Company profile - Canadian Company Capabilities
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Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
T3CS Consulting Group Inc
Mailing Address:
183 Waller StWHITBY, Ontario
L1R 2R5
Location Address:
183 Waller StWHITBY, Ontario
L1R 2R5
Tel.:<PHONE_NUMBER>
Fax:
Company Profile
T3CS provides SMEs with key resources for business development, project management and general consulting service needs. This will enable customers to select high quality business ventures for business plan development and implementation. The core activities of T3CS are:
1. Business case evaluation
2. Business plan development
3. Project management
4. Coordinating product evaluations, field trials and
implementation
5. Technical documentation of:
i. Test plans
ii. Product specifications
iii. Request for proposals
iv. Trouble shooting guides
v. Engineering packages
vi. Installation guides
These services will be delivered with attention to detail.
Full profile
Full profile
Contact information
Company description
611430 - Professional and Management Development Training
Product / Service / Licensing
Business need
Prospective business owners or small business owners that want to expand their enterprises face the daunting task of evaluating several options and then preparing detailed business plans for their optimal choices. The ability to gather market requirement, competitive environment, pricing and translate this into meaningful return on investment (ROI) and the best implementation approach and timeframes are skills that are needed but often cannot be hired on a fulltime basis.
Solution:
T3CS can help to satisfy this need by providing these services on a contract basis as needed. T3CS has provided business plans for a number of SME which have resulted in the enterprises securing loans totaling almost $300,000. These businesses have since implemented these plans over the last 3 years.
Value Added:
i. Business cases that help the decision makers
understand whether a venture is worthwhile pursuing
ii. Business plans targeted to specific audiences
iii.Metrics that can be used to continuously evaluate the
business performance over time
iv.Lower the cost of the overall service and ensures that it
is targeted at targeting the specific need at the required time.
Turnkey solutions offering the full range of service from Business case analysis, business plan development and project management of the recommended solution will also be available.
Business need
In a fast paced environment this challenge can be compounded when there is a need to launch multiple products/ services in a short time and there is no experienced project manager to assist. Often times the business owner is so passionate about the product that they can get sidetracked and while they may deliver the product on time they loose track of cost and sometimes quality. This often results in significant project cost overruns.
Solution:
T3CS can deliver expert project management services ensuring high quality project delivery, on time, within budget and in scope. Only certified Project Management Professionals (PMP) will lead the projects.
Value Added:
i. Clarifying project scope to ensure that what is delivered
is what was expected.
ii. Ensuring that projects are delivered within budget
iii. Ensuring that the knowledge gained on each project is
maintained and incorporated into future projects.
Business need
While evaluating products and services it was found that product specifications provided by vendors are sometimes incomplete or inaccurate. If the vendor is unable to clearly articulate the benefits of the products this alone can eliminate a company from being considered a potential provider.
In other cases the requirements written by the potential buyer themselves is unclear and also provides another layer of confusion for the teams attempting to evaluate products. Evaluators need to be able to ask the right questions to eliminate unqualified bidders and reduce unnecessary costs
The above stated reasons can lead to extra costs for both firms as well as potential lost revenues
Solution:
T3CS can provide guidance in ensuring that requirements are clearly communicated and that the responses are vetted fairly and without bias. The team at T3CS has many years of experience on both the vendor and buyer side of the relationship and has been involved in numerous evaluations and field trials. These experiences will provide
Value Added:
i. Clarifying requirements.
ii. Providing expert consultation in responding to Requests
for Proposals (RFP) and Requests for Proposals (RFQ).
iii. Coordinating field trials and evaluating products based
on a set of defined criteria.
iv. Lower overall product evaluation costs
Business need
If the business is technical (for example a manufacturing environment) product specifications and technical documentation will need to be produced and supplied with the product as a part of the full product offering. Documents have to be written by the engineers themselves and sometimes the engineering teams, when asked to provide detailed documentation find themselves bogged down with a significant amount of overhead. While the documentation is needed, the engineers just do not have the time to complete this task.
Solution:
T3CS can deliver expert documentation services ensuring high quality clearly communicated product specifications and requirements.
Value Added:
i. Refining and clarifying product specifications
ii. A sanity check to ensure that the specifications meet
industry standards and regulations
iii. Asking the right questions to help highlight short falls in
product design
iv. Ensuring that knowledge is transferred throughout the
company and to the target customers
R006-Technical Writing Services
R019-Other Professional Services
Sector information
Key / Major clients:
1.) City of KitchenerEnterprise Division
2.) MR Meat Market
Windsor, Ontario
Phone:<PHONE_NUMBER>
3.) Caribbean Kitchen
Kitchener
Phone:<PHONE_NUMBER>
4. Buckingham Meat Company
Whitby, Ontario
Success stories:
1.) The owner of Mr Meat Market worked with T3CS to deliver a solid business plan.2.) The owners of :
a. The Caribbean Kitchen
b. The Croatian Cuisine
Used the services of T3CS to develop business plans that helped them to launch their success small businesses in the Kitchener Farmer's Market.
3. The City of Kitchener has engaged T3CS to deliver business case analysis for strategic decision decision making.
Testimonial:
1.) MR Meat Market - Windsor, OntarioPhone:<PHONE_NUMBER>
2.) Carnival Cuisine: Ajax, Ontario
Phone:<PHONE_NUMBER>.
www.carnivalcuisine.ca
3. HPM Courier and delivery services: Ajax, Ontario
Phone:(647)242-8718
www.hpmcourier.com
4. Buckingham Meat Company- Whitby, Ontario
Phone:<PHONE_NUMBER>
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
611430 - Professional and Management Development Training
Products, services and licensing
Product / Service / Licensing
Business need
Prospective business owners or small business owners that want to expand their enterprises face the daunting task of evaluating several options and then preparing detailed business plans for their optimal choices. The ability to gather market requirement, competitive environment, pricing and translate this into meaningful return on investment (ROI) and the best implementation approach and timeframes are skills that are needed but often cannot be hired on a fulltime basis.
Solution:
T3CS can help to satisfy this need by providing these services on a contract basis as needed. T3CS has provided business plans for a number of SME which have resulted in the enterprises securing loans totaling almost $300,000. These businesses have since implemented these plans over the last 3 years.
Value Added:
i. Business cases that help the decision makers
understand whether a venture is worthwhile pursuing
ii. Business plans targeted to specific audiences
iii.Metrics that can be used to continuously evaluate the
business performance over time
iv.Lower the cost of the overall service and ensures that it
is targeted at targeting the specific need at the required time.
Turnkey solutions offering the full range of service from Business case analysis, business plan development and project management of the recommended solution will also be available.
Business need
In a fast paced environment this challenge can be compounded when there is a need to launch multiple products/ services in a short time and there is no experienced project manager to assist. Often times the business owner is so passionate about the product that they can get sidetracked and while they may deliver the product on time they loose track of cost and sometimes quality. This often results in significant project cost overruns.
Solution:
T3CS can deliver expert project management services ensuring high quality project delivery, on time, within budget and in scope. Only certified Project Management Professionals (PMP) will lead the projects.
Value Added:
i. Clarifying project scope to ensure that what is delivered
is what was expected.
ii. Ensuring that projects are delivered within budget
iii. Ensuring that the knowledge gained on each project is
maintained and incorporated into future projects.
Business need
While evaluating products and services it was found that product specifications provided by vendors are sometimes incomplete or inaccurate. If the vendor is unable to clearly articulate the benefits of the products this alone can eliminate a company from being considered a potential provider.
In other cases the requirements written by the potential buyer themselves is unclear and also provides another layer of confusion for the teams attempting to evaluate products. Evaluators need to be able to ask the right questions to eliminate unqualified bidders and reduce unnecessary costs
The above stated reasons can lead to extra costs for both firms as well as potential lost revenues
Solution:
T3CS can provide guidance in ensuring that requirements are clearly communicated and that the responses are vetted fairly and without bias. The team at T3CS has many years of experience on both the vendor and buyer side of the relationship and has been involved in numerous evaluations and field trials. These experiences will provide
Value Added:
i. Clarifying requirements.
ii. Providing expert consultation in responding to Requests
for Proposals (RFP) and Requests for Proposals (RFQ).
iii. Coordinating field trials and evaluating products based
on a set of defined criteria.
iv. Lower overall product evaluation costs
Business need
If the business is technical (for example a manufacturing environment) product specifications and technical documentation will need to be produced and supplied with the product as a part of the full product offering. Documents have to be written by the engineers themselves and sometimes the engineering teams, when asked to provide detailed documentation find themselves bogged down with a significant amount of overhead. While the documentation is needed, the engineers just do not have the time to complete this task.
Solution:
T3CS can deliver expert documentation services ensuring high quality clearly communicated product specifications and requirements.
Value Added:
i. Refining and clarifying product specifications
ii. A sanity check to ensure that the specifications meet
industry standards and regulations
iii. Asking the right questions to help highlight short falls in
product design
iv. Ensuring that knowledge is transferred throughout the
company and to the target customers
R006-Technical Writing Services
R019-Other Professional Services
Sector
Sector information
Key / Major clients:
1.) City of KitchenerEnterprise Division
2.) MR Meat Market
Windsor, Ontario
Phone:<PHONE_NUMBER>
3.) Caribbean Kitchen
Kitchener
Phone:<PHONE_NUMBER>
4. Buckingham Meat Company
Whitby, Ontario
Success stories:
1.) The owner of Mr Meat Market worked with T3CS to deliver a solid business plan.2.) The owners of :
a. The Caribbean Kitchen
b. The Croatian Cuisine
Used the services of T3CS to develop business plans that helped them to launch their success small businesses in the Kitchener Farmer's Market.
3. The City of Kitchener has engaged T3CS to deliver business case analysis for strategic decision decision making.
Testimonial:
1.) MR Meat Market - Windsor, OntarioPhone:<PHONE_NUMBER>
2.) Carnival Cuisine: Ajax, Ontario
Phone:<PHONE_NUMBER>.
www.carnivalcuisine.ca
3. HPM Courier and delivery services: Ajax, Ontario
Phone:(647)242-8718
www.hpmcourier.com
4. Buckingham Meat Company- Whitby, Ontario
Phone:<PHONE_NUMBER>


