Your technical director wants to ensure that the company’s devices are secure and users at Woodgrove can see the benefit of applying updates to remain productive and stay current. Your manager has asked you to produce a report on why Woodgrove should stay current, and how it can do so.
Keeping your organization current with updates and new features helps protect against threats, improves performance, and ensures you’re using the best available features.
If your organization doesn’t stay current with the latest updates, your organization could be more vulnerable to threats like:
If you don’t stay current, your environment’s various endpoints won’t be able to receive updates that can help improve the quality and performance of the underlying operating system. As a result, your overall environment’s performance could be affected adversely. This in turn can have an effect on the productivity of your organization’s internal users and could ultimately impact your organization’s customers. Stay current to help keep your organization’s systems responsive, and decrease or eliminate downtime. This way, you maintain and improve your organization’s productivity. You’re also helping ensure that customers have more positive experiences with your organization’s products and services.
Windows gets better and smarter over time. Every feature update release comes with enhancements and capabilities. A single update could include a wide range of improvements and features. For example, it could include:
Make sure to stay current so you don’t miss out on important features and improvements.
You can use the Windows servicing process to help Woodgrove to stay current. There are four phases that span across four workstreams.
Plan: During this phase, you will define readiness criteria to pass through each of the Prepare and Deploy phases, and determine what needs to be updated by performing an assessment of your environment. You’ll prioritize applications, define go or no-go criteria, and put together a list of infrastructure tools, agents, or settings that should be updated during the next phase. At this point, you can also learn from previous Windows deployments to further reduce the effort, time, and cost for subsequent Windows 10 feature updates.
Prepare: In this phase, you’ll assign appropriate test approaches to your important and critical applications, and test applications that must work during Pilot deployment. You also update infrastructure and configuration defined earlier in the plan phase. Based on compatibility data and critical app testing, you put together a list of devices that you’ll use for a pilot deployment.
Deploy: In this phase you deploy the update across your organization. There are two parts to the Deploy phase:
Pilot: Your organization deploys the update on targeted devices to find any problems ahead of a wider deployment. At the end of this phase, you ensure that you’ve tested infrastructure, settings, and applications as defined in the plan phase, and certify that the update can be pushed to Broad deployment across the organization’s environment. If the update includes new capabilities, you ensure that they are all tested during this phase.
Broad deployment: The update is pushed to the broader environment so that all devices are up to date. You capture any learnings from this phase so that you can use them in the plan phase for the next update cycle. This will help you improve the update process in the next cycle.
The phases in the process help you deal with the following workstreams for your updates:
Compatibility and deployment are core workstreams required for Woodgrove to successfully roll out the update. Capability and modernization are optional but recommended workstreams that Woodgrove can use to improve its servicing process for updates.
Each workstream goes through the four phases, starting at the plan phase, right through to Broad deployment. The table below show how the phases are mapped to each workstream, along with the tasks that Woodgrove can carry out in each workstream:
Plan phase
Tasks for the Plan phase:
Prepare phase
Tasks for the Prepare phase:
Deploy phase
Tasks for Pilot deployment:
Tasks for Broad deployment:
There are different tools you can use to accelerate the stay current process for Woodgrove.
You can use the cloud-based Windows Update for Business service to reduce administrative and compatibility effort to deploy updates, and reduce bandwidth constraints for remote workers. Windows Update for Business helps simplify the process for your organization during the Deploy phase and eliminates much of the work that otherwise would need to be done to stay current.
Windows Update for Business makes it possible for you to keep your Windows 10 devices always up to date with the latest version of Windows. At the same time, you remain in control of when and how your devices are updated. For example, you can defer or pause updates for a device for a certain period of time. You can configure different policies for feature updates, quality updates, driver updates, or updates for specific Microsoft products like Microsoft 365 Apps. You can use Windows Update for Business to test specific updates on a smaller segment of your environment, before pushing them out to the wider environment. You can integrate Windows Update for Business with Configuration Manager or Microsoft Intune.
You can use Desktop Analytics to accelerate the Prepare phase and Pilot deployment. Often, the biggest challenge for organizations wanting to stay current is testing. It can involve complex manual work. It can be time-consuming for the organization to continuously and thoroughly analyze application and device compatibility, and remediate issues across the whole environment.
Desktop Analytics is a cloud-based service that integrates with Configuration Manager. The service gives you a better insight into your organization’s update readiness. You can use it to:


--- creation_epoch:<PHONE_NUMBER> lastmod_epoch:<PHONE_NUMBER> categories: Misc title: Web site down recently --- Sorry about this site being down so often recently. I use a free hosting provider x10hosting.com that lost my account in a server move.
They offer lots of neat features for free, but they have way to much down time (like many free hosting sites). I'm working on finding a new one.


Products
Store location:Time Of Opening
location
Address
Opining Time
Closing Time
Seattle
23th Street
7PM
8PM
Dubai
2nd Main Street
7PM
8PM
Paris
23th Street
7PM
8PM
Lima
21th Street
7PM
8PM
Tokyo
11th Street
7PM
8PM
| location | Address | Opining Time | Closing Time |
|---|---|---|---|
| Seattle | 23th Street | 7PM | 8PM |
| Dubai | 2nd Main Street | 7PM | 8PM |
| Paris | 23th Street | 7PM | 8PM |
| Lima | 21th Street | 7PM | 8PM |
| Tokyo | 11th Street | 7PM | 8PM |


--- layout: page title: Input Dialog ---
Activate button to open the input dialog.
Please read the documentation to understand how these examples were created.
Bid Amount:
Updated: April 24th, 2020


/** * Number of pixels between the component's left border * and the left edge of its content area. *
The default value is 0.
*The default value for a Button control is 10.
*The default value for the ComboBox control is 5.
*The default value for the Form container is 16.
*The default value for the Tree control is 2.
*/ [Style(name="paddingLeft", type="Number", format="Length", inherit="no")] /** * Number of pixels between the component's right border * and the right edge of its content area. *The default value is 0.
*The default value for a Button control is 10.
*The default value for the ComboBox control is 5.
*The default value for the Form container is 16.
*/ [Style(name="paddingRight", type="Number", format="Length", inherit="no")]


