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C
call()
- Method in class compbio.engine.local.
ExecutableWrapper
It is vital that output and error streams are captured immediately for this call() to succeed.
canAcceptMoreWork()
- Method in class compbio.engine.local.
LocalExecutorService
If the Executor queue is empty
CancelJob
- Class in
compbio.data.msa.jaxws
CancelJob()
- Constructor for class compbio.data.msa.jaxws.
CancelJob
cancelJob(String)
- Method in interface compbio.data.msa.
JManagement
Stop running the job
jobId
but leave its output untouched
cancelJob(String)
- Method in interface compbio.engine.
AsyncExecutor
Stop running job.
cancelJob(String)
- Method in class compbio.engine.cluster.drmaa.
AsyncJobRunner
cancelJob(String, ClusterSession)
- Static method in class compbio.engine.cluster.drmaa.
ClusterUtil
cancelJob()
- Method in class compbio.engine.cluster.drmaa.
JobRunner
cancelJob(String)
- Method in class compbio.engine.local.
AsyncLocalRunner
cancelJob(Future<ConfiguredExecutable<?>>, String)
- Static method in class compbio.engine.local.
LocalEngineUtil
cancelJob()
- Method in class compbio.engine.local.
LocalRunner
cancelJob()
- Method in interface compbio.engine.
SyncExecutor
Stops running job.
cancelJob(String)
- Method in class compbio.ws.server.
ClustalOWS
cancelJob(String)
- Method in class compbio.ws.server.
ClustalWS
cancelJob(String)
- Method in class compbio.ws.server.
GenericMetadataService
cancelJob(String)
- Method in class compbio.ws.server.
MafftWS
cancelJob(String)
- Method in class compbio.ws.server.
MuscleWS
cancelJob(String)
- Method in class compbio.ws.server.
ProbconsWS
cancelJob(String)
- Method in class compbio.ws.server.
TcoffeeWS
cancelJob(String)
- Static method in class compbio.ws.server.
WSUtil
CancelJobResponse
- Class in
compbio.data.msa.jaxws
CancelJobResponse()
- Constructor for class compbio.data.msa.jaxws.
CancelJobResponse
Category
- Class in
compbio.data.msa
Class that splits
Services
to categories.
CATEGORY_ALIGNMENT
- Static variable in class compbio.data.msa.
Category
All of the Category names
CATEGORY_CONSERVATION
- Static variable in class compbio.data.msa.
Category
CATEGORY_DISORDER
- Static variable in class compbio.data.msa.
Category
checkService(Services)
- Method in class compbio.ws.client.
WSTester
Test JABA web service
ChunkHolder
- Class in
compbio.metadata
Represents a chunk of a string data together with the position in a file for the next read operation.
ChunkHolder(String, long)
- Constructor for class compbio.metadata.
ChunkHolder
Cleaner
- Class in
compbio.engine
Deprecated.
Cleaner()
- Constructor for class compbio.engine.
Cleaner
Deprecated.
cleanProteinSequence(String)
- Static method in class compbio.data.sequence.
SequenceUtil
Remove all non AA chars from the sequence
cleanSequence(String)
- Static method in class compbio.data.sequence.
SequenceUtil
Removes all whitespace chars in the sequence string
cleanup(String)
- Method in interface compbio.engine.
AsyncExecutor
Remove all files and a job directory for a jobid.
cleanup(String)
- Method in class compbio.engine.cluster.drmaa.
AsyncJobRunner
cleanup()
- Method in class compbio.engine.cluster.drmaa.
JobRunner
cleanup(String)
- Method in class compbio.engine.local.
AsyncLocalRunner
cleanup(ConfiguredExecutable<?>)
- Static method in class compbio.engine.local.
LocalEngineUtil
cleanup()
- Method in class compbio.engine.local.
LocalRunner
cleanup()
- Method in interface compbio.engine.
SyncExecutor
Clean up after the job
close()
- Method in class compbio.data.sequence.
