!\ \ \% \ \ MOD$ when MOD is prime
def factorial_mod(n, p) :
ans = 1
for i in range(1, n+1):
ans = ans * i % p
return ans
MOD = 10**9+7
%timeit factorial_mod(10**6, MOD)


Toggle navigation
CIS 411 - Assignment 4
Home
Contact
About
This is my contact page for assignment 4.
The purpose of this page is to experiment with bootstrap. Everything here is filler content.
Name
Email
Message
2 + 3 = ?


Hi there! Don't you wanna know about me?
I am a Personal care Chatbot, I can make your life more confortable, you can easily interact with me. You can do some cool stuff with me
How can I be useful to you ?
 You can also have an friendly chat with me.


#include <BottomRightCustomArea.h>
Public Member Functions | |
| BallWidget () | |
| void | draw (DrawingContext &context) |
| virtual bool | isHitAt (Vector point) |
Protected Member Functions | |
| virtual DraggingSurrogate * | onDragStart (const Vector &startPosition) |
| virtual void | onPress (Vector position, bool option1, bool option2) |
| virtual void | onRelease (bool option1, bool option2) |
Private Attributes | |
| friend | BallWidgetSurrogate |
| friend | BottomRightCustomArea |
| Vector | direction |
| Vector | position |
| bool | pressed |
| bool | show |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Definition at line 29 of file BottomRightCustomArea.h. |
|
|
Definition at line 30 of file BottomRightCustomArea.h. |
|
|
Definition at line 34 of file BottomRightCustomArea.h. |
|
|
Definition at line 35 of file BottomRightCustomArea.h. |
|
|
Definition at line 36 of file BottomRightCustomArea.h. |
|
|
Definition at line 37 of file BottomRightCustomArea.h. |


IMPORTANT - PLEASE READ CAREFULLY
1. Terms & Conditions for the ClaimRide Prepaid Debit Card Program (the "Card Program") This document constitutes the agreement ("Agreement") outlining the terms and conditions under which a Prepaid Debit Card has been issued to you. By accepting and using a Card, you agree to be bound by the terms and conditions of this Agreement. The Card is a prepaid debit card. The Card allows you to access funds you place on the Card. The Card does not constitute a checking, savings or other bank account and is not connected in any way to any other account you may have. The Card is not a credit card. You will not receive any interest on your funds on the Card. The Card will remain the property of Holders Bank and must be surrendered upon demand. The Card is nontransferable, and it may be canceled, repossessed, or revoked at any time without prior notice subject to applicable law.


{% extends "base.html" %} {% block title %}
Medieval catalogues > UNIVERSITY AND COLLEGE LIBRARIES OF CAMBRIDGE > King's College > Inventory, c. 1457
{% endif %}162 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


apk v1.1 available in dist director to demonstrate and test ALog
See GitHub: https://github.com/landenlabs2/all_Log
ALog is an Android Log wrapper which has the following features:
The core class ALog is an enumeration. Using an enueration restricts memory allocation of the class to startup and subsequent use is light-weight singleton.
Core class or enum files:
Optional Extended abstraction enum file:
Change your code from old style:
// Determine debug state once in main activity.
// Use flag in code to determine if logging is active.
boolean showOldLog = ((getApplicationInfo().flags & ApplicationInfo.FLAG_DEBUGGABLE) != 0);
// Places in code where logging is required.
final String TAG = getClass().getSimpleName();
if (showOldLog) {
Log.d(TAG, "old style log message #1");
}
// ... do more stuff ...
if (showOldLog) {
Log.d(TAG, "old style log message #2");
}
To new style:
// Determine debug state once in main activity.
// Use flag in code to determine if logging is active.
boolean showNewLog = ((getApplicationInfo().flags & ApplicationInfo.FLAG_DEBUGGABLE) != 0);
AppLog.setMinLevel(showNewLog ? VERBOSE : NOLOGGING);
// Places in code where logging is required.
AppLog.LOG.d().msg("new style log message #1");
// ... do more stuff
AppLog.LOG.d().msg("new style log message #2");
ALog is the main log wrapper class which supports the standard priority levels:
public enum ALog {
// Log levels to system log file.
v(ALog.VERBOSE),
d(ALog.DEBUG),
i(ALog.INFO),
w(ALog.WARN),
e(ALog.ERROR),
a(ALog.ASSERT),
none(ALog.ASSERT+1),
. . .
ALog enumeration includes chainable methods to configure output.
Example Syntax of ALog
| Call | Logged message |
|---|---|
| ALog.i.self().msg("#log info message"); | /I (MainActivity.java:211)(pid):#log info message |
| ALog.d.tag("myTag1").msg("#log debug message"); | D/myTag1 (pid):#log debug message |
| ALog.w.tagMsg("myTag2", "#log warning message"); | W/myTag2 (pid):#log warning message |
| ALog.e.tag("classTag").fmt("#error FIRST:%s LAST:%s", "first", "last"); | E/classTag (pid):#error FIRST:first LAST:last |
| ALog.i.tag("catTag").cat(" ", "Info", "Log", "a", "new", "msg"); | I/catTag (pid):info Log a new msg |
To control logging, set the static global minLevel in ALog.
File: ALog.java:
/**
* Minimum level to log.
*/
public static int minLevel = ALog.VERBOSE;
The logging is active if the calling log level exceeds or is equal to the priority of the minimum global log level.
ALog.minLevel = ALog.WARN;
ALog.d.msg("this log is ignored, below min level");
ALog.w.msg("this log is sent");
ALog.e.msg("this log is also sent");
The ALog enumeration supports chaining of methods to customize the logging output. The tag and self method set the Tag field which persists between calls within a thread.
| Method | Description |
|---|---|
| tag(String tagStr) | Set output tag field, default is 'self()'. Setting persists between calls. |
| self() | Set output tag to self which parses the call stack and generates a tag value of syntax filename:lineNumber |
The key advantage of ALog is the cat and fmt methods do no processing unless the log is active. This avoids wasting time and memory formating strings only to be discarded.
The following methods cause an immediate printing of the log message if logging is active.
| Method | Description |
|---|---|
| msg(String msgStr) | Print msgStr using current tag to log target |
| msg(String msgStr, Throwable tr) | Print msgStr using current tag to log target along with Throwable stack trace |
| tagMsg(String tagStr, String msgStr) | Print tag and message |
| cat(String separator, Object... args) | Print concatenaated objects |
| fmt(String fmt, Object... args) | Print formatted objects |
Example when Tag set once in a thread and multiple logs generate without setting tag:
| Call | Logged message |
|---|---|
| ALog.w.tag("tag3"); | |
| ALog.w.msg("with tag3, msg#1"); | W/tag3 (pid):msg#1 |
| w.msg("with tag3, msg#2"); | W/tag3 (pid):msg#2 |
Using following syntax to log an exception:
Exception ex = new Exception("test exception");
ALog.e.tr(ex);
This implementation futher extends the logging by wrapping ALog in another enumeration called AppLog. The AppLog enumeration is used to create and manage named loggers which can be used by common code or actions such as Network access, Parsing XML, Fragments, etc. The AppLog enumeration futher extends the logging flavors by supporting alternate logging destinations, such as a private log file or No Logging. By configuring these named loggers you can control which features in your code are actively logging and where they log.
public enum AppLog {
/**
* Logging to android log system.
*/
LOG(LogSys),
/**
* Logging to private log file.
*/
LOGFILE(LogFile),
// =========== Application specific log flavors ===========
/**
* General Fragment logging
*/
LOG_FRAG(LogSys),
/**
* General Network activity (currently disabled).
*/
LOG_NETWORK(LogNone),
/**
* General Parinsg activity (currently disabled).
*/
LOG_PARSING(LogNone),
Example of using higher level Application level wrapper which provides flavors of logging which gives an added level of abstraction to setup default behavior.
AppLog.LOG.i().tag("TestTag").msg("Log fixed test");
AppLog.LOG_FRAG.i().self().msg("Frag fixed test");
AppLog.LOGFILE.e().tag("LogFile").msg("LogFile fixed Test");
Since ALog is just an enumeration pluas two support files you just need to include these files in your project.
To use the full implemenation copy all four files from the alog subdirectory into your project and tune the AppLog enumeration to your needs.
The private log fie target ALogFileWriter requires initialization to create the file. Call init(Context) on this class in your startup code.
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
// Initialize ALog private log file target.
ALogFileWriter.init(this);
No special building is required. Just include the files in your Android project.
Core class files:
Optional Extended abstraction class file:
See above API samples, JavaDoc, Code and Demo app for examples of usage.
/*
* Copyright (c) 2019 Dennis Lang (LanDen Labs<EMAIL_ADDRESS>*
* Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
* associated documentation files (the "Software"), to deal in the Software without restriction, including
* without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the
* following conditions:
*
* The above copyright notice and this permission notice shall be included in all copies or substantial
* portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
* LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
* NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* @author Dennis Lang (Jan-2017)
* @see http://landenlabs.com
*
*/
Code is provided, documentation and examples provided at these locations:


Circuit Breaker Contests
Ongoing: TikTok Challenge
Make an awesome TikTok, it can be a dance or a funny skit!


