GOAL: The goal of each level is to create as large of a bubble as possible without it touching the moving circles. Clicking on the game will create a small bubble which gets larger with a constant speed. The bubble stops growing when any other circle runs into it. To pass the level you need to reach a size of over 100. If you beat the first 10 levels you get to create custom levels of your own.
HOW TO PLAY: At first there will be a few slow moving circles. Click anywhere on the board (probably in a space where other circles are moving away) and a bubble will begin to grow. There is a threshold size for the bubble to advance to the next level. Each level has more, faster moving obstacles.


PyCharm keeps track of which of its features you are using and will offer you some tips to let you know about the features you may have missed. These tips will be shown while the startup process is going on. You can always get back to them by selecting Help | Productivity Guide on the menu bar.


[1]
your pocket and to volume down , you to it and its pointed the right way .
[2]
down , you have to take it out and make sure its pointed the way .


--- imgsrc: "matzahballsoupwilldeutsch.jpg" http://store.notesfromthetribe.com/product/8-5x11-matzah-ball-soup-print postDate: "03.22.10" layout: post ---
ATTENTION MEMBERS OF THE TRIBE: THIS WEEK I WILL BE CELEBRATING THE WONDERFUL SIMCHA THAT IS MY BEST FRIEND’S WEDDING IN NEW YORK, CONSEQUENTLY, ORDERS THAT ARE PLACED THIS WEEK, WILL SHIP MONDAY THE 22.
We now return our regularly scheduled programming:
I’ve added a fifth question to the seder this year:
Why is it that in any other form, this matzoh we eat is the bread of affliction which our forefathers ate when the holy one, blessed be he brought us forth with an outstretched arm from bondage in the land of egypt. But, when combined with salt, schmaltz, and chicken broth, becomes a delicious comfort food served by Jewish bubbys year round?


--- layout: default title: "Sleek - Admin Dashboard Template" bodyClass: "sidebar-fixed sidebar-dark header-light header-fixed" parent: "documentation" sub_parent: "" activePage: "customization" ---
We use Jekyll and Liquid templating language to process templates. Here is a list of components that are found on most pages:
We have many ready to use UI elements with detailed documentation. You can explore and use these by visiting UI Elements
page in the Left Sidebar. Such as Alert,
Card, Modal etc.
There are 2 basic ways to customize your theme...
sleek.css
or sleek.min.css
file located in the dist/assets/css
directory._variables.scss
) to easily customize theme styles, plus you never have to touch Bootstrap source, meaning future updates will be much, much, simpler.


Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.2.0), future (≥ 1.6.2) |
| Imports: | globals (≥ 0.10.3), rlang (≥ 0.2.0), purrr (≥ 0.2.4) |
| Suggests: | listenv (≥ 0.6.0), dplyr (≥ 0.7.4), testthat |
| Published: | 2018-05-16 |
| Author: | Davis Vaughan [aut, cre], Matt Dancho [aut] |
| Maintainer: | Davis Vaughan <dvaughan at business-science.io> |
| BugReports: | https://github.com/DavisVaughan/furrr/issues |
| License: | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)] |
| URL: | https://github.com/DavisVaughan/furrr |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | furrr results |
| Reference manual: | furrr.pdf |
| Package source: | furrr_0.1.0.tar.gz |
| Windows binaries: | r-devel: furrr_0.1.0.zip, r-release: furrr_0.1.0.zip, r-oldrel: furrr_0.1.0.zip |
| OS X binaries: | r-release: furrr_0.1.0.tgz, r-oldrel: furrr_0.1.0.tgz |
| Reverse imports: | evaluator, jstor, vinereg |
| Reverse suggests: | tsibble |
Please use the canonical form https://CRAN.R-project.org/package=furrr to link to this page.


Active devices are devices that are fully working as daily drivers, it requires to have wifi, call, sms, data, OTA, apparmor and sound to be working to be an "Active device".
| Priority | Name | Device | Comment | |
|---|---|---|---|---|
| {{item.pri}} | {{item.name}} | {{item.device}} | {{item.comment}} | |
| Sorry, there is no "Active" devices yet! |
| Priority | Name | Device | Devopment done | |
|---|---|---|---|---|
| {{item.pri}} | {{item.name}} | {{item.device}} |
|
| Priority | Name | Device | Donations to Debug device | |
|---|---|---|---|---|
| {{item.pri}} | {{item.name}} | {{item.device}} |
|