FastaReader
Call this method to close the connection to the input file if you want to free up the resources.
close()
- Method in class compbio.engine.cluster.drmaa.
ClusterSession
closeSilently(Logger, Closeable)
- Static method in class compbio.data.sequence.
SequenceUtil
Closes the Closable and logs the exception if any
CLUSTAL_INFO
- Static variable in enum compbio.ws.client.
Services
CLUSTAL_OMEGA_INFO
- Static variable in enum compbio.ws.client.
Services
ClustalAlignmentUtil
- Class in
compbio.data.sequence
Tools to read and write clustal formated files
ClustalAlignmentUtil()
- Constructor for class compbio.data.sequence.
ClustalAlignmentUtil
ClustalO
- Class in
compbio.runner.msa
ClustalO()
- Constructor for class compbio.runner.msa.
ClustalO
--threads=
Number of processors to use -l, --log=
Log all non-essential output to this file
ClustalOWS
- Class in
compbio.ws.server
ClustalOWS()
- Constructor for class compbio.ws.server.
ClustalOWS
ClustalW
- Class in
compbio.runner.msa
ClustalW()
- Constructor for class compbio.runner.msa.
ClustalW
ClustalWS
- Class in
compbio.ws.server
ClustalWS()
- Constructor for class compbio.ws.server.
ClustalWS
CLUSTER_STAT_IN_SEC
- Static variable in class compbio.engine.cluster.drmaa.
ClusterUtil
CLUSTER_TASK_ID_PREFIX
- Static variable in class compbio.engine.client.
ConfExecutable
CLUSTER_WORK_DIRECTORY
- Static variable in class compbio.engine.
Configurator
ClusterJobId
- Class in
compbio.engine
ClusterJobId(String)
- Constructor for class compbio.engine.
ClusterJobId
ClusterSession
- Class in
compbio.engine.cluster.drmaa
ClusterUtil
- Class in
compbio.engine.cluster.drmaa
ClusterUtil()
- Constructor for class compbio.engine.cluster.drmaa.
ClusterUtil
COILS_EXPECTATION_THRESHOLD
- Variable in class compbio.runner.disorder.
Disembl
For the region to be considered disordered the values must exceed these
CommandBuilder
<
T
> - Class in
compbio.engine.client
CommandBuilder(String)
- Constructor for class compbio.engine.client.
CommandBuilder
compare(Hit, Hit)
- Method in class compbio.pipeline._jpred.
Hit.EvalueComporator
compare(Hit, Hit)
- Method in class compbio.pipeline._jpred.
Hit.NumberComporator
compareTo(Range)
- Method in class compbio.data.sequence.
Range
compareTo(Score)
- Method in class compbio.data.sequence.
Score
compareTo(Delayed)
- Method in class compbio.engine.
FilePuller
compbio.data._structure
- package compbio.data._structure
compbio.data.msa.jaxws
- package compbio.data.msa.jaxws
compbio.pipeline._jpred
- package compbio.pipeline._jpred
compbio.runner._jpred
- package compbio.runner._jpred
confDir
- Static variable in class compbio.engine.conf.
PropertyHelperManager
ConfExecutable
<
T
> - Class in
compbio.engine.client
ConfExecutable(Executable<T>, String)
- Constructor for class compbio.engine.client.
ConfExecutable
Configurator
- Class in
compbio.engine
Configurator()
- Constructor for class compbio.engine.
Configurator
ConfiguredExecutable
<
T
> - Interface in
compbio.engine.client
configureExecutable(Executable<T>)
- Static method in class compbio.engine.
Configurator
configureExecutable(Executable<T>, List<FastaSequence>)
- Static method in class compbio.engine.
Configurator
configureExecutable(Executable<T>, Executable.ExecProvider)
- Static method in class compbio.engine.
Configurator
connect()
- Static method in class compbio.ws.client.
AAConClient
Connects to a AACon web service by the host and the service name
connect(String, Services)
- Static method in class compbio.ws.client.