Zhiqiang Cao
Contact Information
Email<EMAIL_ADDRESS>Personal website: https://statczq.rbind.io/.
Research Interests
Survival analysis, Time series analysis, Statistical learning, Measurement error models.
Education Background
- 2015.09 ~ 2019.05, PhD in Statistics, Department of Mathematics, The Hong Kong University of Science and Technology. Supervisor: Prof. Man-Yu Wong.
- 2012.09 ~ 2015.07, Master of Probability Theory and Mathematical Statistics, School of Mathematical Science, Beijing Normal University. Supervisor: Dr. Hui Li.
- 2008.09 ~ 2012.07, Bachelor of Statistics, School of Mathematics and Statistics, Chongqing University.
Honors & Awards
- The Third Prize of Global Development Challenge (AI theme), Huawei Technologies Co. Ltd, 2019
- Epsilon Fund Award, HKUST, 2019
- The George K Lee Award, HKUST, 2019
- Research Travel Grant (three times), HKUST, 2017-2018.
- Best Oral Presentation Award: “Tests for Zero Inflation in Count Data with Measurement Error in Covariates”. 19th International Conference on Statistics and Analysis, Singapore, 2017.
- Din-Yu Hsieh Teaching award, HKUST, 2016.
- Excellent teaching assistant award, Beijing Normal University, 2015.
- The Second-class Scholarship, Beijing Normal University. 2014.
- Third Prize in National Undergraduate Statistical Modeling Contest, Statistics Education Society of China. 2011.
- Third Prize in the Chinese Mathematics Competitions (Division of Chongqing, Math class), Chinese Mathematical Society. 2010.
- National Encouragement Scholarship, Ministry of Education of P.R.C. 2010.
- National Scholarship, Ministry of Education of P.R.C. 2009.
- Various scholarships and awards from Chongqing University. 2008-2012.
Publications and Presentations
In English
Papers:
Zhiqiang Cao, Man-Yu Wong. (2021). Approximate maximum likelihood estimation for logistic model with covariate measurement error. Biometrical Journal, 63(1), 27-45. URL:https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.202000024
Hui Li, Zhiqiang Cao, Guosheng Yin. (2018). Varying-association copula models for multivariate survival data. Canadian Journal of Statistics, 46(4), 556-576. URL:https://onlinelibrary.wiley.com/doi/10.1002/cjs.11474
Zhiqiang Cao, L.L.Hui, M.Y.Wong. (2018). New approaches to obtaining individual peak height velocity and age at peak height velocity from the SITAR model. Computer Methods and Programs in Biomedicine, 163,79-85. URL:https://www.sciencedirect.com/science/article/pii/S0169260717313895
M.Y.Wong, Yingsi Yang, Zhiqiang Cao, Vivian Y.W.Guo, Cindy L.K.Lam, Carlos K.H.Wong. (2018). Effects of health-related quality of life on health service utilization in patients with colorectal neoplasms. European Journal of Cancer Care, 27(6),e12926. URL:https://onlinelibrary.wiley.com/doi/10.1111/ecc.12926
Zhiqiang Cao, Hui Li, M.Y.Wong. (2018). Functional-coefficient autoregressive and linear regression mixed model for nonlinear time series. Proceedings of 33th International Workshop on Statistical Modelling, 2,37-41. PDF
Zhiqiang Cao, Man-Yu Wong. (2020). Moment estimation method of parameters in additive measurement error model, Computer Methods and Programs in Biomedicine, in press
Zhiqiang Cao, Man-Yu Wong. (2020). Approximate profile likelihood estimation for Cox model with covariate measurement error, revision.
Zhiqiang Cao, Man-Yu Wong. (2020). Reduced correlation coefficient estimation, under review.
Zhiqiang Cao, Hui Li. (2021) Varying-association copula models for marginal additive hazard distribution, under review.
Zhiqiang Cao, Hui Li, Guosheng Yin. (2021). Linear Regression and Varying-Coefficient Autoregressive Mixed Model, manuscript.
In Chinese
Papers:
- Zhiqiang Cao, Hui Li, Xingwei Tong. (2015). A long memory regression model of IF1407 contract and investment strategy. Journal of Beijing Normal University, Natural Science, 51(4):348-353.
- Zhiqiang Cao, Hui Li. (2015). Analysis of gold price fluctuations based on intervention model. Journal of Mathematics in Practice and Theory, 45(15):146-153.
- Zhiqiang Cao, Yang Wang, Wei Li.(2015). Aalen model and its application in medical research. Chinese Journal of Health Statistics, 32(2):335-337.
- Zhiqiang Cao, Yang Wang, Wei Li.(2015). Comparison of semi-parametric and nonparametric additive hazards models: with Tamiflu and traditional Chinese medicine to treat H1N1 flu effect for example. Chinese Journal of Health Statistics, 32(1):22-25.
- Jing Lu, Qin Liang, Zhiqiang Cao.(2012). Efficiency evolution of property insurance industry in China based on three-stage data envelopment: Analysis of Unbalance Panel Data from 2004 to 2009. Insurance Studies, (5):23-35.
- Zhiqiang Cao, Hong Liu, Qin Liang.(2012). An empirical study of the life insurance market efficiency based on CCA-three-stage DEA model. Application of statistical models: The 2011 national undergraduate statistical modeling contest winners selected papers. 437-472. Beijing: China Statistics Press.
Presentations
- Functional-Coefficient Autoregressive and Linear Regression Mixed Model for Nonlinear Time Series. The 33rd International Workshop on Statistical Modelling 2018, Bristol, UK, 2018.07
- Partial Varying-coefficient Regression and Autoregressive Model for Nonlinear Time Series. 38th International Symposium on Forecasting, Boulder, Colorado, USA, 2018.06
- Functional-Coefficient Autoregressive and Linear Regression Mixed Model. PhD seminar of Department of Mathematics, HKUST, Hong Kong, 2018.04
- Tests for Zero Inflation in Count Data with Measurement Error in Covariates. The ICSA 2017: 19th International Conference on Statistics and Analysis, Singapore, 2017.09
- Varying-Association Copula Models for Multivariate Survival Data. PhD seminar of Department of Mathematics, HKUST, Hong Kong, 2017.04
- The introduction of AFT model. Medical Research Statistical Center, Fu Wai Hospital, Beijing, 2013.11
Project Experience
Exploratory methods in development work using EPIC-InterAct data, HKUST. 2017.07-2019.03
- Involved in developing statistical methods to deal with covariate measured with error under logistic regression model and Cox regression model based on EPICInterAct data, especially for covariates have relative large measurement error, for example, FFQ (Food Frequency Questionnaire) data in nutrition.
- Performance: We have derived moment method and approximation maximumlikelihood method to deal with covariates measured with error under logistic regression and Cox regression. Simulation results are as expected. Compared to naive and regression calibration methods, real data analysis from our methods are more reasonable.
Statistical modelling of growth and development in assessing the determinants of pubertal timing and growth, Cooperation project between HKUST and School of Public Health, the University of Hong Kong. 2016.01-2017.11
- Involved in building statistical models to analyze determinants of pubertal timing and growth based on “Children in 1997” data. Especially in developing methods to compute individual age at peak velocity (apv), peak velocity (pv) and height at peak velocity (ypv), since pv and apv are useful and objective markers for respectively the velocity and timing of puberty for both boys and girls.
- Performance: Based on the SITAR model, we proposed the numerical method and the quadratic function method to calculate apv, pv and ypv. Our paper has been published on Journal–Computer Methods and Programs in Biomedicine. In addition, corresponding R package– iapvbs has also been created by us.
Analysis of an ongoing large study on diabetes, School of Public Health, The University of Hong Kong. 2016.10-2017.04
- Based on more than 600 thousands sample data of Hong Kong, taking part in building survival model to analyze relationship between mortality of Diabetes and related factors.
- Performance: According to cross-validation, our proposed model performes better than the Joint Asia Diabetes Evaluation (JADE) model conducted by the Hong Kong Diabetes Registry, and the C-statistics of our model is larger. What’s more, we found some new significant factors, which were not included in the JADE model.
Regression model based on survival data study, National Natural Science Foun- dation, Beijing Normal University. 2013.09-2014.10
- Involved in programming and doing numerical simulation.
Structure and efficiency of Chinese insurance market study,The Second Chongqing University College Students’ Scientific Research Training Program, Chongqing University. 2010.07-2011.07
- Co-investigator, mainly responsible for building Three-stage DEA model and analyzing data, involved in writing the research paper.
- Performance: The research paper was published, and our project got the first prize among all 550 final reports (top 1%).
Work Experience
Full-time Job
Assistant Professor, College of Bigdata and Internet, Shenzhen Technology University, Shenzhen, 2020.10-present
- Teaching and research.
Research Engineer, Algorithm and Technology Development Department, Huawei Technologies Co. Ltd, Dongguan, 2019.06-2020.09
- Taking part in AutoML group, mainly mining time series pattern of KPI data from AIOps.
Internship
R&D Centre, Nanjing mint co., LTD, Nanjing, 2014.11-2015.04 (nearly Full-time)
- Taking part in building models and writing programs to analyze image of value added tax invoice produced.
- Performance: Based on the sample image of real invoice produced by the machine, the program found some characteristics of the value added tax invoice. For example, first fixed position of character “No”, then located interested number area and gray processed can save store space. These foundings were helpful in further developing programs for feature extraction and visual inspection.
Medical Research Statistical Center, Fu Wai Hospital, Beijing. 2013.07-2013.11 (nearly Full-time)
- Using additive hazard model to analyze the treatment effect of H1N1 Flu between traditional Chinese medicine and Tamiflu; taking part in PURE (Prescriptive Urban and Rural Epidemiology Study) project, analyzing relative data using descriptive statistics.
- Performance: Based on the real data of H1N1 Flu in China, we applied the Aalen model to study relationship between mortality of H1N1 Flu and related factors from another perspective, and published relative results in the Chinese journal of health statistics. What’s more, we introduced how to use R software to analyze the Aalen model for interested medical statistical researchers.
Teaching Assistant
- HKUST, Hong Kong. 2016.2-2018.12
- Survival analysis, Statistical inference, Regression analysis.
- Beijing Normal University, Beijing. 2013.09-2014.06
- Time series analysis, Probability theory and mathematical statistics.
Vounteer
- The 7th R Conference of China, Beijing. 2014.04-2014.05
- Participating in production meeting manual and analyzing data about participants.
- Meeting on-site service.
R packages
- iapvbs:Individual Age at Peak Velocity Based on SITAR.
- gmusim:Growth Modelling using a Shape Invariant Model with Random Effects.
Computer Skills
- Programing: R(excellent), Matlab(good), C(good), SAS(basic), Python(basic).
- Others: Office software, Latex, Markdown.
Hobbies
- Listening to music
- Travelling
- Hiking
- Swimming.


SUMMARY
1.GUI designing 2.Database Management Systems 3.Problem solving using java 4.Mobile programming


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{% extends "base.html" %} {% load crispy_forms_tags %} {% block body_class %}bind-github{% endblock %} {% block content %}
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LFE Style Guide
9 Data Representation
9.1 Empty Lists
Thanks to Erlang, LFE doesn't have any of the problems of ambiguity around
NIL
, empty lists, and 'false
:
- There is no
NIL
in LFE. If you want to indicate that something has no value, use the Erlang convention: return the'undefined
atom. - The empty
'()
list in LFE is just that: an empty list. It cannot be compared meaningfully to an undefined value or a boolean. - The boolean value for "False" in LFE is the atom
'false
. It cannot be meaningfully compared to an emoty list or an undefined value.
9.2 Do Not Abuse Lists
Even though back in 1958, LISP was short for "LISt Processing", its successors such as Common Lisp, Clojure, and LFE, have been modern programming languages with modern data structures since the 1980s and mid-2000s, respectively. You should use the appropriate data structures in your programs.
You should not abuse the builtin (single-linked) LIST data structure where it is not appropriate, even though LFE may make that easy to do.
You should only use lists when their performance characteristics is appropriate for the algorithm at hand: sequential iteration over the entire contents of the list.
An exception where it is appropriate to use lists is when it is known in advance that the size of the list will remain very short (say, less than 16 elements).
List data structures are often (but not always) appropriate for macros and functions used by macros at compile-time: indeed, not only is source code passed as lists in LFE, but the macro-expansion and compilation processes will typically walk over the entire source code sequentially.
Another exception where it is appropriate to use lists is for introducing literal constants that will be transformed into more appropriate data structures at compile-time or load-time. It is a good to have a function with a relatively short name to build your program's data structures from such literals.
In the many cases when lists are not the appropriate data structure, different data types and various libraries provide plenty of alternatives that should fulfill all the basic needs of your programs.
9.3 Lists vs. Tuples
For very simple tasks that require a little more structure than what lists
provide, you should use tuple
s.
If other parts of your application need to work with this data, if you need to be able to pass data around to functions, it's probably a good time to switch to using records.
9.4 Lists vs. Records
You should avoid using a list as anything besides a container of elements of like type. You should not use a list as method of passing multiple separate values of different types in and out of function calls. Sometimes it is convenient to use a list as a little ad hoc structure, i.e. "the first element of the list is a FOO, and the second is a BAR", but this should be used minimally since it gets harder to remember the little convention. You should only use a list that way when destructuring the list of arguments from a function, or creating a list of arguments to which to APPLY a function.
The proper way to pass around an object comprising several values of
heterogeneous types is to use a record as defined by (defrecord ...)
.
You should use multiple values only when a function returns a small number of values that are meant to be destructured immediately by the caller, rather than passed together as arguments to further functions.
9.5 Lists vs. Arrays
If you have code that requires data structures with nothing but numeric keys, arrays may be a good fit. Arrays provide access to elements with numerical indices. You can also fold over arrays while ignoring undefined slots.
Note that, unlike most other data structures you will find in LFE/Erlang which are 1-indexed, arrays are 0-indexed.
9.5 Lists vs. Sets
If you have data stored in lists and want to start doing union or intersection operations, it's time to switch to a set data type. Don't even think about writing macros for your lists. That'd just be silly.
LFE/Erlang has several to choose from:
- ordsets
- sets
- gb_sets
- sofs
9.6 Lists vs. Erlang Term Storage vs. Mnesia
If your data structures stored in lists need to be fast, support destructive updates, don't need garbage collection, and allow for other processes to access them, ETS tables might be just your thing.
If you need better concurrency than ETS provides, want transactions, or want to store your data in either memory or on disk, you might want to move your data into Mnesia.