|
My Project
|
Public Member Functions | |
| void | init (const Translate *t) |
| virtual void | dump (const Address &addr, OpCode opc, VarnodeData *outvar, VarnodeData *vars, int4 isize) |
| The main pcode emit method. More... | |
| void | disassemble (const Address &addr, DisassemblyResult &res) |
| void | addTarget (const Address &addr) |
| Public Member Functions inherited from PcodeEmit | |
| virtual | ~PcodeEmit (void) |
| Virtual destructor. | |
| void | restoreXmlOp (const Element *el, const AddrSpaceManager *trans) |
| Emit pcode directly from an XML tag. More... | |
| const uint1 * | restorePackedOp (const Address &addr, const uint1 *ptr, const AddrSpaceManager *trans) |
| Emit pcode directly from a packed byte stream. More... | |
Additional Inherited Members | |
| Public Types inherited from PcodeEmit | |
| enum | { unimpl_tag = 0x20, inst_tag = 0x21, op_tag = 0x22, void_tag = 0x23, spaceid_tag = 0x24, addrsz_tag = 0x25, end_tag = 0x60 } |
| Static Public Member Functions inherited from PcodeEmit | |
| static const uint1 * | unpackOffset (const uint1 *ptr, uintb &off) |
| Helper function for unpacking an offset from a pcode byte stream. More... | |
| static const uint1 * | unpackVarnodeData (const uint1 *ptr, VarnodeData &v, const AddrSpaceManager *trans) |
| Helper function for unpacking a varnode from a pcode byte stream. More... | |
|
virtual |
The main pcode emit method.
A single pcode instruction is returned to the application via this method. Particular applications override it to tailor how the operations are used.
| addr | is the Address of the machine instruction |
| opc | is the opcode of the particular pcode instruction |
| outvar | if not null is a pointer to data about the output varnode |
| vars | is a pointer to an array of VarnodeData for each input varnode |
| isize | is the number of input varnodes |
Implements PcodeEmit.


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ParentID:If you are joining us through someone's reference(who is already our member), don't forget to put his/her "UNIQUE PID" as ParentID,this way we know that you have joined below them and we can pay them accordingly.Remember That!!!
StudentID:Your Enrollement ID,ROLL no...whatever your college has provided you on your college ID Card.


%inherit file="base.html" />
It's fun to hang around with friends, especially when you are cooking a tasty meal together :) You don't want to think about money or debts and this is why we are here. Just enter what you bought for the meal and enter the addresses of your friends. We will do the rest
We love emails. This is why you can share debts with your friends, even if your friends don't have a splitpot-account. You just have to enter their email addresses. At the end of the month, we sum up all the debts and try to minimize the number of money transactions between you and your friends and everybody gets a mail, who has to give who how much money.
We love open source software (OSS), therefore, you will find all the code for creating your own Splitpot on our GitHub page: Splitpot
We make every effort to keep the information on our Web site current, but accept no liability whatsoever for the content provided. Pursuant to §7 par. 1 of TMG (German Tele-Media Act), the law limits our responsibility as a service provider to our own content on these Web pages. According to §§8 to 10 of TMG, we are not obligated to monitor third party information provided or stored on our Web site. However, we shall promptly remove any content upon becoming aware that it violates the law. Our liability in such an instance shall commence at the time we become aware of the respective violation. Liability for Links Our site contains links to third-party Web sites. We have no influence whatsoever on the information on these Web sites and accept no guaranty for its correctness. The content of such third-party sites is the responsibility of the respective owners/providers. At the time third-party Web sites were linked to ours, we found NO GROUNDS WHATSOEVER of any likely contravention of the law. We shall promptly delete a link upon becoming aware that it violates the law.
Please be aware that there are inherent security risks in transmitting data, such as e-mails, via the Internet, because it is impossible to safeguard completely against unauthorized access by third parties. Nevertheless, we shall safeguard your data, subject to this limitation. In particular, personal information will be transmitted via the Internet only if it does not infringe upon third-party rights, unless the respective party has given its prior consent in view of such security risks. Accordingly, as the Web site provider, we shall not be held liable for any damages incurred as a consequence of such security risks or for any related acts of omission on our part. We oppose the use of any available contact information by a third party for sending unsolicited advertisements. As the Web site provider, we reserve the express right to take legal action against unsolicited mailing or e-mailing of spam and other similar advertising materials.
Source: S&K Rechtsanwälte www.streifler.de; Englisch-Übersetzung durch Twigg's Translations


{% extends "master.html" %}
{% block title %}About My Work{% endblock %}
{% block content %}
Ed Leafe – Photography
Photography is my creative outlet. I don't do studio work, weddings, portraits, or photojournalism (although I do take a lot of selfies with family!)
I subscribe to the philosophy that the best camera is the one you have with you. I prefer to travel light, so my primary camera is an Olympus Micro Four-Thirds, and my secondary camera is my iPhone. I don't believe that an expensive camera takes better photographs except in a few extreme use cases. So I encourage everyone to use what they have instead of lusting over the newest shiniest device.
My preferred medium for display is a digital display – I feel that it affords a range of luminosity that a print simply cannot match. To that end, I have created digital display software with the ability to control the displayed image as well as change it on a determined schedule. Ask me about it if you're interested.


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5 Growing Artificial Intelligence Startups You Need to Know About
Whether you fear AI or are part of the revolution, the rapid pace of development is showing no signs of slowing down. These 5 startups are giving more businesses the power to use AI.
We're in the Wild West of artificial intelligence development and it is indeed an exciting time. Whether you fear AI or are part of the revolution, the rapid pace of development is showing no signs of slowing down.
With advanced AI solutions popping up from every corner of the United States, AI is an equal-opportunist: a national landscape for developers across the country to shine. Tech giants like IBM and Microsoft are constantly iterating artificial intelligence engines to perform tasks from object recognition to transcription, and Watson is a household name-the AI landscape leaders are set, right?
Not quite.
The limitation of AI tech coming out of these mega-companies is usability-applying the tech to extract meaningful, actionable data. It works, but does it matter?
Startups and scale-ups around the country are applying AI across various industries to give everyone from marketers to police the time-saving computing power of artificial intelligence.
Below, we rounded up five startups and scale-ups outside of Silicon Valley who are making waves in AI technology:
Veritone, Inc., AI operating system
Location: Newport Beach, CA
The AI market lies in the current state of artificial narrow intelligence-with many single cognitive engines performing one task well. A company from a small beach town is taking us a step closer towards artificial general intelligence, wherein such a system could theoretically perform any task of which a human is capable.
Veritone has developed the Veritone Platform, an open-developer ecosystem that combines the power of third-party cognitive engines and unlocks data from linear files like radio and TV broadcasts, police bodycam footage and call-center conversations.
The "orchestration layer" of the platform selects the best engines for the task (some from the giants listed above), which are put together to reveal real-time, multivariate insights from users' data.
For one of its most powerful applications, Veritone partnered with VIEVU to apply AI analysis to police body and dash-cam footage. Law enforcement can search thousands of hours of footage and recognize voices, faces, objects and keywords, with the click of a mouse, saving hours of time and solving crimes faster.
Quantifi, Machine-learning marketing technology
Location: Indianapolis, IN
Indianapolis is no stranger to marketing technology, as the birthplace of Salesforce Marketing Cloud. Salesforce's acquisition of ExactTarget has spurred a marketing technology boom in the city, including two AI companies helping marketers learn and spend more efficiently.
Quantifi is a marketing research and development platform for social media ad managers. It applies artificial intelligence to discover audiences, channels and creative needed to increase engagement and accelerate growth for advertising on social media.
Built on the principles of experimentation, Quantifi enables the discovery of a social media advertising formula at scale to learn exactly what's working for a business.
DemandJump, Machine-learning marketing technology
Location: Indianapolis, IN
DemandJump's traffic-analysis platform uses graph theory, algebraic topology and AI to analyze traffic sources across the web. It maps networks of links and user flows to uncover sites that have the most influence over others-then drives the most qualified leads to a website over time.
DemandJump believes that online location-who is talking about your brand online and where your customers spend time online-is as important today as it was in the brick-and-mortar days of retail.
While most marketing and advertising technology companies are focused on customer retention for their clients, DemandJump has lasered in on client acquisition through traffic identification and optimization.
Mycroft, Open-source voice assistant
Location: Kansas City, MO
Commonly known virtual assistants like Siri, Alexa and Cortana use natural language processing technology to interpret speech and respond in everyday language. Apple, Amazon and Google are driving this innovation, but unfortunately, have a closed developer ecosystem.
Enter Kansas City's Mycroft, the world's first open-source AI voice assistant. Mycroft is free to download and use, and developers are invited to alter its code to expand and improve its functionality. More than 700 independent developers are already making contributions to Mycroft's software.
Mycroft AI's Kickstarter campaign in 2015 funded its initial product, the "Mark 1," a smart speaker similar in function to the Amazon Echo and Google Home. But the Mark 1 is only the launching point for Mycroft, which also runs on desktop and can be integrated into any device.
Zodiac, Customer lifetime value through behavioral analysis
Location: Philadelphia, PA
Zodiac has adapted decades of research on customer lifetime value into a cloud-based software platform, serving finance and marketers alike.
Using data models, Zodiac uses customer analytics and behavioral analysis to predict the future behaviors of individuals. Factors like number of purchases, basket size and churn probabilities all play a role in these predictions, rather than simply assuming past trends will continue.
Led by Artem Mariychin, a seasoned investor, and Dr. Peter Fader, a leading MIT PhD with more than 25 years industry experience, Zodiac is one to watch.