Jws2Client
Connects to a web service by the host and the service name web service type
connectToRegistry(String)
- Static method in class compbio.ws.client.
Jws2Client
Get a connection of JABAWS registry
ConservationMethod
- Enum in
compbio.data.sequence
Enumeration listing of all the supported methods.
contextDestroyed(ServletContextEvent)
- Method in class compbio.stat.servlet.
StatisticCollector
contextDestroyed(ServletContextEvent)
- Method in class compbio.ws.server.
SetExecutableFlag
contextDestroyed(ServletContextEvent)
- Method in class compbio.ws.server.
ShutdownEngines
contextInitialized(ServletContextEvent)
- Method in class compbio.stat.servlet.
StatisticCollector
contextInitialized(ServletContextEvent)
- Method in class compbio.ws.server.
SetExecutableFlag
This listener is designed to run only once when the web application is deployed to set executable flag for binaries.
contextInitialized(ServletContextEvent)
- Method in class compbio.ws.server.
ShutdownEngines
convertToAbsolute(String)
- Static method in class compbio.engine.client.
Util
copyAndValidateRConfig(RunnerConfig<?>)
- Method in class compbio.metadata.
RunnerConfig
countMatchesInSequence(String, String)
- Static method in class compbio.data.sequence.
FastaSequence
CustomAlign
- Class in
compbio.data.msa.jaxws
CustomAlign()
- Constructor for class compbio.data.msa.jaxws.
CustomAlign
customAlign(List<FastaSequence>, List<Option<T>>)
- Method in interface compbio.data.msa.
MsaWS
Align a list of sequences with options.
customAlign(List<FastaSequence>, List<Option<T>>)
- Method in class compbio.ws.server.
_MsaService
customAlign(List<FastaSequence>, List<Option<ClustalO>>)
- Method in class compbio.ws.server.
ClustalOWS
customAlign(List<FastaSequence>, List<Option<ClustalW>>)
- Method in class compbio.ws.server.
ClustalWS
customAlign(List<FastaSequence>, List<Option<Mafft>>)
- Method in class compbio.ws.server.
MafftWS
customAlign(List<FastaSequence>, List<Option<Muscle>>)
- Method in class compbio.ws.server.
MuscleWS
customAlign(List<FastaSequence>, List<Option<Probcons>>)
- Method in class compbio.ws.server.
ProbconsWS
customAlign(List<FastaSequence>, List<Option<Tcoffee>>)
- Method in class compbio.ws.server.
TcoffeeWS
CustomAlignResponse
- Class in
compbio.data.msa.jaxws
CustomAlignResponse()
- Constructor for class compbio.data.msa.jaxws.
CustomAlignResponse
CustomAnalize
- Class in
compbio.data.msa.jaxws
CustomAnalize()
- Constructor for class compbio.data.msa.jaxws.
CustomAnalize
customAnalize(List<FastaSequence>, List<Option<T>>)
- Method in interface compbio.data.msa.
SequenceAnnotation
Analyse the sequences according to custom settings defined in options list.
customAnalize(List<FastaSequence>, List<Option<Disembl>>)
- Method in class compbio.ws.server.
DisemblWS
customAnalize(List<FastaSequence>, List<Option<GlobPlot>>)
- Method in class compbio.ws.server.
GlobPlotWS
customAnalize(List<FastaSequence>, List<Option<T>>)
- Method in class compbio.ws.server.
SequenceAnnotationService
CustomAnalizeResponse
- Class in
compbio.data.msa.jaxws
CustomAnalizeResponse()
- Constructor for class compbio.data.msa.jaxws.
CustomAnalizeResponse
Overview
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| Seq | Arrival | Departure | Nights | Rmtype | Created By | Room | blockcode | Status |
|---|---|---|---|---|---|---|---|---|
| {{i+1}} | {{resvlist.res_arrival}} | {{resvlist.res_depature}} | {{resvlist.res_nights}} | {{resvlist.res_room_type}} | {{resvlist.created_by}} | {{resvlist.res_number_of_rooms}} | {{resvlist.res_block_code}} | {{resvlist.res_guest_status}} |


Use this service to file your company’s annual accounts with Companies House.