Mark Favorite
Display Information
Send Command
D
Switches
F
Lamps
G
Coils / Flash
Flippers
×
Keyboard Reference
Key
Device


FLESH AND BONE A film review by Scott Renshaw Copyright 1993 Scott Renshaw
Starring: Dennis Quaid, Meg Ryan, James Caan, Gwyneth Paltrow. Screenplay/Director: Steve Kloves.
It's about fifteen minutes into FLESH AND BONE before you see the first speck of sunlight, or hear the first note of underscore. In fact, there's almost no movement or sound of any kind. It's clear from the outset that writer/director Steve Kloves (THE FABULOUS BAKER BOYS) is going for a dark and moody feel. However, there's moody, and then there's comatose. FLESH AND BONE is the perfect example of a solid story choking nearly to death on its own atmosphere.
FLESH AND BONE opens with a rural Texas family taking in an apparently homeless boy for the night. In fact, the boy is the son of a thief who has fequently pulled this con, letting his dad into homes after everyone is asleep. This time, the robbery is botched and three members of the family are slain, leaving only an infant girl. Thirty years later, grown-up Arlis Sweeney (Dennis Quaid) is eking out a living running a vending machine supply business, content with a simple, predictable life. While on a job, he meets Kay Davies (Meg Ryan), a runaway wife jumping drunkenly out of a cake at a bachelor party. Arlis brings Kay along on his rounds, and an attraction grows. Trouble also grows when Papa Roy (James Caan) reappears, linked up with a slick young grifter (Gwyneth Paltrow). Despite every attempt to distance himself from his past, Arlis finds himself drawn into his father's obsession with tying up loose ends.
At the heart of FLESH AND BONE is an intriguing character in an intriguing premise. Arlis Sweeney is a man desperately trying to convince himself that he's not like his father, trying to live out his days meekly as a personal penance for whatever crimes he might have been party to. He feels completely unworthy of the too-trusting Kay, giving their relationship a nice dynamic. Unfortunately, Dennis Quaid is playing Arlis. As charming as he can be, Quaid simply doesn't have the dramatic range necessary to dig into this complicated character; when he should be looking introspective and guilt-ridden, he just looks stiff. I was left imagining a Jeff Bridges in the role, and what might have been given a more versatile actor.
Other aspects of the performances also left me unsatisfied. Meg Ryan has the light touch required for the film's few moments of humor, but she doesn't quite strike the balance of strength and accumulated pain required for this role. James Caan has a surprisingly small part, given his prominence in the advertising campaign, and never quite hits his stride.  It's a major drawback, because Caan's Roy is supposed to be FLESH AND BONE's living manifestation of evil, and he's nowhere near threatening enough. Surprisingly, the best performance is turned in by Gwyneth Paltrow as the savvy Gennie, but it's hard to figure what purpose her character serves, other than perhaps to remind Arlis of what he could be.
For all the problems with the acting, it's another issue entirely that proves to be FLESH AND BONE's ultimate undoing: it's positively snore-inducing. At two hours and ten minutes, it's at *least* twenty minutes too long. The second act is interminable, chock full of scenes best left on the cutting room floor, like Kay's confrontation with her husband (apparently a refugee from THELMA AND LOUISE). There were establishing shots which lasted twice as long as necessary, and meaningful pauses which weren't very meaningful. To make matters worse, there were two minute stretches of dialogue between Caan and Quaid which were practically indecipherable (I'm not sure whether to blame the sound mixing or the theater for that one). In live theater, there is an expression: "Tighten it up." What FLESH AND BONE needed was a good tightening up.
The tone that FLESH AND BONE is going for is fully achieved. The West Texas locations are expertly captured by cinematographer Phillippe Rousselot, and the spare score by Thomas Newman has a nice edgy feel. Everything is working towards a grim tension, and it succeeds, but tension requires release and it's there that FLESH AND BONE doesn't deliver. Thanks to an ill-conceived ad campaign, one of the film's key plot points is given away in the trailers, blowing one of those release points for anyone who has seen them. There is a great deal of craft to the broad strokes of FLESH AND BONE. However, in this tale of evil, the devil is truly in the lack of details.
On the Renshaw scale of 0 to 10 guilty memories: 4.
-- Scott Renshaw Stanford University Office of the General Counsel
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


>From The Desk Of Mr.Jim Brown Address: No 135 Bishopsgate, London,E2CM Email<EMAIL_ADDRESS>Phone: +447040107137 15th June 2006 For your kind attention, Transfer of fund / Investment cum joint venture I discovered an account of a late customer in my office, as credit officer of our Bank here in London. It will be in my interest to transfer this fund worth 15,000,000 million pounds to your account in your country. If you can be a collaborator to this please indicate interest immediately for us to proceed by responding as soon as possible with the following: 1-Your full Name and Contact Address 2-Phone Number and Age 3-Account details to be used.It can be a zero balance account but not dormant. Once the funds have been transferred to your nominated bank account I will come to your country for sharing between us.You can get in touch with me on my mobile number +447040107137 for more clarification and details or through Email<EMAIL_ADDRESS>Regards and Respect, Mr.Jim Brown ______________________________________ Koncert Kry¹tof, Heleny Ze»ové nebo Pehy zadarmo? Ano! V nedìli 18. èervna na pra¾ské Letenské pláni na Sony Ericsson On Air! http://www.SonyEricssonOnAir.cz


Welcome to
  PyNMC
| PYNRC_PATH: |
|
| PYSYN_CDBS: |
|
| WEBBPSF_DATA: |
|
| Optical FILTER you want to convert from: | |
| Spectral Type: | |
| Source (Star) Magnitude: | |
| Photometric System: |
| NIRCam FILTER you want to convert to: | |
| Photometric System: |