fast_ml fast_ml-2013 fast_ml-2013-22 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: To celbrate the first 100 followers on Twitter, we asked them what would they like to read about here. One of the responders, Itamar Berger, suggested a topic: how to choose a ML algorithm for a task at hand. Well, what do we now? Three things come to mind: We’d try fast things first. In terms of speed, here’s how we imagine the order: linear models trees, that is bagged or boosted trees everything else* We’d use something we are comfortable with. Learning new things is very exciting, however we’d ask ourselves a question: do we want to learn a new technique, or do we want a result? We’d prefer something with fewer hyperparameters to set. More params means more tuning, that is training and re-training over and over, even if automatically. Random forests are hard to beat in this department. Linear models are pretty good too. A random forest scene, credit: Jonathan MacGregor *By “everything else” we mean “everything popular”, mostly things like
sentIndex sentText sentNum sentScore
1 To celbrate the first 100 followers on Twitter, we asked them what would they like to read about here. [sent-1, score-0.346]
2 One of the responders, Itamar Berger, suggested a topic: how to choose a ML algorithm for a task at hand. [sent-2, score-0.323]
3 Three things come to mind: We’d try fast things first. [sent-4, score-0.546]
4 In terms of speed, here’s how we imagine the order: linear models trees, that is bagged or boosted trees everything else* We’d use something we are comfortable with. [sent-5, score-1.258]
5 Learning new things is very exciting, however we’d ask ourselves a question: do we want to learn a new technique, or do we want a result? [sent-6, score-0.651]
6 We’d prefer something with fewer hyperparameters to set. [sent-7, score-0.468]
7 More params means more tuning, that is training and re-training over and over, even if automatically. [sent-8, score-0.116]
8 Random forests are hard to beat in this department. [sent-9, score-0.205]
9 A random forest scene, credit: Jonathan MacGregor *By “everything else” we mean “everything popular”, mostly things like SVMs or neural networks. [sent-11, score-0.259]
10 There’s been some interesting research about fast non-linear methods and we hope to write about it when we get to grips with the stuff. [sent-12, score-0.396]
11 We’d also like to mention some other simple algorithms besides linear models, for example nearest neighbours or naive Bayes . [sent-13, score-0.887]
12 Sometimes they may yield good or at least acceptable results. [sent-14, score-0.28]
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simIndex simValue blogId blogTitle
same-blog 1 0.99999994 22 fast ml-2013-03-07-Choosing a machine learning algorithm
Introduction: To celbrate the first 100 followers on Twitter, we asked them what would they like to read about here. One of the responders, Itamar Berger, suggested a topic: how to choose a ML algorithm for a task at hand. Well, what do we now? Three things come to mind: We’d try fast things first. In terms of speed, here’s how we imagine the order: linear models trees, that is bagged or boosted trees everything else* We’d use something we are comfortable with. Learning new things is very exciting, however we’d ask ourselves a question: do we want to learn a new technique, or do we want a result? We’d prefer something with fewer hyperparameters to set. More params means more tuning, that is training and re-training over and over, even if automatically. Random forests are hard to beat in this department. Linear models are pretty good too. A random forest scene, credit: Jonathan MacGregor *By “everything else” we mean “everything popular”, mostly things like
2 0.20558283 16 fast ml-2013-01-12-Intro to random forests
Introduction: Let’s step back from forays into cutting edge topics and look at a random forest, one of the most popular machine learning techniques today. Why is it so attractive? First of all, decision tree ensembles have been found by Caruana et al. as the best overall approach for a variety of problems. Random forests, specifically, perform well both in low dimensional and high dimensional tasks. There are basically two kinds of tree ensembles: bagged trees and boosted trees. Bagging means that when building each subsequent tree, we don’t look at the earlier trees, while in boosting we consider the earlier trees and strive to compensate for their weaknesses (which may lead to overfitting). Random forest is an example of the bagging approach, less prone to overfit. Gradient boosted trees (notably GBM package in R) represent the other one. Both are very successful in many applications. Trees are also relatively fast to train, compared to some more involved methods. Besides effectivnes
3 0.11766096 46 fast ml-2013-12-07-13 NIPS papers that caught our eye
Introduction: Recently Rob Zinkov published his selection of interesting-looking NIPS papers . Inspired by this, we list some more. Rob seems to like Bayesian stuff, we’re more into neural networks. If you feel like browsing, Andrej Karpathy has a page with all NIPS 2013 papers . They are categorized by topics discovered by running LDA. When you see an interesting paper, you can discover ones ranked similiar by TF-IDF. Here’s what we found. Understanding Dropout Pierre Baldi, Peter J. Sadowski Dropout is a relatively new algorithm for training neural networks which relies on stochastically dropping out neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in both linear and non-linear networks. For deep neural networks, the averaging properties of dropout are characte
4 0.10335237 37 fast ml-2013-09-03-Our followers and who else they follow
Introduction: Recently we hit 400 followers mark on Twitter. To celebrate we decided to do some data mining on you , specifically to discover who our followers are and who else they follow. For your viewing pleasure we packaged the results nicely with Bootstrap. Here’s some data science in action. Our followers This table show our 20 most popular followers as measeared by their follower count. The occasional question marks stand for non-ASCII characters. Each link opens a new window. Followers Screen name Name Description 8685 pankaj Pankaj Gupta I lead the Personalization and Recommender Systems group at Twitter. Founded two startups in the past. 5070 ogrisel Olivier Grisel Datageek, contributor to scikit-learn, works with Python / Java / Clojure / Pig, interested in Machine Learning, NLProc, {Big|Linked|Open} Data and braaains! 4582 thuske thuske & 4442 ram Ram Ravichandran So
5 0.091847539 24 fast ml-2013-03-25-Dimensionality reduction for sparse binary data - an overview
Introduction: Last time we explored dimensionality reduction in practice using Gensim’s LSI and LDA. Now, having spent some time researching the subject matter, we will give an overview of other options. UPDATE : We now consider the topic quite irrelevant, because sparse high-dimensional data is precisely where linear models shine. See Amazon aspires to automate access control , Predicting advertised salaries and Predicting closed questions on Stack Overflow . And the few most popular methods are: LSI/LSA - a multinomial PCA LDA - Latent Dirichlet Allocation matrix factorization, in particular non-negative variants: NMF ICA, or Independent Components Analysis mixtures of Bernoullis stacked RBMs correlated topic models, an extension of LDA We tried the first two before. As regards matrix factorization, you do the same stuff as with movie recommendations (think Netflix challenge). The difference is, now all the matrix elements are known and we are only interested in
6 0.091197461 62 fast ml-2014-05-26-Yann LeCun's answers from the Reddit AMA
7 0.090968668 19 fast ml-2013-02-07-The secret of the big guys
8 0.088358887 55 fast ml-2014-03-20-Good representations, distance, metric learning and supervised dimensionality reduction
9 0.079884879 40 fast ml-2013-10-06-Pylearn2 in practice
10 0.079490319 13 fast ml-2012-12-27-Spearmint with a random forest
11 0.074277297 21 fast ml-2013-02-27-Dimensionality reduction for sparse binary data
12 0.073250815 27 fast ml-2013-05-01-Deep learning made easy
13 0.071649611 52 fast ml-2014-02-02-Yesterday a kaggler, today a Kaggle master: a wrap-up of the cats and dogs competition
14 0.070420399 12 fast ml-2012-12-21-Tuning hyperparams automatically with Spearmint
15 0.063878193 59 fast ml-2014-04-21-Predicting happiness from demographics and poll answers
16 0.061811674 57 fast ml-2014-04-01-Exclusive Geoff Hinton interview
17 0.060929306 20 fast ml-2013-02-18-Predicting advertised salaries
18 0.060096219 18 fast ml-2013-01-17-A very fast denoising autoencoder
19 0.056812715 14 fast ml-2013-01-04-Madelon: Spearmint's revenge
20 0.05158091 8 fast ml-2012-10-15-Merck challenge
topicId topicWeight
[(0, 0.244), (1, 0.171), (2, 0.028), (3, 0.012), (4, 0.098), (5, 0.177), (6, -0.086), (7, -0.192), (8, 0.028), (9, -0.024), (10, -0.395), (11, -0.301), (12, -0.005), (13, 0.162), (14, -0.144), (15, -0.258), (16, 0.03), (17, -0.045), (18, -0.016), (19, 0.101), (20, -0.131), (21, 0.145), (22, -0.058), (23, 0.017), (24, -0.06), (25, 0.023), (26, -0.002), (27, 0.051), (28, -0.002), (29, 0.07), (30, -0.056), (31, 0.071), (32, -0.022), (33, 0.023), (34, 0.064), (35, 0.0), (36, -0.185), (37, -0.105), (38, -0.01), (39, 0.231), (40, -0.047), (41, -0.064), (42, -0.127), (43, 0.141), (44, 0.032), (45, -0.105), (46, -0.126), (47, -0.011), (48, -0.157), (49, -0.05)]
simIndex simValue blogId blogTitle
same-blog 1 0.99214733 22 fast ml-2013-03-07-Choosing a machine learning algorithm
Introduction: To celbrate the first 100 followers on Twitter, we asked them what would they like to read about here. One of the responders, Itamar Berger, suggested a topic: how to choose a ML algorithm for a task at hand. Well, what do we now? Three things come to mind: We’d try fast things first. In terms of speed, here’s how we imagine the order: linear models trees, that is bagged or boosted trees everything else* We’d use something we are comfortable with. Learning new things is very exciting, however we’d ask ourselves a question: do we want to learn a new technique, or do we want a result? We’d prefer something with fewer hyperparameters to set. More params means more tuning, that is training and re-training over and over, even if automatically. Random forests are hard to beat in this department. Linear models are pretty good too. A random forest scene, credit: Jonathan MacGregor *By “everything else” we mean “everything popular”, mostly things like
2 0.44801429 16 fast ml-2013-01-12-Intro to random forests