You'll need:
You can register online. You’ll need your email address and create a password.
Once registered, we’ll post an authentication code to your registered office. If your company already has a code, a reminder will be sent.
Allow up to 5 days for delivery of your code.
For security reasons your code can’t be provided by email or over the phone.


{% extends "registration/base.html" %} {% block title %}Add action cluster - {{ block.super }}{% endblock title %} {% block section_title %} Add an Action Cluster {% endblock section_title %} {% block section %}
Please visit the GCTC Participation Guide for the requirements to form an Action Cluster and submit the completed Worksheet to<EMAIL_ADDRESS>and<EMAIL_ADDRESS>for review.
{% comment %}Please fill out the form below to have your action cluster added to our searchable database. Once you have submitted your application and it has been approved, it will be included on our Action Clusters page.
If you need to make changes to your action cluster description it can be accessed via your user profile.
{% endcomment %} {% endblock section %} {% block extra_scripts %} {{ form.media }} {% endblock extra_scripts %}


Handling long claims (256 char limit?) and small ones the same way will be difficult, no matter what odd CSS trickery I employ, issues are likely to arise, especially considering how hard simply aligning a div already is, even using the nice CSS3 features.


IEEE Day Celebration 2019
You need to be aware of what others are doing, applaud their efforts, acknowledge their successes, and encourage them in their pursuits. When we all help one another, everybody wins. IEEE Student Branch DITU is organizing a MEET-UP to celebrate IEEE DAY 2019 for all Student Branch Counsellors , Chapter Advisors and heads. The meet up is scheduled on 11th of October. The delegates will share their ideas and experiences in regard to the current technologies prevailing and provide a chance to interact with people of the same field.
Skill India
Ministry of Skill Development and Entrepreneurship (MSDE) & National Skill Development Corporation (NSDC) launched India’s first National Skills Competition- IndiaSkills, a biennial competition.The competion was divided into various levels and today was the state level competition in DIT, university. Our two teams participated and secured second and third positions and also qualified for national training and regional competitions.
Awards
The achievements of an organisation are the results of the combined effort of each individual. The hardwork of all the members of various clubs and societies of DIT University was acknowledged in the Annual Student Society Meet which was held on 14th April, 2018.We congratulate all the members of IEEE and wish them good luck for the coming years.
Tech Fest
‘Youthopia’ is the Annual Technical & Cultural Fest organised by DIT University, Dehradun. In this 3 day fest 3 events were organized namely Robo soccer,Line maze and sherlocked under IEEE.
Embedded Workshop
Workshop on “Embedded system for IOT and Product development approach” was conducted on 14th October 2018. It was delivered by-
- Harsh Joshi Sir, Team Lead, Smart Joules Pvt Ltd
- Amit Phulera Sir, Software Development Team Lead, Smart Joules Pvt Ltd
- Farogh Iftekhar Sir, Embedded System, Team lead, Smart Joules Pvt Ltd.
Sprite Animation
IEEE Student Chapter of DIT University,Dehradun brought the opportunity to start creating an illusion ‘Animation’ through code by organizing a workshop on “SPRITE ANIMATION”. The workshop was successfully conducted on 1st and 2nd February,2018.


---
layout: post
title: links for 2007-01-26
date: 2007-01-26 00:00:00
---
-
Unusually constructive criticism...
-
"To ignore casual is a mistake. To ignore casual is to ignore the reality of the games industry. This isn’t a matter of what we believe is the right content strategy - this is about the fact that huge numbers of people are playing casual games."
-
“This agreement is part of a broader strategy to establish commercial relationships with mobile operators"
-
-
"Lewes is a combination of a large number of people who are well-organised, intelligent and dislike being told what to do and if you add that to the high number of people who have a lot of skill with small explosives then this is the effect."