TextField
open class TextField : UITextField, UITextFieldDelegate
TextField is the base class for the specialized text fields UI elements for collecting credit card information.
-
Delegate for validation state change
Seealso
TextFieldValidationDelegate
Declaration
Swift
public weak var validationDelegate: ValidationDelegate?
-
Text formatter Default implementation does not do any formatting
Declaration
Swift
open var format: (String) -> String
Return Value
the formatted text
-
Text validation Default implementation return false
Declaration
Swift
open var validate: (String) -> Bool
Return Value
true if is valid
-
Undocumented
Declaration
Swift
override public init(frame: CGRect)
-
Undocumented
Declaration
Swift
required public init?(coder aDecoder: NSCoder)
-
Undocumented
Declaration
Swift
open func textFieldDidBeginEditing(_ textField: UITextField)
-
Undocumented
Declaration
Swift
open func textFieldDidEndEditing(_ textField: UITextField)
-
Undocumented
Declaration
Swift
open func textField(_ textField: UITextField, shouldChangeCharactersIn range: NSRange, replacementString string: String) -> Bool
-
Undocumented
Declaration
Swift
override open var text: String? { get set }
-
Undocumented
Declaration
Swift
override open var bounds: CGRect { get set }
-
Undocumented
Declaration
Swift
override open var placeholder: String? { get set }
-
Undocumented
Declaration
Swift
open override func placeholderRect(forBounds bounds: CGRect) -> CGRect
-
Undocumented
Declaration
Swift
open func animateViewsForTextEntry()
-
Undocumented
Declaration
Swift
open func animateViewsForTextDisplay()
-
Undocumented
Declaration
Swift
open func drawViewsForRect(_ rect: CGRect)
-
Undocumented
Declaration
Swift
override open func draw(_ rect: CGRect)
-
Undocumented
Declaration
Swift
override open func drawPlaceholder(in rect: CGRect)
-
Undocumented
Declaration
Swift
open var textFieldType: TextFieldType?
-
Undocumented
Declaration
Swift
override open func textRect(forBounds bounds: CGRect) -> CGRect
-
Undocumented
Declaration
Swift
open override func editingRect(forBounds bounds: CGRect) -> CGRect
-
Undocumented
Declaration
Swift
override open func leftViewRect(forBounds bounds: CGRect) -> CGRect


crbug.com/377520: Invalidate the rect of an element that is being removed from the tree even when it is overflow-clipped at the time of invalidation.


Buah Melaka
- 5 Pandan leaves
- 1 cup glutinous rice flour
- 2 tablespoons rice flour
- Palm sugar-diced into small cubes
- Desiccated coconut
- 1 teaspoon salt
- Put 3/4 of water into a pot and leave it to boil.
- Meanwhile, blend pandan leaves with some water (about 1/2 to 1 cup) and sieve it. Pour it slowly into the mixture of glutinous rice flour and rice flour until the dough can be shaped.
- Take about a thumb-sized of dough and flatten it on your hand. Put the diced palm sugar at the centre and roll it into a ball.
- Make a few of those until the dough is used up. Then, add them into the boiling water and cook until the dough ball float. Set aside to dry.
- Mixed the desiccated coconut with salt.
 Serve with your favourite drink.


If you’ve ever worried about protecting the privacy of your digital data or your conversations with others, we’re here to help.
Surveillance Self-Defense is a digital security guide that teaches you how to assess your personal risk from online spying. It can help protect you from surveillance by those who might want to find out your secrets, from petty criminals to nation states. We offer guides to the best privacy-enhancing tools and explain how to incorporate protecting yourself against surveillance into your daily routine.
If you’re ready to take the first steps, our series of basic guides (below) will help you to understand what digital surveillance is and how you can fight it. We suggest starting with the Assessing your risks guide.


Pillage galleon Jolly Roger Sea Legs rigging rope's end loot knave code of conduct smartly. Keelhaul holystone list cog sutler scourge of the seven seas fathom parley walk the plank killick. Avast booty square-rigged Blimey no prey, no pay gangplank cackle fruit strike colors smartly tender.
Jack Ketch rum scallywag clipper belaying pin ye measured fer yer chains doubloon draft cutlass. Jack tack main sheet fathom matey scurvy lanyard deadlights bring a spring upon her cable port. Shrouds belay maroon warp jolly boat deadlights line barque run a rig gabion.
Shrouds handsomely bowsprit grog Spanish Main haul wind yo-ho-ho six pounders interloper aye. Weigh anchor ye loot fire ship Sea Legs draught splice the main brace barkadeer bilge rat Privateer. Marooned Yellow Jack fore bring a spring upon her cable Sail ho aye pressgang boatswain gaff Pieces of Eight.