rectorch.nets¶
Class list¶
|
Abstract Autoencoder network. |
|
Denoising Autoencoder network for collaborative filtering. |
|
Variational Autoencoder network. |
|
Variational Autoencoder network for collaborative filtering. |
|
Conditioned Variational Autoencoder network for collaborative filtering. |
|
Generator network of the CFGAN model. |
|
Discriminator network of the CFGAN model. |
|
Sequential Variational Autoencoders for Collaborative Filtering. |
This module contains definitions of the neural newtork architectures used by the rectorch models.
See also
Modules:
models
-
class
rectorch.nets.
AE_net
(dec_dims, enc_dims=None)[source]¶ Bases:
torch.nn.modules.module.Module
Abstract Autoencoder network.
This abstract class must be inherited anytime a new autoencoder network is defined. The following methods must be implemented in the sub-classes:
- Parameters
- dec_dimslist or array_like
Dimensions of the decoder network.
dec_dims[0]
indicates the dimension of the latent space, anddec_dims[-1]
indicates the dimension of the input space.- enc_dimslist, array_like or
None
[optional]Dimensions of the encoder network.
end_dims[0]
indicates the dimension of the input space, andend_dims[-1]
indicates the dimension of the latent space. If evaluates to False,enc_dims = dec_dims[::-1]
. By defaultNone
.
- Attributes
- dec_dims
list
or array_like ofint
See
dec_dims
parameter.- enc_dims
list
or array_like ofint
See
end_dims
parameter.- dec_dims
-
decode
(self, z)[source]¶ Forward propagate the latent represenation in the decoder network.
- Parameters
- z
torch.Tensor
The latent tensor
- z
-
encode
(self, x)[source]¶ Forward propagate the input in the encoder network.
- Parameters
- x
torch.Tensor
The input tensor
- x
-
forward
(self, x)[source]¶ Forward propagate the input in the network.
- Parameters
- x
torch.Tensor
The input tensor to feed to the network.
- x
-
class
rectorch.nets.
MultiDAE_net
(dec_dims, enc_dims=None, dropout=0.5)[source]¶ Bases:
rectorch.nets.AE_net
Denoising Autoencoder network for collaborative filtering.
The network structure follows the definition as in [R2dd2e16d1ef4-VAE]. Hidden layers are fully connected and tanh activated. The output layer of both the encoder and the decoder are linearly activated.
References
- R2dd2e16d1ef4-VAE
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 689–698. DOI: https://doi.org/10.1145/3178876.3186150
- Attributes
- dec_dims
list
or array_like ofint
See
dec_dims
parameter.- enc_dims
list
or array_like ofint
See
end_dims
parameter.- dropout
float
The dropout layer that is applied to the input during the
AE_net.forward()
.- dec_dims
-
decode
(self, z)[source]¶ Forward propagate the latent represenation in the decoder network.
- Parameters
- z
torch.Tensor
The latent tensor
- z
-
encode
(self, x)[source]¶ Forward propagate the input in the encoder network.
- Parameters
- x
torch.Tensor
The input tensor
- x
-
init_weights
(self)[source]¶ Initialize the weights of the network.
Weights are initialized with the
torch.nn.init.xavier_uniform_()
initializer, while biases are initalized with thetorch.nn.init.normal_()
initializer.
-
class
rectorch.nets.
VAE_net
(dec_dims, enc_dims=None)[source]¶ Bases:
rectorch.nets.AE_net
Variational Autoencoder network.
Layers are fully connected and ReLU activated with the exception of the ouput layers of both the encoder and decoder that are linearly activated.
Note
See
AE_net
for parameters and attributes.-
decode
(self, z)[source]¶ Apply the decoder network to the sampled latent representation.
- Parameters
- z
torch.Tensor
The sampled (trhough the reparameterization trick) latent tensor.
- z
- Returns
torch.Tensor
The output tensor of the decoder network.
-
-
encode
(self, x)[source]¶ Apply the encoder network of the Variational Autoencoder.
- Parameters
- x
torch.Tensor
The input tensor
- x
- Returns
- mu, logvar
tuple
oftorch.Tensor
The tensors in the latent space representing the mean and standard deviation (actually the logarithm of the variance) of the probability distributions over the latent variables.
- mu, logvar
-
forward
(self, x)[source]¶ Apply the full Variational Autoencoder network to the input.
- Parameters
- x
torch.Tensor
The input tensor
- x
- Returns
- x’, mu, logvar
tuple
oftorch.Tensor
The reconstructed input (x’) along with the intermediate tensors in the latent space representing the mean and standard deviation (actually the logarithm of the variance) of the probability distributions over the latent variables.
- x’, mu, logvar
-
init_weights
(self)[source]¶ Initialize the weights of the network.
Weights are initialized with the
torch.nn.init.xavier_uniform_()
initializer, while biases are initalized with thetorch.nn.init.normal_()
initializer.
-
class
rectorch.nets.
MultiVAE_net
(dec_dims, enc_dims=None, dropout=0.5)[source]¶ Bases:
rectorch.nets.VAE_net
Variational Autoencoder network for collaborative filtering.
The network structure follows the definition as in [Rb6211bc148e3-VAE]. Hidden layers are fully connected and tanh activated. The output layer of both the encoder and the decoder are linearly activated.
References
- Rb6211bc148e3-VAE
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 689–698. DOI: https://doi.org/10.1145/3178876.3186150
- Attributes
- dec_dims
list
or array_like ofint
See
dec_dims
parameter.- enc_dims
list
or array_like ofint
See
end_dims
parameter.- dropout
float
The dropout layer that is applied to the input during the
VAE_net.forward()
.- dec_dims
-
decode
(self, z)[source]¶ Apply the decoder network to the sampled latent representation.
- Parameters
- z
torch.Tensor
The sampled (trhough the reparameterization trick) latent tensor.
- z
- Returns
torch.Tensor
The output tensor of the decoder network.
-
encode
(self, x)[source]¶ Apply the encoder network of the Variational Autoencoder.
- Parameters
- x
torch.Tensor
The input tensor
- x
- Returns
- mu, logvar
tuple
oftorch.Tensor
The tensors in the latent space representing the mean and standard deviation (actually the logarithm of the variance) of the probability distributions over the latent variables.
- mu, logvar
-
class
rectorch.nets.
CMultiVAE_net
(cond_dim, dec_dims, enc_dims=None, dropout=0.5)[source]¶ Bases:
rectorch.nets.MultiVAE_net
Conditioned Variational Autoencoder network for collaborative filtering.
The network structure follows the definition as in [R9611bc073931-CVAE]. Hidden layers are fully connected and tanh activated. The output layer of both the encoder and the decoder are linearly activated.
References
- R9611bc073931-CVAE
Tommaso Carraro, Mirko Polato and Fabio Aiolli. Conditioned Variational Autoencoder for top-N item recommendation, 2020. arXiv pre-print: https://arxiv.org/abs/2004.11141
- Attributes
- cond_dim
int
See
cond_dim
parameter.- dec_dims
list
or array_like ofint
See
dec_dims
parameter.- enc_dims
list
or array_likeSee
end_dims
parameter.- dropout
float
The dropout layer that is applied to the input during the
VAE_net.forward()
.- cond_dim
-
encode
(self, x)[source]¶ Apply the encoder network of the Variational Autoencoder.
- Parameters
- x
torch.Tensor
The input tensor
- x
- Returns
- mu, logvar
tuple
oftorch.Tensor
The tensors in the latent space representing the mean and standard deviation (actually the logarithm of the variance) of the probability distributions over the latent variables.
- mu, logvar
-
class
rectorch.nets.
CFGAN_G_net
(layers_dim)[source]¶ Bases:
torch.nn.modules.module.Module
Generator network of the CFGAN model.
The generator newtork of CFGAN is a simple Multi Layer perceptron. Each internal layer is fully connected and ReLU activated. The output layer insted has a sigmoid as activation funciton. See [R6425608c9e5d-CFGAN] for a full description.
References
- R6425608c9e5d-CFGAN
Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 137–146. DOI: https://doi.org/10.1145/3269206.3271743
- Attributes
- layers_dim
list
ofint
See the
layers_dim
parameter.- input_dim
int
The dimension of the output of the generator, i.e., the input of the discriminator.
- latent_dim
int
The dimension of the latent space, i.e., the dimension of the input of the generator.
- layers_dim
-
forward
(self, z)[source]¶ Apply the generator network to the input.
- Parameters
- x
torch.Tensor
The input tensor to be forwarded.
- x
- Returns
torch.Tensor
The output tensor results of the application of the generator network.
-
init_weights
(self, layer)[source]¶ Initialize the weights of the network.
Weights are initialized with the
torch.nn.init.xavier_uniform_()
initializer, while biases are initalized with thetorch.nn.init.normal_()
initializer.
-
class
rectorch.nets.
CFGAN_D_net
(layers_dim)[source]¶ Bases:
torch.nn.modules.module.Module
Discriminator network of the CFGAN model.
The discriminator newtork of CFGAN is a simple Multi Layer perceptron. Each internal layer is fully connected and ReLU activated. The output layer insted has a sigmoid as activation funciton. See [R59de8c9a4ef6-CFGAN] for a full description.
References
- R59de8c9a4ef6-CFGAN
Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 137–146. DOI: https://doi.org/10.1145/3269206.3271743
- Attributes
- layers_dim
list
ofint
See the
layers_dim
parameter.- input_dim
int
The dimension of the input of the discriminator.
- layers_dim
-
forward
(self, x, cond)[source]¶ Apply the discriminator network to the input.
- Parameters
- x
torch.Tensor
The input tensor to be forwarded.
- cond
torch.Tensor
The condition tensor. Note that must hold that
x.shape[0] == cond.shape[0]
.- x
- Returns
torch.Tensor
The output tensor results of the application of the discriminator to the input concatenated with the condition.
-
init_weights
(self, layer)[source]¶ Initialize the weights of the network.
Weights are initialized with the
torch.nn.init.xavier_uniform_()
initializer, while biases are initalized with thetorch.nn.init.normal_()
initializer.
-
class
rectorch.nets.
SVAE_net
(n_items, embed_size, rnn_size, dec_dims, enc_dims)[source]¶ Bases:
rectorch.nets.VAE_net
Sequential Variational Autoencoders for Collaborative Filtering.
UNDOCUMENTED [R894e5278d4e4-SVAE]
References
- R894e5278d4e4-SVAE
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, and Vikram Pudi. 2019. Sequential Variational Autoencoders for Collaborative Filtering. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ‘19). Association for Computing Machinery, New York, NY, USA, 600–608. DOI: https://doi.org/10.1145/3289600.3291007
- Attributes
- See *Parameres* section.
-
decode
(self, z)[source]¶ Apply the decoder network to the sampled latent representation.
- Parameters
- z
torch.Tensor
The sampled (trhough the reparameterization trick) latent tensor.
- z
- Returns
torch.Tensor
The output tensor of the decoder network.
-
forward
(self, x)[source]¶ Apply the full Variational Autoencoder network to the input.
- Parameters
- x
torch.Tensor
The input tensor
- x
- Returns
- x’, mu, logvar
tuple
oftorch.Tensor
The reconstructed input (x’) along with the intermediate tensors in the latent space representing the mean and standard deviation (actually the logarithm of the variance) of the probability distributions over the latent variables.
- x’, mu, logvar
-
init_weights
(self)[source]¶ Initialize the weights of the network.
Weights are initialized with the
torch.nn.init.xavier_uniform_()
initializer, while biases are initalized with thetorch.nn.init.normal_()
initializer.


{% extends "base/base.html" %} {% block title %}Contact — {{ block.super }}{% endblock %} {% block breadcrumbs_header %}
Do get in touch with any comments or questions, or just to say hello.
London, SE6 1NJ
...coming soon
{{ settings.DEFAULT_FROM_EMAIL_MAILTO }}


--- title: Home description: ---
My name is Stefan Oderbolz ("Odi") and I live in Zurich. I'm currently working for Avaloq as a software engineer and I'm studying computer science at the HSR (University of Applied Science) in Rapperswil.


A clone of the classic windows game, Minesweeper.
Right click to mark a mine with a flag or question mark. On mobile devices, tap and hold the cell to mark mines (working on this for iOS).
Built by Ryan Hamacher. Open source and available on GitHub.


this is a wrapper for yify torrent api, access is granted by yify website itself, developed by Ghazwan Aliesh under MIT licenses


It is often convenient to define explicitly the lower bound and upper bound of a type map, and override the definition given on page Lower-Bound and Upper-Bound Markers . This allows one to define a datatype that has ``holes'' at its beginning or its end, or a datatype with entries that extend above the upper bound or below the lower bound. Examples of such usage are provided in Section Examples . Also, the user may want to overide the alignment rules that are used to compute upper bounds and extents. E.g., a C compiler may allow the user to overide default alignment rules for some of the structures within a program. The user has to specify explicitly the bounds of the datatypes that match these structures.
To achieve this, we add two additional ``pseudo-datatypes,'' MPI_LB and MPI_UB, that can be used, respectively, to mark the lower bound or the upper bound of a datatype. These pseudo-datatypes occupy no space (extent(typeMPI_LB) = extent(typeMPI_UB) =0). They do not affect the size or count of a datatype, and do not affect the content of a message created with this datatype. However, they do affect the definition of the extent of a datatype and, therefore, affect the outcome of a replication of this datatype by a datatype constructor.
Example
Let D = (-3, 0, 6); T = (MPI_LB, MPI_INT, MPI_UB),
and B = (1, 1, 1). Then a call to
MPI_TYPE_STRUCT(3, B, D, T, type1)
creates a new datatype that has an
extent of 9 (from -3 to 5, 5 included), and contains an integer at
displacement 0. This is the datatype defined by the sequence
{(lb, -3), (int, 0), (ub, 6) } .
If this type is replicated twice by a call to
MPI_TYPE_CONTIGUOUS(2, type1, type2) then the newly created
type can
be described by the sequence
{(lb, -3), (int, 0), (int,9), (ub, 15) } .
(An entry of type
ub
can be deleted if there is another entry of type ub with a higher
displacement; an entry of type lb can be deleted if there is another
entry of type lb with a lower displacement.)
In general, if Typemap = { (type0 , disp0 ) , ... , (typen-1 , dispn-1) } , then the lower bound of Typemap is defined to be
Similarly, the upper bound of Typemap is defined to be
Then
extent(Typemap) = ub(Typemap) - lb(Typemap)
If typei requires alignment to a byte address that is a multiple of ki,
then
is the least non-negative increment needed to round
extent(Typemap) to the next multiple of
.
The formal definitions given for the various datatype constructors
apply now, with the amended definition of extent.
Up: Derived Datatypes
Next: Extent and Bounds of Datatypes
Previous: Address and Size Functions
Return to MPI-2.2 Standard Index
Return to MPI Forum Home Page
(Unofficial) MPI-2.2 of September 4, 2009
HTML Generated on September 10, 2009


Home
Home - Default
Home - Band
Home - Multiple
Home - Personal
About Events
Performers
Blog
Gallery
Contact
Pages
Album Page
Album Details
Artist Page
Shop Page
Shop Details
Photo Gallery
Video Gallery
Blog Details
Sponsor Page
Upcoming Event
Performer Details
Contact Page
Register now!
Released
Recent Albums
Rock
Ryan Putnam
Availability:
In stock
$100.00
Quantity
Add to cart
Add to Wishlist
Update Required
To play the media you will need to either update your browser to a recent version or update your
Flash plugin
.
Related Recent Albums
Rock
Ryan Putnam
Hiphop
Cetinkaya
rock
Ryan Putnam
Rock
Cetinkaya
Contact With Us
[email protected]
+012 3456 78910


AirBrush® Edit your photos to perfection with this fast, powerful, and easy photo editor! Remove pimples, smooth wrinkles, even out skin tone, whiten teeth, and more to get the perfect photo every time.
#1 PHOTO EDITING APP
Near perfect app star rating!
Professional results! Easy to use!
POWERFUL RESULTS
AirBrush brings you the most natural, easy-to-use photo editing tools designed to give your photos the perfect aesthetic.