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projects.ryantaylor.net
Projects
Red Squirrel GBS test run
Red Squirrel Male Selection
Chipmunk fluctuating selection (Le Cœur et al. 2015)


The TMOverAccess API call transmits a TMTouchReset signal and returns 0 if no Presence signal is detected. If a Presence signal is detected, it sends an Overdrive match to the device whose ROM code is in the internal eight byte buffer. The Overdrive match readies the 1-Wire device to accept memory function commands such as read scratchpad and also sets the device in Overdrive speed. A successful return only guarantees that some 1-Wire device is on the 1-Wire network and if the desired 1-Wire device is present then it is selected, in Overdrive communication mode, and ready for a device specific command.
For a description of the above types, see the TMEX Type Descriptions.
Parameters
session_handle
Specifies the session handle returned from the API function TMExtendedStartSession that specifies the desired 1-Wire network port. This parameter is required by most TMEX API functions.
state_buffer
Specifies a pointer to a memory location that TMEX keeps all of the state information for the 1-Wire networks. This parameter is required by most TMEX API functions.
Return Value
0 => no presence on 1-Wire network
1 => presence on 1-Wire network, in Overdrive mode, and ROM selected
<0 => TMEX Network Error Return Code
See Also
TMRom; TMStrongAccess, TMStrongAlarmAccess; TMAccess


The future of Digital Construction is an idea and proposal for a cross-collaborative design effort where various technical experts have an equal design stake for an outcome that is unachievable without one another. The goal of this research is to engage new technologies such as digital fabrication, in combination with material science, computer science and robotics, as a method for a new building system in architecture.
Architecture today uses technology and building systems which parallel a construction method used over 50 years ago. The most common system is that of a steel or wood framed construction, insulated, then wrapped with an outer skin. Other industries such as the aviation, automotive and medical fields have used progressive digital technologies throughout their design and construction process; resulting in dramatic advances in customization and rapid changeability.
Architects and designers alike should be working to develop systems that respond in conjunction with our current technological advancements. This model of cross pollination currently doesn’t exist as a mainstream approach within architecture, but it could.
In 2008, I started working professionally for a large architecture firm in Chicago. What I immediately noticed then, which is the same today, is how architects often design for the lowest common denominator - meaning this object / building / design must be successfully buildable by anyone who has the capacity to complete the job. Architecture should be an innovation approach, not a design model that relies on outdated construction types for completion.
As designers and innovators, we often have ideas that are unobtainable without the computer. Complex geometries, boolean subtractions, hyper-parabolic shapes, all possible because of the tools we use. “The new digitally-enabled processes of production imply that the constructibility in building design becomes a direct function of computability.”
Use technology as a tool.
The diagrams above are meant to represent the future of digital construction. The technical skills won’t be troweling mortar onto a masonry row, it’ll be writing a script for a robotic arm. That script will outline how and where to cut a custom shape into the side of a building. This is the future.
“The classic activity of the architect has been the creation of built space. Yet in recent years, the element that defines this space - the boundary - is steadily moving into the focus of architectural interest. New technologies and developments are continually expanding the design repertoire.”
This proposal is meant to be a conversation about how design and engineering disciplines can integrate as a single team to produce rich, creative outcomes that are only possible from this type of collaborative method. Architects only design what they know can be built, not what they don’t know. The ones who are best, often ask a lot of questions and seek advice from all ends of the spectrum.


vignettes/NonNormalEffectSynthesis.Rmd
NonNormalEffectSynthesis.Rmd
Observational studies on the effects of treatments are often done across a distributed network of health care databases, with the following characteristics
Sharing of individual patient data (IPD) is often not allowed due to patient privacy concerns and local governance regulations.
Patients are often exposed for varying lengths of time, and may stop being followed for various reasons, making a time-to-event analysis with right-censoring such as a Cox proportional hazards regression the most prevalent analysis.
Due to the observational nature of the data, some correction for baseline differences between exposure groups is often necessary to avoid confounding. This is often done by stratifying, matching, or weighting by propensity score or disease risk score.
Since data sites often differ in the population they capture or in their capture process, some heterogeneity between sites should be assumed to exist.
Because no IPD may be shared, data must be aggregated in some way to avoid patient identifiability before communication. Current practice is to estimate the hazard ratio (with standard error) per database, and combine these across sites using a traditional meta-analysis for random effects. However, with small counts the true per-database likelihood function may no longer be approximately normally distributed, as is assumed by this approach. Especially when zero events are observed in one of the treatment arms but not both, the likelihood is far from normally distributed, yet still holds information on the parameter of interest. Even though databases in a network are typically large in terms of the total number of patients covered, when restricting to patients having specific outcomes while exposed to specific treatments the numbers quickly dwindle to numbers small enough for this issue to be of concern.
This vignette describes how this problem can be solved by using a non-normal approximation of the per-database likelihood. The EvidenceSynthesis
package supports various approximations of the likelihood that are more appropriate when sample size is small and also still apply when sample sizes are large, allowing sites to contribute information on the overall likelihood distribution of a parameter of interest (ie. the effect of the treatment on the outcome) without sharing IPD. This package further provides functions for synthesizing the likelihood approximations from the various sites to a single estimate.
To demonstrate how to use the EvidenceSynthesis
package, we will generate some simple simulated data. For this, we will use the simulatePopulations()
function included in the EvidenceSynthesis
package:
library(EvidenceSynthesis)
simulationSettings <- createSimulationSettings(nSites = 5,
n = 10000,
treatedFraction = 0.75,
nStrata = 5,
hazardRatio = 2,
randomEffectSd = 0.5)
populations <- simulatePopulations(simulationSettings)
Here we specify we want to simulate 5 data sites, each with 10,000 subjects. Of these, 75% should be exposed to the target treatment (the remaining 25% will be in the comparator cohort). At each site, the data are stratified in to 5 strata (e.g. propensity score strata), each with a different baseline risk of the outcome. The target hazard ratio is 2, but there is a random effect with standard deviation = 2 (ie. each site draws its log(hazard ratio) from a normal distribution with mean = log(2) and standard deviation = 0.5).
The resulting populations
object is a list of data frames, each with person-level data:
In a real study, we would not have all the person-level data available at one site. Instead, each site would have their data behind their organization’s firewall, without the ability to share person-level data. To be able to synthesize evidence across sites, we need to share information about the parameter of interest (the effect size) without sharing person-level data. We can do this by approximating the likelihood function of the parameter at each site, and communicating only the parameters of the approximation.
First, we fit our model for the effect of interest. For this we use the Cyclops
package:
library(Cyclops)
# Assume we are at site 1:
population <- populations[[1]]
cyclopsData <- createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
data = population,
modelType = "cox")
cyclopsFit <- fitCyclopsModel(cyclopsData)
If we are interested, we can see what the local estimate was of the hazard ratio and its confidence interval:
## x
## 0.8000462
## [1] 0.2965148 2.5159796
Note that an estimate may not exist at a database, for example if no one in the target or comparator group has the outcome. However, the likelihood may still be informative.
Next, we approximate the likelihood function of our parameter of interest:
approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
approximation
## mu sigma gamma
## 1 -0.2204294 0.5455648 -0.1293467
Here we use the custom parametric approximation, which in research has shown to provide a good approximation in many situations. This function has only three parameters (\(\mu\), \(\sigma\), and \(\gamma\)). These parameters can be shared with the study coordinator, without sharing patient-level data.
The EvidenceSynthesis
package offers three types of approximation:
In case we’re interested, we can see how closely our fitted function approximates the likelihood:
plotLikelihoodFit(approximation = approximation,
cyclopsFit = cyclopsFit,
parameter = "x")
## Detected custom parameric approximation
For this example we will create the approximations for all sites in one short script. In reality, each site will have to execute the code described above.
fitModelInDatabase <- function(population) {
cyclopsData <- createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
data = population,
modelType = "cox")
cyclopsFit <- fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit,
parameter = "x",
approximation = "custom")
return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations
## [[1]]
## mu sigma gamma
## 1 -0.2204294 0.5455648 -0.1293467
##
## [[2]]
## mu sigma gamma
## 1 1.631419 0.9599598 -0.21736
##
## [[3]]
## mu sigma gamma
## 1 1.113098 1.04303 -0.2147777
##
## [[4]]
## mu sigma gamma
## 1 1.699838 0.947783 -0.2255899
##
## [[5]]
## mu sigma gamma
## 1 0.2528568 0.810072 -0.1684032
Here, for each population we fit the model, and create the approximation. The resulting object is a list of approximations, one for each site.
Assuming we have gathered the approximations from all participating sites, we can now produce a single estimate of the effect size. The EvidenceSynthesis
package implements two different approaches to achieve this: assuming fixed-effects, or using a Bayesian model for random-effects.
A fixed-effects model assumes the true effect size is identical at each site. This assumption is reasonable if the sites are highly homogeneous, and have recruited very similar individuals. We use the computeFixedEffectMetaAnalysis()
function, which takes a single table containing all per-database approximations as input:
# Combine the various approximations into a single data frame, one row per database:
approximationsTable <- do.call(rbind, approximations)
# Do meta-analysis
estimate <- computeFixedEffectMetaAnalysis(approximationsTable)
estimate
## rr lb ub logRr seLogRr
## 1 2.051065 1.088817 4.188596 0.7183594 0.3436986
Alternatively, we can use a random-effects model, assuming that the effect sizes at each site might be slightly different, but are all drawn from the same distribution of effect sizes. We assume that distribution is a normal distribution with mean \(\mu\) and standard deviation \(\tau\). Because such a model tends to be unstable when sample sizes are small, we treat this as a Bayesian problem, and use Markov chain Monte Carlo sampling to sample from the posterior distribution.
We can use the computeBayesianMetaAnalysis()
function to combine the approximations into a single estimate. This function also takes a single table containing all per-database approximations as input:
# Combine the various approximations into a single data frame, one row per database:
approximationsTable <- do.call(rbind, approximations)
# Do meta-analysis
estimate <- computeBayesianMetaAnalysis(approximationsTable)
exp(estimate[1:3])
## mu mu95Lb mu95Ub
## 1 2.179863 1.012596 4.953978
We output the point estimate and 95% credible interval of our main parameter of interest \(\mu\), and exponentiate these to convert to the hazard ratio scale.
We can also view the full posterior distribution:
plotPosterior(estimate)
And the trace of the MCMC:
plotMcmcTrace(estimate)
The computeBayesianMetaAnalysis
uses a normal and half-normal prior for the \(\mu\) and \(\tau\) parameters, respectively. By default, the standard deviations are set to 2 for \(\mu\) and 0.5 for \(\tau\). A standard deviation of 2 on the \(\mu\) parameter expresses the belief of a 95% probability that the true value of \(\mu\) is between -3.92 and 3.92, corresponding to hazard ratios from 0.02 to 50.40.
With less data, the choice of prior becomes more important. In a small-sample scenario, we may for example choose a stronger prior on \(\tau\) if we have reasons to believe between-site heterogeneity of the effect size will be small:
estimate2 <- computeBayesianMetaAnalysis(approximationsTable, priorSd = c(2, 0.1))
exp(estimate2[1:3])
## mu mu95Lb mu95Ub
## 1 2.138004 1.072448 4.10942
Here we still use a prior with a standard deviation of 2 on \(\mu\), but for \(\tau\) we specify a prior with a standard deviation of 0.1 instead of 0.5. As can be seen by the narrow credible interval, this stronger prior on \(\tau\) leads to less uncertainty around \(\mu\).
We can plot the results using a standard forest plot:
# Make up some data site labels:
labels <- paste("Data site", LETTERS[1:length(populations)])
plotMetaAnalysisForest(approximationsTable, labels, estimate)


