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You can ask the court to recover the debt for you if Mary Richards hasn't paid you on time.
You have to pay a fee for each type of recovery. You can apply for as many as you think might get you your money.
I don't know much about the defendant's financial situation
You can ask the court to order the defendant to attend a hearing and find out what they can afford to pay.
How to apply: Download form
N316(opens in a new window), complete it and return it to make your
application.
The application costs £110. The court adds this fee to the total
debt owed to you.
Where to send paper forms
County Court Money Claims Centre
PO Box 527
Salford
M5 0BY
I believe the defendant has the money, or items to sell at auction
You can ask the court to send bailiffs to collect the money. You'll need to apply for either a warrant of control
or a writ of control.
There are differences between a writ and a warrant. You must pay a fee for whichever you choose. If you choose to apply for both you will have to pay two fees.
Apply for a warrant of control
A warrant of control instructs a county court enforcement officer, known as a bailiff, to visit the defendant to try to get your money. They have fewer powers than bailiffs that enforce a writ of control, but you won't have to pay an extra fee to cover their costs if they're not able to collect the debt.
You can only apply for a warrant of control online for a debt of less than £5,000, including both:
If you apply for a writ of control, a high court enforcement officer will visit the defendant to try to get your money.
High court enforcement officers have greater powers to collect money and items from defendants. However, you will have to pay an extra fee to cover their costs if they're not able to collect the debt.
You can only apply for a writ of control if the debt is over £600.
How to apply: Download a paper form (opens in a new window), complete it and return it to make your
application.
The application costs £66. The court adds this fee to the total
debt owed to you.
Where to send paper forms
County Court Money Claims Centre
PO Box 527
Salford
M5 0BY
I believe the defendant is in paid employment
You can ask the court to take money from the debtor’s wages to pay the debt. They do this by sending an order to the debtor’s employer.
You should choose a different option if you know Mary Richards is self-employed or unemployed.
How to apply: Download form
N337(opens in a new window), complete it and return it to make your
application.
The application costs £110. The court adds this fee to the total
debt owed to you.
Where to send paper forms
County Court Money Claims Centre
PO Box 527
Salford
M5 0BY
I believe the defendant has a bank account
You can ask the court to freeze money in the defendant's bank, building society or business account.
The court will decide if money from the account can be used to pay the debt.
How to apply: Download form
N349(opens in a new window), complete it and return it to make your
application.
The application costs £110. The court adds this fee to the total
debt owed to you.
Where to send paper forms
County Court Money Claims Centre
PO Box 527
Salford
M5 0BY
I believe the defendant has property or land
You can ask the court to charge the defendant's land or property. If the land or property is sold, the debtor must pay this charge before they get their money.
How to apply: Download form
N379(opens in a new window), complete it and return it to make your
application.
The application costs £110. The court adds this fee to the total
debt owed to you.
Where to send paper forms
County Court Money Claims Centre
PO Box 527
Salford
M5 0BY
{% include 'prototypes/prototype-jan-2019/includes/contact.html' %}
{% endblock %}


Streak Search
An automatic search for "killstreak" on the Steam Marketplace that allows you to filter results so you only see kits, fabricators, weapons of a certain quality, etc. The data is scraped from Valve every 10 minutes.
{% extends "main.html" %} {% block title %} - Miscellaneous Features {% endblock %} {% block scripts %} {% endblock %} {% block content %}


Job
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Find a Job in
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industry. text ever sispecimen bookever sispecimen book.
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industry. text ever sispecimen bookever sispecimen book.


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batch_difficulty_level==$i)?'checked':''}}@else{{"Input::old('batch_difficulty_level')"}}@endif type="radio" >{{$data}}
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@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @(params: Map[String, Any]) @uk.gov.hmrc.hmrcemailrenderer.templates.helpers.html.template_main(params, "We’ve received your VAT registration application") {
Dear @params("name")
Reference number: @params("ref")
We’ve received your application and we will write to you with a decision within 40 working days.
You should wait until we confirm your VAT registration before you get software to follow the rules for ‘Making Tax Digital for VAT’.
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nips nips2000 nips2000-58 nips2000-58-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Thore Graepel, Ralf Herbrich, Robert C. Williamson
Abstract: We present an improvement of Novikoff's perceptron convergence theorem. Reinterpreting this mistake bound as a margin dependent sparsity guarantee allows us to give a PAC-style generalisation error bound for the classifier learned by the perceptron learning algorithm. The bound value crucially depends on the margin a support vector machine would achieve on the same data set using the same kernel. Ironically, the bound yields better guarantees than are currently available for the support vector solution itself. 1
[I] M. Aizerman, E. Braverman, and L. Rozonoer. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25:821- 837, 1964.
[2] Y. Freund and R. E. Schapire. Large margin classification using the perceptron algorithm. Machine Learning, 1999.
[3] T. Friess, N. Cristianini, and C. Campbell. The Kernel-Adatron: A fast and simple learning procedure for Support Vector Machines. In Proceedings of the 15- th International Conference in Machine Learning, pages 188- 196, 1998.
[4] T. Graepel, R. Herbrich, and J. Shawe-Taylor. Generalisation error bounds for sparse linear classifiers. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pages 298- 303, 2000. in press.
[5] R. Herbrich. Learning Linear Classifiers - Theory and Algorithms. PhD thesis, Technische Universitiit Berlin, 2000. accepted for publication by MIT Press.
[6] R. Herbrich and T. Graepel. A PAC-Bayesian margin bound for linear classifiers: Why SVMs work. In Advances in Neural Information System Processing 13, 2001.
[7] H. Konig. Eigenvalue Distribution of Compact Operators. Birkhiiuser, Basel, 1986.
[8] N. Littlestone and M. Warmuth. Relating data compression and learn ability. Technical report, University of California Santa Cruz, 1986.
[9] T. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Transaction of London Philosophy Society (A), 209:415446, 1909.
[10] A. Novikoff. On convergence proofs for perceptrons. In Report at the Symposium on Mathematical Theory of Automata, pages 24- 26, Politechnical Institute Brooklyn, 1962.
[11] M. Rosenblatt. Principles of neurodynamics: Perceptron and Theory of Brain Mechanisms. Spartan- Books, Washington D.C., 1962.
[12] J . Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5):1926- 1940, 1998.
[13] V. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.
[14] V. Vapnik. The Nature of Statistical Learning Theory. Springer, second edition, 1999.
[15] G. Wahba. Support Vector Machines, Reproducing Kernel Hilbert Spaces and the randomized GACV. Technical report, Department of Statistics, University of Wisconsin, Madison, 1997. TR- NO- 984.


Rfft
| input | (Tensor) the input tensor of at least |
|---|---|
| signal_ndim | (int) the number of dimensions in each signal. |
| normalized | (bool, optional) controls whether to return normalized results. Default: |
| onesided | (bool, optional) controls whether to return half of results to avoid redundancy. Default: |
For CUDA tensors, an LRU cache is used for cuFFT plans to speed up repeatedly running FFT methods on tensors of same geometry with same configuration. See cufft-plan-cache for more details on how to monitor and control the cache.
Real-to-complex Discrete Fourier Transform
This method computes the real-to-complex discrete Fourier transform. It is
mathematically equivalent with torch_fft
with differences only in
formats of the input and output.
This method supports 1D, 2D and 3D real-to-complex transforms, indicated
by signal_ndim
. input
must be a tensor with at least
signal_ndim
dimensions with optionally arbitrary number of leading batch
dimensions. If normalized
is set to True
, this normalizes the result
by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
unitary, where \(N_i\) is the size of signal dimension \(i\).
The real-to-complex Fourier transform results follow conjugate symmetry:
$$
X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
$$
where the index arithmetic is computed modulus the size of the corresponding
dimension, \(\ ^*\) is the conjugate operator, and
\(d\) = signal_ndim
. onesided
flag controls whether to avoid
redundancy in the output results. If set to True
(default), the output will
not be full complex result of shape \((*, 2)\), where \(*\) is the shape
of input
, but instead the last dimension will be halfed as of size
\(\lfloor \frac{N_d}{2} \rfloor + 1\).
The inverse of this function is torch_irfft
.
For CPU tensors, this method is currently only available with MKL. Use
torch_backends.mkl.is_available
to check if MKL is installed.
#> torch_tensor #> (1,.,.) = #> 4.9202 0.0000 #> 2.9093 2.7036 #> 1.8227 -4.8232 #> #> (2,.,.) = #> -2.0160 4.4730 #> 1.8297 -3.0301 #> 0.4639 0.6830 #> #> (3,.,.) = #> -6.8659 1.3907 #> -5.6917 -4.3527 #> 2.7115 0.4562 #> #> (4,.,.) = #> -6.8659 -1.3907 #> -3.4394 -1.4155 #> 4.7454 -1.6532 #> #> (5,.,.) = #> -2.0160 -4.4730 #> -1.0448 5.6595 #> 5.7855 -3.2515 #> [ CPUFloatType{5,3,2} ]torch_rfft(x, 2, onesided=FALSE)#> torch_tensor #> (1,.,.) = #> 4.9202 0.0000 #> 2.9093 2.7036 #> 1.8227 -4.8232 #> 1.8227 4.8232 #> 2.9093 -2.7036 #> #> (2,.,.) = #> -2.0160 4.4730 #> 1.8297 -3.0301 #> 0.4639 0.6830 #> 5.7855 3.2515 #> -1.0448 -5.6595 #> #> (3,.,.) = #> -6.8659 1.3907 #> -5.6917 -4.3527 #> 2.7115 0.4562 #> 4.7454 1.6532 #> -3.4394 1.4155 #> #> (4,.,.) = #> -6.8659 -1.3907 #> -3.4394 -1.4155 #> 4.7454 -1.6532 #> 2.7115 -0.4562 #> -5.6917 4.3527 #> #> (5,.,.) = #> -2.0160 -4.4730 #> -1.0448 5.6595 #> 5.7855 -3.2515 #> 0.4639 -0.6830 #> 1.8297 3.0301 #> [ CPUFloatType{5,5,2} ]# }