-------------------------------------
-------------------------------------
GLOWING SKIN
• Remove acne and blemishes
• Even out skin tone
• Smooth and glowing skin
DAZZLING SMILE
• Whiten teeth
• Widen or nudge your smile
• Plump up lips
BRIGHT EYES
• Brighten eyes
• Look awake and refreshed
• Enlarge eyes
SCULPT AND SHAPE
• Expand or slim any facial feature
• Shift position of facial features
• Reduce or enlarge any area of the photo
• Add volume to your hair
• Stretch tool for added height
PROFESSIONAL PHOTO EFFECTS
• Add depth with our blur tool
• Frame your photos with vignettes
• Crop photos with custom or pre-set sizes
• Control color and light
FILTERS FOR DAYS!
• Dozens of looks
• Themed filters and gorgeous light effects
• New filters always being added
READY TO SHARE?
• Directly share your AirBrush photos
onto your favorite social media accounts.
INSTAL NOW
👉🏽 Download AirBrush


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use IOFileFilter | |
| org.apache.commons.io | This package defines utility classes for working with streams, readers, writers and files. |
| org.apache.commons.io.filefilter | This package defines an interface (IOFileFilter) that combines both
FileFilter and FilenameFilter . |
| Uses of IOFileFilter in org.apache.commons.io |
| Methods in org.apache.commons.io with parameters of type IOFileFilter | |
private static void |
FileUtils.innerListFiles(Collection files,
File directory,
IOFileFilter filter)
Finds files within a given directory (and optionally its subdirectories). |
static Collection |
FileUtils.listFiles(File directory,
IOFileFilter fileFilter,
IOFileFilter dirFilter)
Finds files within a given directory (and optionally its subdirectories). |
static Iterator |
FileUtils.iterateFiles(File directory,
IOFileFilter fileFilter,
IOFileFilter dirFilter)
Allows iteration over the files in given directory (and optionally its subdirectories). |
| Constructors in org.apache.commons.io with parameters of type IOFileFilter | |
DirectoryWalker(IOFileFilter directoryFilter,
IOFileFilter fileFilter,
int depthLimit)
Construct an instance with a directory and a file filter and an optional limit on the depth navigated to. |
| Uses of IOFileFilter in org.apache.commons.io.filefilter |
| Classes in org.apache.commons.io.filefilter that implement IOFileFilter | |
class |
AbstractFileFilter
An abstract class which implements the Java FileFilter and FilenameFilter interfaces via the IOFileFilter interface. |
class |
AgeFileFilter
Filters files based on a cutoff time, can filter either newer files or files equal to or older. |
class |
AndFileFilter
A FileFilter providing conditional AND logic across a list of
file filters. |
class |
CanReadFileFilter
This filter accepts File s that can be read. |
class |
CanWriteFileFilter
This filter accepts File s that can be written to. |
class |
DelegateFileFilter
This class turns a Java FileFilter or FilenameFilter into an IO FileFilter. |
class |
DirectoryFileFilter
This filter accepts File s that are directories. |
class |
EmptyFileFilter
This filter accepts files or directories that are empty. |
class |
FalseFileFilter
A file filter that always returns false. |
class |
FileFileFilter
This filter accepts File s that are files (not directories). |
class |
HiddenFileFilter
This filter accepts File s that are hidden. |
class |
NameFileFilter
Filters filenames for a certain name. |
class |
NotFileFilter
This filter produces a logical NOT of the filters specified. |
class |
OrFileFilter
A FileFilter providing conditional OR logic across a list of
file filters. |
class |
PrefixFileFilter
Filters filenames for a certain prefix. |
class |
SizeFileFilter
Filters files based on size, can filter either smaller files or files equal to or larger than a given threshold. |
class |
SuffixFileFilter
Filters files based on the suffix (what the filename ends with). |
class |
TrueFileFilter
A file filter that always returns true. |
class |
WildcardFileFilter
Filters files using the supplied wildcards. |
class |
WildcardFilter
Deprecated. Use WilcardFileFilter. Deprecated as this class performs directory filtering which it shouldn't do, but that can't be removed due to compatability. |
| Fields in org.apache.commons.io.filefilter declared as IOFileFilter | |
static IOFileFilter |
TrueFileFilter.TRUE
Singleton instance of true filter. |
static IOFileFilter |
TrueFileFilter.INSTANCE
Singleton instance of true filter. |
static IOFileFilter |
CanWriteFileFilter.CAN_WRITE
Singleton instance of writable filter |
static IOFileFilter |
CanWriteFileFilter.CANNOT_WRITE
Singleton instance of not writable filter |
static IOFileFilter |
HiddenFileFilter.HIDDEN
Singleton instance of hidden filter |
static IOFileFilter |
HiddenFileFilter.VISIBLE
Singleton instance of visible filter |
static IOFileFilter |
FalseFileFilter.FALSE
Singleton instance of false filter. |
static IOFileFilter |
FalseFileFilter.INSTANCE
Singleton instance of false filter. |
private static IOFileFilter |
FileFilterUtils.cvsFilter
|
private static IOFileFilter |
FileFilterUtils.svnFilter
|
static IOFileFilter |
DirectoryFileFilter.DIRECTORY
Singleton instance of directory filter. |
static IOFileFilter |
DirectoryFileFilter.INSTANCE
Singleton instance of directory filter. |
static IOFileFilter |
EmptyFileFilter.EMPTY
Singleton instance of empty filter |
static IOFileFilter |
EmptyFileFilter.NOT_EMPTY
Singleton instance of not-empty filter |
static IOFileFilter |
CanReadFileFilter.CAN_READ
Singleton instance of readable filter |
static IOFileFilter |
CanReadFileFilter.CANNOT_READ
Singleton instance of not readable filter |
static IOFileFilter |
CanReadFileFilter.READ_ONLY
Singleton instance of read-only filter |
private IOFileFilter |
NotFileFilter.filter
The filter |
static IOFileFilter |
FileFileFilter.FILE
Singleton instance of file filter |
| Methods in org.apache.commons.io.filefilter that return IOFileFilter | |
static IOFileFilter |
FileFilterUtils.prefixFileFilter(String prefix)
Returns a filter that returns true if the filename starts with the specified text. |
static IOFileFilter |
FileFilterUtils.suffixFileFilter(String suffix)
Returns a filter that returns true if the filename ends with the specified text. |
static IOFileFilter |
FileFilterUtils.nameFileFilter(String name)
Returns a filter that returns true if the filename matches the specified text. |
static IOFileFilter |
FileFilterUtils.directoryFileFilter()
Returns a filter that checks if the file is a directory. |
static IOFileFilter |
FileFilterUtils.fileFileFilter()
Returns a filter that checks if the file is a file (and not a directory). |
static IOFileFilter |
FileFilterUtils.andFileFilter(IOFileFilter filter1,
IOFileFilter filter2)
Returns a filter that ANDs the two specified filters. |
static IOFileFilter |
FileFilterUtils.orFileFilter(IOFileFilter filter1,
IOFileFilter filter2)
Returns a filter that ORs the two specified filters. |
static IOFileFilter |
FileFilterUtils.notFileFilter(IOFileFilter filter)
Returns a filter that NOTs the specified filter. |
static IOFileFilter |
FileFilterUtils.trueFileFilter()
Returns a filter that always returns true. |
static IOFileFilter |
FileFilterUtils.falseFileFilter()
Returns a filter that always returns false. |
static IOFileFilter |
FileFilterUtils.asFileFilter(FileFilter filter)
Returns an IOFileFilter that wraps the
FileFilter instance. |
static IOFileFilter |
FileFilterUtils.asFileFilter(FilenameFilter filter)
Returns an IOFileFilter that wraps the
FilenameFilter instance. |
static IOFileFilter |
FileFilterUtils.ageFileFilter(long cutoff)
Returns a filter that returns true if the file was last modified after the specified cutoff time. |
static IOFileFilter |
FileFilterUtils.ageFileFilter(long cutoff,
boolean acceptOlder)
Returns a filter that filters files based on a cutoff time. |
static IOFileFilter |
FileFilterUtils.ageFileFilter(Date cutoffDate)
Returns a filter that returns true if the file was last modified after the specified cutoff date. |
static IOFileFilter |
FileFilterUtils.ageFileFilter(Date cutoffDate,
boolean acceptOlder)
Returns a filter that filters files based on a cutoff date. |
static IOFileFilter |
FileFilterUtils.ageFileFilter(File cutoffReference)
Returns a filter that returns true if the file was last modified after the specified reference file. |
static IOFileFilter |
FileFilterUtils.ageFileFilter(File cutoffReference,
boolean acceptOlder)
Returns a filter that filters files based on a cutoff reference file. |
static IOFileFilter |
FileFilterUtils.sizeFileFilter(long threshold)
Returns a filter that returns true if the file is bigger than a certain size. |
static IOFileFilter |
FileFilterUtils.sizeFileFilter(long threshold,
boolean acceptLarger)
Returns a filter that filters based on file size. |
static IOFileFilter |
FileFilterUtils.sizeRangeFileFilter(long minSizeInclusive,
long maxSizeInclusive)
Returns a filter that accepts files whose size is >= minimum size and <= maximum size. |
static IOFileFilter |
FileFilterUtils.makeCVSAware(IOFileFilter filter)
Decorates a filter to make it ignore CVS directories. |
static IOFileFilter |
FileFilterUtils.makeSVNAware(IOFileFilter filter)
Decorates a filter to make it ignore SVN directories. |
static IOFileFilter |
FileFilterUtils.makeDirectoryOnly(IOFileFilter filter)
Decorates a filter so that it only applies to directories and not to files. |
static IOFileFilter |
FileFilterUtils.makeFileOnly(IOFileFilter filter)
Decorates a filter so that it only applies to files and not to directories. |
| Methods in org.apache.commons.io.filefilter with parameters of type IOFileFilter | |
void |
ConditionalFileFilter.addFileFilter(IOFileFilter ioFileFilter)
Adds the specified file filter to the list of file filters at the end of the list. |
boolean |
ConditionalFileFilter.removeFileFilter(IOFileFilter ioFileFilter)
Removes the specified file filter. |
static IOFileFilter |
FileFilterUtils.andFileFilter(IOFileFilter filter1,
IOFileFilter filter2)
Returns a filter that ANDs the two specified filters. |
static IOFileFilter |
FileFilterUtils.orFileFilter(IOFileFilter filter1,
IOFileFilter filter2)
Returns a filter that ORs the two specified filters. |
static IOFileFilter |
FileFilterUtils.notFileFilter(IOFileFilter filter)
Returns a filter that NOTs the specified filter. |
static IOFileFilter |
FileFilterUtils.makeCVSAware(IOFileFilter filter)
Decorates a filter to make it ignore CVS directories. |
static IOFileFilter |
FileFilterUtils.makeSVNAware(IOFileFilter filter)
Decorates a filter to make it ignore SVN directories. |
static IOFileFilter |
FileFilterUtils.makeDirectoryOnly(IOFileFilter filter)
Decorates a filter so that it only applies to directories and not to files. |
static IOFileFilter |
FileFilterUtils.makeFileOnly(IOFileFilter filter)
Decorates a filter so that it only applies to files and not to directories. |
void |
AndFileFilter.addFileFilter(IOFileFilter ioFileFilter)
{@inheritDoc} |
boolean |
AndFileFilter.removeFileFilter(IOFileFilter ioFileFilter)
{@inheritDoc} |
void |
OrFileFilter.addFileFilter(IOFileFilter ioFileFilter)
{@inheritDoc} |
boolean |
OrFileFilter.removeFileFilter(IOFileFilter ioFileFilter)
{@inheritDoc} |
| Constructors in org.apache.commons.io.filefilter with parameters of type IOFileFilter | |
AndFileFilter(IOFileFilter filter1,
IOFileFilter filter2)
Constructs a new file filter that ANDs the result of two other filters. |
|
NotFileFilter(IOFileFilter filter)
Constructs a new file filter that NOTs the result of another filters. |
|
OrFileFilter(IOFileFilter filter1,
IOFileFilter filter2)
Constructs a new file filter that ORs the result of two other filters. |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