-
grrrr


CoriEstimator.Rd
An estimator of the reproduction number (\(R_t\)) using a renewal equation method.
Version: 0.03
Generated: 2022-05-24T22:12:30.975629
| r0Mean | r0Mean - The mean of the gamma distribution of the baseline prior assumption about R0 - the theorerical reproduction number in an infinite pool of evenly mixing susceptible individuals - (java expects a double) |
|---|---|
| r0SD | r0SD - The SD of the prior gamma distribution - (java expects a double) |
| maxWindow | maxWindow - The maximum sliding window over which to estimate \(R_t\). - (java expects a int) |
The renewal equation method depends on a time series of infections, and on the infectivity profile - a measure of the probability that a secondary infection occurred on a specific day after the primary case, given a secondary infection occurred. A Bayesian framework is then used to update a prior probabilistic estimate of \(R_t\) on any given day with both information gained from the time series of infections in the epidemic to date and the infectivity profile to produce a posterior estimate of \(R_t\). This is based on the article:

A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, ‘A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics’, Am J Epidemiol, vol. 178, no. 9, pp. 1505–1512, Nov. 2013, doi: 10.1093/aje/kwt133.

 and the implementation of it in the R Library EpiEstim. It assumes that infections (or other type of observation) are a Poisson distributed process and that the reproduction number is a gamma distributed quantity. The estimates of \(R_t\) may be aggregated over a specific "window" to provide a more stable estimate.
J$CoriEstimator$defaultEpiEstim(infectivityProfile, meanR0, sdR0, window)
J$CoriEstimator$defaultUncertainEpiEstim(infectivityProfiles, meanR0, sdR0, window, n2)
instance$withInfectivityProfile(infectivityProfile, replace)
instance$estimateRt(incidence, dateColName, incidenceColName)
instance$estimateRtSingle(incidence, dateColName, incidenceColName)
instance$estimateRtFromRates(rates, dateColName, rateColName, samplesPerProfile)
instance$estimateRtFromSingleRate(rate, dateColName, rateColName, samplesPerProfile)
instance$clone()
instance$print()
new()
The Estimator is configured with the baseline \(R_0\) assumptions which are used as prior values for \(R_t\) when no other values are available, or the @link CoriEstimator#withDefaultPrior() option is used.
J = jepidemic::JavaApi$get() J$CoriEstimator$new(r0Mean, r0SD, maxWindow)
r0Mean - The mean of the gamma distribution of the baseline prior assumption about R0 - the theorerical reproduction number in an infinite pool of evenly mixing susceptible individuals - (java expects a double)
r0SD - The SD of the prior gamma distribution - (java expects a double)
maxWindow - The maximum sliding window over which to estimate \(R_t\). - (java expects a int)
R6 CoriEstimator object:
not available
legacySupport()
no description
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$legacySupport(on)
on - a boolean flag to switch on (TRUE) or off (FALSE) eipestim naming convention - (java expects a boolean)
R6 CoriEstimator object: the estimator itself (a fluent method)
not available
defaultEpiEstim()
Produces a CoriEstimator that is configured to behave like the default settings of EpiEstim. In that it uses a fixed prior for Rt and returns a fixed window size

J = jepidemic::JavaApi$get() J$CoriEstimator$defaultEpiEstim(infectivityProfile, meanR0, sdR0, window)
infectivityProfile - a vector with the discrete probability distribution of the serial interval with \(P_(t=0) = 0\) - (java expects a RNumericVector)
meanR0 - the mean prior of R0 - (java expects a double)
sdR0 - the sd prior of R0 - (java expects a double)
window - the window size - (java expects a int)
R6 CoriEstimator object: a default cori estimator
not available
defaultUncertainEpiEstim()
Produces a CoriEstimator that is configured to behave like the default settings of EpiEstim. In that it uses a fixed prior for Rt and returns a fixed window size and aggregates uncertain SI distributions with a random resampling strategy

J = jepidemic::JavaApi$get() J$CoriEstimator$defaultUncertainEpiEstim(infectivityProfiles, meanR0, sdR0, window, n2)
infectivityProfiles - a numeric matrix with the discrete probability distribution of the serial interval with \(P_(t=0) = 0\) - (java expects a RNumericArray)
meanR0 - the mean prior of R0 - (java expects a double)
sdR0 - the sd prior of R0 - (java expects a double)
window - the window size - (java expects a int)
n2 - the number of samples to use for estimating confidence limits on bootstrapped infectivity profiles - (java expects a int)
R6 CoriEstimator object: a default cori estimator
not available
selectSpecificWindow()
The select methods define how the posterior estimate of \(R_t\) is selected. In this case a set value for the window is used. This must be less or equal to the value of maxWindow supplied in the CoriEstimator::new constructor. The window defines how many data points prior to the time of the estimate are used to synthesise the posterior estimate of \(R_t\), and so tends to reduce the uncertainty around the value of \(R_t\), but longer values of window introduces temporal uncertainty into the estimates. This is as implemented in EpiEstim (with a default value of 7 for the window).