Louise Jensen
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During my undergraduate studies, I focused on identifying potential anerobic bacteria for bioremedation of heavily contaiminated riverbed sediment using various bioinformatic techniques.
As master's student, I investigated the effects yogurt consumption has on the gut microbiome and the role these microbes may have in affecting stress levels.
I hope to combine my understaning of biology with computer science to create user friendly web applications for scientist looking to analyze large genomic data sets.


Since January 2015
- Front-end web development of the SLL.com consumer and enterprise solution using Angular.js, CSS3, and JavaScript.
I'm a self-taught programmer and overall geek at heart. I began by teaching myself C++, HTML, and CSS and started developing basic websites and applications at the age of 8. I love technology, both hardware and software. I'm a programming, computer hardware, and hackathon enthusiast.
In the past year at Coursatliy I've dedicated a lot of my time to UI/UX research and development. We aim to create a UI that addresses the most effective UX for each user.
In recent years I've become a hackathon and event organizer. I organize various events inluding CodeRED and CodeDay Houston. I also help with organizing local and private events for organizations and companies.
| @Aaron7pm | ||
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| Skype | Aaron7pm | |
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|<EMAIL_ADDRESS>|


Texturize namespace
The root namespace, that contains all framework classes.
Classes
- class AppearanceSpace
- A search space implementation based on pixel neighborhood appearances.
- class AppearanceSpaceAsset
- An object that allows to store
AppearanceSpace
instances inside an asset. -
template<typename Base, typename T, typename... Ts>struct are_base_of
- Generic type trait that checks if a each type in a variadic template pack is derived from a certain base class
Base
. -
template<typename Base, typename T>struct are_base_of<Base, T>
- Generic type trait that checks if a each type in a variadic template pack is derived from a certain base class
Base
. -
template<typename TAsset>class AssetPersistence
- An object that is used to store/restore assets to/from an file storage.
- class CoherentIndex
- Implements a search index, that matches pixel neighborhoods based on coherent pixels.
- class CoordinateHash
- Generates a permutation vector from a set of coordinates.
- class DefaultCodec
- Implements an image codec that uses OpenCV's
cv::imread
andcv::imwrite
methods to read and write image files. - class DescriptorExtractor
- Provides access to runtime neighborhood descriptors during synthesis.
- class DynamicThresholdFilter
- Implements an dynamic threshold filter.
- class EdgeDetector
- The base class for filters that are capable of detecting edges.
-
template<typename TResult, typename... TArgs>class EventDispatcher
- A class that can be used to report events.
- class Exception
- An exception object that gets thrown by the
raiseError
methods. - class FeatureDistanceFilter
- Implements a euclidean distance filter.
- class FeatureExtractor
- Implements a filter that calculates a feature map
- class FeatureMatchingIndex deprecated
- A search index based on OpenCV's feature matching library.
-
template<typename TStorage>class FileStorageWrapper
- Creates a wrapper for a file storage implementation.
- class FilterCascade
- A filter that can be used to execute multiple filters as a cascade.
- class FunctionFilter
- A filter that accepts a function or a lambda that is applied to each pixel of the input sample.
- class GrayscaleFilter
- Implements a grayscale filter.
- class Hdf5FileStorage
- A file storage implementation, that uses HDF5 to store files.
- class IFileStorage
- An interface that provides storing and restoring data from a file.
- class IFilter
- A base class for filter functions that can be applied to
Sample
instances. - class ISampleCodec
- An interface that is used to load and save images.
- class ISearchIndex
- An interface that is used to access index search spaces.
- class ISearchSpace
- An interface that defines methods to create, transform and work with search spaces.
- class ISynthesizer
- The base interface for synthesizer implementations.
- class NormalizationFilter
- Implements a normalization filter.
- class OpenCvFileStorage
- A file storage implementation, that uses OpenCV's file storage implementation to store files.
- class ParallelPyramidSynthesizer
- Implements a parallel, pyramid-bases, non-parametric, per-pixel synthesizer.
- class PyramidSynthesisSettings
- A set of settings to initialize instances of
Texturize::
with.PyramidSynthesizer - class PyramidSynthesizer
- Implements a pyramid-bases, non-parametric, per-pixel synthesizer.
- class PyramidSynthesizerState
- The runtime state of a pyramid-based synthesizer, persistent between multiple synthesis passes.
- class RandomWalkIndex
- Implements a search index, that matches pixels by randomly walking coherent candidate neighborhoods.
- class Sample
- A class that provides unified access to image samples.
-
template<size_t cn>class Sample_
- A class that provides unified access to image samples.
- class SamplePersistence
- An object that allows to manage multiple codecs and dispatch persistence requests accordingly.
-
template<typename TDefaultCodec = DefaultCodec>class SamplePersistence_
- An object that allows to manage multiple codecs and dispatch persistence requests accordingly.
- class SearchIndex
- Implements a search index, based on pixel neighborhood appearances.
- class StorageFactory
- The default storage factory that can be used to create a file storage.
- class StructuredEdgeDetector
- Implements an edge detection filter, based on structured forrests.
- class SynthesisSettings
- A set of settings to initialize a synthesizer with.
- class SynthesizerBase
- A base implementation for exemplar-based synthesizers.
- class SynthesizerState
- The runtime state of a synthesizer, persistent between multiple synthesis passes.
Typedefs
- using TrivialSignalDispatcher = EventDispatcher<void>
- A trivial event dispatcher that accepts no parameters and returns nothing.
Functions
- auto raiseError(const Exception& ex) -> void TEXTURIZE_API
- Raises an error based on an
Exception
object. - auto raiseError(const char* func, const char* file, int line, int code, const char* msg) -> void TEXTURIZE_API
- Creates a new error and raises it.
Function documentation
void TEXTURIZE_API Texturize:: raiseError(const Exception& ex)
Raises an error based on an Exception
object.
| Parameters | |
|---|---|
| ex | The exception to throw. |
void TEXTURIZE_API Texturize:: raiseError(const char* func,
const char* file,
int line,
int code,
const char* msg)
Creates a new error and raises it.
| Parameters | |
|---|---|
| func | The name of the function that reported the error. |
| file | The name of the source file, that contains the function, reporting the error. |
| line | The line number, the error was raised at. |
| code | An error code. |
| msg | A description of the error. |


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attoparser is Open Source Software created in his spare time by a Spanish Software Engineer called Daniel Fernández, who's also the author of other java libraries like thymeleaf, jasypt, op4j or javatuples.
It is neither made nor backed by any software (or any other type of) company, and it is offered to the public totally free of charge, both in binary and in source code forms, under the Apache License 2.0.
No, it does not. Commercial support is not offered at the moment, but there is an issue tracking system at your disposal.
Yes, all stable (non-beta) releases can be considered production-ready.
In many ways:
If you want to contribute some code to attoparser, please read first the page on Contributing to attoparser.
Strictly speaking, no, it isn't, because it does not implement the standard SAX API.
But it is a SAX-style parser in the sense that it works in a similar way to standard SAX parsers, this is, by triggering parsing events like element, attribute, text, document start, etc. so that they can be handled by specifically created handler methods. This contrasts with the way DOM parsers work, which parse entire documents and convert them into DOM (Document Object Model) node trees before returning control to the user.
From time to time, attoparser will produce a snapshot release that will allow you to test new or being-developed features. These are non production ready releases, but will be stable enough for most testing purposes, and will enable you to try new features before they are generally available.
In order to use snapshot releases, you need to add the Sonatype OSS Snapshots repository in your Maven pom.xml file, like this:
<repositories> <repository> <id>sonatype-nexus-snapshots</id> <name>Sonatype Nexus Snapshots</name> <url>https://oss.sonatype.org/content/repositories/snapshots</url> <snapshots> <enabled>true</enabled> </snapshots> </repository> </repositories>
Also, you will have to modify your attoparser dependency/ies to use a snapshot version, which usually consists of the version number of the next release (not the current one), followed by -SNAPSHOT. For example, while 2.0.6.RELEASE is the latest published stable release, snapshots will probably appear under the 2.0.7-SNAPSHOT or maybe 2.1.0-SNAPSHOT version numbers:
<dependency> <groupId>org.attoparser</groupId> <artifactId>attoparser</artifactId> <version>2.0.7-SNAPSHOT</version> <scope>compile</scope> </dependency>