Please View Full Resume Online Here
• Perform client invoicing submissions for intranet accounts
• Direct contact for vendors and inter-company accounting department for invoicing discrepancies and statements.
• Execute audit procedures, calculate account summaries, and update system spreadsheets for month end close using v-lookup, pivot tables, and various calculation functions.
• Managed the quality of goods, costs of services, and the budget efficiency of the corporation.
• Re-engineered purchasing processes to improve efficiency.
• Provided support to the Director of Operations by assisting with financial reporting and monthly budget analysis.
• Conducted the monthly Operational Management meeting that displayed details of budget related projects.
• Handled purchasing and logistics; reviewed and audited legal purchase requisitions, and managed company supply list for all departments. Scanned, dated, and processed purchase orders. Submitted invoices to Executive Officers for approval, then directed purchase orders through the accounting process.
•Balanced financial accounts by performing debit and credit entries.
• Resolved discrepancies by analyzing customers' payment history, and payment schedules with collections departments.
• Performed extensive data deciphering and analysis using pivot tables, formulas (v-lookup and macros), data validation, and graph and chart manipulations.
•Learn to create software solutions with front-end and back-end software programming. •Build features and applications with a mobile responsive design. •Design client-side and server-side architecture layouts for projects. •Build the front-end applications with appealing visual design. •Develop and manage well-functioning databases and applications and write effective APIs. •Test software to ensure responsiveness and web efficiency. •Troubleshoot, debug, and upgrade projects. •Create security and data protection settings. •Write technical documentation/ReadME.md files.
•Achieved competency in communications and marketing
•Accomplished courses in Film and Television production that enhanced artist perspective of images and layouts
•Excelled at team collaborations in Marketing and Film projects
•Designed, developed and completed art projects.
•Earned computer software programming credit towards degree.


“From the day that consciousness came to me in this world I have been miserable… Too well do I know what it is to feel the burning and jagged links of the devil's chain cutting through my quivering flesh to the shrinking bone—to feel my nerves tremble with agony, and my brain burn as if bathed in liquids of fire—too well, I say, do I know what these things are, for I have felt them intensified again and again, ten thousand times.” — Luther Benson, Fifteen Years In Hell


Opaque cpConstraint struct. More...
#include <cpConstraint.h>
Data Fields | |
| cpBody * | a |
| The first body connected to this constraint. | |
| cpBody * | b |
| The second body connected to this constraint. | |
| cpFloat | maxForce |
| The maximum force that this constraint is allowed to use. | |
| cpFloat | errorBias |
| The rate at which joint error is corrected. | |
| cpFloat | maxBias |
| The maximum rate at which joint error is corrected. | |
| cpConstraintPreSolveFunc | preSolve |
| Function called before the solver runs. | |
| cpConstraintPostSolveFunc | postSolve |
| Function called after the solver runs. | |
| cpDataPointer | data |
| User definable data pointer. |
Opaque cpConstraint struct.
Definition at line 46 of file cpConstraint.h.
User definable data pointer.
Generally this points to your the game object class so you can access it when given a cpConstraint reference in a callback.
Definition at line 81 of file cpConstraint.h.
| cpFloat errorBias |
The rate at which joint error is corrected.
Defaults to pow(1.0 - 0.1, 60.0) meaning that it will correct 10% of the error every 1/60th of a second.
Definition at line 65 of file cpConstraint.h.
| cpFloat maxBias |
The maximum rate at which joint error is corrected.
Defaults to infinity.
Definition at line 68 of file cpConstraint.h.
| cpFloat maxForce |
The maximum force that this constraint is allowed to use.
Defaults to infinity.
Definition at line 61 of file cpConstraint.h.
| cpConstraintPostSolveFunc postSolve |
Function called after the solver runs.
Use the applied impulse to perform effects like breakable joints.
Definition at line 76 of file cpConstraint.h.
| cpConstraintPreSolveFunc preSolve |
Function called before the solver runs.
Animate your joint anchors, update your motor torque, etc.
Definition at line 72 of file cpConstraint.h.


Highscore
Use your arrow keys or [W,A,S,D] keys to navigate pacman.
To pause / resume the game press [SPACE] or [ESC] or just click into the game area.
Use swipe gestures to navigate pacman.
Alternatively use the Arrow Buttons underneath the game area to navigate pacman.
To pause / resume the game, touch the game area once.

Every ghost has its own strategy to chase down pacman.
This is the default mood. When ghosts are in scatter mood, they will just go to their home corner and stay there.
After a certain time the ghosts change their mood and want to go chasing pacman. This is indicated through the walls turning red.
The goal of every level is, to eat all the white pills without getting catched by the ghosts. One pill results in 10 points.
In every level there are 4 powerpills, which are a bit bigger than the regular ones. If Pacman eats those, he will get strong enough to eat the ghosts. You can see this indicated by the ghosts turning blue. One powerpill results in 50 points.
Eating a ghost results in 100 points. The soul of the ghost will return to the ghost house before starting to chase Pacman again.
Current Version: 1.0.6 (2022-08-14)
Pacman Canvas is Open Source, written by platzh1rsch. You can get the code on github.
Click to Play
This whole thing was written in HTML5, CSS3 and Javascript (using small bits of jquery). For the basics I was following the "Exploring HTML5 Canvas" Tutorials (Part 1 - 6) by Devhammer. Thanks for the great Tutorial!
For some other stuff, like how to write objectorientated javascript I was following the tutorials over at http://www.codecademy.com/, which is a really great site to learn Javascript and also other languages.
If you understand German you can also read my blogpost about this site: "Pacman in HTML5 Canvas".