[read-only] The four direction value as a number of the numpad, or 0 for neutral.
[read-only] The eight direction value as a number of the numpad, or 0 for neutral.
A hash table to convert from a gamepad button to a mapped key name.
A hash table to convert from a virtual key code to a mapped key name.
The interval of the key repeat in frames.
The wait time of the key repeat in frames.
Clears all the input data.
Initializes the input system.
Checks whether a key is kept depressed.
The mapped name of the key
True if the key is long-pressed
Checks whether a key is currently pressed down.
The mapped name of the key
True if the key is pressed
Checks whether a key is just pressed.
The mapped name of the key
True if the key is triggered
Checks whether a key is just pressed or a key repeat occurred.
The mapped name of the key
True if the key is repeated
Updates the input data.
Generated using TypeDoc
[read-only] The time of the last input in milliseconds.
date


---
layout: post
title: Tripwire -- what a great product
tags:
- Configuration Audit
status: publish
type: post
published: true
meta: {}
---
<vendor_post type="spruiking">
As regular readers know I recently joined Tripwire as a technical manager. I recently had my first round of presentations to customers and an opportunity to show off the product. It was interesting to hear a variety of needs around different platforms, applications and processes.
Tripwire Enterprise was easy to talk about -- it has a great user interface and a really well defined set of features. Sales presentations are all abut aligning these features to the customers priorities, instead of bending the product the to look like it's what the customer might need (which has often been my experience with other applications).
If you need to identity the changes going on in your data centre then make sure you give it a look
</vendor_post>


Turkey says will begin repatriation of Islamic State prisoners on Monday: Anadolu
ANKARA (Reuters) - Turkey will begin to repatriate captured Islamic State militants to their home countries as of Monday, Interior Minister Suleyman Soylu was quoted as saying by state media on Friday.
Turkey has long criticised its European allies for refusing to take back Islamic State fighters who are their citizens, and on Monday warned that Ankara would send captured jihadists back to their countries even if their citizenships have been revoked.
“We are telling them: ‘We’ll repatriate these people to you’, and we are starting as of Monday,” the state-run Anadolu news agency quoted Soylu as saying.


On 17 April 2017, 12 new articles were published in 7 journals.
The Sociological Imagination Revisited: Lessons From America’s Safest City
The Second Life of Kowloon Walled City: Crime, Media and Cultural Memory
Book Review: David Brotherton, Youth Street Gangs: A Critical Appraisal
Twenty Reasons to Publish in Dignity
Book Review: the Neighborhood Has Its Own Rules
Differential Perceptions of Fear of Crime Among College Students: the Race Factor
Note: although this article was published on 31 March 2017, the publisher only added it to the journal RSS feed on 17 April 2017, which is why it is shown in the list of articles for that date.
Note: although this article was published on 31 March 2017, the publisher only added it to the journal RSS feed on 17 April 2017, which is why it is shown in the list of articles for that date.
Pairing Fieldworkers With Patrol Officers: A Study of Supervising Officers’ Selections
Joanne Sweeney-Burke. Social Media Under Investigation: Law Enforcement and the Social Web.


HEADER
NAVIGATION
Latest 4 Posts
More posts can be found in
the archive
.
Philippe De Pauw - Waterschoot


|
Perl Weekly
Issue #71 - 2012-12-03 - The Advent calendars are here!
latest | archive | by Gabor Szabo
|
|
Hi,
as I expected there are are several Perl-related Advent calendars. In case you don't know, these are basically sites where you will have a Perl-related article every day till 24th December. Lots of great stuff there. For the actual calendars, please see the links bellow.
Unfortunately I don't know much about other, non-Perl Advent calendars. If you know any such calendar, please send me a link!
There is also an 'Open source motivation survey' running. If you have 2-3 minutes, please answer the questions.
In any case, enjoy the articles!
|
|
|
Advent Calendars
This year again, there are several Perl-related Advent calendars and several others.
|
|
|
Advent Calendars Calendar
Len Jaffe created an Advent Calendar pulling together the links to the Advent postings on the various other calendars. Mostly Perl though there are some others as well.
|
|
Must reads
Two stories written in a very enjoyable way from the two sides of The Channel.
|
|
|
|
|
Articles
|
Operate with Impunity
Data validation should be an important subject for everyone who is accepting date from the outside world. Al Newkirk has re-written the Validation::Class module and also added a white-paper discussing the whole field of data validation. Since this post he already released version 7.900001 of the module with 95% backward compatibility. I wonder what does that mean and how could a user know if her code will work correctly on the new version of the module?
|
|
Categorizing CPAN modules
Steven Haryanto brings up the age-old problem of difficulty in finding things on CPAN. All kinds of suggestions were raised, mostly around MetaCPAN. Actually there is a site that already provides the possibility to tag modules, but unfortunately CPAN::Forum never got really off the ground and in recent years I did not have any time to work on it.
|
|
Open source motivation survey
Last week I mentioned the Play Perl project Vyacheslav Matjukhin started to build. I think in a very clever way he is now running a survey - a market research, if you wish - to understand how his project could help you. It is only a few minutes to answer the questions. I think it took me less than 2.
|
|
Hash key order : beware of implicit assumptions
In every Perl training class I try to make sure people understand that they can never ever rely on the order keys() returns the keys of a hash. This was hard as in the trivial cases the values always returned in the same order. As you can see in this article, even expert Perl programmers might have used this assumption. This will all change when 5.18 is released as the hash keys will be now further randomized. Expect some (incorrect) code to start breaking.
|
|
Discussion
|
Why Corporates Hate Perl
This is basically an old proposal of Dave Cross to talk about the subject. Why are there companies in which management is blaming Perl for the lack of management in old Perl code? I asked there and I am still not sure, is this a problem specific to Perl? Is it a bigger problem for Perl than for other languages? Why?
|
|
Security
|
USN-1643-1: Perl vulnerabilities
Ubuntu has released a set of security fixes for perl. If you are on Ubuntu, or on other systems, you might want to take a look at these.
|
|
Releases
|
Method::Signatures: where and when
Last week I mentioned that I looked at a couple of modules that would allow me to use real function signatures, but I have not actually looked deep into Method::Signatures. Now Buddy Burden reports about the new release of this module with some beautiful new features. I also love the examples in the article. Especially I was wondering if there is a way to pass several arrays to a function like this f(@x, @y) and handle them correctly, or at least as array references inside the function.
|
|
Moose
|
|
|
|
|
Grants
|
|
|
Alien::Base Grant - Report #7
Toby Inkster has started to build Alien::LibXML using the new Alien::Base Joel Berger is creating. This lead to some bug reports and fixes. If you too need to install some external dependency, trying to build and Alien::* module now will further help Joel improving his module.
|
|
Perl 6
|
|
|
Perl 6 Advent Calendar 2012: Table of Contents
From here you'll have links to the articles appearing every day in the next 3 weeks. Currently there is the 'State of Perl 6 in 2012' which is a must read if you have ever been interested in Perl 6 and 'Anonymous functions for great good' which is 'just' interesting.
|
|
Weekly collections
|
|
|
|
|
|
|
Events
I usually list the next 3-4 events here. The list of all the events can be found on the web site. If your Perl event is not listed there, please let me know.
|
|
|
|
|
|
|
You know, you could get the Perl Weekly right in your mailbox. Every Week. Free of charge!
|


Why OptiPol
Restricted Resources
Police have had to deal with increasingly limited resources. Policing solutions that not only shows where crimes will happen, but also how to more effectively deploy units are necessary to make the unit deployment planning process faster and more efficient.
Public Perception of Discrimination
There's been high criticism of police work on the media and by the public, especially regarding police misconduct and ethnical discrimination. This is reflected in historical crime data, which in turns infects any intelligent predictive model with the same bias. Police departments need to control this bias and avoid perpetuating discriminatory behavior in future engagements.
Meet OptiPol
The solution to help police departments prevent crime more effectively and plan faster and fairer.
Reduce Discrimination
The fairness of your crime predictions and deployment plans are always there for you to check.
Plan Faster
Optipol leverages mathematical optimization techniques to prescribe you the more efficient deployment plans in seconds.
View crime predictions and details in a dashboard
Ensure less discriminatory policing
Fairness Indicators
Fairness indicators in the crime prediction screen and patrol deployment screen help you ensure that the your predictive model's predictions and your unit deployment plan are not discriminatory.
Have a collection of different machine learning models to choose from, and switch between models on-the-fly. Train new models through an easy-to-use interface using a collection of additional features to remove bias and create fairer, less discriminatory models.
-
Plan Deployments
Unit deployment planning in a fast and responsive interface. -
Deployment Fairness
Real time evaluation of fairness to avoid discrimination. -
Mathematical Optimization
Get the best course of action in seconds.
Experiment on our live demo
Check out the prediction, model training and switching and patrol deployment features.


Bindings (read only)
{{this.inline}}
{{this.text}}
Bindings (read only)
Muplitple inline binding
{{this.inline}} {{this.text}} {{this.html}}
Filters
{{this.text | uppercase}}
{{this.text | lowercase}}
{{this.text | limitTo:this.a}}
Expressions
{{this.a + this.b}}
{{this.bool ? "true": "false"}}
mjs-class
mjs-if, mjs-show, mjs-hide
this should appear if .bool = true
this should show if .bool = true
this should hide if .bool = true
mjs-include
transcluded text
mjs-options
{{ this.title }}
mjs-include change template:
a + b |
a * b
This element is ignored by renderer: {{ this.$root.a }}
Dynamically created component on existing element will go here
Dynamically created component with renderTo will go here
This is another instance with deferred value: {{ this.deferred }}