Jury to start deciding Oklahoma officer’s fate in killing
by The Associated PressOKLAHOMA CITY (AP) — A jury is expected to begin deliberations Monday in the trial of an Oklahoma City police officer who fatally shot an unarmed man who had doused himself in lighter fluid and was threatening to set himself on fire.
Sgt. Keith Sweeney is charged with second-degree murder in the Nov. 15, 2017, shooting death of 29-year-old Dustin Pigeon. If convicted, he faces 10 years to life in prison.
The 34-year-old Sweeney was among the officers responding to reports of a suicidal person who found Pigeon holding a lighter and threatening to set himself on fire.
Prosecutors say Sweeney shot Pigeon after another officer fired a bean bag. Two fellow officers testified they didn’t believe Pigeon posed a threat.
Sweeney’s attorney Gary James says Sweeney didn’t know whether Pigeon had a weapon.
The Associated Press


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wargame package¶
Submodules¶
wargame.abstractgameunit module¶
wargame.abstractgameunit
This module contains the AbstractGameUnit class implementation.
This module is compatible with Python 3.4.x and beyond. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
-
class
wargame.abstractgameunit.
AbstractGameUnit
(name='')¶ Bases:
object
Abstract class to represent a game character (or a ‘unit’)
Variables: - max_hp – Maximum ‘hit points’ or ‘health points’ for the unit This is set by the subclasses.
- health_meter – Keeps track of the current health of the unit
- name – Name of the character (set by subclasses)
- enemy – Present enemy of this unit, At any time, it can have only one enemy.
- unit_type – Tells is this is a ‘friend’ or an ‘enemy’ from the player’s point ot view.
Parameters: name – Accept the name of this game character
See also
Classes
Knight
andOrcRider
-
attack
(enemy)¶ Attack the enemy unit
This method handles combat between the player (Knight instance) and the given enemy (at the moment OrcRider instance). In the combat, one of the units could get injured or both will escape unhurt. The method reduces the ‘health’ of the injured unit by a randomly selected amount.
Parameters: enemy – The enemy to be attacked (instance of subclass of AbstractGameUnit See also
Knight.acquire_hut()
-
heal
(heal_by=2, full_healing=True)¶ Heal this unit (increase health points)
This method is called when you (the player) enters a friendly hut.
Parameters: - heal_by – health_meter will be updated by this amount if full healing is not requested/
- full_healing – Fully heal this unit by resetting the heal_meter to the maximum limit.
See also
Knight.acquire_hut()
-
info
()¶ Print information about this game unit.
Abstract method. See subclasses for implementation.
-
reset_health_meter
()¶ Rest the health_meter to the maximum limit
-
show_health
(bold=False, end='\n')¶ Print information on the current health reading of this game unit
The arguments to this method are mainly to customize the message display style.
Parameters: - bold – Flag to indicate whether information should be printed in bold style or normal style.
- end – Specify how the message should end i.e whether a new line character should be appended in the end or you just want to add a space or a tab (for message continuation)
wargame.attackoftheorcs module¶
wargame.attackoftheorcs
This module contains the AttackOfTheOrcs class implementation.
This module is compatible with Python 3.4.x. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
-
class
wargame.attackoftheorcs.
AttackOfTheOrcs
¶ Bases:
object
Main class with the high level logic to play Attack of The Orcs game
ivar huts: List object to hold instances of Hut class. ivar player: Represents the player playing this game. This is an instance of class Knight in current implementation. See also
self.play()
where the main action happens.-
get_occupants
()¶ Return a list of occupant types for all huts.
This is mainly used for printing information on current status of the hut (whether unoccupied or acquired etc)
If the occupant is not None the occupant type will be ‘enemy’ or ‘friend’. But if there is no occupant or is already ‘acquired’ the occupant_type will display that information instead. See Hut.get_occupant_type() for more details.
Return a list that collects this information from all the huts. This is a list comprehension example. More on the list comprehension in a later chapter on Performance.
Returns: A list containing occupant types (strings) See also
Hut.get_occupant_type()
-
play
()¶ Workhorse method to play the game.
Controls the high level logic to play the game. This is called from the main program to begin the game execution.
In summary, this method has the high level logic that does the following by calling appropriate functionality:
- Set up instance variables for the game
- Accept the user input for hut number to enter
- Attempt to acquire the hut (
Knight.acquire_hut()
) - Determine if the player wins or loses.
See also
Knight.acquire_hut()
-
show_game_mission
()¶ Print the game mission in the console
-
wargame.gameuniterror module¶
wargame.gameuniterror
This module contains the GameUnitError class implementation. It shows how to create a custom exception class. See the chapter on exception handling for more details. The code in this module is demonstrated in that chapter.
This module is compatible with Python 3.4.x. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
-
exception
wargame.gameuniterror.
GameUnitError
(message='', code=0)¶ Bases:
Exception
Custom exceptions class for the `AbstractGameUnit and its subclasses.
Inherits built-in Exception class.
Variables: - error_message – Print the error message with an error code.
- error_dict – Python dictionary object that stores error number as the keys and the detailed error message as its value.
See also
:py:meth: abstractgameunit.AbstractGameUnit.heal for an example usage.
wargame.gameutils module¶
wargame.gameutils
This module contains some utility function for the game Attack of the Orcs
This module is compatible with Python 3.4.x. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
-
wargame.gameutils.
print_bold
(msg, end='\n')¶ Convenience function to print a message in bold style
Optionally you can also specify how the bold text should end. By default it ends with a new line character.
Parameters: - msg – Message to be converted to bold style
- end – Tell how the printed string should end (newline, space etc)
-
wargame.gameutils.
weighted_random_selection
(obj1, obj2)¶ Randomly return one of the following, obj1 or obj2
Parameters: - obj1 – An instance of class AbstractGameUnit. It can be any object. The calling code should ensure the correct object is passed to this function.
- obj2 – Another instance of class AbstractGameUnit. See the comment for o
Returns: obj1 or obj2
wargame.hut module¶
wargame.hut
This module contains the Hut class implementation.
This module is compatible with Python 3.4.x. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
-
class
wargame.hut.
Hut
(number, occupant)¶ Bases:
object
Class to create hut objects in the game Attack of the Orcs
Parameters: - number (int) – Hut number to be assigned
- occupant (AbstractGameUnit) – The new occupant of the Hut
Variables: - number (int) – A number assigned to this hut
- is_acquired (boolean) – A boolean flag to indicate if the hut is acquired. In the current implementation this is viewed from the players perspective.
- occupant (AbstractGameUnit) – The occupant of this hut. Needs to be an instance of a subclass of AbstractGameUnit
See also
Where it is used –
attackoftheorcs.AttackOfTheOrcs.setup_game_scenario()
-
acquire
(new_occupant)¶ Update the occupant of this hut and set is_acquired flag.
Update the occupant instance variable with the parameter new_occupant and set the is_acquired flag to True.
Parameters: new_occupant – self.occupant will be updated with this parameter
-
get_occupant_type
()¶ Return a string giving info on occupant type.
Used only for the printing information on who is present in the hut the information it will return depends on the occupant and can be one of these strings: ‘enemy’, ‘friend’, ‘ACQUIRED’, ‘unoccupied’
The logic is as follows: If the hut.occupant is one of the game characters, it will simply retrieve this info from that instance. Otherwise determine whether it is acquired or unoccupied.
Returns: A string representing the occupant type
wargame.knight module¶
wargame.knight
This module contains the Knight class implementation.
This module is compatible with Python 3.4.x. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
-
class
wargame.knight.
Knight
(name='Sir Foo')¶ Bases:
abstractgameunit.AbstractGameUnit
Class that represents the game character ‘Knight’
The player instance in the game is a Knight instance. Other Knight instances are also considered as ‘friends’ of the player and is indicated by the attribute self.unit_type .
Parameters: name (str) – Name of this game character (optional)
Variables: - max_hp (int) – Maximum number of hit points (health points)
- health_meter (int) – The actual number of hit points or health points
- unit_type (str) – Stores id this character(unit) a friend or an enemy
- enemy (AbstractGameUnit) – Stores who is the enemy (not implemented)
-
info
()¶ Print basic information about this character.
Overrides AbstractGameUnit.info
-
run_away
()¶ Leave the hut
If the player feels he/she is losing the combat, there is an option to leave the hut. A strategy to rejuvenate and restart the combat for better chance of winning.
..seealso ::
self.acquire_hut()
wargame.orcrider module¶
wargame.orcrider
This module contains the OrcRider class implementation.
This module is compatible with Python 3.4.x. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |
Module contents¶
wargame.__init__.py
This is the __init__.py file for the wargame package.
This module is compatible with Python 3.4.x and beyond. It contains supporting code for the book, Learning Python Application Development, Packt Publishing.
| copyright: | 2015, Ninad Sathaye |
|---|---|
| license: | The MIT License (MIT) . See LICENSE file for further details. |