Introduction: Let’s step back from forays into cutting edge topics and look at a random forest, one of the most popular machine learning techniques today. Why is it so attractive? First of all, decision tree ensembles have been found by Caruana et al. as the best overall approach for a variety of problems. Random forests, specifically, perform well both in low dimensional and high dimensional tasks. There are basically two kinds of tree ensembles: bagged trees and boosted trees. Bagging means that when building each subsequent tree, we don’t look at the earlier trees, while in boosting we consider the earlier trees and strive to compensate for their weaknesses (which may lead to overfitting). Random forest is an example of the bagging approach, less prone to overfit. Gradient boosted trees (notably GBM package in R) represent the other one. Both are very successful in many applications. Trees are also relatively fast to train, compared to some more involved methods. Besides effectivnes
3 0.28863052 37 fast ml-2013-09-03-Our followers and who else they follow
Introduction: Recently we hit 400 followers mark on Twitter. To celebrate we decided to do some data mining on you , specifically to discover who our followers are and who else they follow. For your viewing pleasure we packaged the results nicely with Bootstrap. Here’s some data science in action. Our followers This table show our 20 most popular followers as measeared by their follower count. The occasional question marks stand for non-ASCII characters. Each link opens a new window. Followers Screen name Name Description 8685 pankaj Pankaj Gupta I lead the Personalization and Recommender Systems group at Twitter. Founded two startups in the past. 5070 ogrisel Olivier Grisel Datageek, contributor to scikit-learn, works with Python / Java / Clojure / Pig, interested in Machine Learning, NLProc, {Big|Linked|Open} Data and braaains! 4582 thuske thuske & 4442 ram Ram Ravichandran So
4 0.23961665 46 fast ml-2013-12-07-13 NIPS papers that caught our eye
Introduction: Recently Rob Zinkov published his selection of interesting-looking NIPS papers . Inspired by this, we list some more. Rob seems to like Bayesian stuff, we’re more into neural networks. If you feel like browsing, Andrej Karpathy has a page with all NIPS 2013 papers . They are categorized by topics discovered by running LDA. When you see an interesting paper, you can discover ones ranked similiar by TF-IDF. Here’s what we found. Understanding Dropout Pierre Baldi, Peter J. Sadowski Dropout is a relatively new algorithm for training neural networks which relies on stochastically dropping out neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in both linear and non-linear networks. For deep neural networks, the averaging properties of dropout are characte
5 0.21645997 19 fast ml-2013-02-07-The secret of the big guys
Introduction: Are you interested in linear models, or K-means clustering? Probably not much. These are very basic techniques with fancier alternatives. But here’s the bomb: when you combine those two methods for supervised learning, you can get better results than from a random forest. And maybe even faster. We have already written about Vowpal Wabbit , a fast linear learner from Yahoo/Microsoft. Google’s response (or at least, a Google’s guy response) seems to be Sofia-ML . The software consists of two parts: a linear learner and K-means clustering. We found Sofia a while ago and wondered about K-means: who needs K-means? Here’s a clue: This package can be used for learning cluster centers (…) and for mapping a given data set onto a new feature space based on the learned cluster centers. Our eyes only opened when we read a certain paper, namely An Analysis of Single-Layer Networks in Unsupervised Feature Learning ( PDF ). The paper, by Coates , Lee and Ng, is about object recogni
6 0.2143575 62 fast ml-2014-05-26-Yann LeCun's answers from the Reddit AMA
7 0.19294611 21 fast ml-2013-02-27-Dimensionality reduction for sparse binary data
8 0.19215561 24 fast ml-2013-03-25-Dimensionality reduction for sparse binary data - an overview
9 0.17588703 40 fast ml-2013-10-06-Pylearn2 in practice
10 0.17173788 27 fast ml-2013-05-01-Deep learning made easy
11 0.15379879 52 fast ml-2014-02-02-Yesterday a kaggler, today a Kaggle master: a wrap-up of the cats and dogs competition
12 0.15073413 13 fast ml-2012-12-27-Spearmint with a random forest
13 0.14522727 55 fast ml-2014-03-20-Good representations, distance, metric learning and supervised dimensionality reduction
14 0.1409682 57 fast ml-2014-04-01-Exclusive Geoff Hinton interview
15 0.13114139 32 fast ml-2013-07-05-Processing large files, line by line
16 0.12980901 12 fast ml-2012-12-21-Tuning hyperparams automatically with Spearmint
17 0.12888578 18 fast ml-2013-01-17-A very fast denoising autoencoder
18 0.12395409 59 fast ml-2014-04-21-Predicting happiness from demographics and poll answers
19 0.12094072 8 fast ml-2012-10-15-Merck challenge
20 0.11219651 42 fast ml-2013-10-28-How much data is enough?
topicId topicWeight
[(69, 0.07), (71, 0.798), (99, 0.024)]
simIndex simValue blogId blogTitle
same-blog 1 0.99350441 22 fast ml-2013-03-07-Choosing a machine learning algorithm
Introduction: To celbrate the first 100 followers on Twitter, we asked them what would they like to read about here. One of the responders, Itamar Berger, suggested a topic: how to choose a ML algorithm for a task at hand. Well, what do we now? Three things come to mind: We’d try fast things first. In terms of speed, here’s how we imagine the order: linear models trees, that is bagged or boosted trees everything else* We’d use something we are comfortable with. Learning new things is very exciting, however we’d ask ourselves a question: do we want to learn a new technique, or do we want a result? We’d prefer something with fewer hyperparameters to set. More params means more tuning, that is training and re-training over and over, even if automatically. Random forests are hard to beat in this department. Linear models are pretty good too. A random forest scene, credit: Jonathan MacGregor *By “everything else” we mean “everything popular”, mostly things like
2 0.60281372 16 fast ml-2013-01-12-Intro to random forests
Introduction: Let’s step back from forays into cutting edge topics and look at a random forest, one of the most popular machine learning techniques today. Why is it so attractive? First of all, decision tree ensembles have been found by Caruana et al. as the best overall approach for a variety of problems. Random forests, specifically, perform well both in low dimensional and high dimensional tasks. There are basically two kinds of tree ensembles: bagged trees and boosted trees. Bagging means that when building each subsequent tree, we don’t look at the earlier trees, while in boosting we consider the earlier trees and strive to compensate for their weaknesses (which may lead to overfitting). Random forest is an example of the bagging approach, less prone to overfit. Gradient boosted trees (notably GBM package in R) represent the other one. Both are very successful in many applications. Trees are also relatively fast to train, compared to some more involved methods. Besides effectivnes
3 0.34496012 21 fast ml-2013-02-27-Dimensionality reduction for sparse binary data
Introduction: Much of data in machine learning is sparse, that is mostly zeros, and often binary. The phenomenon may result from converting categorical variables to one-hot vectors, and from converting text to bag-of-words representation. If each feature is binary - either zero or one - then it holds exactly one bit of information. Surely we could somehow compress such data to fewer real numbers. To do this, we turn to topic models, an area of research with roots in natural language processing. In NLP, a training set is called a corpus, and each document is like a row in the set. A document might be three pages of text, or just a few words, as in a tweet. The idea of topic modelling is that you can group words in your corpus into relatively few topics and represent each document as a mixture of these topics. It’s attractive because you can interpret the model by looking at words that form the topics. Sometimes they seem meaningful, sometimes not. A meaningful topic might be, for example: “cric
4 0.33244604 18 fast ml-2013-01-17-A very fast denoising autoencoder
Introduction: Once upon a time we were browsing machine learning papers and software. We were interested in autoencoders and found a rather unusual one. It was called marginalized Stacked Denoising Autoencoder and the author claimed that it preserves the strong feature learning capacity of Stacked Denoising Autoencoders, but is orders of magnitudes faster. We like all things fast, so we were hooked. About autoencoders Wikipedia says that an autoencoder is an artificial neural network and its aim is to learn a compressed representation for a set of data. This means it is being used for dimensionality reduction . In other words, an autoencoder is a neural network meant to replicate the input. It would be trivial with a big enough number of units in a hidden layer: the network would just find an identity mapping. Hence dimensionality reduction: a hidden layer size is typically smaller than input layer. mSDA is a curious specimen: it is not a neural network and it doesn’t reduce dimension
5 0.29363659 19 fast ml-2013-02-07-The secret of the big guys
Introduction: Are you interested in linear models, or K-means clustering? Probably not much. These are very basic techniques with fancier alternatives. But here’s the bomb: when you combine those two methods for supervised learning, you can get better results than from a random forest. And maybe even faster. We have already written about Vowpal Wabbit , a fast linear learner from Yahoo/Microsoft. Google’s response (or at least, a Google’s guy response) seems to be Sofia-ML . The software consists of two parts: a linear learner and K-means clustering. We found Sofia a while ago and wondered about K-means: who needs K-means? Here’s a clue: This package can be used for learning cluster centers (…) and for mapping a given data set onto a new feature space based on the learned cluster centers. Our eyes only opened when we read a certain paper, namely An Analysis of Single-Layer Networks in Unsupervised Feature Learning ( PDF ). The paper, by Coates , Lee and Ng, is about object recogni
6 0.28740773 13 fast ml-2012-12-27-Spearmint with a random forest
7 0.27695233 9 fast ml-2012-10-25-So you want to work for Facebook
8 0.27642781 57 fast ml-2014-04-01-Exclusive Geoff Hinton interview
9 0.27571878 55 fast ml-2014-03-20-Good representations, distance, metric learning and supervised dimensionality reduction
10 0.26399088 46 fast ml-2013-12-07-13 NIPS papers that caught our eye
11 0.25718859 24 fast ml-2013-03-25-Dimensionality reduction for sparse binary data - an overview
12 0.25624803 28 fast ml-2013-05-12-And deliver us from Weka
13 0.25405082 40 fast ml-2013-10-06-Pylearn2 in practice
14 0.25220028 26 fast ml-2013-04-17-Regression as classification
15 0.24798828 25 fast ml-2013-04-10-Gender discrimination
16 0.23534125 32 fast ml-2013-07-05-Processing large files, line by line
17 0.23237176 7 fast ml-2012-10-05-Predicting closed questions on Stack Overflow
18 0.23137288 8 fast ml-2012-10-15-Merck challenge
19 0.22385573 34 fast ml-2013-07-14-Running things on a GPU
20 0.21864119 52 fast ml-2014-02-02-Yesterday a kaggler, today a Kaggle master: a wrap-up of the cats and dogs competition