{% extends "base.html" %} {% block head_title %} Welcome | {{ block.super }} {%endblock %} {% block content %}
This tool is used to graph data in the experiment directory.
NOTE: All experiment names must have 'expt' in the name to be properly graphed.


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Ok, good slideShow we have here but can we ehance it a little bit ?
Why not with a little more interaction.
Scan that Qr-code and take control over it.
Thanks Lorempixel for the random placeholder images.
Don't hesitate to check the AirPad Project for more informations.
Slider done with Easy slider.
If you want more web experiments you can check my dedicated page


Recommendation engine or recommendation systems are among the most popular applications of data science today. Almost every major tech company has applied them in some forms. Thus, there is no doubt that a good recommendation engine can bring substantial profit to the company. So the goal of this post is to try to talk about my understanding of constructing a good recommendation engine.
Contents
First, let’s look at the structure of this post.
- Overview
- Business Analysis
- 2.1 Analytical Framework
- 2.2 Deep Dive into Amazon.com
- 2.3 Conclusions
- Data Science Analysis
- 3.1 Introduction
- 3.2 Model Construction
- 3.3 Conclusions
- Combination
I divide my presentation into four parts, first I will give a brief introduction about recommendation engine, and then I will take a general business analysis about recommendation systems in Amazon; Next, I will take a pure data science analysis about recommendation systems; Finally I will combine these two analyses together.
1. Overview
When I met the word "Recommendation Engine", the first question came to my mind was: why we need it?
Briefly speaking, it is all because of money. For example, Amazon and Netflix recommend items and movies to attract the user to buy directly; YouTube and Facebook are not selling anything but with your stay in its website, they can show more ads and also increase the income.
So, now we know why we need a recommendation system, actually it is said that 30% of Amazon’s income was due to its recommendation. Then how the recommendations look like? Can we analyze them from a business perspective? Then the next step, I will try to deep dive into Amazon.com to have a general business analysis of these recommendation engines.
2. Business Analysis
2.1 Analytical Framework
To make my analysis more structured, I referred to one of the recommendation analysis framework raised by Joseph from University of Minnesota and then revised a little bit.
By following this framework, we could make it very clear about each of the recommendation engine. Let’s then use this analytical framework while we deep dive into Amazon.com.
2.2 Deep Dive into Amazon.com
Here are the things Amazon recommended to me.
At the top is an advertisement of Amazon Echo and other staffs. As far as I know, these ads are not highly personalized, and they will choose to show some recent popular items.
Next, we notice that Amazon put its deals recommendation at the most obvious location. That makes sense because personalized and cheap items are very easy to catch customer’s eyes, so it has a more probability to be clicked on.
For me, the bike and some magazines do attract me a lot since I like cycling and reading and once bought a bicycle lock from Amazon. So it seems like these recommendations aim to recommend me similar items I bought before and now have discounts.
2.3 Conclusion
Based on my tour on Amazon pages, I found that:
- For different recommendation purposes, we may need to generate different recommendation results
- Different recommendations may need to be put in different web locations.
So, it is very important to define our recommendation’s business goal before creating any models.
3. Data Science Analysis
Now let’s look at the recommendation system in a data science perspective.
3.1 Introduction
Typically, we have two types of recommendation engines.
Collaborate Filtering
This type of model tries to find the similarities between each user and predict user’s behavior based on other similar users’ behavior. So for example, if we want to predict the fifth user’s preference for watching TV, we could see other two users preferences since they are very similar to the fifth user.
Content-based Filtering
Another type of recommendation engine is called content-based filtering. It tries to use keywords or other description to construct a profile for user and item. And then recommend similar one.
For example, we could use the overview description for each item to construct TF-IDF matrix and find a similar item with it or use some keywords to generalize these products such as category, material, function and so on.
Collection of Data
Except for the model, the data from recommendation engines are different from our normal data set. It can be either explicit or implicit.
Explicit Data: Data inputted by users such as ratings and comments
Implicit Data: Order history/return history, Cart events, Click thru and search log.
For my later model, I suppose we have enough explicit data which is the rating information from each user.
Here is how the ideal data frame looks like: each entry represents the rating one user gave to the item.
| ID | user1 | user2 | user3 |
|---|---|---|---|
| item1 | 1 | 5 | 3 |
| item2 | 2 | 2 | |
| item3 | 2 | 5 |
Here Each entry $r(i,u)$ represents the rating of this $u$ user for the $i$ item.
3.2 Model Construction
Memory-based Model
Now it is time to construct a real recommendation engine. The first method I used is the traditional CF memory-based model, what it does is that:
- We first find a similarity measure between each user. There are several ways we can do, here I choose to use cosine similarity. This similarity measure is very intuitive since if we deem two users' rating as two vectors, Cosine is just the measure of the intersection angle between these two vectors. The smaller the angle, the more similar of the users.
- Then we make predictions based our average rating plus other similar users rating.
- Finally, we evaluate our model by calculating the mean absolute differences between our rating prediction and the real rating.
Here is the summary information for this model:
- Advantages:
- Intuitive: Very easy to understand
- Simple: Easy to operate
- Drawbacks:
- Scalability: Computation cost grows as data getting larger
- Sparsity: Commonly rated items for two users are quite few
Model-based Model
So here comes to a more advanced model that perfectly solves the above two problems and it actually won the Netflix Recommendation Competition in 2009. This model is called latent factor model. Essentially, we want to turn the recommendation problem into a machine learning optimization problem.
But, how we transform it? Basically, we view our problem in a different way. Suppose we use squared error as our metric, now our problem is viewed as how good we are in predicting the rating for items, so our goal now is to try to minimize our loss instead of finding the most similar user.
Let us see the image illustration to understand how it works. Where Q matrix represents the item information per row and the P matrix represents the user information per column. K is the factor parameter we will choose; The latent factor K is a broad idea which describes a property or concept that a user or an item has. For instance, if the items are music, so the latent factor can refer to the genre that music belongs to. In a word, this method tries to map our original data matrix into two low dimensional matrices by only using the existing data. So now we can see how this model perfectly solves two previous problems.
But how this model solves our previous problems?
- For sparsity, this model only uses the data that we already have, do not need to fill the NA or suppose them to be zero.
- For scalability, this model maps our user-item matrix to two low dimensional ones, and the dimension is controlled by the number of factors you choose.
Finally, to achieve this mapping, we always use SVD technique. Briefly sparking, SVD is a matrix decomposition techniques that decompose our original matrix into some low dimensional matrices and also keep the minimum reconstruction error, which is perfectly matching our goal, right?
Then let’s see how we construct this model:
- First we need to solve this optimization equation, basically, it means we try to find the matrix p and q that minimizing the square error by using our existing data (the yellow region). This optimization problem can be achieved by using gradient descent.
- We need to choose the best factor number for performing cross-validation.
- Next, we can make a prediction by multiple p and q matrix.
- Finally, we also use mean absolute error to check the goodness of our prediction.
Here is the summary information for this model:
- Advantages:
- Ignore sparsity: No need to fill value into the empty entry
- Efficient: Commutation cost is not that heavy
- Drawbacks:
- Not Intuitive: Hard to explain the reason that we recommend the item
3.3 Conclusion
From this part, I performed two collaborate filtering models. Actually, there are so many other models we can use, such as content-based model or clustering model.
But I find that each of them has their own drawback, for collaborate filtering model, it suffers cold start, which means we are not able to make a good recommendation if we have little rating information for this user. For content-based model, it is hard to recommend different categories items; and clustering has difficulties with sparsity.
So to make a good prediction, it is more reasonable to have a hybrid model: what I think is that, at the beginning if we have little data, we could use content-based model first, and then, using clustering algorithm such as EM algorithm to segment our user, and finally use the target cluster’s user data to perform latent factor model.
4. Combination
In the end, let me combine my two parts analysis together.
I think at the beginning; you may wonder why I have a such a structure that starts with business analysis and then technology-based analysis. The reason is that I think a good data science application should always be the combination of business plus data science analysis.
Based on my previous analysis, a good recommendation should always start with a clear business goal: where to put the recommendation, what type of items to recommend, personalized or not. After that, we are able to collect the targeted data to achieve the goal. Finally, always keep in mind each model’s drawback, a hybrid model may be a better choice.
Following this pipeline, I also summarized what technology stack I will use in each step. We may first use some BI tools to perform our business analysis such as A/B test, and the data normally will be stored in the cloud database, so I will use Spark SQL to connect to a cloud SQL instance and then load data into Spark DataFrame or RDD format. Spark Streaming is used to update our dataset in real time. Finally, we can either use Spark MLlib recommendation module or use Python Surprise library to construct our recommendation model. At the same time, We can also use AWS or google cloud to help us quickly get results.
Hope this post could give you my understanding of constructing a good recommendation engine!
Reference: https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75 https://github.com/rounakbanik/movies/blob/master/movies_recommender.ipynb https://www.youtube.com/watch?v=E8aMcwmqsTg&list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV&index=55 https://www.coursera.org/specializations/recommender-systems