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$selectSpecificWindow(window)
window - a number of days - (java expects a int)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
selectAdaptiveWindow()
The adaptive windowing strategy selects a posterior estimate of \(R_t\) by selecting the shortest window length (> minWindow) which spans at least incidenceSum infection (or observation) counts. This ensures that where numbers are low the estimation window is lengthened (up ot the value of maxWindow provided to the CoriEstimator::new constructor) to include more data at the cost of temporal precision. This prevents stochastic noise from dominating the estimates of \(R_t\).
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$selectAdaptiveWindow(incidenceSum, minWindow)
incidenceSum - the number of cases that must be observed in a window. It there are fewer that this the window will be lengthened. Larger values lead to smoother estiamtes at the cost of reduced temporal precision. - (java expects a double)
minWindow - the minimum size of the window that will be selected. In times of very high case numbers a minimum window prevents spurious outliers from completely dominating that days estimate of \(R_t\). - (java expects a int)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
selectMinimumUncertainty()
The minimum uncertainty strategy aims to select a window for the posterior \(R_t\) estimate based on minimising a combined value for uncertainty. The uncertainty in this case is the product of the (timeVsRt)th root of SD of the posterior estimate and the window size to the power of timeVsRt.

$$window^timeVsRt * SD_posterior^1timeVsRt$$
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$selectMinimumUncertainty(timeVsRt, minWindow)
timeVsRt - the relative importance parameter. Values above one favour temporal certainty; values under one favour \(R_t\) estimate certainty - (java expects a double)
minWindow - the minimum size of the window that will be selected. - (java expects a int)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
selectMixtureCombination()
The mixture combination strategy for selecting a posterior \(R_t\) involves finding all the estimates that have a window that span the estimate date (this may involve longer windowed estimate from dates in the future) and estimating a gamma distribution with the same mean and SD as the mixture of all the estimates. This combined estimate involves estimates of all different windows.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$selectMixtureCombination()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
selectWeightedMixtureCombination()
The mixture combination strategy for selecting a posterior \(R_t\) involves finding all the estimates that have a window that span the estimate date (this may involve longer windowed estimate from dates in the future) and estimating a gamma distribution with the same mean and SD as the mixture of all the estimates. This combined estimate involves estimates of all different windows.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$selectWeightedMixtureCombination(weights)
weights - the weighting of the different windows according to their length as a vector which much be the same length as the longest window, maxTau. - (java expects a RNumericVector)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
withDefaultPrior()
This sets the selection strategy for prior estimate of the reproduction number to be the fixed R0 value provided to the CoriEstimator::new constructor. All estimates use the same prior and this is equivalent to the strategy implemented in EpiEstim (which has a default value of 5 for the mean and 5 for the SD of the R0 prior). This is an uninformed prior strategy.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$withDefaultPrior()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
withFixedPrior()
This sets the selection strategy for prior estimate of the reproduction number to be fixed value. All estimates use the same value as an uninformed prior, but enables us to compare the impact of different priors.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$withFixedPrior(mean, sd)
mean - the mean of the fixed prior \(R_t\) - (java expects a double)
sd - the sd of the fixed prior \(R_t\) - (java expects a double)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
withAdaptivePrior()
This sets the prior to be the posterior for the previous timestep, but with the SD of the previous posterior widened to allow the equivalent of a random walk. The widening factor defines how much change we expect to see in each time step
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$withAdaptivePrior(factor)
factor - must be above 1 (or else the estimate will become locked on a specific value) - (java expects a double)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
collectResampledQuantiles()