C3G Learning Resources
Home
Workshops
RNASeq Jan2019
RNASeq workshop Jan 2019
Material
C3G Analysis Workshop Module: RNA-Seq
Date:
22-23 January, 2019 at 9:00 AM
Location:
McGill McIntyre Building (3655 Promenade Sir-William-Osler, Montreal, QC H3G 0B1); Room 210/11
Instructors from C3G:
Rola Dali, Hector Galvez, Eloi Mercier, Pierre-Olivier Quirion
Preparation
Compute Canada Account
Software Installation
CyberDuck Installation
Presentations
Lect1: Intro
Lect2: Intro to Unix
Lect3: Intro to RNASeq
Lect4: GenPipes Files
Lect5: Recap
Lect6: Differential Expression
Lect7: The End!
Practicals
Practical1: Intro to Unix
Practical2: GenPipes Set Up
Practical3: GenPipes Files
Practical4: Mapping
Practical5: Differential Expression
Practical6: Visualization
Practical7: GenPipes Progress
Extras
mugqicValidator
nanuq_example
Resources Page
Workshop Schedule
Unix Cheat Sheet
GenPipes RNASeq Workflow
csvToreadset.R
workshop.ini
Homo_sapiens.GRCh38.genes.txt
Full Package
RNASeq Test Dataset
C3GAW_All_Material
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--- layout: post esbma_id: 1556 title: Vault 2.0: Major Price Reduction date: 2004-02-13 15:45:15 keywords: cornsharp sourcegear teaser: We are hoping this new pricing makes Vault compelling for a lot more people than before. ---
I am very pleased to announce that SourceGear has just released version 2.0 of Vault, our source control tool for Windows developers. This new release is a major step forward in features and performance (and it is a free upgrade for all Vault users). If you have been hearing great things about Vault from the early adopters, but have been waiting for it to grow out of its 1.x immaturity, we invite you to give our product a fresh look.
And for those of you who have found Vault's price to be an obstacle, we have good news for you too.
One of the most difficult decisions in a small ISV is product pricing. When we shipped Vault 1.0 (almost exactly one year ago), we spent a lot of time trying to decide how much to charge for it. In the end, we settled on a base price of $399 per user, with no standard volume discounts available. We were very tempted to price it lower, but we didn't want our product to be priced dramatically cheaper than the offerings from other source control vendors. All the other serious source control tools are several hundreds dollars and up. We think our product is just as good as theirs. Why should we not price it accordingly? Many small ISVs make the mistake of setting their initial pricing too low, and I know better.
However, the popularity of Vault has overwhelmed us. Sales have exceeded our projections. The basic message of our product has resonated with lots of people who are grateful to have found a compelling replacement for Visual SourceSafe.
As the months have passed, we have been constantly getting feedback from people who say Vault is too expensive. Granted, a certain amount of this kind of feedback is very healthy for a small ISV. There will always been someone who thinks any price is too high. If absolutely nobody is complaining, then it is very likely your price is too low.
Still, the feedback from these people was unsettling. We had started to feel almost like an evil drug company with an important new medicine which is priced so high that only the rich can afford it.
Seriously, we want to attract a much larger user base for Vault, so we have decided to reduce our prices quite significantly. The new prices vary by quantity. The gory details are available in our online store, but in a nutshell, the good news is this:
With volume discounts, Vault user licenses
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We are hoping this new pricing makes Vault compelling for a lot more people than before.


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Honda
Toyota
Acura
g.2.d repeat-index precedence over repeat-item (non-normative)
You must see a list of cars. Also, the repeat item
with the current index must have a dashed box around it with a
yellow background. When you change the index with the triggers at the
bottom the selected repeat-item must have the dashed box and yellow
background. All other repeat-items must have a dotted border with an
orange background.
Set Index to 1
Set Index to 2
Set Index to 3
Index :


Your partner's message to you is
Instructions
-
If both of you choose OPTION A, then both of you will earn $1.00.
-
If you choose OPTION A and your pair chooses OPTION B, then you
will earn $0.20 and your pair will earn $0.90.
-
If you choose OPTION B and your pair chooses OPTION A, then you
will earn $0.90 and your pair will earn $0.20.
-
If both of you choose OPTION 2, then both of you will earn $0.50.
Make your choice


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| Values |
| Cancelled |
The interactive selection of a property value was cancelled.
|
| Success |
The interactive selection of a property value succeeded, and the
new property value chosen by the user has already been set at the
inspected component.
|
| ObtainedValue |
The interactive selection of a property value succeeded, a new
property value has been obtained, but not yet set at the inspected
component.
|
| Pending |
The interactive selection of a property value is still pending.
|
| Values' Details |
Cancelled
Cancelled,
- Description
- The interactive selection of a property value was cancelled.
|
|
Success
Success,
- Description
- The interactive selection of a property value succeeded, and the
new property value chosen by the user has already been set at the
inspected component.
|
|
ObtainedValue
ObtainedValue,
- Description
- The interactive selection of a property value succeeded, a new
property value has been obtained, but not yet set at the inspected
component.
In this case, the obtained value is passed to the caller of
which is
responsible for forwarding this value to the inspected component.
|
|
Pending
Pending,
- Description
- The interactive selection of a property value is still pending.
This is usually used when this selection involves non-modal user interface.
|
|
Top of Page
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Download company profile :
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{{ template "header.html" .}}
| Name | Count |
|---|---|
| Km sum | {{ printf "%.2f" .payload.Km}} |
| Bike km | {{ printf "%.2f" .payload.Kmbike}} |
| Walk km | {{ printf "%.2f" .payload.Kmwalk}} |
| AVG KM on Trip | {{ printf "%.2f" .payload.AvgKm}} |
| Villages count | {{.payload.VillagesCount}} |
| Trip count | {{.payload.TripCount}} |
| Max in one trip | {{.payload.Maxkm}} |
| Max peak | {{.payload.PoiStats.MaxPeak}} |
| Ruin count | {{.payload.PoiStats.RuinCount}} |
| Railway station count | {{.payload.PoiStats.StationCount}} |
| Peak count | {{.payload.PoiStats.PeakCount}} |
| Attraction count | {{.payload.PoiStats.AttractionCount}} |
| Worship count | {{.payload.PoiStats.WorshipCount}} |
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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/the.20type.20of.20a.20pi.20type.2E.html ---
After a few months break I am revisiting lean basics. I'm looking back at dependent types.
I understand why this typechecks: ((λ k: nat, eq.refl k) : (Π k : nat, k = k))
essentially we have a function, which returns a type, which depends on the parameter the function takes. We need this because we want to have a family of proofs 0 = 0, 1 = 1, etc.
But I don't get why these typecheck: ((Π k : nat, k = k) : Prop)
or ((Π k: nat, Prop) : Type)
. The rule seems to be that the type signature of any pi type "forgets" the parameters that the type on the left depends on and behaves as if it was a simple type.
Are there any particular reasons of why that is or is it just the way it is?
Yes. Let's look at the non-dependent case first. Let's first notice that everything has a type:
(λ n : ℕ, n+1) : ℕ → ℕ ℕ → ℕ : Type 0 Type 0 : Type 1 -- and so on
@Adam Kurkiewicz if A has type u and B has type v, then A -> B
has type max u v
And ℕ → ℕ
is basically syntactic sugar for Π n : ℕ, ℕ
The rule seems to be that the type signature of any pi type "forgets" the parameters that the type on the left depends on and behaves as if it was a simple type.
That's correct.
Are there any particular reasons of why that is or is it just the way it is?
For one thing it is useful. But let's look at an alternative:
(Π k : nat, k = k) : nat -> Prop
That means that that proposition is not true on its own. It would be true about a given number. But that's not the case. Π
is the same as ∀
. The statement is not "if you give me a number, I'll state that it is equal to itself". It is "every number is equal to itself". If you want the first, you'll use (λ k : nat, k = k) : nat -> Prop
. It is not an assertion on its own. You feed it a natural number and then the result will state something about that natural number.
I see. So it doesn't forget the type of the parameter, it just takes the maximum of the type of the parameter and the type of the type. So Prop
is effectively Type -1
, right? And since nat : Type 0
, then k : Type -1
and k = k : Type -1
is Type -1
, so the total result is Type max -1 -1
, which is Type -1
, which is Prop
. This might be a little bit barbarian, but I think I'm getting it.
A less barbarian way of seeing is that Prop
and Type
are both syntactic sugar for Sort
: Prop = Sort 0
and Type u = Sort (u+1)
I am surprised you would care so much about universes (given that you are a beginner I assume). This information is not very useful in my opinion :).
@Simon Hudon I actually do not think that my original statement was correct. The type of the parameter is not forgotten, it's the maximum like @Kenny Lau said, for example this typechecks #check ((Π k: Type 0, Prop) : Type 1)
Really? For small proofs maybe but as soon as you have types within types, you have to start caring about universes
Not quite. You can rely on Type*
to do the work for you.
@Simon Hudon I actually do not think that my original statement was correct. The type of the parameter is not forgotten, it's the maximum like @Kenny Lau said, for example this typechecks
#check ((Π k: Type 0, Prop) : Type 1)
That's true, in that sense, it does not forget. More specifically, it does not forget the universe of the parameter. The type itself becomes unavailable though
I've largely ignored universes so far, and most of the underlying type theory, but given that I might be teaching lean to others from next semester I'm trying to make sure I know at least a little bit of the foundations.
Interesting detail:
#check ((Π k: Type 7, k = k) : Prop)
If the result type is Prop, the whole thing can stay in Prop
I think you've done well to ignore them for the most part :P!
I think I agree. I don't particularly like to have to specify them. I wonder what justify that design choice
I use Type*
and only think about it once I run into issues. From my experience, I find it not too difficult to fix them post-facto. I have yet to encounter a case where I wish I had considered it beforehand. But perhaps I avoid the kinds of things that might require more universe-thought. I mostly write certificates of functional programs.
@Simon Hudon hmm... So it turns out it's not max u v
after all? It can't be, since in this case it would have to be Type 7
as opposed to Prop
?
It's actually imax u v
which is 0 if v
is 0, and otherwise max u v
.
<deleted>
Useful to know mainly because you might see imax
in Lean output/error messages and wonder what it means, I think.
Thanks @Reid Barton, this will definitely come useful at some point.
@Simon Hudon I really like your example with natural numbers. I think it's another productive way of thinking about Pi types.
Do you mean that nat -> Prop
example?
Last updated: Apr 24 2020 at 19:02 UTC