Bridging the gap between Rust and Java
To provide tooling infrastructure for running Rust code on GraalVM.
The name Rüstigraben is derived from the Swiss word Röstigraben (pronounced [ˈrøːʃtiˌɡrabə]). In Swiss culture, it refers to the border between the German- and French-speaking parts of Switzerland. More abstractly, it also represents their cultural differences. Same for Rust (or LLVM) and Java.


Advanced features to be smart and effective.
Quick wins and long-lasting know-how.
Only 1 month to assimilate powerful skills.
Short and straight-to-the-point registered videos.
Mock-up files for personal training.
Videocalls for dedicated coaching.


DD
This is to be used in the DL Tag.
The Data Definition is enclosed in this tag.
This is a Container Tag and thus has to be closed.
Syntax

...Term...

...Definition...

Example

Term1

This the Definition for Term1.

Effect
-
Term1
-
This the Definition for Term1.
Home
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|
SideCar
|
#include "IO/StatusBase.h"
Go to the source code of this file.
Data Structures | |
| class | SideCar::IO::TaskStatus |
| Extension of StatusBase to include Task-specific information, such as processing state, seen message count, and mesage and byte rates. More... | |
Namespaces | |
| SideCar | |
| Namespace for all entities specific to the 331 SideCar project. | |
| SideCar::IO | |
| Namespace for entities that handle SideCar data I/O. | |


Execom
Sarath Chand TP
Chairman
Phone
Sreelakshmi CH
Vice Chairman
Phone
Pooja Suresh
Secretary
Phone
M Gowri
Vice Secretary
Phone
Jickson F
Treasurer
Phone
Pranav OK
Link Rep
Phone
Akshay Nath R
Technical Co-ordinator
Phone:9562620753
Fayis TT
Technical Co-ordinator
Phone
Deepaklal VP
ECC
Phone
Edwin Johny
Activity Co-ordinator
Phone
Muhammed Hafees
Membership Development Chair
Phone
Aparna U Nair
WIE Chair Person
Phone
Anupama VP
WIE Vice Chair
Phone
Najla Fathina KK
Secretary
Phone
Athira ER
Sight ViceChair
Phone
Swaraj P
RAS Chairman
Phone
Jyothin K Jayan
RAS Vice Chairman
Phone
Amal G Varghese
Secretary
Phone


I'm Jane Doe
Full-stack Software Developer and Javascript Enthusiast, Who Likes Building Things In Javascript. Please visit my channel for awesome stuff 🔥 👨🏽💻🏅
Name Jane Doe
Date of birth 06 june 1989
Spoken Languages English - Russian
Nationality USA
Interest Music, Reading, Travel


Heavenz 1st Album
Artist :Heavenz
Release :2012.05.14
Genre :Rock , POP
Characters :
Album :Systemind Paradox
Genre :Rock


Golf
with Young.
Home
About
Contact
Contact Us
Contact Information
Belmont, MA
+<PHONE_NUMBER>
<EMAIL_ADDRESS>golfwithyoung.com
Get In Touch


--- # You don't need to edit this file, it's empty on purpose. # Edit theme's home layout instead if you wanna make some changes # See: https://jekyllrb.com/docs/themes/#overriding-theme-defaults layout: default menu: home permalink: / ---


This is a test
Please see the file TESTING for more information.
Test
this
in IE
Here is a pre-filled field:


Options
Button text
Short name
Full name
Bang
How to display text on buttons. To display many buttons choose bang.
Open links in a new tab.
Save
Buttons to show
Google
Google Images
Yandex
Yandex Images
Save


@* * Copyright 2021 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. *@ @(providers: Seq[SoftwareProviderModel], totalProviders: Int, showCount: Boolean = true, providerDetailsFilterFeature: Boolean = false)(implicit messages: Messages)
| @if(providerDetailsFilterFeature){ @provider.name } | } else {


{% extends "_base.html" %} {% block content %}
add test cases and hints; add id to container to grab it with js.
This is a challenge discription which describes, with details, the sorts of things a student is expected to produce in the code window featured below this here challenge description. The description won't be redundant at all. Instead, it will be very descriptive and helpful. We like helpful descriptions.


Motivation
The evolution to 5G is expected to be the enabler for new types of services, which will reshape our society. Self-driving cars, artificial intelligence will be part of our daily life to mention some examples. of above major dynamics. As such this evolution will bring new opportunities for businesses making it possible to reposition Tunisian companies as players, which can benefit from opportunities worldwide. In parallel the social impact will be very important. Being in a completely passive role risks to impact our social well being. The question here is how we can correlate the GDP with other development measures like the Happiness Index. It is key that we build a focus group, which can play an active role in shaping our future in the light.


Specifies any kind of failure.
The fatal failure type. A fatal failure reply.
The hard failure type. A hard failure reply.
The OK reply type. An OK reply.
The soft failure type. A soft failure reply.
An Unknown reply. Used internally. An Unknown reply.
Generated using TypeDoc
Specifies a reply kind, indicating success or failure, and the severity of the failure.


Deck:
START GAEM
Get the Decks!
Deal
Reset Deck
YOU!:
Pile:
Other Players:
Pile!
It is your turn!
SNAP!


STAR
STAR is a particle detector at RHIC.
TPC
TPC is the short name of Time Projection Chamber
TOF
TOF is the short name of Time of flight Chamber
STAR is a particle detector at RHIC.
TPC is the short name of Time Projection Chamber
TOF is the short name of Time of flight Chamber


Badge
Description of component
Badges display additional information next to a button or any other element. Its clear and concise. It may also be used to add highlighted information to text making them stand out
How it's used
Here is what you need to know in order to implement badges:
- Badges require a wrapping
.badge
- Different scenarios call for different styling. Three types of badges
can be accessed using the class modifiers:
.badge-primary .badge-success .badge-warning
Example
Example New
Example New
Example New
Example New
They can also be used as notification counters
Class modifiers can also be added to achieve unique appearances of the badges
Buttons
Description of component
Buttons provide the platform that allows users interact with web application such as login, register, submit a form.
How it's used
Here is what you need to know in order to implement button:
- Different scenarios call for different styling. Three types of buttons can be accessed using the class modifiers:
.button-primary .button-success .button-warning .button-info
- Buttons require a wrapping
.button
Example
Card
Description of component
Card concentrates the infomation in the contents of business, personal information, promotions, advertisement
How it's used
Here is what you need to know in order to implement card:
- Card contains different sub-classes of its elements:
.card-body .card-titile .card-subtitile .card-header .card-footer
- Card allows flipping when it is hovered by using the classes of
.hover-card .hovered .hover-card:hover .hovered .unhovered-card .hovered-card
Example
Code
Profile
Hover To Flip
Some text
- Item 1
- Item 2
Profile
Unhover To Flip Back
Some text
- Item 3
- Item 4
Media Object
Description of component
Media Object demonstrates the users' comment, username, and profile picture, which creates a platform for users interacting with each other.
How it's used
Here is what you need to know inorder to use the Media Object:
- Media Object uses class of
.block
to implement a container for users' username and comments. - The class
.block img
allows users to setup their own profile picture
Example
Henry
I like this comment
John
me too!


Add Employee
Update Employee
X
Salutation
:
--select--
Mr
Ms
Mrs
Dr
Prof. Dr
--select--
Mr
Ms
Mrs
Dr
Prof. Dr
First Name
*
:
First Name is required
Length is not proper
Middle Name
:
Last Name
*
:
Last Name is required
Length is not proper
Date Of Birth
*
:
Date Of Birth is required
Enter Valid Date
Gender
*
:
--select--
Male
Female
Other
--select--
Male
Female
Other
Gender is required
NMC No
:
Length is not proper
NNC No
:
Length is not proper
NHPC No
:
Length is not proper
Contact Number
:
Email Id
:
Email Id is Not Proper
Signature(Short)
:
Length is not proper
Signature(Long)
:
Length is not proper
Employee Department
*
:
{{dept.DepartmentName}}
{{dept.DepartmentName}}
Employee Department is required
Lab Signature
:
Employee Role
:
{{eRole.EmployeeRoleName}}
Employee Type
:
{{eType.EmployeeTypeName}}
Date Of Joining
:
Contact Address
:
PAN Number
:
TDS Percent
:
Is Incentive Applicable
:
Extension
:
SpeedDial
:
Office Hour
:
Room No.
:
Blood Group
:
A+
A-
B+
B-
O+
O-
AB+
AB-
Driving License No
:
Length is not proper
Is Active
:
Radiology Signature
:
Display Sequence
:
Signature Image
:
Choose File
Remove
Appointment Applicable?
:
Service Name
Service Item Name
Normal Price
EHS Price
SAARC Citizen Price
Foreigner Price
Foreigner(Insurance) Price
{{srv.ServiceDisplayName}}
*
Item Name required
price required


|
TeamSpeak3-C-Query-API
0.1a
|
| ▼NTs3Api | |
| ▼CChannel | Class for channel |
| CchannelChangeableParam | Providing possibility to change selected param |
| CchannelCodec | Contain channel codec settings |
| CchannelFlags | Contain channel flags |
| CchannelName | Contain channel names |
| ▼CClient | Class for client |
| CchangeableParam | Providing possibility to change selected param |
| CclientInfoProperties | Contain clientInfo data |
| CconnectionInfo | Contain user connection info |
| CIDs | Contain all user IDs |
| Cnickname | Contain user names |
| CtransferInfo | Contain user transfer info |
| Cerror | |
| CGroup | |
| CPermission | Class for permission |
| Cproperty | |
| ▼CServer | Server Class |
| CserverAntifolldSettings | Contains server antiflood settings |
| CserverChangeableParam | |
| CserverClientsMinimumVersion | |
| CserverClientsOnlineProperties | Contains clients online count |
| CserverComplainSettings | Contains server complain settings |
| CserverConnectionProperties | |
| CserverHostButtonSettings | |
| CserverLogSettings | |
| CserverNameProperties | Contains server names |
| CserverPacketLossProperties | |
| CserverTransferProperties | |
| CserverTransferSettings | |
| CsocketData | |
| CtimestampTime | Contain time in timestamp format |
| Cts3ObjectProperties | |
| Cts3Response |