logo
Home
(current)
gallery
advertisement
Triangle bajar advertisemnt
Triangle bajar advertisemnt
Triangle bajar advertisemnt
second card
third card
fourth card


when a vm start,such file will auto load and cached:
subvm:
block:
jslib: var abc=requir e('./abc.js')
style:
cache used for open multi vm with same url to save http request.
vm.clean() is not automatic,you need excute it mannuly,
vm.clean should excute after vm.close,
these will not clean: app.use(vm|lib|elhook|block), app.config.path[vm|lib|block|elhook]
there is a refcount about every loaded file,if refcount>0,will not clean()
when vm.clean(),if same url not closed all,not clean
if refcount==0,these will clean:bodytext,styletext,vmlib,and loaded file in this vm not in config.path and app.use:
subvm-block-style: clean all,
jslib:when a jslib need check clean,if other vm use this jslib,means refcount>0,will not clean,
pvm.removeChild(cid) will auto close the child,
when a vm.close(),pvm still has a child with the id,but empty,you can start again use the cached body,style,vmlib
when pvm.removeChild(cid) ,if you want restart,you need pvm.appendChild(),
restart need not reload abc.html,it is in path restart need not reload abc.html,it is in path restart need reload abd.html restart need reload abd.html


My Favorite Restaurants
In my favorite city, Charlotte, my favorite restaurants are:
Thai Taste
Chik-fil-A
Wendys
Wayback Burgers
Smoke Pit
Pad Thai is the best food there is.
I want Pad Thai


The PWG meetings will be held on November 3-4 via WebEx teleconference.
There is no registration fee or RSVP survey for this meeting.
Call-in toll-free number (US/Canada): +1<PHONE_NUMBER>
Call-in toll number (US/Canada): +1<PHONE_NUMBER>
Call-in toll number (US/Canada): +1<PHONE_NUMBER>
Attendee access code: (by request only)
To join the online meeting:
Note:All times are US Eastern Standard Time (4 hours behind GMT/UTC) - standard time takes effect November 1st, 2015 in the US.
Discussions at PWG face-to-face meetings are covered by the PWG Policy on Intellectual Property and Confidentiality - basically everything we discuss is non-confidential and can be included in an open standard.
| Date | Times | Description |
|---|---|---|
| November 3 | 9:00 AM to 9:45 AM | PWG Plenary (Slides) |
| 9:45 AM to 10:45 AM | IDS - Charter, HCD-PP, ICCC (Slides · Charter) | |
| 10:45 AM to 11:00 AM | Break | |
| 11:00 AM to 12:30 PM | Semantic Model - Schema, JDFMAP (Slides) | |
| 12:30 PM to 1:30 PM | Lunch | |
| 1:30 PM to 4:00 PM | 3D Printing BOF (Slides · White Paper) | |
| November 4 | 9:00 AM to 12:00 PM | IPP - IPP/1.1, IPP/2.0, IPP Everywhere (Slides · RFC2910bis · RFC2911bis · Self-Certification) |
| 12:00 PM to 1:00 PM | Lunch | |
| 1:00 PM to 4:00 PM | IPP - System Service (SYSTEM) |


Example of modal in CSS only, here I use the pseudo selector ":target" and no javascript for modal action.
One modal example here! :D


The Polyline
component in a combination with DirectionsRequest
can be used to route the way
on the map.
DirectionsRequest
creates a collection of points between the start and end coordinates, and
Polyline
connects them by straight line segments.


Test of crosstab: two autosomal loci
In each of these tables, hover over the
row and column headings to view conditional genotype percentages among
non-missing values (and, in parentheses, the overall percent of
missing values).
Test of crosstab: two X-linked loci
Test of crosstab: an autosomal locus and an X-linked locus
Source at GitHub:
source |
documentation |
test code


shophere
This time around, the campaign intends to make SHOPHERE, a habit
for consumers and drive them to interact more with the platform, with the promise of providing
the latest fashion, updated daily.
"to make our life our client look good,trendy,and obviously as they like to look themselves"
Learn more
Online shopping for women's clothing at Shophere comes with a lot of variety -
in terms of product type, style, design, cut, and other features. Making a choice from such a
wide assortment is now an easy task, as online shopping portal - shophere brings to you a
wide range of women's clothing that are stylish and comfortable.
Online Shopping for Women's Dresses on shophere
Online shopping for evening gowns available here if you are attending a formal event.


|
Panda3D
|
Public Member Functions | |
| def | __init__ (self, nodePath, bboxColor=None) |
| def | __repr__ (self) |
| def | computeBounds (self) |
| def | computeTightBounds (self) |
| def | createBBoxLines (self, bboxColor=None) |
| def | getBounds (self) |
| def | getCenter (self) |
| def | getMax (self) |
| def | getMin (self) |
| def | getRadius (self) |
| def | hide (self) |
| def | recompute (self) |
| def | setBoxColorScale (self, r, g, b, a) |
| def | show (self) |
| def | updateBBoxLines (self) |
| def | vecAsString (self, vec) |
Public Attributes | |
| bounds | |
| center | |
| lines | |
| max | |
| min | |
| nodePath | |
| radius | |
| def __init__ | ( | self, | |
| nodePath, | |||
bboxColor = None |
|||
| ) |
| def __repr__ | ( | self | ) |
| def computeBounds | ( | self | ) |
| def computeTightBounds | ( | self | ) |
| def createBBoxLines | ( | self, | |
bboxColor = None |
|||
| ) |
| def getBounds | ( | self | ) |
| def getCenter | ( | self | ) |
| def getMax | ( | self | ) |
| def getMin | ( | self | ) |
| def getRadius | ( | self | ) |
| def hide | ( | self | ) |
| def recompute | ( | self | ) |
| def setBoxColorScale | ( | self, | |
| r, | |||
| g, | |||
| b, | |||
| a | |||
| ) |
| def show | ( | self | ) |
| def updateBBoxLines | ( | self | ) |
| def vecAsString | ( | self, | |
| vec | |||
| ) |
| bounds |
| center |
| lines |
| max |
| min |
| nodePath |
| radius |


Time Series for Work Flow
Time Series service allows a user to plot one or more variables as function(s) of time. At each time point, the values are computed by averaging physical variables over a longitude and latitude box. For 3-d variables, the values are for specified pressure levels. In addition to the usual way of specifying data using source and variable names, this service allows user to specify an input file by its path. The input data file can be generated from another CMDA service or uploaded by user. It currently supports only netcdf format.