[CHAPTER 293
BICYCLES]
Section
293-1 Defacing serial numbers, etc., of bicycles
293-2 Bicycle dealers, records required
Cross References
Registration tax, see chapter 249.
Traffic regulations, see §§291C-141 to 150.


The Liquidizer is a highly interactive online platform for
political discussion and ongoing opinion polling. Its focus lies
in the simplicity of the voting system and the rich visual
feedback displaying current interests, trends and
interdependencies. The system provides swift and precise feedback
to the user's expression of preferences. Optimizing their impact
on the polling result users must allocate a limited total voting
weight. This leads to a game like experience where the formation
of allys and coordinated voting tactics can reveal the innate
structure of rivalling political fractions along side each
participant's political profile.
Main menu
A summary of all queries in the system. From this list, you can
navigate to each queries detail page showing voters' behaviour and
graphical analysis options.
A summary of all users registered in the system. From this list,
you can navigate to each user's profile page.
This help page.
The home button is only shown when you are logged in. From here,
you can change your profile, see your voting behaviour and
delegations.
Voting
The voting system of the Liquidizer is based on limited overall,
such that participation on each query must be well balanced. The
maximal weight which can be used to support (or reject) a
proposition is 1. If a user supports multiple propositions, the
voting weight on each proposition is reduced. To optimize voting
influence, you need to log in at regular intervals and pay close
attention to what propositions you support. The total voting
weight also decreases over time when you do not log into the
system, such that the influence slackers and dead accounts is
diminished.
The user can express her preferences for individual queries on
scale between -3 and 3. By clicking on a positive number you
declare your support for a proposition, by clicking a negative
value you reject it. The preferences are not directly visible to
other users, but they define the proportions between your limited
total voting weights.
Your can also assign a positive preferences to other users, in
which case you delegate a part of your voting weight. Based on
delegation strength between 0 and 3 this user's votes will be
considered in the calculation of your final voting weights.
The most influential strategy to gain support for your political
initiative still consists in placing a good argument. All places
that show this edit icon can be commented by the user who is
logged in. Except mail addresses, postings are visible to all.
The texts you type can include links. To create a list, type
an asterisk (*) at the beginning of each new row. The html tags
<img>, <em>, and <i> are allowed.
Popularity
If you are using the Liquidizer you are doing so because you want to
find a good solution not only for you, but also for your group. The
popularity factor that is displayed next to the users' profiles tells
you how well you are doing. If all participants agree perfectly then
everyone has a popularity of 100%. In case the group fights fiercefully
over each and every question then all members will have a popularity
close to 0%.
Obviously, you should try to increase your popularity factor. In doing
so you will actively participate in finding a good group compromise. As
a result you will see more happy faces not only in the system but also
in real life.
Emoticons
When you are logged in, your relationships with other users is
visualized with the help of emoticons. The generation is based on
the components Sympathy, Excitement, Strength and Distance.
Valence
Arousal
Potency
Distance
The Valence component depends on the extent your positions
coincide with the positions of the selected user.
The Arousal component shows whether the other components have changed recently.
The Potency component shows how many topics you and the selected
user have in common (i.e. you both voted on them), independent of
the positions on that topics.
The Distance component shows how you and the selected user
influence each other through direct or indirect delegation.
You can test the emoticons at:
Voting system and tactics
Voting weight
Preference
Query
Sum
The voting system of Liquidizer is optimized from a
game-theoretic point of view. As each user's voting weight is
limited, they have to optimize their influence to achieve a
favorbale result. There is no time limit on polls. Thus, the final
goal is not to "win", but to make ideas, proposals and topics more
visible.
The voting weight of a user on a single topic lies between -1 and
1. The sum of the weights' squares across all topics is equal to
1. Thus, if you vote on a high number of topics, your vote's
weight on each one will be low (e.g., you can participate in 100
topics with a weight of 0.1 each, as 0.1² = 0.01). If a topic is
important or controversial and you want to influence it more
strongly, you may choose to vote on that topic only (then the
weight of your vote will become 1).
In the following example, users A and B participate in the polls
on three topics. A is indifferent about topic 1, is strongly in
favor of topic 2 and rejects topic 3. B favors all three topics,
with A being most important.
When A votes according to his preferences, the voting weights for
the respective topics will be 0, 0.95 and -0.32, as
B's voting weights will be 0.90, 0.30 und 0.30:
The voting weight's ratios are equal to the preference ratios, but
the weights are limited by the constraint on their absolute value:
In this example, both A and B "waste" a part of their limited
voting weight. Their votes on topic 3 "cancel out", so both can
increase their influence by shifting more weight onto topics they
agree upon.
If both A and B decide to change their voring behaviour to
(1,1,0), the summed result would be preferred by both:
Thus, it is worthwhile to look for allies with similar
positions. You may, for example, delegate your vote to another
user and look whether your resulting voting behaviour is still
acceptable.
Analysis tools
Charts
Histogram
Delegation graph
The following analysis tools are available on a topic's detail
page. They provide in depth visual feedback about the voters'
behaviour. This might be usefull to understand trends or to
discover frauds.
Charts show the history of votes on a topic. The line at the top
shows the history of positive votes and the line on the bottom
shows the negative votes (weighted). The sum in the middle is
shown yellow (if it's positive) or red (if negative). As voting
weights decrease with time, the trend lines converge towards 0 if
users do not refresh their votes.
The histogram shows how many users are participating in a poll and
what intensities (preferences) they have. All votes have a weight
between -1 and +1, but only few users would spend all their weight
on a single topic. Thus, lower weights in the center of the graph
are more common.
The delegation graph shows the influence of delegations on your
votes. You may choose multiple users to whom you delegate, in
which case your resulting voting weights will be cast as a
average of your delegates. Even if you have delegated
your vote, you need to log in to check your resulting voting
behaviour regularly, otherwise your voting weight will decrease
with time.
Links


You WON'T believe this but...
The earth swallowed my thesis and now I have to find it
Generate excuse


Angelo
What is Angelo?
Angelo is a library to assist developers in using procedural generation in their apps. It’s written purely in Swift and currently supports the following methods:
- Weighted Lists
- L-Systems
Docs
Angelo Docs are currently in Notion: https://www.notion.so/Angelo-Docs-c14404883fcb4fa88dac8a0cac348794
Installation
Angelo is available through CocoaPods. To install it, simply add the following line to your Podfile:
pod 'Angelo'
Angelo is also available using Carthage and the Swift Package Manager
Example
To run the example project, clone the repo, and run pod install
from the Example directory first.
Author
Angelo is made by me, Enzo Maruffa. Feel free to contribute or reach out to me!
License
Angelo is available under the MIT license. See the LICENSE file for more info.