public class FilesystemLogReplayer extends AbstractLogReplayer
AbstractLogReplayer
, using the FSReader
to replay IMonitoringRecord
s from a list of file
system monitoring logs.MAX_TIMESTAMP, MIN_TIMESTAMP
| Constructor and Description |
|---|
FilesystemLogReplayer(java.lang.String monitoringConfigurationFile,
boolean realtimeMode,
double realtimeAccelerationFactor,
boolean keepOriginalLoggingTimestamps,
int numRealtimeWorkerThreads,
long ignoreRecordsBeforeTimestamp,
long ignoreRecordsAfterTimestamp,
java.lang.String[] inputDirs)
Creates a new instance of this class using the given parameters.
|
| Modifier and Type | Method and Description |
|---|---|
protected AbstractReaderPlugin |
createReader(IAnalysisController analysisInstance)
Implementing classes return the reader to be used for reading the monitoring log.
|
protected java.lang.String |
readerOutputPortName()
Implementing classes returns the name of the reader's output port which provides the
IMonitoringRecord s from the monitoring log. |
replay
public FilesystemLogReplayer(java.lang.String monitoringConfigurationFile, boolean realtimeMode, double realtimeAccelerationFactor, boolean keepOriginalLoggingTimestamps, int numRealtimeWorkerThreads, long ignoreRecordsBeforeTimestamp, long ignoreRecordsAfterTimestamp, java.lang.String[] inputDirs)
monitoringConfigurationFile
- The name of the monitoring configuration file.realtimeMode
- Whether realtime mode should be used.realtimeAccelerationFactor
- Determines whether to accelerate (value > 1.0) or slow down (<1.0) the replay in realtime mode by the given factor.
Choose a value of 1.0 for "real" realtime mode (i.e., no acceleration/slow down)keepOriginalLoggingTimestamps
- Whether to keep the original logging timestamps or not.numRealtimeWorkerThreads
- The number of realtime worker threads to be used.ignoreRecordsBeforeTimestamp
- The lower limit for the timestamps.ignoreRecordsAfterTimestamp
- The upper limit for the timestamps.inputDirs
- The array containing the input directories.protected AbstractReaderPlugin createReader(IAnalysisController analysisInstance)
createReader
in class AbstractLogReplayer
analysisInstance
- The analysis controller which will be the parent of the reader.protected java.lang.String readerOutputPortName()
IMonitoringRecord
s from the monitoring log.readerOutputPortName
in class AbstractLogReplayer
Copyright 2017 Kieker Project, http://kieker-monitoring.net


---
title: "23+ Mercury News Logo
Images"
description: "Mercury news daily | the latest news, headlines, breaking news and analysis at www.mercurynewsdaily.com."
date: "2020-11-25"
categories:
- "Logo"
images:
- "https://img.favpng.com/11/17/15/san-francisco-bay-area-news-group-the-mercury-news-media-png-favpng-xvmzmjP6gXBc826KhejnAEg8K.jpg"
featuredImage: "https://1hdlxe1oe0ll1eghud24le5v-wpengine.netdna-ssl.com/wp-content/uploads/2015/11/SiliconBeat_header_559x124.jpg"
featured_image: "https://upload.wikimedia.org/wikipedia/en/3/34/CA_SJMN.jpeg"
image: "https://zenprospect-production.s3.amazonaws.com/uploads/pictures/5f512b0a9c56d40001ccaadc/picture"
---
23+ Mercury News Logo
Images. Download the vector logo of the the san jose mercury news brand designed by in encapsulated postscript (eps) format. Latest silicon valley and san francisco bay area news from reporters & columnists at the mercury news.
The mercury news logo black and white. Download the the mercury news logo vector file in eps format (encapsulated postscript). 183,839 likes · 19,188 talking about this.
Photos from around the bay area by mercury news and east bay times staff photographers.
Download the the mercury news logo vector file in eps format (encapsulated postscript). The mercury news, san jose, california. Explore life stories, offer condolences & send flowers. Logo of viet mercury from 1999 to 2005.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 6
If you thought soufflé was decadent, this recipe rises to another level. Crispy bacon, reduced cream and melting cheese happily make a hearty base for puffed up mustardy soufflés.
Preheat the oven to 200C/400F/Gas 6 (convection mode) and generously grease six ramekins of 7cm x 4cm/2¾in x 1½in.
Place the milk in a saucepan, add the thyme, bay leaf and nutmeg, and bring to a simmer.
Turn off the heat and leave to infuse for a few minutes.
Heat a frying pan until hot, add the olive oil and bacon and cook until crisp. Remove and place on a plate lined with kitchen paper.
Melt the butter in a saucepan and add the flour and mustard. Cook for a couple of minutes until a nutty aroma is released.
Strain the milk and add it to the saucepan containing the flour and mustard. Whisk continuously until thick, then beat in the egg yolks and season with salt and freshly ground black pepper. Allow to cool slightly.
Whisk the egg whites in a clean bowl until firm, adding a pinch of salt.
Add one-third of the egg white to the soufflé base mix and beat in. Stir in the chives and then carefully fold in the remaining egg whites. Spoon the mixture into the buttered ramekins and bake for 5-7 minutes, or until the tops begin to brown.
To serve, preheat the grill to high.
Warm the cream in a saucepan and season with salt and freshly ground black pepper. Increase the temperature and cook until the volume of liquid has reduced by one-third.
Add the crispy bacon and mix well, then add salt and pepper, to taste. Pour the cream into six serving bowls and turn the soufflés out onto the cream.
Cover the soufflés with the grated gruyère cheese and place under the grill. Once golden-brown and bubbling, remove from the grill and serve immediately.