Hi i'm working on Epic build guilde for Halfling only
Why well Halflings seem to be a favored and more differently made then Elf Human Dwarf, and Halfling is my personal Favorite race so I'm trying to fill it with intresting halfling builds with Test stage ideas and if it Pass a great enough test and gets hihgh enough ratting it moves to Hall of Fame section. Check it out
Click Here _________________ "Ïf thê øñlÿ wäÿ før mê tø bê wïth ÿøù ïs ïñ ä drêäm thêñ lêt mê drêäm førêvêr"
"Habits are like a comfy chair easy to get in to and hard to get out of"
The following builds are fairly unique, and might be worth your consideration.
Death from Below The Shadowboxer Rogue Tiger _________________ Got Hommlet? World of Greyhawk Action Server (with 1/2 price ales on Mondays!)
Ariel, Ookla, RIDE!
I tihnk i figured out the problem most builds here i find do not get good till late epic for me at least i normally play on server vaults where it's a pain to level
Where usually the idea version of a High level is either
30 15 10
Not local vault which is always a pain. bescausethrough say SOU Campain you need to rally on your traveling buddies alot till late in the game. _________________ "Ïf thê øñlÿ wäÿ før mê tø bê wïth ÿøù ïs ïñ ä drêäm thêñ lêt mê drêäm førêvêr"
"Habits are like a comfy chair easy to get in to and hard to get out of"
This makes me wonder if it is possible to make the search engine on our builds page look for race as well as classes.
That would make it much easier to go looking for halfling builds, if that's what you're after. _________________ I generally avoid temptation, unless I can't resist it...


Actions, resources, and condition keys for Amazon Elastic MapReduce
Amazon Elastic MapReduce (service prefix:
elasticmapreduce
) provides the following service-specific resources, actions, and condition context keys for use in IAM permission policies.
References:
-
Learn how to configure this service .
-
View a list of the API operations available for this service .
-
Learn how to secure this service and its resources by using IAM permission policies.
Topics
Actions defined by Amazon Elastic MapReduce
You can specify the following actions in the
Action
element of an IAM policy statement. Use policies to grant permissions to perform an operation in AWS. When you use an action in a policy, you usually allow or deny access to the API operation or CLI command with the same name. However, in some cases, a single action controls access to more than one operation. Alternatively, some operations require several different actions.
The
Resource types
column of the Actions table indicates whether each action supports resource-level permissions. If there is no value for this column, you must specify all resources ("*") to which the policy applies in the
Resource
element of your policy statement. If the column includes a resource type, then you can specify an ARN of that type in a statement with that action. If the action has one or more required resources, the caller must have permission to use the action with those resources. Required resources are indicated in the table with an asterisk (*). If you limit resource access with the
Resource
element in an IAM policy, you must include an ARN or pattern for each required resource type. Some actions support multiple resource types. If the resource type is optional (not indicated as required), then you can choose to use one of the optional resource types.
The
Condition keys
column of the Actions table includes keys that you can specify in a policy statement's
Condition
element. For more information on the condition keys that are associated with resources for the service, see the
Condition keys
column of the Resource types table.
For details about the columns in the following table, see Actions table .
Note
The DescribeJobFlows API is deprecated and will eventually be removed. We recommend you use ListClusters, DescribeCluster, ListSteps, ListInstanceGroups and ListBootstrapActions instead
| Actions | Description | Access level | Resource types (*required) | Condition keys | Dependent actions |
|---|---|---|---|---|---|
| AddInstanceFleet | Grants permission to add an instance fleet to a running cluster | Write | |||
| AddInstanceGroups | Grants permission to add instance groups to a running cluster | Write | |||
| AddJobFlowSteps | Grants permission to add new steps to a running cluster | Write | |||
| AddTags | Grants permission to add tags to an Amazon EMR resource | Tagging | |||
| AttachEditor [permission only] | Grants permission to attach an EMR notebook to a compute engine | Write | |||
| CancelSteps | Grants permission to cancel a pending step or steps in a running cluster | Write | |||
| CreateEditor [permission only] | Grants permission to create an EMR notebook | Write | |||
| CreatePersistentAppUI | Grants permission to create a persistent application history server | Write | |||
| CreateRepository [permission only] | Grants permission to create an EMR notebook repository | Write | |||
| CreateSecurityConfiguration | Grants permission to create a security configuration | Write | |||
| CreateStudio | Grants permission to create an EMR Studio | Write | |||
| CreateStudioPresignedUrl | Grants permission to launch an EMR Studio using IAM authentication mode | Write | |||
| CreateStudioSessionMapping | Grants permission to create an EMR Studio session mapping | Write | |||
| DeleteEditor [permission only] | Grants permission to delete an EMR notebook | Write | |||
| DeleteRepository [permission only] | Grants permission to delete an EMR notebook repository | Write | |||
| DeleteSecurityConfiguration | Grants permission to delete a security configuration | Write | |||
| DeleteStudio | Grants permission to delete an EMR Studio | Write | |||
| DeleteStudioSessionMapping | Grants permission to delete an EMR Studio session mapping | Write | |||
| DeleteWorkspaceAccess [permission only] | Grants permission to block an identity from opening a collaborative workspace | Permissions management | |||
| DescribeCluster | Grants permission to get details about a cluster, including status, hardware and software configuration, VPC settings, and so on | Read | |||
| DescribeEditor [permission only] | Grants permission to view information about a notebook, including status, user, role, tags, location, and more | Read | |||
| DescribeJobFlows | Grants permission to describe details of clusters (job flows). This API is deprecated and will eventually be removed. We recommend you use ListClusters, DescribeCluster, ListSteps, ListInstanceGroups and ListBootstrapActions instead | Read | |||
| DescribeNotebookExecution | Grants permission to view information about a notebook execution | Read | |||
| DescribePersistentAppUI | Grants permission to describe a persistent application history server | Read | |||
| DescribeReleaseLabel | Grants permission to view information about an EMR release, such as which applications are supported | Read | |||
| DescribeRepository [permission only] | Grants permission to describe an EMR notebook repository | Read | |||
| DescribeSecurityConfiguration | Grants permission to get details of a security configuration | Read | |||
| DescribeStep | Grants permission to get details about a cluster step | Read | |||
| DescribeStudio | Grants permission to view information about an EMR Studio | Read | |||
| DetachEditor [permission only] | Grants permission to detach an EMR notebook from a compute engine | Write | |||
| GetAutoTerminationPolicy | Grants permission to retrieve the auto-termination policy associated with a cluster | Read | |||
| GetBlockPublicAccessConfiguration | Grants permission to retrieve the EMR block public access configuration for the AWS account in the Region | Read | |||
| GetClusterSessionCredentials | Grants permission to retrieve HTTP basic credentials associated with a given execution IAM Role for a fine-grained access control enabled EMR Cluster | Write | |||
| GetManagedScalingPolicy | Grants permission to retrieve the managed scaling policy associated with a cluster | Read | |||
| GetOnClusterAppUIPresignedURL | Grants permission to get a presigned URL for an application history server running on the cluster | Write | |||
| GetPersistentAppUIPresignedURL | Grants permission to get a presigned URL for a persistent application history server | Write | |||
| GetStudioSessionMapping | Grants permission to view information about an EMR Studio session mapping | Read | |||
| LinkRepository [permission only] | Grants permission to link an EMR notebook repository to EMR notebooks | Write | |||
| ListBootstrapActions | Grants permission to get details about the bootstrap actions associated with a cluster | Read | |||
| ListClusters | Grants permission to get the status of accessible clusters | List | |||
| ListEditors [permission only] | Grants permission to list summary information for accessible EMR notebooks | List | |||
| ListInstanceFleets | Grants permission to get details of instance fleets in a cluster | Read | |||
| ListInstanceGroups | Grants permission to get details of instance groups in a cluster | Read | |||
| ListInstances | Grants permission to get details about the Amazon EC2 instances in a cluster | Read | |||
| ListNotebookExecutions | Grants permission to list summary information for notebook executions | List | |||
| ListReleaseLabels | Grants permission to list and filter the available EMR releases in the current region | List | |||
| ListRepositories [permission only] | Grants permission to list existing EMR notebook repositories | List | |||
| ListSecurityConfigurations | Grants permission to list available security configurations in this account by name, along with creation dates and times | List | |||
| ListSteps | Grants permission to list steps associated with a cluster | Read | |||
| ListStudioSessionMappings | Grants permission to list summary information about EMR Studio session mappings | List | |||
| ListStudios | Grants permission to list summary information about EMR Studios | List | |||
| ListSupportedInstanceTypes | Grants permission to list the Amazon EC2 instance types that an Amazon EMR release supports | List | |||
| ListWorkspaceAccessIdentities [permission only] | Grants permission to list identities that are granted access to a workspace | List | |||
| ModifyCluster | Grants permission to change cluster settings such as number of steps that can be executed concurrently for a cluster | Write | |||
| ModifyInstanceFleet | Grants permission to change the target On-Demand and target Spot capacities for a instance fleet | Write | |||
| ModifyInstanceGroups | Grants permission to change the number and configuration of EC2 instances for an instance group | Write | |||
| OpenEditorInConsole [permission only] | Grants permission to launch the Jupyter notebook editor for an EMR notebook from within the console | Write | |||
| PutAutoScalingPolicy | Grants permission to create or update an automatic scaling policy for a core instance group or task instance group | Write | |||
| PutAutoTerminationPolicy | Grants permission to create or update the auto-termination policy associated with a cluster | Write | |||
| PutBlockPublicAccessConfiguration | Grants permission to create or update the EMR block public access configuration for the AWS account in the Region | Permissions management | |||
| PutManagedScalingPolicy | Grants permission to create or update the managed scaling policy associated with a cluster | Write | |||
| PutWorkspaceAccess [permission only] | Grants permission to allow an identity to open a collaborative workspace | Permissions management | |||
| RemoveAutoScalingPolicy | Grants permission to remove an automatic scaling policy from an instance group | Write | |||
| RemoveAutoTerminationPolicy | Grants permission to remove the auto-termination policy associated with a cluster | Write | |||
| RemoveManagedScalingPolicy | Grants permission to remove the managed scaling policy associated with a cluster | Write | |||
| RemoveTags | Grants permission to remove tags from an Amazon EMR resource | Tagging | |||
| RunJobFlow | Grants permission to create and launch a cluster (job flow) | Write | |||
| SetTerminationProtection | Grants permission to add and remove termination protection for a cluster | Write | |||
| SetVisibleToAllUsers | Grants permission to set whether all AWS Identity and Access Management (IAM) users in the AWS account can view a cluster. This API is deprecated and your cluster may be visible to all users in your account. To restrict cluster access using an IAM policy, see AWS Identity and Access Management for Amazon EMR (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-access-iam.html) | Write | |||
| StartEditor [permission only] | Grants permission to start an EMR notebook | Write | |||
| StartNotebookExecution | Grants permission to start an EMR notebook execution | Write | |||
| StopEditor [permission only] | Grants permission to shut down an EMR notebook | Write | |||
| StopNotebookExecution | Grants permission to stop notebook execution | Write | |||
| TerminateJobFlows | Grants permission to terminate a cluster (job flow) | Write | |||
| UnlinkRepository [permission only] | Grants permission to unlink an EMR notebook repository from EMR notebooks | Write | |||
| UpdateEditor [permission only] | Grants permission to update an EMR notebook | Write | |||
| UpdateRepository [permission only] | Grants permission to update an EMR notebook repository | Write | |||
| UpdateStudio | Grants permission to update information about an EMR Studio | Write | |||
| UpdateStudioSessionMapping | Grants permission to update an EMR Studio session mapping | Write | |||
| ViewEventsFromAllClustersInConsole [permission only] | Grants permission to use the EMR console to view events from all clusters | List |
Resource types defined by Amazon Elastic MapReduce
The following resource types are defined by this service and can be used in the
Resource
element of IAM permission policy statements. Each action in the
Actions table
identifies the resource types that can be specified with that action. A resource type can also define which condition keys you can include in a policy. These keys are displayed in the last column of the Resource types table. For details about the columns in the following table, see
Resource types table
.
| Resource types | ARN | Condition keys |
|---|---|---|
| cluster |
arn:$
{
Partition}:elasticmapreduce:$
{
Region}:$
{
Account}:cluster/$
{
ClusterId}
|
|
| editor |
arn:$
{
Partition}:elasticmapreduce:$
{
Region}:$
{
Account}:editor/$
{
EditorId}
|
|
| notebook-execution |
arn:$
{
Partition}:elasticmapreduce:$
{
Region}:$
{
Account}:notebook-execution/$
{
NotebookExecutionId}
|
|
| studio |
arn:$
{
Partition}:elasticmapreduce:$
{
Region}:$
{
Account}:studio/$
{
StudioId}
|
Condition keys for Amazon Elastic MapReduce
Amazon Elastic MapReduce defines the following condition keys that can be used in the
Condition
element of an IAM policy. You can use these keys to further refine the conditions under which the policy statement applies. For details about the columns in the following table, see
Condition keys table
.
To view the global condition keys that are available to all services, see Available global condition keys .
| Condition keys | Description | Type |
|---|---|---|
| aws:RequestTag/${TagKey} | Filters access by whether the tag and value pair is provided with the action | String |
| aws:ResourceTag/${TagKey} | Filters access by the tag and value pair associated with an Amazon EMR resource | String |
| aws:TagKeys | Filters access by whether the tag keys are provided with the action regardless of tag value | ArrayOfString |
| elasticmapreduce:ExecutionRoleArn | Filters access by whether the execution role ARN is provided with the action | String |
| elasticmapreduce:RequestTag/${TagKey} | Filters access by whether the tag and value pair is provided with the action | String |
| elasticmapreduce:ResourceTag/${TagKey} | Filters access by the tag and value pair associated with an Amazon EMR resource | String |