[1]
breakfast was fresh and the staff routine replaced items , even though they were not empty .
[2]
quiet but the breakfast area became swamped and was difficult to eat due to lines .


--- description: 'Individual product page.' action: 'shop:product' template: main protocol: all published: true name: 'Product' url: '/product/:urlName' ---
| {{ attr.name }}: |
- [[ customerReviews[currentReview].from_name ]]
Please check again later, or write your own!
{{ product.description | raw }}
{{ theme.storeReturnPolicy | raw }}


---
layout: post
title: 'BSG: Episode "You Can''t Go Home Again"'
categories: []
tags: []
status: publish
type: post
published: true
meta:
_original_post_id: ''
_original_import_author: John Duprey
author:
---
The BSG writers are skating on thin ice. In this week's episode "
You Can't Go Home Again," Starbuck is stranded on a moon after a serious solo dog fight with 8+? cylon fighters. On the moon, she manages to find a downed cylon craft and get it working. The ship is based on organic technology. She cuts her way into it. The ship's cockpit has a big hole in it from the dog fight and she plugs it with part of her space suit. She figures out how to fly it.. somehow.. and takes it into space. I found it hard to suspend disbelief that this ship could pressurize and protect her body from the vacuum of space using a just a space suit "plug." Perhaps it is my child-like understanding of space.?
The other tough pill to swallow is to know that Adama would put the last vestige of humanity at risk to save one fighter pilot - as hot as she might be. For the show to succeed, it has to be about the drama and not the tech., but it must still follow some sort of rule book for what is and is not allowed. Otherwise, the drama becomes meaningless. If in the next episode I see Starbuck get out of a sticky situation by clicking her heels three times and saying "There's no place like Caprica! There's no place like Carpica! ..." I'm turning the TV off!


Complex Dropout¶
-
class
ComplexDropout
¶ Applies Dropout to the input. It works also with complex inputs! The Dropout layer randomly sets input units to 0 with a frequency of
rate
at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by1/(1 - rate)
such that the sum over all inputs is unchanged. Note that the Dropout layer only applies whentraining
is set to True such that no values are dropped during inference. When usingmodel.fit
,training
will be appropriately set to True automatically, and in other contexts, you can set thekwarg
explicitly toTrue
when calling the layer. This is in contrast to settingtrainable=False
for a Dropout layer.trainable
does not affect the layer’s behavior, as Dropout does not have any variables/weights that can be frozen during training.Dropout [CIT2014-SRIVASTAVA] consists in randomly setting a fraction
rate
of input units to 0 at each update during training time, which helps prevent overfitting.
-
__init__
(self, rate, noise_shape=None, seed=None)¶ - Parameters
rate – Float between 0 and 1. Fraction of the input units to drop.
noise_shape – 1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape
(batch_size, timesteps, features)
and you want the dropout mask to be the same for all timesteps, you can usenoise_shape=(batch_size, 1, features)
.seed – A Python integer to use as random seed.
-
call
(self, inputs, training=None)¶ - Parameters
inputs – Input tensor (of any rank).
training – Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).
Code example
Let’s first create some data:
import tensorflow as tf
import numpy as np
import cvnn.layers as complex_layers
tf.random.set_seed(0)
layer = complex_layers.ComplexDropout(.2, input_shape=(2,))
data = np.arange(10).reshape(5, 2).astype(np.float32)
data = tf.complex(data, data)
print(data)
Data will therefore be:
<tf.Tensor: shape=(5, 2), dtype=complex64, numpy=
array([[0.+0.j, 1.+1.j],
[2.+2.j, 3.+3.j],
[4.+4.j, 5.+5.j],
[6.+6.j, 7.+7.j],
[8.+8.j, 9.+9.j]], dtype=complex64)>
Now when we apply the dropout layer:
outputs = layer(data, training=True)
print(output)
It outputs:
<tf.Tensor: shape=(5, 2), dtype=complex64, numpy=
array([[ 0. +0.j , 1.25 +1.25j],
[ 2.5 +2.5j , 3.75 +3.75j],
[ 5. +5.j , 6.25 +6.25j],
[ 7.5 +7.5j , 8.75 +8.75j],
[10. +10.j , 0. +0.j ]], dtype=complex64)>
However, if you use training=False
, you will get the data unchanged:
assert np.all(data == layer(data, training=False))
- CIT2014-SRIVASTAVA
Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958, Jan. 2014


--- layout: post title: Shooting storyboards date: '2013-08-10T12:18:35+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/57892031000/shooting-Storyboards ---
Shooting Storyboards.


Upload a single or multiple files Only complete (and draft) assembled genomes in valid .fasta files are accepted.
Identity percentage cutoff:
Length percentage cutoff:
Serotype only Full information
I want the results in a table (default is JSON format)


{{ partial "header" . }} Get in touch We're here for you, and we're wearing our thinking caps. Message Received Thank you for getting in touch {{ partial "footer" . }}
We're here for you, and we're wearing our thinking caps.
Thank you for getting in touch


Quickly calculate weight for power lifting training based on the Westside Barbell Conjugate Method.
This calculator is especially useful when coaching a group of athletes that are all working on the same lift (or at least the same type of effort).
Enter each athlete's 1 Rep Max and select the effort of the day.
Custom percentages can also be entered.


Start Saving Today!
Cash Cache is a smart piggy bank with features and tools to help you track and grow your savings.
Set goals for yourself and share your achievements with your friends and family members on social media.
Customize your profile with inspirational imagery and notes to stay motivated and keep your eye on the prize.
Gain access to helpful tips and information to assist you along your savings journey.