Week four
Software Clocks with capture
This week we kept building and improving the clock codes with creating new functions, adding capture camera, visual and dynamic elements. From Mario game; the visual elements I incorporated in my java sketches are; pixelated screen, simple graphics made of rectangles and squares, basic animations and colourful screen. The interaction dynamics I chose for codes such as scale, rotation, vertical and horizontal movements.Return to home page


#assign primary="active">
@Primary @Secondary are equivalent to Spring DI @Primary @Secondary and Micronaut @Primary @Secondary.
Note that JSR 330 does not have any equivalent functionality. CDI has a different approach and instead has @Priority, @Default and @Alternative annotations to achieve similar goals.
@Primary
and @Secondary
are used when there are
multiple candidates to inject. They provide a "priority" to determine which dependency to
inject and use when injecting a single implementation and multiple candidates are available
to inject.
We typically use @Primary and @Secondary when we are building multi-module applications. We have multiple modules (jars) that provide implementations. We use @Secondary to indicate a "default" or "fallback" implementation to use and we use @Primary to indicate the best implementation to use when it is available. DInject DI will then wire depending on which modules (jars) are included in the classpath.
A bean with @Primary
is deemed to be highest priority and will be injected and used
when it is available.
There should only ever be one bean implementation marked as @Primary for a given dependency.
This is functionally the same as Spring and Micronaut @Primary.
A bean with @Secondary
is deemed to be lowest priority and will only be injected
if there are no other candidates to inject. We use @Secondary
to indicate a
"default" or "fallback" implementation that will be superseded by any other available implementation.
This is functionally the same as Spring and Micronaut @Secondary.
<@next "Test doubles" "test-doubles"/>


 Students range from freshmen to seniors and are majoring in concentrations ranging from biology to economics. Without the hard work and dedication from our team, this year's event would not be possible.
Shirley Lin '19
Shirley is a junior studying Neurobiology. In high school, she lived and breathed Science Olympiad, and could often be found consuming copious amounts of bagel chips after a long day of 5 events. She’s done too many events to recall, but her favorite was Forensics. On campus, she also serves as a director for the Harvard Summit for Young Leaders in China conference run by the Harvard Association of U.S.-China Relations impacting over 1300 students every August. When not speed-walking from classes to meetings, Shirley enjoys drinking tea, watching Friends for the tenth time, and playing piano.
Cameron Akker '18
Cameron is a senior at Harvard College studying electrical engineering with a background in both computer science and mechanical engineering. Cameron comes from Redmond High School in Redmond, Washington and was part of his Science Olympiad team for 3 years. He primarily competed in Thermodynamics, MagLev, and Robot Arm among other events. Cameron has also worked at both Apple and Formlabs, in roles ranging from engineering project management to hardware design.
Bovey Rao '19
Bovey is a Junior at Harvard College studying Molecular and Cellular Biology and Computer Science. Originally from Salt Lake City, Utah, he has a professed love of nature, food, and biology. During high school Science Olympiad, he competed in a wide range of physics and zoology events with a strong preference for Entomology and Thermodynamics. When not trying to catch up on sleep, Bovey runs one of the student restaurants on campus, conducts research on ALS, and organizes conferences through the Harvard Association for US-China Relations.
Lily Lu '19
Lily Lu grew up in Boston, Massachusetts, and is currently a junior at Harvard College studying Integrative Biology. Lily spent the majority of her time in middle and high school competing with and organizing her school’s Science Olympiad team, where she met some of her closest friends and discovered her passion for birds through the ornithology event. Outside of Science Olympiad, Lily is also an editor of Ecdysis, a recently established Harvard undergraduate journal on the intersection between art and science, and plays piano accompaniment for the Harvard Noteables, a non-audition show choir. In her free time, Lily enjoys drawing, playing piano, taking photos of her pet cat, and looking at pictures of fat birds.
Kevin Bao '19
Kevin Bao is from Walnut in the LA region. He has had a lifelong passion for biology and just generally discovering new morsels of knowledge. He competed for Troy High School Science Olympiad and today spends the bulk of his day lounging about on obscure pages of Wikipedia.
Jeanna Qiu '20
Jeanna Qiu is a sophomore in Harvard College, studying Chemical and Physical Biology with a secondary in Sociology. She has been involved in Science Olympiad since she was 11 years old and her favorite event was Experimental Design. In addition to HCSO, Jeanna plays cello in the Harvard-Radcliffe Orchestra and conducts research about the bacterial cell wall at Harvard Medical School. In her free time, Jeanna enjoys exploring Boston and finding good dessert restaurants.
Haig Cholakian '21
Haig is from Dubai and is currently a freshman at Harvard College planning to study electrical engineering, applied math, and/or computer science. In high school, he did not compete in Science Olympiad, but he is excited to be part of Harvard’s first SciOly invitational. When he’s not busy with classes and meetings, he can be found binge watching TV shows, baking in his dorm room, and trying new foodie spots in the Square.
Vivian Lee '21
Vivian, hailing from Los Angeles, California, is a freshman at Harvard planning to study Human Developmental and Regenerative Biology. For three years in high school, she spent many weekends with her beloved Science Olympiad team competing in Disease Detectives, Experimental Design, Chemistry Lab, and Anatomy and Physiology, eating snacks, and making puns. When she isn’t staying up late to work on problem sets, Vivian enjoys reading Neil Gaiman novels, watching anime, and napping.
Karissa Huang '21
Karissa Huang is a freshman at Harvard, originally from Cleveland, OH, and will most likely study Applied Math. Karissa has been doing Science Olympiad since the tender age of 10 and in high school, Science Olympiad was a large part of her life. Her favorite events were Chem Lab and Cell Biology. Outside of SciOly, Karissa plays flute in the Harvard Radcliffe Orchestra, and tutors. In her free time, Karissa enjoys going for a swim and/or watching Sherlock.
Grace Greason '21
Grace is from Rochester, Minnesota and is a freshman at Harvard College planning on studying Government and languages. In high school, she was an active participant in Science Olympiad and enjoyed competing in Anatomy and Physiology and Green Generation. She also enjoys traveling, hiking, and Netflix.
Marina Andrawis '21
Marina Andrawis is from Staten Island, New York and is a freshman at Harvard College. Marina is planning to study Neurobiology, but also enjoys studying Psychology and Global Health and Public Policy. Marina has participated in Science Olympiad for most of her high school years, and participated in events such as Write It Do It, Wind Power, Disease Detectives, Chemistry Lab, and Anatomy & Physiology. Outside of SciOly, Marina enjoys playing basketball, drinking coffee, and watching thrillers.
Nina Chen '21
Nina is from Brooklyn New York and is currently a freshman at Harvard College. She plans to concentrate in Applied Math and Statistics, but she is also interested in computer programing and biological engineering. In high school she participated in many Science Olympiad events such as Protein Modeling, Disease Detective, Cell Biology, Microbe Mission, Ecology, and Experimental Design. Now she is part of Harvard College Science Olympiad, hoping to plan Harvard’s first SciOly invitational!


Sorry. No details available.
Max Points: {{assignment.points_possible}} {{assignment.grading_type}}
Created at : {{assignment.created_at}}
Due at : {{assignment.due_at}}
You have successfully submitted the assignment.
Kindly review the submission here
You have not submitted the assignment.
Kindly submit the assignment here


{{c.classType.name}}
{{c.date}}
{{c.startTime}}
No classes available
{{customer.forename}} {{customer.surname}}
{{customer.gender}}
{{customer.phone || '-'}}
{{customer.user.email}}
Forename field is required
Please enter a valid forename
Surname field is required
Please enter a valid surname
Email field is required
Please enter a valid email address
Current password is required
New password is required
Confirm new password is required
New passwords do not match