DRUG MULE A film review by Will FitzHugh Copyright 1997 Will FitzHugh
This film starts out as the thematic sequel to the 1995 film festival hit 'Flesh Suitcase'. In that film, two men swallow balloons full of heroin in order to smuggle it from Malaysia to the States. The majority of the movie takes place in a hotel as the men wait for the balloons to re-emerge. One of the men is a first-timer and the plot unfolds as the more experienced one relates stories and advice from past runs. The hotel is populated with go-go girls, an apocalypse-obsessed religious freak and the eccentric manager and his dog, all of whom get swept up in the plot. Eventually, a balloon comes up missing and the drug's owners show up, looking for someone to blame. The movie succeeds on most fronts with a blend of black humor, sharp dialog, competant acting and nearly ridiculous plot details. The violent conclusion is a bit disjointed, but the film is a welcome, personalized look at the pawns in the worldwide drug trade.
The follow-up, 'Drug Mule', pushs the suspension of disbelief even further. The two drug runners, this time a man and a woman, use a donkey as their 'flesh suitcase', loading the beast's digestive tract up with well-wrapped heroin. Once over the border Mexican border into Arizona, the two hole up at a ranch to wait for the results of their scheme. As in 'Flesh Suitcase', the details of the plot are exposed in a series of flashbacks as the veteran woman and the neophyte man pass the time. This doesn't last long, however.
The plot starts to twist unrecognizably as the donkey turns out to be more than it appears. William S. Burroughs' 'The Place of Dead Roads' seems to be an inspiration as the ranch soon becomes populated with the ghosts of indians and gunfighters and competing spys from unnamed and possibly extraterrestrial sources. The donkey (or whatever), now able to talk, shrink to half its original size and mentally control anybody past puberty, has assimilated the heroin and sets up a pre-teen drug empire at the ranch to feed his massive habit.
The beast uses the two runners as bait to get their bosses under his control and soon pushes his influence worldwide. There are some darkly comic scenes of children negotiating with American and Middle Eastern drug kingpins. The plot trys to stay on course as the two original characters try to escape the beast and get back to reality, but they (and us) are in way too deep. The movie goes nearly out of control in a wigged-out space opera across the solar system as the beast and his army of prepubescent psychotics fight it out against the real intergalactic government power (the Leptoids), an organization that claims that the CIA and KGB are its 'tiniest, most insignificant field offices'. Good, twisted design (David Lynch would have been proud, even scared) makes up for a small budget (think 'Doctor Who' if designed by Francis Bacon and H. R. Giger as a boy).
'Drug Mule' lasts for almost three hours and I had a splitting headache after I saw it. Don't let that deter you, though. Do what you have to do prepare yourself; it probably won't help, but take a few painkillers before you sit down. The plot and characters, the main building blocks for most movies, become secondary to the overriding vision and horror of the scenario. This film is the most powerful piece of modern cinema since, well, since there has been modern cinema. To properly view this movie, it should be viewed through a telescope while being projected on the dark side of the moon with the soundtrack being spliced directly into your otic nerves.
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


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| glance.factanal {broom} | R Documentation |
Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA
.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
## S3 method for class 'factanal' glance(x, ...)
x |
A |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with exactly one row and columns:
converged |
Logical indicating if the model fitting procedure was succesful and converged. |
df |
Degrees of freedom used by the model. |
method |
Which method was used. |
n |
The total number of observations. |
n.factors |
The number of fitted factors. |
nobs |
Number of observations used. |
p.value |
P-value corresponding to the test statistic. |
statistic |
Test statistic. |
total.variance |
Total cumulative proportion of variance accounted for by all factors. |
Other factanal tidiers:
augment.factanal()
,
tidy.factanal()
set.seed(123) # data m1 <- dplyr::tibble( v1 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 5, 6), v2 = c(1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 3, 4, 3, 3, 3, 4, 6, 5), v3 = c(3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 4, 6), v4 = c(3, 3, 4, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 5, 6, 4), v5 = c(1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 6, 4, 5), v6 = c(1, 1, 1, 2, 1, 3, 3, 3, 4, 3, 1, 1, 1, 2, 1, 6, 5, 4) ) # new data m2 <- purrr::map_dfr(m1, rev) # factor analysis objects fit1 <- stats::factanal(m1, factors = 3, scores = "Bartlett") fit2 <- stats::factanal(m1, factors = 3, scores = "regression") # tidying the object tidy(fit1) tidy(fit2) # augmented dataframe augment(fit1) augment(fit2) # augmented dataframe (with new data) augment(fit1, data = m2) augment(fit2, data = m2)


Variables |
|
|---|---|
Arguments
|
audio **If max_outputs is greater the summary value tags are *generated sequentially as *tag *tag etc **
|
audio
|
audio **If max_outputs is greater the summary value tags are *generated sequentially as *tag *
|
bad_color_ = Input::Initializer({255, 0, 0, 255}, {4}).AsTensorProto()
|
TensorProto
|
image
|
image **If max_images is greater the summary value tags are *generated sequentially as *tag *
|
max_images_ = 3
|
int64
|
max_outputs_ = x
|
ret
|
operation
|
Operation
|
ret
|
return
|
sample_rate
|
audio **If max_outputs is greater the summary value tags are *generated sequentially as *tag *tag etc frames **
|
summary
|
|
than
|
audio **If max_outputs is greater
Outputs a
Summary protocol buffer with audio. |
Functions |
|
|---|---|
AudioSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, ::tensorflow::Input sample_rate)
|
|
AudioSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, ::tensorflow::Input sample_rate, const AudioSummary::Attrs & attrs)
|
|
BadColor(const TensorProto & x)
|
TF_MUST_USE_RESULT Attrs
Color to use for pixels with non-finite values.
|
ImageSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor)
|
|
ImageSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, const ImageSummary::Attrs & attrs)
|
|
MaxImages(int64 x)
|
Attrs
|
MaxOutputs(int64 x)
|
Attrs
|
node() const
|
::tensorflow::Node *
|
range(It represents the value of a *pixel in the output image).Non-finite values in the input tensor are *replaced by this tensor in the output image.The default value is the color *red.**Arguments
|
image **If max_images is greater the summary value tags are *generated sequentially as *tag *tag etc **The bad_color argument is the color to use in the generated images for *non finite input values It is a uint8 D tensor of length channels *Each element must be in the
Max number of batch elements to generate images for.
|
Classes |
|
|---|---|
|
tensorflow:: |
Asserts that the given condition is true. |
|
tensorflow:: |
Outputs a |
|
tensorflow:: |
Merges summaries. |
|
tensorflow:: |
Prints a list of tensors. |
|
tensorflow:: |
Prints a string scalar. |
|
tensorflow:: |
Outputs a |
|
tensorflow:: |
Outputs a |
|
tensorflow:: |
Outputs a |
|
tensorflow:: |
Provides the time since epoch in seconds. |
audio **If max_outputs is greater the summary value tags are *generated sequentially as *tag *tag etc ** Arguments
audio **If max_outputs is greater the summary value tags are *generated sequentially as *tag * audio
TensorProto bad_color_ = Input::Initializer({255, 0, 0, 255}, {4}).AsTensorProto()
image **If max_images is greater the summary value tags are *generated sequentially as *tag * image
int64 max_images_ = 3
ret max_outputs_ = x
Operation operation
return ret
audio **If max_outputs is greater the summary value tags are *generated sequentially as *tag *tag etc frames ** sample_rate
::tensorflow::Output summary
audio **If max_outputs is greater than
Outputs a Summary
protocol buffer with audio.
Outputs a Summary
protocol buffer with images.
The summary has up to max_outputs
summary values containing audio. The audio is built from tensor
which must be 3-D with shape [batch_size, frames, channels]
or 2-D with shape [batch_size, frames]
. The values are assumed to be in the range of [-1.0, 1.0]
with a sample rate of sample_rate
.
The tag
argument is a scalar Tensor
of type string
. It is used to build the tag
of the summary values:
max_outputs
is 1, the summary value tag is '*tagThe summary has up to max_images
summary values containing images. The images are built from tensor
which must be 4-D with shape [batch_size, height, width, channels]
and where channels
can be:
tensor
is interpreted as Grayscale.tensor
is interpreted as RGB.tensor
is interpreted as RGBA.The images have the same number of channels as the input tensor. For float input, the values are normalized one image at a time to fit in the range [0, 255]
. uint8
values are unchanged. The op uses two different normalization algorithms:
The tag
argument is a scalar Tensor
of type string
. It is used to build the tag
of the summary values:
max_images
is 1, the summary value tag is '*tag AudioSummary( const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, ::tensorflow::Input sample_rate )
AudioSummary( const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, ::tensorflow::Input sample_rate, const AudioSummary::Attrs & attrs )
TF_MUST_USE_RESULT Attrs BadColor( const TensorProto & x )
Color to use for pixels with non-finite values.
Defaults to Tensor
ImageSummary( const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor )
ImageSummary( const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, const ImageSummary::Attrs & attrs )
Attrs MaxImages( int64 x )
Attrs MaxOutputs( int64 x )
::tensorflow::Node * node() const
image **If max_images is greater the summary value tags are *generated sequentially as *tag *tag etc **The bad_color argument is the color to use in the generated images for *non finite input values It is a uint8 D tensor of length channels *Each element must be in the range( It represents the value of a *pixel in the output image ).Non-finite values in the input tensor are *replaced by this tensor in the output image.The default value is the color *red.**Arguments
Max number of batch elements to generate images for.
Defaults to 3


Model:
Poisson-based direct solver
DC Approximation GCR solver
Nodes list
# Generation(GW) Consumption(GW)
Edges list
# From To
Service actions


---
layout: post
title: Nice transition from splash screen to main application screen using fade out and zoom in animation
---
Change the -application:didFinishLaunchingWithOptions: method of your application
delegate class like this (assuming your splash image is named Default.png):


The assertEquals
command can currently be used on any HTML element.
There are currently no checks.
However, you should avoid using the command on elements that cannot legally
have a
child (e.g.

,

,

,

).
All of these will work:
Fred
Fred

Fred

 Fred


Seasar2 TOP page
Seasar2 Samples
Sample files to learn Seasar2
Introductory
ssp_suga_20040902_en.zip
DI and AOP sample.
seasarExamples.zip
DI and AOP sample.
Intermediate
s2daoNM1.zip
S2DAO sample concatenating 3 tables
Advanced
Miscellaneous
Trans. by H.Ozawa
Copyright© 2004-2006, The Seasar Foundation and the others. All rights reserved.