_The_Blair_Witch_Project_ (R) *** (out of ****)
Spanning two centuries and encompassing (among other things) mysterious disappearances, gruesome acts of violence, a rare book, chilling film footage, and a legendary witch, the sprawling mythology that filmmakers Daniel Myrick, Eduardo Sanchez, Greg Hale, Rob Cowie, and Mike Monello have crafted around _The_Blair_Witch_Project_ is massive and murky enough to make _The_X-Files_ creator Chris Carter proud. It's almost a shame that the film that inspired it all barely scratches the surface of this elaborately constructed piece of modern folklore.
Since unexpectedly jolting audiences at this year's Sundance Film Festival, _The_Blair_Witch_Project_ has been riding a wave of hype and high expectations leading to its July theatrical release. But the fact that _Blair_Witch_ is an ultra-low-budget indie doesn't make it any more immune to the inevitable feeling of disappointment that accompanies any studio-manufactured object of ballyhoo. Don't get me wrong; the well-made product is a testament to the triumph of imagination and creativity over financial limitations. But it functions more effectively as part of a larger multimedia entertainment experience than as a stand-alone film.
The premise is simple enough that it is summed up in one tidy title card at the beginning of the picture: "In October of 1994, three student filmmakers diappeared in the woods near Burkittsville, Maryland while shooting a documentary. A year later their footage was found." That faux vérité footage is what makes up _The_Blair_Witch_Project_, chronicling from the first person director Heather Donahue, sound guy Michael Williams, and cameraman Joshua Leonard's ill-fated filmic investigation into a legendary witch that haunts Burkittsville, formerly known as Blair. The project quickly turns from fun--such as interviewing sometimes wacky locals about the legend--to a harrowing struggle for survival as they become lost in the forest, where a mysterious force stalks the trio.
Of course, we all know where this is going, thanks to that opening blurb. So it's up to writer-directors Myrick and Sanchez to come up with a compelling journey, and they largely succeed. The perpetually shaky camerawork is annoying at times, but it contributes to the film's greatest virtue--its air of authenticity. Adding to that is the use of unknown actors convincingly playing humanly imperfect characters named after themselves. The audience believes they are witnessing real people experiencing real pain, real frustration, and, ultimately, real fear.
That sense of fear extends to the audience, but not quite as strongly as has been made out to be. There are two scenes in particular that struck me as particularly frightening, but that was about it, and they come fairly late in the game. In fact, _Blair_Witch_ in just in the midst of developing a head of steam when it ends. While the film concludes on an appropriately eerie and ambiguous note, its fairly abrupt nature is far from satisfying.
For greater satisfaction, one must see the Sci-Fi Channel's forthcoming tie-in special on the _Blair_Witch_ legend or visit the website, which offers a wealth of information on the _Blair_Witch_ mythology, from the legend's origins to events that take place after those depicted in the film--not to mention the "story" behind the piecing together of the "lost footage" into the 87-minute film. A taste of this information is provided in the press notes, which I did not read until after the film. Reading them has indeed made the experience and understanding of the film a bit richer, but that doesn't change what is printed on the celluloid. As smartly conceived and executed as it is, _Blair_Witch_ the film is basically a fairly familiar tale of three people who must learn to live together and fend for themselves when lost in the forest.
Which is basically to say that _The_Blair_Witch_Project_ isn't quite as special as it has been made out to be; it won't be the breakout hit Artisan Entertainment is hoping for, just a sizable arthouse success. This is not to say that the film is not worth a look; it is a diverting work of great ingenuity and invention. It's just that to appreciate the full breadth of the five filmmakers' vision, one must bone up on the supplemental materials. _Blair_Witch_ the film is ultimately as much of a teaser for its website as is vice versa. (opens July 16)
Michael Dequina<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown CompuServe Hollywood Hotline: http://www.HollywoodHotline.com on ICQ: #25289934 | on AOL Instant Messenger: MrBrown23
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| 3.3V: | ~v3p3~ | |
| 3.8V: | ~v3p8~ | |
| 5.0V: | ~v5~ | |
| 12.0V: | ~v12~ | |
| UHF Radio1: | ~vUHF1~ | |
| UHF Radio2: | ~vUHF2~ | |
| Temperature: | ~temp~ | |
| Charger Temp: | ~chargeTemp~ | |
| Charge Count: | ~chargeCount~ | |
| Charge Max Count: | ~maxCount~ | |
| Charge Min Count: | ~minCount~ |
| Ext Charger In: | ~vExtCharge~ | |
| Ext Charger Reg: | ~vExtReg~ | |
| Gen In: | ~vGen~ | |
| Gen Reg: | ~vReg~ | |
| Charging Voltage: | ~vIn~ | |
| Charging Current : | ~iIn~ | |
| Battery Voltage: | ~vBatt~ | |
| Battery Current: | ~iBatt~ | |
| System Load Voltage: | ~vSys~ | |
| System Load Current : | ~iLoad~ | |
| Acceleration X: | ~accelX~ | |
| Acceleration Y: | ~accelY~ | |
| Acceleration Z: | ~accelZ~ |
Note: This page updates automatically.


EntryWay to Cicada
I'd like to record information about a
.
This
_______
has these attributes:
JSON Schema with Form helpers
© 2010
CouchOne


Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
| Version: | 0.7-2 |
| Depends: | glmnet, mboost |
| Published: | 2018-10-08 |
| Author: | Nicolai Meinshausen |
| Maintainer: | Nicolai Meinshausen <meinshausen at stat.math.ethz.ch> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | InvariantCausalPrediction results |
| Reference manual: | InvariantCausalPrediction.pdf |
| Package source: | InvariantCausalPrediction_0.7-2.tar.gz |
| Windows binaries: | r-devel: InvariantCausalPrediction_0.7-2.zip, r-release: InvariantCausalPrediction_0.7-2.zip, r-oldrel: InvariantCausalPrediction_0.7-2.zip |
| OS X binaries: | r-release: InvariantCausalPrediction_0.7-2.tgz, r-oldrel: InvariantCausalPrediction_0.7-2.tgz |
| Old sources: | InvariantCausalPrediction archive |
| Reverse suggests: | CompareCausalNetworks |
Please use the canonical form https://CRAN.R-project.org/package=InvariantCausalPrediction to link to this page.