Research Interests
I'm interested in a number of different topics within computational and cognitive neuroscience. Firstly, I'm intrigued by human memory, especially in the visual and auditory domains. Once we understand exactly how memories are encoded, stored, and recalled, I'm interested to know if we could then augment these processes. You could imagine a world where we are able to "back up" our memories to the cloud, and even share our memories with others.
It also fascinates me how we process and perceive visual and auditory information concurrently. Often, we use information received in the auditory domain to help us process a visual cue, and vice versa. Although this sometimes leads to negative biases, I wonder if it can be utilized in a way that enhances information uptake — perhaps for use in a learning environment. Additionally, I'm interested in using imaging techniques such as fMRI and MEG to combine my experience with crossmodal correspondences with memory research. The advent of machine learning and computer vision also presents many interesting opportunities to combine with neuroscientific data which I'm eager to explore.
Although I've switched fields, I've kept my love for Astrophysics! I enjoy reading the latest news, and hope someday I'll have the opportunity to research supernovae once more.
Past Projects
MSc Thesis:
Neural Correlates of Crossmodal Correspondence Between Pitch and Visual Motion
Supervisor: Prof Joydeep Bhattacharya FRSA
Our senses are not independent entities — instead, they work together to build our realities. When one sense influences another, we call this phenomenon crossmodal correspondence. I studied a particular crossmodal correspondence in which an ascending/descending pitch as well as the spoken words "up"/"down" (auditory domain) were able to bias perception of visual motion (visual domain).
I designed and conducted an EEG experiment in which the participant judged the direction of motion of an ambiguous (same upwards and downwards components) moving grating whilst listening to the auditory stimuli. Using behavioural and ERP analysis, I showed that auditory stimuli were enough to bias people's perception of visual motion — an ascending tone caused more people to perceive upwards motion. This was the first time that this effect was studied using brain imaging techniques.
You can read my MSc thesis here or take a look at my poster for the behavioural data here.
Anvil Hack 2018:
Speed Reader
While at Goldsmiths, I participated in a number of different hackathons including Anvil Hack. During this event, I worked in a team of 3 to make a platform that allows you to convert text into a form that was much more quickly readable. We were inspired by this advert by Honda. Perhaps in the future, devices that use this technology might be commonplace.
MPhys Thesis:
Realistic MHD Modeling of Wind-Driven Processes in Cataclysmic Variable-Like Binaries
Supervisors: Dr Cecilia Garraffo and Dr Jeremy Drake
When main sequence stars such as our Sun orbit a star known as a white dwarf, they can form something called a cataclysmic variable-like binary. The charged ions contained within the magnetic fields of these stars creates a complex wind structure as the two bodies orbit each other. When this wind structure decouples from the system it carries away mass and angular momentum, which impacts how the stars spin and how their orbits evolve. Using 3-D magnetohydrodynamic simulations, I explored the effects of orbital separation and magnetic field configuration on these mass and angular momentum loss rates for binary systems.
The current models don't include the magnetic fields of the stars. It was hypothesized that including the magnetic field of the white dwarf in the binary system could alter these rates. I found that when including these magnetic fields, the mass and angular momentum loss rates dropped by factors of 4 and 6 respectively, suggesting that the current models for these systems should be amended to include the magnetic field.
You can read my MPhys thesis here or watch a 20-min video of my thesis defense given at the Harvard-Smithsonian Center for Astrophysics High Energy Seminar here.
Hack @ Brown 2017:
Saguaro: An open source, extensible home automation platform
I attended a hackathon at Brown University where I worked within a team of 5 to design and build an IOT platform called Saguaro. Saguaro allows hardware devices to be controlled by a simple web interface.
Activating hardware in your home — lights, windows, anything you can run a wire to — is as simple as pushing a button. Additionally, Saguaro learns your schedule, and over time will be able to anticipate your actions and automate your home for you.
You can learn more about this project here.
Bottom image credit: Lynette Cook / extrasolar.spaceart.org
Computing Project #1:
Planetary Orbits Around Binary Star Systems
Incredibly, almost half of the star systems that we see in the sky contain multiple stars! This project aimed to simulate planetary orbits around a binary star system. This is important to study because such simulations could be used to detect habitable planets outside our solar system (exoplanets). For life to exist, the planet on which it occurs must keep a stable orbit over long time-scales.
To find stable orbits, I used a Runge-Kutta approach to solve the differential equations involved in such a 3-body system and tested various initial velocities and separations. I found many different p- and s-type orbits (the two species of stable planetary orbit), as well as some chaotic orbits, and discovered whether they possessed a habitable zone where the planet could sustain life.
You can read my report on this project here.
Computing Project #2:
The Structure of White Dwarf Stars
White dwarf (WD) stars are extremely dense objects — the only thing preventing them from collapsing into a black hole is a force called electron degeneracy pressure, which has to do with the fact that electrons cannot be close enough to occupy the same energy state. They are very important within astronomy and appear in many areas of study, including galaxy formation, stellar evolution, and supernovae. Since over 95% of the stars we observe will end their lives as WDs, knowing how they function and how their parameters behave is crucial.
In this project, I created a model for determining the mass-radius variation in WDs, and ultimately found the critical mass of a WD (Chandrasekhar limit), beyond which the electron degeneracy pressure can no longer support the star and it must collapse into a neutron star or a black hole.
You can read more about this project here.
BAE Systems Project:
Submarine Drag Reduction Study
Before I started university I spent a summer internship at BAE Systems Maritime completing a project focusing on how to reduce physical drag on the new class of nuclear submarines.
I investigated how changes to the bow, control surfaces, fins, and body of the submarine would affect the hydrodynamics of the submarine. I also investigated if it would be possible to inject polymers or micro-bubbles to change the way the water flowed over the submarine's surface. Finally, I looked into more radical solutions such as supercavitation — a technology used in some underwater missiles that involves boiling the water in front of the supercavitating body to reduce friction drag.
You can read the original report for this project here.
Copyright © 2018 Alex Lascelles.


ATtiny2313 is a low-power CMOS 8-bit microcontroller based on the AVR enhanced RISC
architecture.
By executing powerful instructions in a single clock cycle, the ATtiny2313 achieves
throughputs approaching 1 MIPS per MHz allowing the system designer to optimize power con-
sumption versus processing speed.
This microcontroller has a set with 32 general purpose registers. All those are connected to the arithmetic logic unit.
Provides this features:
- 2K bytes of In-System Programmable Flash
- 128 bytes EEPROM
- 128 bytes SRAM
- 18 general purpose I/O lines
- 32 general purpose working registers
- A single-wire Interface for On-chip Debugging
- Two flexible Timer/Counters with compare modes
- Internal and external interrupts
- A serial programmable USART
- Universal Serial Interface with Start Condition Detector
- A programmable Watchdog Timer with internal Oscillator
- Three software selectable power saving modes
The Diagram of the ATtiny2313 architecture:
This is a very powerful microcontroller and with a low cost too. In next post i will show you a utilization sample of this microcontroller.
thanks.


| Interface | Description |
|---|---|
| BusinessDayConvention |
A convention defining how to adjust a date if it falls on a day other than a business day.
|
| DateAdjuster |
Functional interface that can adjust a date.
|
| DateSequence |
A series of dates identified by name.
|
| DayCount |
A convention defining how to calculate fractions of a year.
|
| DayCount.ScheduleInfo |
Information about the schedule necessary to calculate the day count.
|
| HolidayCalendar |
A holiday calendar, classifying dates as holidays or business days.
|
| PeriodAdditionConvention |
A convention defining how a period is added to a date.
|
| Class | Description |
|---|---|
| AdjustableDate |
An adjustable date.
|
| AdjustableDate.Meta |
The meta-bean for
AdjustableDate . |
| BusinessDayAdjustment |
An adjustment that alters a date if it falls on a day other than a business day.
|
| BusinessDayAdjustment.Builder |
The bean-builder for
BusinessDayAdjustment . |
| BusinessDayAdjustment.Meta |
The meta-bean for
BusinessDayAdjustment . |
| BusinessDayConventions |
Constants and implementations for standard business day conventions.
|
| DateAdjusters |
Date adjusters that perform useful operations on
LocalDate . |
| DateSequences |
Constants and implementations for standard date sequences.
|
| DayCounts |
Constants and implementations for standard day count conventions.
|
| DaysAdjustment |
An adjustment that alters a date by adding a period of days.
|
| DaysAdjustment.Builder |
The bean-builder for
DaysAdjustment . |
| DaysAdjustment.Meta |
The meta-bean for
DaysAdjustment . |
| HolidayCalendarId |
An identifier for a holiday calendar.
|
| HolidayCalendarIds |
Identifiers for common holiday calendars.
|
| HolidayCalendars |
Constants and implementations for standard holiday calendars.
|
| ImmutableHolidayCalendar |
A holiday calendar implementation based on an immutable set of holiday dates and weekends.
|
| ImmutableHolidayCalendar.Meta |
The meta-bean for
ImmutableHolidayCalendar . |
| PeriodAdditionConventions |
Constants and implementations for standard period addition conventions.
|
| PeriodAdjustment |
An adjustment that alters a date by adding a period of calendar days, months and years.
|
| PeriodAdjustment.Builder |
The bean-builder for
PeriodAdjustment . |
| PeriodAdjustment.Meta |
The meta-bean for
PeriodAdjustment . |
| Tenor |
A tenor indicating how long it will take for a financial instrument to reach maturity.
|
| TenorAdjustment |
An adjustment that alters a date by adding a tenor.
|
| TenorAdjustment.Builder |
The bean-builder for
TenorAdjustment . |
| TenorAdjustment.Meta |
The meta-bean for
TenorAdjustment . |
This package contains data objects and tools for manipulating dates.
DayCount
provides the standard
mechanism used to convert dates to fractions of a year.
HolidayCalendar
provides
data on whether a date is a holiday or business day.
BusinessDayConvention
provides standard rules for converting a holiday to the nearest business day,
such as 'Following', 'ModifiedFollowing' and 'Preceding'.
PeriodAdditionConvention
provides rules for adding months, such as 'LastDay' and 'LastBusinessDay'.
Tenor
is used to represent
the length of time that a financial instrument takes to reach maturity.
AdjustableDate
,
BusinessDayAdjustment
,
DaysAdjustment
,
PeriodAdjustment
and
TenorAdjustment
provide
entity objects to represent different kinds of adjustment.
Copyright 2009-Present by OpenGamma Inc. and individual contributors
Apache v2 licensed
Additional documentation can be found at opengamma.github.io/StrataDocs.


By engaging me in business you accept the following terms.
- All payments to be made within 28 days of invoice date.
- Late payments will be charged at a daily rate of interest 8% above Bank of England base rate, plus a £45 fee per reminder.
- Overtime (OT), time off clock (TOC) and meal penalties (NLB, NSB) will all be charged per hour at T1.5 currently £50 per hour.
- TOC charged per hour, for each hour under 11 hours rest given between wrap and call the following day.
- Cancellations within 48 hours charged at 50% of agreed labour and kit rates.
- Cancellations within 24 hours charged at 100% of agreed labour and kit rates.


Algorithm Bubble sort Cocktail sort Comb sort Gnome sort Heap sort Insertion sort Odd-even sort Selection sort Quick sort
Pivot Random First Middle Last
Size
Value Distribution Linear Random
Shuffle Random Ascending Descending
Delay
Loop Count
Events in Queue Highest Events in Queue Events Completed
Test X Test Y Test Count
Swap X Swap Y Swap Count


Tags of HTML
have been styled in Double to add some custom styles and
reset browser defaults. The default font-family
used by Double is Open
Sans. Here a list of all
typography tags

Heading 1

Heading 2

Heading 3

Heading 4

Heading 5

Heading 6

This is a sample text with strong and small styles
and also some code
used inside a paragraphs

This is a sample text with strong and small styles
and also some code used inside a paragraph

Some code here

Some code here


Examples
Below are a list of examples to get you started using this interface.
Bilinearly constrained NLP
Nonlinearly constrained NLP
Nonlinearly constrained MINLP
The MATLAB/BARON interfarce is provided from http://www.minlp.com.