This configures how the estimator combines multiple estimates of \(R_t\) using different infectivity profiles. In this case the estimator will randomly sample from the selected posterior gamma distribution produced for each infectivity profile. These samples are combined and a mean, sd and empirical quantiles calculated.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$collectResampledQuantiles(sampleSize)
sampleSize - the number of samples to draw for each posterior estimate of \(R_t\). This number will be multiplied by the number of infectivity profile estimates and the number time points, so should not be set too high or it will lead to memory consumption issues. This is the equivalent of the $n2$ parameter in EpiEstim - (java expects a int)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
collectMixtureQuantiles()
This configures how the estimator combines multiple estimates of \(R_t\) using different infectivity profiles. In this case it calculates a mixture distribution of all the posterior estimates, and combined values for mean, and standard deviation, of the mixture plus the quantiles using a brent solver.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$collectMixtureQuantiles()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
collectMixtureApproximation()
This configures how the estimator combines multiple estimates of \(R_t\) using different infectivity profiles. In this case it calculates a mixture distribution of all the posterior estimates, and then estimates a single gamma distribution with the same mean and SD as the mixture. This will only be a valid approximation if all the posterior estimates are close to each other, which will be the case if the infectivitiy profiles are fairly similar. It shoudl tend to produce estimates with less confidence. It is however the quickest method.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$collectMixtureApproximation()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
detailedOutput()
Configure the estimator to output full details for all infectivity profiles.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$detailedOutput()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
withInfectivityProfile()
Configure the estimator with an infectivity profile. At least one profile must be added
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$withInfectivityProfile(infectivityProfile, replace)
infectivityProfile - A numeric vector of the daily discrete probability of a secondary infection event occurring given an infected individual. It is assumed that this vector starts at time zero with a probability of zero. - (java expects a RNumericVector)
replace - replace the existing infectivity profile with this new one? if false then the profile is added to a list. - (java expects a boolean)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
withInfectivityProfileMatrix()
Configure the estimator with an infectivity profile by specifying it as a matrix. This will replace all previously set profiles.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$withInfectivityProfileMatrix(infectivityProfiles)
infectivityProfiles - A numeric array of the daily discrete probability of a secondary infection event occurring given an infected individual. It is assumed that this vector starts at time zero with a probability of zero. - (java expects a RNumericArray)
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
atStartOfTimeseries()
If the timeseries is at the start then we can safely impute zero values prior to the start of the timeseries. If the timeseries starts in the middle of the epidemic we must infer the infections prior to the beginning of the time series if we are to prevent sudden jumps in the estimates over the initial part of the timeseries.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$atStartOfTimeseries()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
inMiddleOfTimeseries()
If the timeseries starts in the middle of the epidemic we must infer the infections prior to the beginning of the time series if we are to prevent sudden jumps in the estimates over the initial part of the timeseries. This is done using the supplied value of R0 to infer a growth rate.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$inMiddleOfTimeseries()
none
R6 CoriEstimator object: The estimator itself (a fluent method)
not available
estimateRt()
Estimates \(R_t\) for a one or more timeseries of infections in an epidemic, where the supplied dataframe is grouped
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$estimateRt(incidence, dateColName, incidenceColName)
incidence - a dataframe containing at least 2 columns (one containing a date, the other a numeric count of cases). The dataframe may be grouped, in which case grouping is preserved, and each group is treated as a seperate time seriesincidenceColName - the name of the incidence column - (java expects a RDataframe)
dateColName - the name of the date column - (java expects a String)
incidenceColName - the name of the incidence column - (java expects a String)
RDataframe: a dataframe, containing either a summary of the estimates of \(R_t\) with mean, sd, and quantiles for each day, or a full breakdown of the component \(R_t\) estimates.