Standard controller implementations for the MVC framework that comes with Spring. Provides both abstract base classes and concrete implementations for often seen use cases.
A Controller
- as defined in this package - is analogous to a Struts
Action
. Usually Controllers
are JavaBeans
to allow easy configuration using the {@link com.interface21.beans com.interface21.beans}
package. Controllers define the C
from so-called MVC paradigm
and can be used in conjunction with the {@link com.interface21.web.servlet.ModelAndView ModelAndView}
to achieve interactive applications. The view might be represented by a
HTML interface, but, because of model and the controller being completely
independent of the view, PDF views are possible, as well as for instance Excel
views.
How to actually set up a (web)application using the MVC framework Spring provides is explained in more detail in the MVC-Step-by-Step tutorial, also provided in this package (or have a look here for an online version). The classes contained by this package explain in more detail the actual workflow of some of the abstract and concrete controller and how to extend and fully use their functionality.
Especially useful to read, while getting into the Spring MVC framework are the following:


Outbreak.info is a resource to aggregate, integrate, organize, and share information crucial for scientific research on emerging diseases such as COVID-19. Your help is needed for organizing sources of information.
This involves reading short definitions for the categories of information in Outbreak.info, looking at the titles, descriptions, and keywords of dataset described in Outbreak.info and categorizing them based on the definitions. Your best guesses will be combined with others to identify the best ways to organize the information in Outbreak.info


By default, CrashRpt archive's
bin directory contains binary files compiled in Visual Studio 2010. These files are provided for demonstration purposes only. It is strongly recommended that you compile CrashRpt yourself using your version of Visual Studio. This is needed to ensure CrashRpt uses the same version of C run-time libraries (CRT) as your application does.
The top-level directory of CrashRpt distribution contains solution file for Visual Studio 2010 (
CrashRpt_vs2010.sln). Open this solution file to compile CrashRpt in
Visual C++ 2010 or in
Visual C++ Express 2010.
To build the solution, choose the Release build configuration and press F7 to start building process.
When build finishes, CrashRpt executable files can be found in bin directory, and library files can be found inside of lib directory.
The Release LIB configuration is an addition to the standard Debug and Release build configurations. Typically, you do not compile CrashRpt in this configuration. However, it may be useful if you use static CRT linkage.
In the Release LIB configuration, the build generates CrashSender.exe executable file and CrashRptLIB.lib static library. In this configuration, CrashSender.exe and CrashRptLIB.lib use static linkage to CRT.
If you use another version (not 2010) of Visual Studio, you can generate solution/project files for your version of Visual Studio using CMake. See below for instructions.
You can generate Visual Studio project files using CMake cross-platform make system. Although CrashRpt doesn't support operating systems other than Windows and doesn't support compilers other than Visual C++, CMake is useful, because it makes it easier to maintain multiple build configurations between different versions of Visual Studio.
To generate CrashRpt solution/project files for your favorite version of Visual Studio with CMake, do the following:
- Download CMake (version 2.8 or later) and install it. This will create CMake shortcuts in All Programs menu in the Start menu.
- Run CMake (cmake-gui) application. This will display the cmake-gui window.
- In the Where is the source code: field, enter the path to your top-level directory of CrashRpt distribution. The top-level directory and its subdirectories contain CMakeLists.txt files which are CMake configuration files.
- In the Where to build the binaries: field, enter the path to the directory where you want to generate project files. It is recommended to enter the path to your top-level directory of CrashRpt distribution here, too.
- Press the Configure button. When prompted, select your version of Visual Studio from the list. If everything is OK, you should see the message "Configuring done".
- In the option list, modify build options as you wish.
- CRASHRPT_BUILD_SHARED_LIBS option controls whether to build CrashRpt as DLL or as a static LIB. It is recommended that you have this checked. Unchecking the option will force CrashRpt to build static libs instead of DLLs.
- CRASHRPT_LINK_CRT_AS_DLL option controls how CrashRpt should be linked to C run-time (CRT) libraries. Unchecking this option will force CrashRpt to link CRT as a multi-threaded static library. It is recommended that you have this checked, which results in linking CRT as multi-threaded DLL.
- After modifying options, press the Configure button the second time to force changes to make effect. If everything is OK, you should see the message "Configuring done".
- Press the Generate button. This will generate project files to the directory you have specified. If everything is OK, you should see the message "Generating done".
- And finally, go to the directory you have specified in the Where to build the binaries: field. You should see CrashRpt.sln file that you can use to compile CrashRpt.
cmake-gui window
There are several projects in CrashRpt solution.
Structure of CrashRpt solution as seen in Solution Explorer window
- ConsoleDemo is a console application that helps to test how CrashRpt works with console applications.
- CrashRpt project contains API implementation and provides functionality for intercepting exceptions.
- CrashRptProbe project contains functionality for processing error reports.
- CrashSender project contains functionality for displaying GUI, sending the error report and showing error report sending progress.
- crprober is a console tool for error reports processing.
- jpeg project contains JPEG file management functionality.
- libpng project contains PNG file management functionality.
- MFCDemo is a GUI application that helps to test that CrashRpt API functions work as expected with a MFC-based application.
- minizip project contains ZIP file management functionality.
- Tests project contains automated tests.
- tinyxml project contains XML file management functionality.
- WTLDemo is a GUI application that helps to test that CrashRpt API functions work as expected with a WTL-based application.
- zlib project contains file compression functionality.
CrashRpt can be compiled for both 32-bit (Win32) and 64-bit (x64) platforms. You can choose the current platform by opening menu Build->Configuration Manager... and by selecting the desired platform from the 'Active solution platform:' combo box.
If you use CMake to generate project files, select a 64-bit compiler from the drop-down list when configuring CMake.
You should have x64 compiler tools and SDK files properly installed (ensure you have checked these components during Visual C++ installation).
Compilation in free Visual C++ Express is a little more complicated than compilation in a commercial edition of Visual C++.
CrashRpt depends on WTL. WTL itself depends on Microsoft's library called ATL (Active Template Library). ATL library is a part of Visual Studio SDKs. An older version, ATL 3.0, is included into Microsoft Platform SDK for Windows Server 2003. Since then, Platform SDK was renamed into Windows SDK. However, Microsoft doesn't include ATL into Windows SDK to encourage using of commercial Visual Studio having ATL.
So, there are two ways to compile CrashRpt in Visual C++ Express:
Recommended way. Install Windows Driver Kit, version 7.0 or later, because it contains ATL 7.1 source code. When WinDDK is installed, in VC++ Express window, open Tools->Options...->Projects and Solutions-> VC++ Directories. In the 'Show directories for' combo, select 'Include files' and then add C:\WinDDK\6001.18002\inc\crt\atl71 path to the list of include directories.
Alternative way. You need to have Platform SDK for Windows Server 2003, because this Platform SDK contains ATL 3.0 source code. Download and install Microsoft Platform SDK for Windows Server 2003, for example from here.
In VC++ Express window, open Tools->Options...->Projects and Solutions-> VC++ Directories. In the 'Show directories for' combo, select 'Include files' and then add <Platform_SDK_Folder>\Include\atl path to the list of include directories (replace the <Platform_SDK_Folder> with the actual directory name).
By default, project documentation is already generated and included into the CrashRpt distribution. To open the documentation, click
index.html
file in the top-level directory of CrashRpt distribution.
You can also build documentation yourself. CrashRpt documentation can be generated using the Doxygen tool. You can download the tool from here.
To build the documentation, go to the docs
folder and type in the command line:
When doxygen finishes, go to the top level directory and open index.html
.
Further reading: Configuring Your Project's Build Settings.


[gecode-users] propagation consistency of count (single value) constraint
Christian Schulte
cschulte at kth.se
Thu Sep 3 15:55:24 CEST 2015
Yep, that was a bug introduced by a bugfix some time ago! Thanks! Fixed in
the trunk. Cheers Christian
--
Christian Schulte, www.gecode.org/~schulte
Professor of Computer Science, KTH, cschulte at kth.se
Expert Researcher, SICS, cschulte at sics.se
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Kish Shen
Sent: Thursday, August 27, 2015 05:48 PM
To: users at gecode.org
Subject: Re: [gecode-users] propagation consistency of count (single value)
constraint
As a follow up to my previous post on this, then from the reference
documentation, for
count(home, x, y, IRT_EQ, z)
where y and z are int, then there is only one consistency level supported,
and it is not domain consistent.
We have a rostering program (originally written for our ic solvder), which I
used to test the Gecode interface, and it uses count constraints, and
changing the consistency level affected the execution times significantly
for certasin problem instances. I took a look again at the code, and the
count constraints are with y and z being integers.
(the execution times are faster with ICL_BND than ICL_DOM)
The count single vsalue constraint is posted to Gecode using only one
propagator, the version with y and z being IntVar (and if they are integers,
they are posted as IntVar with a singleton domain). This is done for all
constraints that have variants that accept both IntVar and int, as this
avoid me having to write a lot of code to distinguish the argument types,
and I remember asking about this here a while ago and I think Chirstian said
that Gecode will figure out the best propagator to use for the argument.
So my questions are: is the propagator posted the one with y and z being
int? If so, is there more than consistency level for the propagator, as my
result seem to suggest? The difference is very significant, e.g. for one
instance:
ICL_BND: 0.73s ICL_DOM: 198s
the ICL_BND times are more than 100 times faster, and the only difference
between the two runs are the count constraint posted with ICL_BND rather
than ICL_DOM (running on the same machine, of course).
Cheers,
Kish
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