Design | Research | Strategy
Story
Methods
Works
Hobbies
Inspiration
X
Contact Beth
<EMAIL_ADDRESS>(713) 320-2818
Resume


| Method | Defined by | ||
|---|---|---|---|
|
clear(p_sStr:String, p_aExpand:Array):String
[static]
Strips "\r" and "\n" characters from a string and removes consecutive spaces.
| StringUtil | ||
|
clearRepeat(p_sStr:String, p_sRepeat:String, p_sReplace:String = ""):String
[static]
Clears the source string from multiple occurences of a pattern.
| StringUtil | ||
|
findBlockIndex(p_sStr:String, p_sStart:String, p_sEnd:String):Array
[static]
Returns pairs of start and end indices of a block in a source string.
| StringUtil | ||
|
getBlockByIndex(p_sStr:String, p_aPairs:Array, p_nIndex:Number, p_bNoSymbol:Boolean):String
[static]
Returns a specific sub string of the source string.
| StringUtil | ||
|
getBlockRange(p_aPairs:Array, p_nIndex:Number):Array
[static]
Returns the start and end indices for a substring, analyzed by findBlockIndex.
| StringUtil | ||
|
getBlocks(p_sStr:String, p_aPairs:Array, p_bWithSymbol:Boolean):Array
[static]
Returns all blocks between start and end indices from a source string.
| StringUtil | ||
|
getTextBetween(p_sStr:String, p_sStart:String, p_sEnd:String):Array
[static]
Returns all sub strings in a string between two given delimiter patterns.
| StringUtil | ||
|
indexTimes(p_sStr:String, p_sSubstr:String, p_nOccur:Number, p_nStart:Number):Number
[static]
Returns the index of the Nth occurance of sub string.
| StringUtil | ||
|
replace(p_sStr:String, p_sOld:String, p_sNew:String):String
[static]
Replaces a sub string with another in the target string.
| StringUtil |
| clear | () | method |
public static function clear(p_sStr:String, p_aExpand:Array):String
Strips "\r" and "\n" characters from a string and removes consecutive spaces.
Parametersp_sStr:String — The string to modify.
|
|
p_aExpand:Array — Other strings you want to clear out.
|
String — The modified string.
|
| clearRepeat | () | method |
public static function clearRepeat(p_sStr:String, p_sRepeat:String, p_sReplace:String = ""):String
Clears the source string from multiple occurences of a pattern.
Ex: If you want to clear "ababab", you can use "ab" as a pattern.
Parametersp_sStr:String — The pattern to clear out.
|
|
p_sRepeat:String — Replace chars, default is a space.
|
|
p_sReplace:String (default = " ")
|
String — The modified string.
|
| findBlockIndex | () | method |
public static function findBlockIndex(p_sStr:String, p_sStart:String, p_sEnd:String):Array
Returns pairs of start and end indices of a block in a source string.
The target string is searched for blocks of text between a start and and an end delimiter string.
The start and end indices into the searched string, for each found block is stored in a two dimensional array.
All inices are returned as an array of index pairs.
array struct:
array[0] = [Block_Start_String_Length, Block_End_String_Length]
array[1] = [Start_Index_1, End_Index_1]
array[x] = [Start_Index_x, End_Index_x]
Parametersp_sStr:String — The string to search.
|
|
p_sStart:String — Block start with.
|
|
p_sEnd:String — Block end with.
|
Array — An array containtng all the start and end indices.
|
| getBlockByIndex | () | method |
public static function getBlockByIndex(p_sStr:String, p_aPairs:Array, p_nIndex:Number, p_bNoSymbol:Boolean):String
Returns a specific sub string of the source string.
Parametersp_sStr:String — The string to search.
|
|
p_aPairs:Array — Index pairs created by findBlockIndex.
|
|
p_nIndex:Number — Start index into the string of the block to search for.
|
|
p_bNoSymbol:Boolean — Do not include start and end symbols.
|
String — The searched sub string or an empty string if not found.
|
| getBlockRange | () | method |
public static function getBlockRange(p_aPairs:Array, p_nIndex:Number):Array
Returns the start and end indices for a substring, analyzed by findBlockIndex.
Parametersp_aPairs:Array — Index pairs created by findBlockIndex.
|
|
p_nIndex:Number — Start index into the string of the block of interest.
|
Array — The pair of start and end indices, [-1, -1] if not found.
|
| getBlocks | () | method |
public static function getBlocks(p_sStr:String, p_aPairs:Array, p_bWithSymbol:Boolean):Array
Returns all blocks between start and end indices from a source string.
Given an array of index pairs, normally created by a call to findBlockIndex, all blocks between index pairs are extracted and stored in an array
Parametersp_sStr:String — The string to search.
|
|
p_aPairs:Array — Index pairs created by findBlockIndex.
|
|
p_bWithSymbol:Boolean — Include start and end symbols if set to true.
|
Array — All blocks between the start and end indices.
|
| getTextBetween | () | method |
public static function getTextBetween(p_sStr:String, p_sStart:String, p_sEnd:String):Array
Returns all sub strings in a string between two given delimiter patterns.
The delimiters are not included in the results.
Parametersp_sStr:String — The string to search.
|
|
p_sStart:String — The start delimiter.
|
|
p_sEnd:String — The end delimiter.
|
Array — An array of found sub strings. Empty if no matching patterns were found.
|
| indexTimes | () | method |
public static function indexTimes(p_sStr:String, p_sSubstr:String, p_nOccur:Number, p_nStart:Number):Number
Returns the index of the Nth occurance of sub string.
Parametersp_sStr:String — The string to search.
|
|
p_sSubstr:String — Sub string you want find.
|
|
p_nOccur:Number — The occurance of the sub string to find.
|
|
p_nStart:Number — Start index for the search.
|
Number — Start index of the search sub string.
|
| replace | () | method |
public static function replace(p_sStr:String, p_sOld:String, p_sNew:String):String
Replaces a sub string with another in the target string.
Parametersp_sStr:String — The string to modify.
|
|
p_sOld:String — The pattern to replace.
|
|
p_sNew:String — What to replace the pattern with.
|
String — The modified string.
|


Options
R2D2 Bag
Banana Slicer
Bathroom Tablet Holder
Breakfast Station
Open-toed Rain Boots
Meatball Bubble Gum
Shitty Chair
Chtulhu Figurine
Duck Bill for Dogs
Dragon Meat
Pentencils
Pet Sweeper
Pizza Scissors
Shark Sleeping Bag
Baby Sweeper
Tauntaun Sleeping Bag
Unicorn Meat
Monster Tail USB Drive
Watering Can't
Pythagorean Wine Glass
Quantity:


Bio
My name is Tala Jallad. I am 22 years old.
I was born in November 29th, 1998.
My passions inculde Psychology,
volunteering and self-development.
What I do for fun is watching movies
or hanging out with my friends;
sometimes I like to learn spanish.
Education History
- I graduated Twjihi in 2017 from the scientific sector.
- In college, I majored in Applied English and graduted
August 2020 with an excellent GPA.
Job Experience and Career Goals
- During univerity I volunteered for a month as a translator in a magazine in Amman.
- In June 2020, I interned at the Jordan Times for a month.
- In the period of octobor 2019 and july 2020, I was volunteering with AIESEC in Jordan where I was the marketing team leader the last 6 months.
- My goal is to work in a prestigious company as a software developer.
Top 10 Favourite Movies
- Taare Zameen Par
- August Rush
- Keith
- The Holiday
- The Shawshank Redemption
- About Time
- Home Alone
- Harry Potter
- Groundhog Day
- The Dark Knight Rises


Home
About Us
Devlog
Projects
Current Projects
Past Projects
Future Projects
Meet the Catalina Studios Team
Kai Johnson
CEO & Lead Software Developer
LinkedIn Page
Email Kai
John Bethel
CFO & Lead Game Designer
LinkedIn Page
Email John
Matt Rodosky
Lead Game Developer
LinkedIn Page
Email Matt
Website designed by Kai Johnson


Paul Merson slams ’embarrassing’ Pep Guardiola and Manchester City, stating the Premier League title is won
by Matt AddisonFormer Arsenal hero Paul Merson has slammed Pep Guardiola and Manchester City, claiming that they have been ’embarrassed’ by Liverpool so far this season, and that the Reds already have the Premier League title wrapped up.
Merson says not enough questions are being asked of the Spaniard, who has now lost three league matches already this campaign. His Manchester City side currently occupy fourth spot in the table, behind both Chelsea and Leicester City.
Writing for the Daily Star, he explained: “The title race is over so you might as well crown Liverpool champions now. Do I think City can come back from nine points behind? No chance. Liverpool have lost once in about 52 games.
“They would have to lose three times before the end of the season and that’s without City losing a game. Can anyone see that happening?
“If Liverpool win it by eight or nine points that’s running away with it for me. But double digits would be embarrassing for City. It’s already embarrassing to see them in fourth.
“City have spent so much money. How are they nine points behind Liverpool already? Serious questions should be asked.”
Manchester City play Chelsea on Saturday evening at the Etihad Stadium, by which time they could be 12 points off the summit should the Reds beat Crystal Palace earlier in the day.
They also have a tough few games coming up, with fixtures scheduled against the likes of Manchester United, Arsenal, Leicester and Wolves in the league.
Nothing is done and dusted just yet but there can be no doubting who the best side in the country is at the moment. The Reds are head and shoulders above the others right now – but that needs to continue for another few months for it to mean anything.


Developer Students Club
Navarachna University
At DSC NUV, our aim is to learn and teach. Developers, designers and managers come together under one roof to create a community which inspires many. Join Us!
Developer Student Clubs are university based community groups for students interested in Google developer technologies. Students from all undergraduate or graduate programs with an interest in growing as a developer are welcome. By joining a DSC, students grow their knowledge in a peer-to-peer learning environment and build solutions for local businesses and their community.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.