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Hugues Sibille, Labo de l’ESS (french think-tank in social economy)
« Social innovation is a Great Promise. Our responsibility is to recognize, transform and, before all, accelerate it to thrive results as high as unsatisfied social needs. »
Hundreds of websites already collect social innovation projects. We want this information to be more accessible. Community members’ websites are automatically scraped, they don’t have to fill again all the information. Every actor already highlighting social innovations can join the community.
You capitalize social innovation projects and you’d like to share them in the Social Innovation Crossroad?
Discover how to join the projectSocial innovation crossroad federates public institutions, networks, foundations, associations, medias and research centers.
« Social innovation is a Great Promise. Our responsibility is to recognize, transform and, before all, accelerate it to thrive results as high as unsatisfied social needs. »
« Facing the growing risk of social and territorial divide linked with the crisis, social economy appears today as a chance to grasp. We therefore chose to join Social Innovation Crossroad to share good practices and impacting initiatives that disrupt practices by creating shared social value. »
« As regional cooperative banks, through our federation, we are proud to contribute to the co-construction of this platform serving territories and its social innovations »
« Social Innovation Crossroad can become a lever to transform the world. It will enable to understand and develop capacity of initiative and cooperation in the society, the cornerstone of social innovation and its promises. »
« At a crossroad meet people from every horizon, including some who ignored the existence of the other. It’s to give light to rural territories as they really are, that local, rural projects are legitimate in this exchange forum »
« This project is fulfilling meetings, other perspectives for social innovation, the experience of an original governance and discovering new inclusive projects. »
« Sparknews’ mission is to identify and highlight the most inspiring social innovations. It was thus natural for us to join the Crossroad in order to contribute and find new topics »
« Identify, communicate and support social innovations in Europe is part of AEIDL’s priorities »