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object java.awt.geom.Dimension2D java.awt.Dimension
public class Dimension
The Dimension
class encapsulates the width and
height of a component (in integer precision) in a single object.
The class is
associated with certain properties of components. Several methods
defined by the Component
class and the
LayoutManager
interface return a
Dimension
object.
Normally the values of width
and height
are non-negative integers.
The constructors that allow you to create a dimension do
not prevent you from setting a negative value for these properties.
If the value of width
or height
is
negative, the behavior of some methods defined by other objects is
undefined.
Component
,
LayoutManager
,
Serialized Form| Field Summary | |
|---|---|
int |
height
The height dimension; negative values can be used. |
int |
width
The width dimension; negative values can be used. |
| Constructor Summary | |
|---|---|
Dimension()
Creates an instance of Dimension with a width
of zero and a height of zero. |
|
Dimension(Dimension d)
Creates an instance of Dimension whose width
and height are the same as for the specified dimension. |
|
Dimension(int width,
int height)
Constructs a Dimension and initializes
it to the specified width and specified height. |
| Method Summary | |
|---|---|
boolean |
equals(Object obj)
Checks whether two dimension objects have equal values. |
double |
getHeight()
Returns the height of this dimension in double precision. |
Dimension |
getSize()
Gets the size of this Dimension object. |
double |
getWidth()
Returns the width of this dimension in double precision. |
int |
hashCode()
Returns the hash code for this Dimension . |
void |
setSize(Dimension d)
Sets the size of this Dimension object to the specified size. |
void |
setSize(double width,
double height)
Sets the size of this Dimension object to
the specified width and height in double precision. |
void |
setSize(int width,
int height)
Sets the size of this Dimension object
to the specified width and height. |
String |
toString()
Returns a string representation of the values of this Dimension object's height and
width fields. |
| Methods inherited from class java.awt.geom.Dimension2D |
|---|
clone, setSize |
| Methods inherited from class java.lang.Object |
|---|
finalize, getClass, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
public int width
getSize()
,
setSize(double, double)
public int height
getSize()
,
setSize(double, double)
| Constructor Detail |
|---|
public Dimension()
Dimension
with a width
of zero and a height of zero.
public Dimension(Dimension d)
Dimension
whose width
and height are the same as for the specified dimension.
d
- the specified dimension for the
width
and
height
valuespublic Dimension(int width, int height)
Dimension
and initializes
it to the specified width and specified height.
width
- the specified widthheight
- the specified height| Method Detail |
|---|
public double getWidth()
getWidth
in class Dimension2D
public double getHeight()
getHeight
in class Dimension2D
public void setSize(double width, double height)
Dimension
object to
the specified width and height in double precision.
Note that if width
or height
are larger than Integer.MAX_VALUE
, they will
be reset to Integer.MAX_VALUE
.
setSize
in class Dimension2D
width
- the new width for the Dimension
objectheight
- the new height for the Dimension
objectpublic Dimension getSize()
Dimension
object.
This method is included for completeness, to parallel the
getSize
method defined by Component
.
Dimension
with the same width and heightsetSize(double, double)
,
Component.getSize()
public void setSize(Dimension d)
Dimension
object to the specified size.
This method is included for completeness, to parallel the
setSize
method defined by Component
.
d
- the new size for this Dimension
objectgetSize()
,
Component.setSize(int, int)
public void setSize(int width, int height)
Dimension
object
to the specified width and height.
This method is included for completeness, to parallel the
setSize
method defined by Component
.
width
- the new width for this Dimension
objectheight
- the new height for this Dimension
objectgetSize()
,
Component.setSize(int, int)
public boolean equals(Object obj)
equals
in class Object
obj
- the reference object with which to compare.
true
if this object is the same as the obj
argument; false
otherwise.Object.hashCode()
,
Hashtable
public int hashCode()
Dimension
.
hashCode
in class Object
Dimension
Object.equals(java.lang.Object)
,
Hashtable
public String toString()
Dimension
object's height
and
width
fields. This method is intended to be used only
for debugging purposes, and the content and format of the returned
string may vary between implementations. The returned string may be
empty but may not be null
.
toString
in class Object
Dimension
object
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


What does a reference type represent?
a memory address reference instead of the actual item stored at that address
simple indecomposable values
No hints for this question


| Package | Description |
|---|---|
| s3games.ai | |
| s3games.player |
| Modifier and Type | Class and Description |
|---|---|
class |
DistanceFromGoalHeuristic
a heuristic that compares the number of different elements from the target situation
|
class |
MoreStonesHeuristic
a heuristic class that counts the number of elements of both players on the board.
|
class |
Puzzle8Heuristic
A heuristic class for the puzzle8 game.
|
| Modifier and Type | Field and Description |
|---|---|
protected Heuristic |
Strategy.heuristic
if the strategy needs a heuristic, it is stored here
|
| Modifier and Type | Method and Description |
|---|---|
static Heuristic |
Heuristic.getHeuristic(java.lang.String name,
GameSpecification specs)
constructor method that creates a heuristic of the specified type
|
| Modifier and Type | Method and Description |
|---|---|
static Strategy |
Strategy.getStrategy(java.lang.String name,
Heuristic h)
get a strategy instance of the specified name
|
void |
Strategy.setHeuristic(Heuristic heuristic)
set a heuristic that this strategy should be using
|
| Constructor and Description |
|---|
AStarStrategy(Heuristic h)
construct an instance of the strategy
|
MiniMaxStochasticStrategy(Heuristic h)
constructor requires a heuristic for evaluating states that are beyond the time limit
|
MiniMaxStrategy(Heuristic h)
constructor requires a heuristic for evaluating states that are beyond the time limit
|
| Constructor and Description |
|---|
AStarPlayer(GameSpecification specs,
Heuristic heuristic)
construct a new A* player for the specified game with the given heuristic
|
MiniMaxPlayer(GameSpecification specs,
Heuristic heuristic)
create a minimax player for the specified game with the heuristic provided
|
MiniMaxStochasticPlayer(GameSpecification specs,
Heuristic heuristic)
create a minimax player for the specified game with the heuristic provided
|


Bossggle
Score
0
Words
See the code
Make words by clicking the letters. Only dictionary words count!
START!
Time's up!
Your score is
PLAY AGAIN!
Shuffle
Submit
Clear Word


Goals for the next generation
Naming
2.0 is the opportunity to improve what we call things in the Swagger specification—one of the hardest problems in computer science1.
API design-first workflow
A human-friendly YAML syntax and editor make it easy to create an API spec from scratch.
Extending the metadata
Grow the format to include SLA information, vendor extensions, and other useful metadata.
Best-of-breed tooling
Code generation and Swagger UI will get a major refresh following the new spec.
GET involved!
New to Swagger? Learn more about the specification, or browse the projects on GitHub.
Got ideas for how to make Swagger even better?
GET the Details
What is the timeframe?
The formation of a working group was announced by Tony Tam at Gluecon on May 22, 2014. The goal is to have a draft spec in June for finalization in July.
Who is behind the working group?
Reverb is convening the group with Apigee as a founding member.
Get your organization involved—join the group!
How big a deal is this?
Swagger is estimated to have around 10,000 deployments in production. The Java client alone has been downloaded over 500k times. Swagger means business.
How can I help?
We're looking for designers, developers, and people who just think a lot about APIs. If that sounds like you, then come help a great, open source project get even better.


Quick Update
| Actions |
| 120 |
| Tasks |
| 99 |
| KPI's |
| 64 |
| Risk |
| 298 |
| Risk Action |
| 08 |
| Audit Recommendations |
| 31 |
| Tasks |
| 99 |
| KPI's |
| 64 |
| Risk |
| 298 |
Complete the business case if not already provided and
ensure purpose is well defined
on project character and PID
Target 75%
OctDec
0
Target 75%
OctDec
0
Hold scheduling workshops with the project team so they
understad the plan and likelihood of missed tasks is reduced
Target 75%
OctDec
0
Target 75%
OctDec
0
Complete the business case if not already provided and
ensure purpose is well defined
on project character and PID
Target 75%
OctDec
0


{% extends "base.html" %} {% load i18n %} {% load core_tags %} {% load gravatar %} {% block profile-bar-admin %}active{% endblock %} {# This is included here instead of subclassing base_profile because we don't control all the views, and can't set the `profile` variable reliably #} {% block subnav-bar %}