Overview
VetterAppointments is a desktop application made for vets to manage appointments, clients, pets and staff. The application has a graphical user interface (GUI) created with JavaFX and works via a command line interface (CLI) where users can enter single line commands to interact with the application.
This project is part of a NUS CS2103T module. VetterAppointments was created by building upon an existing code base of the AddressBook-Level 4 project by SE-EDU initiative. The application is written in Java and has about 20kLOC.
This portfolio showcases of some notable contributions that I have made for the CS2103T project. It can be used for the evaluation of my coursework and learning progress from the CS2103T module.
Summary of contributions
-
Major enhancement: added the autocomplete functionality
-
What it does: provides the user with suggestions for completing their commands and allows the user to autocomplete their command by pressing tab.
-
Justification: This feature improves the product significantly because some commands of the application are long. A user can quickly enter the command they wish to type by using this autocomplete feature.
-
Highlights: This enhancement is an additional component to the existing code base, little modification of the existing code base was required. However, the modification of the UI logic was required to handle the tab key press.
-
-
Minor enhancement: added the support for adding and removing clients and vet technicians in the application
-
List of new/modified command enhancements:
-
AddCommand
-
DeleteCommand
-
EditCommand
-
AddVetTechToAppointentCommand
-
RemoveVetTechFromAppointmentCommand
-
-
-
Code contributed: [Functional code] [Reused Functional code] [Test code] [Reused Test code] [Unused code]
-
Other contributions:
-
Project management:
-
Managed Github issue tracker
-
-
Documentation:
-
Community:
-
Tools:
-
Integrated Travis CI, Appveyor, Coveralls to the team repo
-
-
Contributions to the User Guide
Given below are sections I contributed to the User Guide. They showcase my ability to write documentation targeting end-users. |
Removing a client or vet technician: delete
Since v1.1
Command format: delete INDEX
|
The index must be a positive integer. |
Description: Deletes a person in the program according to the index provided.
 Likewise it deletes
the vet technician when viewing the Vet Tech list. |
Here is an example of using the command delete
:
-
delete 1
Figure 5 below shows the result of the command box notification of the program when delete 1
is executed.
delete
command on index 1.Figure 6 below shows the status of client list before executing the delete 1
command.
delete 1
command.Figure 7 below shows the result of the client list being updated upon executing the delete 1
command.
Keyboard Shortcuts
This section shows you the different keyboard shortcuts that are available in VetterAppointments.
The table below shows a list of shortcuts you can use.
Result |
Shortcut |
Clears the Command Box |
Esc |
Auto-complete your text input |
Tab |
Switches panel selection forward |
Control+Tab |
Switches panel selection backward |
Shift+Tab |
|
Pressing the Tab key twice will provide you with suggestions of your current command input.
|
Contributions to the Developer Guide
Given below are sections I contributed to the Developer Guide. They showcase my ability to write technical documentation and the technical depth of my contributions to the project. |
Autocomplete feature
Reasons for implementation
The autocomplete feature will allow vets to type their commands quicker as some of the commands require several inputs from the vet. With the autocomplete feature, vets will be able to execute commands faster to save time.
Current implementation
The autocomplete mechanism is facilitated by AutoCompleteManager
, which resides inside LogicManager
. It supports the auto completion of incomplete commands by providing a list of auto completed command from a given incomplete command.
An underlying Trie
data structure is used to facilitate the AutoCompleteManager
functionality.
The Trie
only supports the auto completion of commands provided by AutoCompleteManager
. The CommandParameterSyntaxHandler
which resides in AutocompleteManager
supports the auto completion of parameters for commands.
Figure 4-18 shows the class diagram of the autocomplete implementation.
The CommandBox
will interact with the AutoCompleteManager from using the LogicManager
.
When the user presses tab in the command box area, the CommandBox
will handle the tab key press and executes the autoCompleteUserInput()
method.
public class CommandBox extends UiPart<Region> {
private void handleKeyPress(KeyEvent keyEvent) {
switch (keyEvent.getCode()) {
// ... other cases ...
case TAB:
keyEvent.consume();
autoCompleteUserInput();
break;
default:
// let JavaFx handle the keypress
}
}
/* Shows auto completed text or suggestions on the UI */
private void autoCompleteUserInput() {
// ... auto complete logic ...
if (isTabDoubleTap()) {
// ... show suggestion on UI logic ...
}
}
}
The isTabDoubleTap()
method will check if the user presses tab twice in quick succession. If true, the CommandBox
will show a list of command suggestions for the current user input.
Figure 4-19 shows the sequence diagram of how the autocomplete operation works.
Design considerations
Aspect: Implementation of autocomplete capability
-
Alternative 1 (current choice): Add a manager class to handle all autocomplete behaviour
-
Pros: The class will allow for more code abstraction and can also be implemented elsewhere.
-
Cons: New developers will need to thoroughly understand the class interactions.
-
-
Alternative 2: Add the autocomplete logic simply in the
Commandbox
-
Pros: Other classes are not involved, making it easier for new developers to understand.
-
Cons: It breaks the Single Responsibility Principle.
-
Aspect: Implementation of algorithm
-
Alternative 1 (current choice): Use a Trie data structure
-
Pros: Software performance will be better.
-
Cons: It is difficult to implement.
-
-
Alternative 2: Iterate through all possible commands to find match prefix
-
Pros: It is easier to implement.
-
Cons: It may result in performance loss if there are too many commands.
-
Client, vet technician, pet, appointment entity
Reasons for implementation
VetterAppointments requires to manage clients, vet technicians, pets and appointments in the application. An implementation of these objects is required for the application to be able to process the data.
Current implementation
The entities of VetterAppointments are contained within the AddressBook
class. AddressBook
maintains the collections of Person
, Appointment
, ClientOwnPet
and Pet
.
Figure 4-17 below shows the class diagram of how the entities are connected in VetterAppointments.
As seen from figure 4-17 VetTechnician` and Client
are sub classes of the Person
class. ClientOwnPet
is an association class that associates the ownership of the Pet
to the Client
. Appointment
will contain the VetTechnician
and ClientOwnPet
that will have the associated Client
and Pet
.
Design considerations
Aspect: Implementation of entity data model
-
Alternative 1 (current choice): Only couple entities if necessary
-
Pros: Low coupling. Classes will have lesser dependency between entities.
-
Cons: New developers will need to thoroughly understand the class interactions.
-
-
Alternative 2: Couple all entities
-
Pros: Classes can easily access highly coupled classes.
-
Cons: High coupling. Integration and testing will be harder.
-


| rappdirs-package {rappdirs} | R Documentation |
An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' (<https://github.com/ActiveState/appdirs>) to R.
Maintainer: Hadley Wickham<EMAIL_ADDRESS>[translator, copyright holder]
Authors:
Sridhar Ratnakumar
Trent Mick
Trevor Davis [translator]
Other contributors:
RStudio [copyright holder]
ActiveState (R/appdir.r, R/cache.r, R/data.r, R/log.r translated from appdirs) [copyright holder]
Eddy Petrisor [contributor]
Gabor Csardi [contributor]
Gregory Jefferis [contributor]
Useful links:
Report bugs at https://github.com/r-lib/rappdirs/issues


After having spent over five years using, tweaking and refining a personal editor theme I decided it was time to release "Tomorrow Theme" - a bright theme with pastel colours and sensible syntax highlighting.
A theme should not get in your way but should aid your programming with easily identifiable colours that add meaning and enhance legibility. This was the main focus when developing Tomorrow.
Check out base16 the next evolution of Tomorrow Theme!
Tomorrow theme variations in shown in TextMate with Ruby code and the font "Menlo".
#1d1f21
Background
#282a2e
Current Line
#373b41
Selection
#c5c8c6
Foreground
#969896
Comment
#cc6666
Red
#de935f
Orange
#f0c674
Yellow
#b5bd68
Green
#8abeb7
Aqua
#81a2be
Blue
#b294bb
Purple
#ffffff
Background
#efefef
Current Line
#d6d6d6
Selection
#4d4d4c
Foreground
#8e908c
Comment
#c82829
Red
#f5871f
Orange
#eab700
Yellow
#718c00
Green
#3e999f
Aqua
#4271ae
Blue
#8959a8
Purple
#2d2d2d
Background
#393939
Current Line
#515151
Selection
#cccccc
Foreground
#999999
Comment
#f2777a
Red
#f99157
Orange
#ffcc66
Yellow
#99cc99
Green
#66cccc
Aqua
#6699cc
Blue
#cc99cc
Purple
#002451
Background
#00346e
Current Line
#003f8e
Selection
#ffffff
Foreground
#7285b7
Comment
#ff9da4
Red
#ffc58f
Orange
#ffeead
Yellow
#d1f1a9
Green
#99ffff
Aqua
#bbdaff
Blue
#ebbbff
Purple
#000000
Background
#2a2a2a
Current Line
#424242
Selection
#eaeaea
Foreground
#969896
Comment
#d54e53
Red
#e78c45
Orange
#e7c547
Yellow
#b9ca4a
Green
#70c0b1
Aqua
#7aa6da
Blue
#c397d8
Purple
The theme has been designed to be language independent and should look great with any language. However, if you find this isn't the case, just send me some sample code and I'll try to fix it!
I may be releasing other variations of this theme and will certainly be adding support for other more text editors/IDEs.
Released under the MIT License


This is simply a place to outline my work and write about things
© 2020. All rights reserved.
Sorry, we've misplaced that URL or it's pointing to something that doesn't exist. Head back home to try finding it again.


Sprta4yn67xd6m
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Does Lastpass Work With Firefox How To Install The Add On
Lastpass 4 68 0 ダウンロードと使い方 ソフタロウ
Passwords Springerlink
Lastpass Enterprise User Manual Manualzz
Enable Two Factor Authentication In Lastpass Ask Leo