Step 4: Generate Project¶
The project generator is run somewhat like venv. That is, I usually go to my Python 3.x installation, and
from there run venv
to create the virtual environment. Therefore, in this guide I take the approach
of installing sjb.pyguide to the default Python 3.x directory.
It is assumed you have installed Python. If not see Install Python
It is assumed you have installed pyguide. If not see Install pyguide
Launch the console menu for the Python Guide generator and the will be displayed:
c:\Python37>scripts\py_guide --show_menu
******************************************
Python Guide for Practicing Wizards
*******************************************
1. Generate new project
2. py_guide help
3. Exit
>>>
Use the menu to start the generator. Provide the information requested, and watch the project get created, including the virtual environment.
******************************************
Python Guide for Practicing Wizards
*******************************************
1. Generate new project
2. py_guide help
3. Exit
>>> 1
What name do you want to use for author, copyright holder? [sburke]
What is the name of your project?py_project
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\.idea
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\.idea\runConfigurations
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\build
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\dist
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\docs
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\docs_source
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\docs_source\_static
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\docs_source\code
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\py_project
INFO | Created directory C:\Users\sburke\PycharmProjects\py_project\venv
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\LICENSE
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\requirements.txt
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\.gitignore
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\docs_source\conf.py
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\docs_source\index.rst
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\docs_source\readme.rst
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\docs_source\next_steps.rst
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\dist_pypi.bat
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\dist_pypi.config.bat
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\jrepl.bat
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\project.cfg
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\setup.py
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\README.rst
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\py_project\__init__.py
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\py_project\__main__.py
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\py_project\console_menu.py
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\py_project\logging_trees.py
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\.idea\runConfigurations\Build_setup_wheel.xml
INFO | Created file C:\Users\sburke\PycharmProjects\py_project\.idea\runConfigurations\Build_Sphinx_docs.xml
INFO | Your project was created at C:\Users\sburke\PycharmProjects
INFO | Deactivating Python Virtual Environment (c:\python37)
'c:\python37\Scripts\deactivate' is not recognized as an internal or external command,operable program or batch file.
INFO | Creating Virtual environment...
INFO | Activating Python Virtual Environment (C:\Users\sburke\PycharmProjects\py_project\venv)
INFO | Downloading pip installation file...
INFO | Installing pip ...
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Installing collected packages: pip, setuptools, wheel
Successfully installed pip-19.2.3 setuptools-41.2.0 wheel-0.33.6
INFO | Installing requirements using pip...
Collecting configparser2==4.0.0 (from -r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 1))
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Requirement already satisfied: pip>=19.0.2 in c:\users\sburke\pycharmprojects\py_project\venv\lib\site-packages (from -r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 4)) (19.2.3)
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Using cached https://files.pythonhosted.org/packages/7d/66/a4af242b4348b729b9d46ce5db23943ce9bca7da9bbe2ece60dc27f26420/Sphinx-1.8.5-py2.py3-none-any.whl
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Requirement already satisfied: setuptools>=39.2.0 in c:\users\sburke\pycharmprojects\py_project\venv\lib\site-packages (from -r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 7)) (41.2.0)
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Collecting Pygments>=2.0 (from Sphinx==1.8.5->-r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 5))
Using cached https://files.pythonhosted.org/packages/5c/73/1dfa428150e3ccb0fa3e68db406e5be48698f2a979ccbcec795f28f44048/Pygments-2.4.2-py2.py3-none-any.whl
Collecting Jinja2>=2.3 (from Sphinx==1.8.5->-r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 5))
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Collecting colorama>=0.3.5; sys_platform == "win32" (from Sphinx==1.8.5->-r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 5))
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Collecting babel!=2.0,>=1.3 (from Sphinx==1.8.5->-r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt(line 5))
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Collecting tqdm>=4.14 (from twine==1.13.0->-r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line8))
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Collecting pytz>=2015.7 (from babel!=2.0,>=1.3->Sphinx==1.8.5->-r C:\Users\sburke\PycharmProjects\py_project\venv\..\requirements.txt (line 5))
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Installing collected packages: configparser2, docutils, Menu, Pygments, MarkupSafe, Jinja2, sphinxcontrib-websupport, alabaster, idna, urllib3, chardet, certifi, requests, six, pyparsing, packaging, snowballstemmer, imagesize, colorama, pytz, babel, Sphinx, sphinx-rtd-theme, pkginfo, tqdm, webencodings, bleach, readme-renderer, requests-toolbelt, twine
INFO | Deactivating Python Virtual Environment (C:\Users\sburke\PycharmProjects\py_project\venv)
INFO | Activating Python Virtual Environment (c:\python37)


Functions for genome-wide association/interaction analysis and rare variant analysis on a continuous/dichotomous trait using family data, and for making genome-wide p-value plot and QQ plot.
| Version: | 2.2 |
| Depends: | R (≥ 2.15.1), geepack, coxme, lme4 |
| Published: | 2015-03-12 |
| Author: | Ming-Huei Chen and Qiong Yang |
| Maintainer: | Ming-Huei Chen <mhchen at bu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | GWAF results |
| Reference manual: | GWAF.pdf |
| Package source: | GWAF_2.2.tar.gz |
| Windows binaries: | r-devel: GWAF_2.2.zip, r-release: GWAF_2.2.zip, r-oldrel: GWAF_2.2.zip |
| OS X binaries: | r-release: GWAF_2.2.tgz, r-oldrel: GWAF_2.2.tgz |
| Old sources: | GWAF archive |
Please use the canonical form https://CRAN.R-project.org/package=GWAF to link to this page.


My name is Samuel Urias. Actually I am a student of computer engineering in Universidad del Valle de Guatemala.
I love reading and learning new thing every day. Most of the time I have a cup of coffee in the hand thinking in something cool to do today.
Now I am working in my own app. For the moment I only can say that something awesome is in progress.
I have experience working as a web developer for many customers in the past, sadly I was working under NDA and I gave all my portfolio to the company that I was working for, but now I am for my own.
I think that If we are going to work together for a while, you deserve the best of the best.
Because of that I get in contact to work with one of the best graphic designers I have ever know: Ximena Vielman for the most demanding projects.
Now you can rest knowing that you project is built by people with a lot of experience in this area.
Ready to start your next project with me? That's great! Send me an email and I will get back to you as soon as possible!


You can't view YouTube videos and that's not a surprise.
This page features Pulsar's custom YouTube video player. It directly interfaces with and leverages the entire YouTube <iframe> API to render YouTube embeds directly into the view. AngularJS methods are used to control the video and update the view's model in real time as the videos are playing.