Sun, 01 Nov 2020 17:08:53 +0000
[17:08:53] Composing titles...
[17:08:53] Composing excerpts...
[17:08:53] Composing duplicate indicators...
[17:08:53] Composing authors...
[17:08:53] Composing slugs...
[17:08:53] Composing menu order...
[17:08:53] Composing contents...
[17:08:53] Composing dates...
[17:08:53] Composing terms for `Categories` taxonomy...
[17:08:53] Composing terms for `Tags` taxonomy...
[17:08:53] Composing URLs for images...
[17:08:53] Composing URLs for attachments files...
[17:08:53] Composing unique keys...
[17:08:53] Processing posts...
[17:08:53] Data parsing via add-ons...
[17:08:53] ---
[17:08:53] Record #1
[17:08:53] ACTION: pmxi_before_post_import ...
[17:08:53] Combine all data for post `Cauliflower with Polish Sauce`...
[17:08:53] Find corresponding article among previously imported for post `Cauliflower with Polish Sauce`...
[17:08:53] Duplicate post wasn't found with unique key `10`...
[17:08:53] Applying filter `pmxi_article_data` for `Cauliflower with Polish Sauce`
[17:08:53] CREATING `Cauliflower with Polish Sauce` `Post`
[17:08:53] Associate post `Cauliflower with Polish Sauce` with current import ...
[17:08:53] IMAGES:
[17:08:53] TAXONOMIES:
[17:08:53] - Importing taxonomy `category` ...
[17:08:53] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:54] - Importing taxonomy `post_tag` ...
[17:08:54] CREATED `Cauliflower with Polish Sauce` `Post` (ID: 80)
[17:08:54] ACTION: pmxi_saved_post
[17:08:54] ACTION: pmxi_after_post_import
[17:08:54] ---
[17:08:54] Record #2
[17:08:54] ACTION: pmxi_before_post_import ...
[17:08:54] Combine all data for post `Cabbage Rolls (Golabki)`...
[17:08:54] Find corresponding article among previously imported for post `Cabbage Rolls (Golabki)`...
[17:08:54] Duplicate post wasn't found with unique key `11`...
[17:08:54] Applying filter `pmxi_article_data` for `Cabbage Rolls (Golabki)`
[17:08:54] CREATING `Cabbage Rolls (Golabki)` `Post`
[17:08:54] Associate post `Cabbage Rolls (Golabki)` with current import ...
[17:08:54] IMAGES:
[17:08:54] TAXONOMIES:
[17:08:54] - Importing taxonomy `category` ...
[17:08:54] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:54] - Importing taxonomy `post_tag` ...
[17:08:54] CREATED `Cabbage Rolls (Golabki)` `Post` (ID: 81)
[17:08:54] ACTION: pmxi_saved_post
[17:08:54] ACTION: pmxi_after_post_import
[17:08:54] ---
[17:08:54] Record #3
[17:08:54] ACTION: pmxi_before_post_import ...
[17:08:54] Combine all data for post `Pierogi (Dumplings)`...
[17:08:54] Find corresponding article among previously imported for post `Pierogi (Dumplings)`...
[17:08:54] Duplicate post wasn't found with unique key `12`...
[17:08:54] Applying filter `pmxi_article_data` for `Pierogi (Dumplings)`
[17:08:54] CREATING `Pierogi (Dumplings)` `Post`
[17:08:54] Associate post `Pierogi (Dumplings)` with current import ...
[17:08:54] IMAGES:
[17:08:54] TAXONOMIES:
[17:08:54] - Importing taxonomy `category` ...
[17:08:54] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:54] - Importing taxonomy `post_tag` ...
[17:08:54] CREATED `Pierogi (Dumplings)` `Post` (ID: 82)
[17:08:54] ACTION: pmxi_saved_post
[17:08:54] ACTION: pmxi_after_post_import
[17:08:54] ---
[17:08:54] Record #4
[17:08:54] ACTION: pmxi_before_post_import ...
[17:08:54] Combine all data for post `Bigos (Hunter&Stew)`...
[17:08:54] Find corresponding article among previously imported for post `Bigos (Hunter&Stew)`...
[17:08:54] Duplicate post wasn't found with unique key `13`...
[17:08:54] Applying filter `pmxi_article_data` for `Bigos (Hunter&Stew)`
[17:08:54] CREATING `Bigos (Hunter&Stew)` `Post`
[17:08:54] Associate post `Bigos (Hunter&Stew)` with current import ...
[17:08:54] IMAGES:
[17:08:54] TAXONOMIES:
[17:08:54] - Importing taxonomy `category` ...
[17:08:54] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:54] - Importing taxonomy `post_tag` ...
[17:08:54] CREATED `Bigos (Hunter&Stew)` `Post` (ID: 83)
[17:08:55] ACTION: pmxi_saved_post
[17:08:55] ACTION: pmxi_after_post_import
[17:08:55] ---
[17:08:55] Record #5
[17:08:55] ACTION: pmxi_before_post_import ...
[17:08:55] Combine all data for post `Krupnik (Barley Soup)`...
[17:08:55] Find corresponding article among previously imported for post `Krupnik (Barley Soup)`...
[17:08:55] Duplicate post wasn't found with unique key `14`...
[17:08:55] Applying filter `pmxi_article_data` for `Krupnik (Barley Soup)`
[17:08:55] CREATING `Krupnik (Barley Soup)` `Post`
[17:08:55] Associate post `Krupnik (Barley Soup)` with current import ...
[17:08:55] IMAGES:
[17:08:55] TAXONOMIES:
[17:08:55] - Importing taxonomy `category` ...
[17:08:55] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:55] - Importing taxonomy `post_tag` ...
[17:08:55] CREATED `Krupnik (Barley Soup)` `Post` (ID: 84)
[17:08:55] ACTION: pmxi_saved_post
[17:08:55] ACTION: pmxi_after_post_import
[17:08:55] ---
[17:08:55] Record #6
[17:08:55] ACTION: pmxi_before_post_import ...
[17:08:55] Combine all data for post `Uszka (Small Dumplings)`...
[17:08:55] Find corresponding article among previously imported for post `Uszka (Small Dumplings)`...
[17:08:55] Duplicate post wasn't found with unique key `15`...
[17:08:55] Applying filter `pmxi_article_data` for `Uszka (Small Dumplings)`
[17:08:55] CREATING `Uszka (Small Dumplings)` `Post`
[17:08:55] Associate post `Uszka (Small Dumplings)` with current import ...
[17:08:55] IMAGES:
[17:08:55] TAXONOMIES:
[17:08:55] - Importing taxonomy `category` ...
[17:08:55] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:55] - Importing taxonomy `post_tag` ...
[17:08:55] CREATED `Uszka (Small Dumplings)` `Post` (ID: 85)
[17:08:55] ACTION: pmxi_saved_post
[17:08:55] ACTION: pmxi_after_post_import
[17:08:55] ---
[17:08:55] Record #7
[17:08:55] ACTION: pmxi_before_post_import ...
[17:08:55] Combine all data for post `White cabbage stewed with neck`...
[17:08:55] Find corresponding article among previously imported for post `White cabbage stewed with neck`...
[17:08:55] Duplicate post wasn't found with unique key `16`...
[17:08:55] Applying filter `pmxi_article_data` for `White cabbage stewed with neck`
[17:08:55] CREATING `White cabbage stewed with neck` `Post`
[17:08:55] Associate post `White cabbage stewed with neck` with current import ...
[17:08:55] IMAGES:
[17:08:55] TAXONOMIES:
[17:08:55] - Importing taxonomy `category` ...
[17:08:55] - Attempted to create parent Post category `Cuisine`, duplicate detected. Importing Post to existing `Cuisine` category, ID 23, slug `cuisine` ...
[17:08:55] - Importing taxonomy `post_tag` ...
[17:08:55] CREATED `White cabbage stewed with neck` `Post` (ID: 86)
[17:08:56] ACTION: pmxi_saved_post
[17:08:56] ACTION: pmxi_after_post_import
[17:08:56] Cleaning temporary data...


Turtle graphics is a popular way for introducing programming to kids. Virtual turtles can be programmed to move around the
screen. The turtle draws lines as it moves. The "turtle" could look like the turtle animal, an arrow, or be invisibile. The user can
write turtle programs that draw beautiful shapes and learn to program at the same time.
There are always four steps you need to do in order to use the turtle module:
1. Import the turtle module. If we skip this step, there’ll be no turtle to control.
2. Create a turtle to control.
3. Draw things. Do stuff. This will also automatically create the screen.
4. Run turtle.done() .
Code:
from turtle import *
forward(100)
left(90)
forward(100)
left(90)
forward(100)
left(90)
forward(100)
o/p:
The instructions in your program tell the "turtle" how to move. The turtle draws a line behind it as it moves. This program
draws a square. The steps given to the program are:
1. Move forward 100 steps. (In the beginning, the turtle is facing to the right.)
2. Turn 90 degrees to the left.
3. Move forward 100 steps.
4. Turn 90 degrees to the left.
5. Move forward 100 steps.
6. Turn 90 degrees to the left.
7. Move forward 100 steps. The turtle has ended up where it started.
Examples:
from turtle import *
for i in range(500): # this "for" loop will repeat these functions 500 times
forward(i)
left(91)
Examples:
from turtle import *
color('red', 'yellow')
begin_fill()
while True:
forward(200)
left(170)
if abs(pos()) < 1:
break
end_fill()
done()
o/p:
Examples:
import turtle
painter = turtle.Turtle()
painter.pencolor("blue")
for i in range(50):
painter.forward(50)
painter.left(123) # Let's go counterclockwise this time
painter.pencolor("red")
turtle.done()
o/p:
Examples:
import turtle
polygon = turtle.Turtle()
num_sides = 6
side_length = 70
angle = 360.0 / num_sides
for i in range(num_sides):
polygon.forward(side_length)
polygon.right(angle)
turtle.done()
o/p:


GitWorks, a tool set to analyze Git-based software repositories.
WUW, P2P decentralized service to measure and improve user satisfaction.
P2Poem, a framework for P2P distributed function optimization.


a team-based multiplayer first-person shooter video game developed and published by Blizzard Entertainment, which released on May 24, 2016 for PlayStation 4, Xbox One, and Windows.
More InfoCharacters in Overwatch come in four varieties: Offense, Defense, Tank, and Support. These roles serve to categorize the heroes by similar characteristics that can be used to describe them and their play style. The game shows tips to the players depending on which heroes have been selected; e.g., the team will recommend that a player selects a Support hero if there are none on the team.
About RolesOffense characters have high mobility and are known for their ability to deal large amounts of damage. To balance this, offense characters have a low number of hit points
Defense characters excel at protecting specific locations and creating choke points. Some can provide several means of field support, such as sentry turrets and traps
Tank characters have many more hit points compared to other characters.   They do not deal a lot of damage, nor do they have many hit points, but the buffs and debuffs they provide aid teammates in eliminating their opponents
For Any problems or Services, let us know it! Call or send us an email and we will get back to you as soon as possible!
1644-0727(KR)
489 952 457(FR)
FR 87 489 952 457(EU)


Applications
3D
Architecture Master Plan Buildings
Dominic 3D Sketchup Buildings
More detailed master plan buildings
Adds missing buildings from master plan
LAAH 3D
Floors, rooms, elevators, stairs, light poles, night mode, true time shadows
Requires toggling individual features in hamburger menu
Low fidelity 3D campus in Unity
Basic building box meshes
On application start, hit ESC key to exit menu and then pan camera with mouse and move around with WASD keys.
High fidelity LAAH building in Unity
Highly detailed building interior spaces
On application start, hit ESC key to exit menu and then pan camera with mouse and move around with WASD keys.
CSFD Firefighter Demo for Hullaballoo
Plays back a call recording with CSFD and goes through the call transcript automatically animating the scenario.
High Fidelity 3D Evans Exterior
Different color meshes with detailed exterior features.
2D
Graduation Arrival
Graduation Departure
Hailstorm
Movein
Signage
Trees
Vector


Allows you to test the pattern, min and max versions.
Run the command you're configuring above and paste its output into this text box. This text will then be checked using the pattern and the version detected (if any) will be compared against the min and max versions.
Note: Text pasted here will not be persisted.


Games at Games Bond :
Games Bond is the largest source of the best free online games including funny games, flash games, arcade games, dress-up games, internet games, shooting games, word games, RPG games, racing games, and much more. Play Games on the One-and-Only Addicting Games! Whatever game you feel like turning on, we've got it here. Including free online games! With tons of reviews & ratings, gamers of all skill levels are sure to find hot new games every day. This is the best place on the web to play games for free! Thousands of choices from some of the best developers around, like Armor Games, Games2Win, and even yours truly, Addicting Games! If you're ready to start playing right now, simply choose from the following categories: Action, Sports, Puzzle & Board, Shooting, Arcade & Classic, Strategy, Adventure, Life & Style, & NewsGames.


sylly: Hyphenation and Syllable Counting for Text Analysis
Provides the hyphenation algorithm used for 'TeX'/'LaTeX'
and similar software, as proposed by Liang (1983,
<https://tug.org/docs/liang/>). Mainly contains the function
hyphen() to be used for hyphenation/syllable counting of text
objects. It was originally developed for and part of the
'koRpus' package, but later released as a separate package so
it's lighter to have this particular functionality available
for other packages. Support for various languages needs be
added on-the-fly or by plugin packages
(<https://undocumeantit.github.io/repos>); this package does
not include any language specific data. Due to some
restrictions on CRAN, the full package sources are only
available from the project homepage. To ask for help, report
bugs, request features, or discuss the development of the
package, please subscribe to the koRpus-dev mailing list
(<http://korpusml.reaktanz.de>).
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=sylly
to link to this page.