npm exec -- <pkg>[@<version>] [args...]
npm exec --package=<pkg>[@<version>] -- <cmd> [args...]
npm exec -c '<cmd> [args...]'
npm exec --package=foo -c '<cmd> [args...]'
npx <pkg>[@<specifier>] [args...]
npx -p <pkg>[@<specifier>] <cmd> [args...]
npx -c '<cmd> [args...]'
npx -p <pkg>[@<specifier>] -c '<cmd> [args...]'
alias: npm x, npx
--package=<pkg> (may be specified multiple times)
-p is a shorthand for --package only when using npx executable
-c <cmd> --call=<cmd> (may not be mixed with positional arguments)
This command allows you to run an arbitrary command from an npm package (either one installed locally, or fetched
remotely), in a similar context as running it via npm run
.
Whatever packages are specified by the --package
option will be provided in the PATH
of
the executed command, along with any locally installed package executables. The --package
option
may be specified multiple times, to execute the supplied command in an environment where all specified packages
are available.
If any requested packages are not present in the local project dependencies, then they are installed to a folder
in the npm cache, which is added to the PATH
environment variable in the executed process. A prompt
is printed (which can be suppressed by providing either --yes
or --no
).
Package names provided without a specifier will be matched with whatever version exists in the local project. Package names with a specifier will only be considered a match if they have the exact same name and version as the local dependency.
If no -c
or --call
option is provided, then the positional arguments are used to
generate the command string. If no --package
options are provided, then npm will attempt to
determine the executable name from the package specifier provided as the first positional argument according to
the following heuristic:
bin
field in package.json
, or if all
entries are aliases of the same command, then that command will be used.bin
entries, and one of them matches the unscoped portion of the
name
field, then that command will be used.
name
of the package), then npm exec
exits with an error.To run a binary other than the named binary, specify one or more --package
options, which
will prevent npm from inferring the package from the first command argument.
npx
vs npm exec
When run via the npx
binary, all flags and options must be set prior to any positional
arguments. When run via npm exec
, a double-hyphen --
flag can be used to suppress
npm’s parsing of switches and options that should be sent to the executed command.
For exae:
$ npx foo@latest bar --package=@npmcli/foo
In this case, npm will resolve the foo
package name, and run the following command:
$ foo bar --package=@npmcli/foo
Since the --package
option comes after the positional arguments, it is treated as an
argument to the executed command.
In contrast, due to npm’s argument parsing logic, running this command is different:
$ npm exec foo@latest bar --package=@npmcli/foo
In this case, npm will parse the --package
option first, resolving the @npmcli/foo
package. Then, it will execute the following command in that context:
$ foo@latest bar
The double-hyphen character is recommended to explicitly tell npm to stop parsing command line options and
switches. The following command would thus be equivalent to the npx
command above:
$ npm exec -- foo@latest bar --package=@npmcli/foo
Run the version of tap
in the local dependencies, with the provided arguments:
$ npm exec -- tap --bail test/foo.js
$ npx tap --bail test/foo.js
Run a command other than the command whose name matches the package name by specifying a
--package
option:
$ npm exec --package=foo -- bar --bar-argument
# ~ or ~
$ npx --package=foo bar --bar-argument
Run an arbitrary shell script, in the context of the current project:
$ npm x -c 'eslint && say "hooray, lint passed"'
$ npx -c 'eslint && say "hooray, lint passed"'
The npx
binary was rewritten in npm v7.0.0, and the standalone npx
package deprecated
at that time. npx
uses the npm exec
command instead of a separate argument parser and
install process, with some affordances to maintain backwards compatibility with the arguments it accepted in
previous versions.
This resulted in some shifts in its functionality:
npm
config value may be provided.npx
prompts
before installing anything. Suppress this prompt with the -y
or --yes
option.--no-install
option is deprecated, and will be converted to --no
.-p
argument is a shorthand for --parseable
in npm, but shorthand for
--package
in npx. This is maintained, but only for the npx
executable.
--ignore-existing
option is removed. Locally installed bins are always present in the
executed process PATH
.--npm
option is removed. npx
will always use the npm
it ships
with.--node-arg
and -n
options are removed.--always-spawn
option is redundant, and thus removed.--shell
option is replaced with --script-shell
, but maintained in the
npx
executable for backwards compatibility.


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Second International Workshop on Energy Efficient Scalable Data Mining and Machine Learning
Co-located with ECML PKDD 2019
September 16, 2019 - Würzburg, Germany
Room 1.002 (Hubland campus of the University of Würzburg)
KEYNOTE SPEAKERS
TU Dortmund University
Arm ML Research Lab and Harvard University
Ekkono Solutions
This workshop aims to bring together people from different areas and backgrounds in data mining and machine learning that have a common interest in energy efficiency, scalability, and edge computing.
For the past years, the main concern in machine learning had been to create highly accurate models, without considering the high computational requirements involved. This has lead to a scenario where most of the machine learning prediction is done in the cloud, incurring in security concerns and increased latency.
However, there is an increasing trend in machine learning which focuses on building models that are able to run in the edge. For instance, Google has released the first speech recognition model running directly on the device, improving latency and reducing energy consumed by the network connectivity. Another example is TensorLite, a powerful tool to deploy models on mobile and IoT devices.
The goal with this workshop is to promote green machine learning even further, by creating a half-day workshop where researchers in different machine learning and data mining areas can bring together their ideas, present them in front of a heterogeneous crowd, and have interesting debates on how to advance machine learning into a more scalable future.
We accept original work, already completed, or in progress. Position papers and extended abstracts are also considered.
Accepted papers (except for extended abstracts) will be published in ECML-PKDD 2019 Workshop proceedings.
For the past years, the main concern in machine learning had been to create highly accurate models, without considering the high computational requirements involved. This has lead to a scenario where most of the machine learning prediction is done in the cloud, incurring in security concerns and increased latency.
However, there is an increasing trend in machine learning which focuses on building models that are able to run in the edge. For instance, Google has released the first speech recognition model running directly on the device, improving latency and reducing energy consumed by the network connectivity. Another example is TensorLite, a powerful tool to deploy models on mobile and IoT devices.
The goal with this workshop is to promote green machine learning even further, by creating a half-day workshop where researchers in different machine learning and data mining areas can bring together their ideas, present them in front of a heterogeneous crowd, and have interesting debates on how to advance machine learning into a more scalable future.
We accept original work, already completed, or in progress. Position papers and extended abstracts are also considered.
Accepted papers (except for extended abstracts) will be published in ECML-PKDD 2019 Workshop proceedings.
Key Dates
- Workshop paper submission deadline: Monday, July 1, 2019
- Workshop paper acceptance notification: Friday, July 19, 2019
- Workshop paper camera-ready deadline: Monday, July 26, 2019
- Workshop date: Friday, September 16, 2019
Workshop Chairs
- Eva García-Martín, Blekinge Institute of Technology
- Albert Bifet, Telecom-ParisTech
- Crefeda Faviola Rodrigues, University of Manchester
- Heitor Murilo Gomes, Telecom-ParisTech
Steering Committee
- Ricardo Baeza-Yates, NTENT
- Christian Nordahl, Blekinge Institute of Technology
- Veselka Boeva, Blekinge Institute of Technology
- Elena Tsiporkova, Sirris (Collective Center for the Belgian technological industry)
- Niklas Lavesson, Jönköping University
- Håkan Grahn, Blekinge Institute of Technology
- Emiliano Casalicchio, Sapienza University of Rome


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I am a software expert with an entrepreneurial focus. I have experience architecting applications and efficiently building them into profitable software products. Through starting my own companies, I have gained the ability to use customer needs to drive product development. I thrive most in small collaborative teams, and I work to make an immediate, positive impact.
Bootstrapped an eCommerce app from an idea, to an MVP, to a growing and profitable product, and ultimately sold it. Drove profitability by building extremely low-cost and scalable APIs.
Leading software contributor to the next-gen ultra-scalable IoT platform which led to Allegion’s 2020 acquisition of Yonomi.
Bootstrapped the development of a smart home hardware product from the ground up through rigorous customer development, project management, and coding work.
I excel at identifying the best software design and tools for each use case. In addition to software architecture, I adapt quickly to different languages and frameworks in order to build fast. The result is an ability to meet customer needs through calculated usage of software.
I am passionate about the outdoors, especially skiing and mountaineering. I enjoy traveling, especially to National Parks and for hiking and biking. I play the guitar and love to run with my dog, Arlo.


{% extends 'layouts/base.html' %} {% load crispy_forms_tags %} {% block content %} You've logged out successfully, this session has now expired. Home Log in {% endblock content %}


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!= include('ui/inc/style.css'); ?> if (mode === 'reviewAdmin2') { ?> != include('diff.js'); ?> } ?>
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Please click 'I will review' if you are able to review this proposal. If there is a conflict of interest or if you are unable to undertake the review for any reasons, click 'I decline review'.
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Takes a sequence length and any two positive numbers in order to produce a fibonacci sequence. The fibonacci sequence is returned as a vector of numbers.
fibonacci(seq_length = 10, first_num = 0, second_num = 1)
| seq_length | The length of the sequence as a number. The sequence length has to be greater than or equal to 2 ( |
|---|---|
| first_num | The starting point of the sequence. Use any positive number that comes before second_num ( |
| second_num | The next number in the sequence after first_num. Use any positive number that comes after first_num ( |
If using the default first and second numbers of 0
and 1
R will return Inf
starting at element number 1478
.
# NOT RUN { fibonacci('eight', 2, -1) # wrong #1 fibonacci(8, 2, -1) # wrong #2 fibonacci(8, 2, 1) # wrong #3 fibonacci(8, 2, 2) # wrong #4 # }fibonacci(8, 10, 12) # correct!#> [1] 10 12 22 34 56 90 146 236fibonacci(seq_length = 20, first_num = 10, second_num = 15) # yay!#> [1] 10 15 25 40 65 105 170 275<PHONE_NUMBER> 1885 #> [13] 3050 4935 7985 12920 20905 33825 54730 88555fibonacci(30)#> [1] 0 1 1 2 3 5 8 13 21 34 #> [11] 55 89 144 233 377<PHONE_NUMBER> 2584 4181 #> [21] 6765 10946 17711 28657 46368 75025<PHONE_NUMBER>18<PHONE_NUMBER>29fibonacci()#> [1] 0 1 1 2 3 5 8 13 21 34fibonacci(1500)[1477]#> [1] 1.306989e+308fibonacci(1500)[1478]#> [1] Inffibonacci(1500)[1500]#> [1] Inf