Before Change
// Iterate thru answers, apply every scoring function
for i, answer in enumerate(answers):
print([function(i+1, question, answer) for function in functions])
scores.append(score_settings["starting_score"] + sum([function(i+1, question, answer) for function in functions]))
// Return score
After Change
//answer_subsentence_diffrence_by_distance,
]
scores = {"score": [], "score_modifiers": []}
// Iterate thru answers, apply every scoring function
for i, answer in enumerate(answers):
score_modifiers = [function(i+1, question, answer) for function in functions]
scores["score"].append(score_settings["starting_score"] + sum(score_modifiers))
scores["score_modifiers"].append(score_modifiers)
// Return score
return scores


Italian Recipe: Chicken Piccata
Ingredients
- 1 pound chicken scaloppini
- Kosher salt and freshly ground black pepper
- 1 tbsp. plus 1 tsp. all-purpose flour, divided
- 2 garlic cloves, finely chopped
- 1/2 cup low-sodium chicken broth
- Finely grated zest and juice of 1 lemon
- 1 tsp. white wine vinegar
- 2 tbsp. chopped fresh parsley, plus more for garnish
- 1 tbsp. unsalted butter
- Baby arugula for garnish
- Whole-wheat angel hair pasta or roasted potatoes
Directions
- Season the chicken with salt and pepper and sprinkle with 1 tablespoon flour.
- Heat 2 teaspoons oil in a large nonstick skillet over medium-high heat until very hot; cook the cutlets until opaque throughout, 1 to 2 minutes per side. Transfer the cutlets to a plate. (Reserve the skillet.) Reduce the heat under the skillet to medium.
- Add the remaining teaspoon oil and garlic to the same skillet; cook until fragrant, about 1 minute. Whisk together the broth, lemon juice and 1 teaspoon flour, and add to the pan. Simmer sauce until reduced and thickened, 2 to 3 minutes. Stir in the vinegar, parsley and capers. Swirl in the butter until sauce is shiny and slightly thickened.
- Serve chicken with the sauce. Toss the arugula and freshly grated lemon zest with a light drizzle of olive oil; season to taste with salt and pepper, scatter over chicken. Serve with pasta or potatoes as desired.


HandlerRequest Object¶
The HandlerRequest object is a wrapper around the HttpServletRequest object. Besides proving access to the entire HttpServletRequest API, the HandlerRequest object also adds some convenient methods which abstract the mundane and arduous tasks for performing some pretty common chores.
protocol() : String¶
Wraps around HttpServletRequest’s getProtocol method
secure() : boolean¶
Returns true if the protocol is https
hostname() : String¶
Wraps around HttpServletRequest’s getRemoteHost method
ip() : String¶
Wraps around HttpServletRequest’s getRemoteAddr method
path() : String¶
Wraps around HttpServletRequest’s getRequestURI method
route() : RouteSearch¶
Returns the route matched together with path parameters discovered as a Map<String, String> pathParams and the RequestAttrs used to search for the route.
param(name: String) : String¶
First searches for a named path parameter extracted from the request uri. This is
defined using {curly braces} in the request path mapping. If none if found,
it falls back to the servlet’s getParameter(name)
method which will return the
named parameter if it exists in the request body. If none is found,
it return s a null value.
pathParams() : Map¶
Returns a map of all named path parameters extracted from the request uri. These are
defined using {curly
braces}
in the request path mapping.
query() : String¶
Wraps around HttpServletRequest’s getQueryString method
header(name: String) : String¶
Wraps around HttpServletRequest’s getHeader(name) method
error() : boolean¶
Return an error status associated with the request if something went wrong and was captured
message() : String¶
Return a message associated with the request if one was saved when the request was processed.
body() : byte[]¶
Return the request body captured if the long capture() was called prior.
body(type: Class) : T¶
Used to return the request’s body() content as either json or xml depending on the respective Content-Type header in the request.
body(provider: BodyReader) : T¶
Used to return the request’s body() content that is transformed using the BodyReader implementation supplied by caller of this method.
capture() : long¶
Captures the request body into a byte[] which is subsequently accessible using the byte[] body() method.
upload(destination: String) : long¶
Used together with a servlet designated for uploading multipart/form-data using the POST method and that is configured with a MultipartConfigElement object. The configured values for (String location, long maxFileSize, long maxRequestSize, int fileSizeThreshold) are (“temp”, 1024 * 1024 * 50, 1024 * 1024, 5) respectively.:
router.get('/upload', function (req, res) {
res.render('upload', {}); //return upload page
});
router.post('/upload', '', 'multipart/form-data', function (req, res) {
var dest = req.param('destination');
req.upload(dest);
res.redirect(app.resolve("/upload")); //redirect to upload page
});
And the equivalent Java syntax would be.:
get("/upload", (HandlerRequest request, HandlerResponse response) -> {
response.render("upload", Maps.newHashMap());
});
post("/www/upload", "", "multipart/form-data", (HandlerRequest request, HandlerResponse response) ->{
String dest = request.param("destination");
request.upload(dest);
response.redirect(app.resolve("/upload"));
});
cookies() : Cookie[]¶
Returns the cookies associated with the request.
session(create: boolean) : HttpSession¶
Returns the session object associated with the request. If the create attribute is true, a new session is created and returned. If it is false it will only return a session if one already exists otherwise it will not create one and returns nothing.


2016-09-19 Using STEEMIT to build a youtube following in short order: How I got 222,000 views on youtube from a fresh account in 14 days steemit
2016-09-18 Inside the bloggers studio- Blair Reich, PhD and satirical alter ego @aggroed discuss blogging, screencasting, youtube, writing, and more introduceyourself
2016-09-18 Satirically Yours S1E18- Colin Kaepernick escalates anthem protest. Converts to Wahabism, burns flag, joins ISIS. SF wins 28, 0. politics
2016-09-17 Satirically Yours S1E17: Inspired by America's Got Talent illusionist Hiroki Hara, Hillary Clinton to include green screen magic in campaign speeches. writing
2016-09-16 Satirically Yours S1E15: Hillary Clinton regrets leaving campaign trail to attend dubstep/electronic music festival headlined by Bassnectar politics
2016-09-16 Hillary Clinton Saga- Evidence for a 2nd body double used at the Greensboro NC rally politics
2016-09-15 In a wholly ironic way HRCs immunization against pneumonia may have been the source vaccines
2016-09-15 Exploring the Mandela Effect S1E3- "Mirror, mirror on the wall" line has changed to "Magic mirror on the wall" in Disney's Snow White and the Seven Dwarves science
2016-09-15 Satirically Yours S1E14- Hillary Clinton experiences dramatic recovery after switching to raw food vegan diet politics
2016-09-15 Satirically Yours S1E13- Radical pro-Harambe candidate Trump tweets armed selfie, states "Don't go to the Cincinnati Zoo tomorrow!" politics
2016-09-14 BREAKING MEDIA SILENCE- Hillary... "Come out! Come out! Wherever you are!" Prolonged absence of Hillary, reported doubles, and potential explanations hillarysdeath
2016-09-14 Exploring the Mandela Effect S1E2- 25,000 respondent quiz results video and 83 page paper. mandelaeffect
2016-09-14 Mainstream Media Ignored Story- North Dakota Access pipeline protest swells to 5,000 people, mercs use dogs, militarized police mass arrest water protectors, Amy & Jill Served, FB censorship, and Bernie visits nodapl
2016-09-14 Satirically Yours S1E12: Cis gendered Caucasian plus sized bisexual millennial social justice warrior experiences brief freedom from patriarchal homophobic consumerist American culture while taking nature walk nature
2016-09-13 Satirically Yours S1E11: Trump finds phone unleashes a slew of anti-Hillary memes on Twitter politics
2016-09-13 Satirically Yours S1E10: @aggroed sits down for an interview with @ned and @dan about STEEMIT economics while chained to a basement chair in STEEMIT headquarters steem
2016-09-13 Exploring the Mandela and Toto Effect S1E1- Term introduction Mandelo. Origination to our current situation seems to have multiple access points. science


Select the date - this must be today's or tomorrow's date
Use the filter buttons to select your region / district
Enter new burns using the "Add Burn" button
Enter new bushfires using the "Add Bushfire" button
The (r) next to a burn indicates that it was rolled over from the previous day's records i.e. it was active yesterday
All previously active burns and bushfires must be updated with the status (active/inactive) and area burnt yeterday (ha or km)
Before SDO can acknowledge the fire load, all burns and bushfires must be entered at the district level and endorsed at the regional level
To enter burns and bushfires at the district level, tick the box/es next to the burn and select "District Entered", click on "Update" button
To acknowledge burns and bushfires at the regional level, tick the box/es next to the burn and select "Regional Acknowledgement", click on "Update" button
To acknowledge burns and bushfires at state level, tick the box/es next to the burn and select "State Acknowledgement", click on "Update" button


Adam Wyeth
<EMAIL_ADDRESS>Bachelor of Arts in Economics, Expected June 2015
Minor in Computer Science, Expected June 2015
Cumulative GPA: 3.61/4.00
Diploma, 2013
Honors included: Valedictorian (1 of 327), National Honor Society Member, and Top Math Student of the Class of 2011
Junior Web Developer, October 2014 - Present
Web Developer Intern, June 2014 - September 2014
Grader, June 2013 - March 2014
Assistant Web Developer, April 2013- December 2013


In one of the first articles, I’ve recommended to remove from the default AppDelegate.cpp
file everything related to work with multiple screen resolutions.
The Cocos2d development started a long long time ago and there were not many different screen sizes available at that time. Later the diversity increased and there a lot of different formats. And now it seems that everybody used more or less same screen sizes again. At some period of time, the resolution was a serious problem: you had to keep a few sets of images for various generations of devices. Again, now it seems that even cheaper devices have decent screens so the problem is not so important (of course you could disagree with this point).
In theory, the development process should look like this:
There was a code like this in default program:
auto visibleSize = Director::getInstance()->getVisibleSize();
Vec2 origin = Director::getInstance()->getVisibleOrigin();
Here visibleSize
is the size of the user’s screen and origin
is the position of the scene’s bottom left corner on that screen
Consider the following situation (numbers look unrealistic for our days, but nevermind):
Here:
visibleSize
will be 1024x768, and origin
will be (128:0). The origin is the position of bottom left corner of large red rectangle on the picture.Cocos2d offers few variants of fitting called (ResolutionPolicy):
EXACT_FIT
means the image will fill the screen anyway, but it may get stretched horizontally or vertically.FIXED_HEIGHT
and FIXED_WIDTH
means the proportions remain the same, but the scene may get cut from the sides to keep correct width or heightNO_BORDER
looks kike automatic selection either FIXED_HEIGHT
or FIXED_WIDTH
SHOW_ALL
means proportions remain the same, the whole scene appears on the screen, but black stripes may be added to the sides.Also, you can implement your own policy like is this article.
And another variant, you can skip specifying design resolution and launch with a real screen size. Following code in AppDelegate.cpp
does the trick:
Size realScreenSize = glview->getFrameSize();
glview->setDesignResolutionSize(realScreenSize.width, realScreenSize.height,
ResolutionPolicy::NO_BORDER);
It doesn’t matter ResolutionPolicy is used here, you get the real screen size anyway.
If you want to use different sets of images for different sizes you’ll have to change the default paths for files downloading. More information about it here, here or in this article.


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