not available
estimateRtSingle()
Estimates \(R_t\) for a single timeseries of infections in an epidemic.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$estimateRtSingle(incidence, dateColName, incidenceColName)
incidence - a dataframe containing at least 2 columns (one containing a date, the other a numeric count of cases). The dataframe may be grouped, in which case grouping is preservedincidenceColName - the name of the incidence column - (java expects a RDataframe)
dateColName - the name of the date column - (java expects a String)
incidenceColName - the name of the incidence column - (java expects a String)
RDataframe: a dataframe, containing either a summary of the estimates of \(R_t\) with mean, sd, and quantiles for each day, or a full breakdown of the component \(R_t\) estimates.
not available
estimateRtFromRates()
Estimates \(R_t\) for a one or more timeseries of infections in an epidemic, where the supplied dataframe is grouped
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$estimateRtFromRates(rates, dateColName, rateColName, samplesPerProfile)
rates - a dataframe containing at least 2 columns (one containing a date, the other a numeric poission rate of cases). The dataframe may be grouped, in which case grouping is preserved, and each group is treated as a seperate time series - (java expects a RDataframe)
dateColName - the name of the date column - (java expects a String)
rateColName - the name of the rate column - (java expects a String)
samplesPerProfile - the number of times to bootstrap per infectivity profile - (java expects a int)
RDataframe: a dataframe, containing either a summary of the estimates of \(R_t\) with mean, sd, and quantiles for each day, or a full breakdown of the component \(R_t\) estimates.
not available
estimateRtFromSingleRate()
Estimates \(R_t\) for a single timeseries of infections in an epidemic.
J = jepidemic::JavaApi$get() instance = J$CoriEstimator$new(...); instance$estimateRtFromSingleRate(rate, dateColName, rateColName, samplesPerProfile)
rate - a dataframe containing at least 2 columns (one containing a date, the other a numeric estimate of possion rate of cases). The dataframe may be grouped, in which case grouping is preservedrateColName - the name of the estimated poisson rate - (java expects a RDataframe)
dateColName - the name of the date column - (java expects a String)
rateColName - the name of the estimated poisson rate - (java expects a String)
samplesPerProfile - the number of times to bootstrap per infectivity profile - (java expects a int)
RDataframe: a dataframe, containing either a summary of the estimates of \(R_t\) with mean, sd, and quantiles for each day, or a full breakdown of the component \(R_t\) estimates.
not available
if (FALSE) { J = jepidemic::JavaApi$get(); instance = J$CoriEstimator$new(r0Mean, r0SD, maxWindow) ## ----------------------------------- ## Method `CoriEstimator$legacySupport` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$defaultEpiEstim` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$defaultUncertainEpiEstim` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$selectSpecificWindow` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$selectAdaptiveWindow` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$selectMinimumUncertainty` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$selectMixtureCombination` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$selectWeightedMixtureCombination` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$withDefaultPrior` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$withFixedPrior` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$withAdaptivePrior` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$collectResampledQuantiles` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$collectMixtureQuantiles` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$collectMixtureApproximation` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$detailedOutput` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$withInfectivityProfile` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$withInfectivityProfileMatrix` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$atStartOfTimeseries` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$inMiddleOfTimeseries` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$estimateRt` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$estimateRtSingle` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$estimateRtFromRates` ## ----------------------------------- ## ----------------------------------- ## Method `CoriEstimator$estimateRtFromSingleRate` ## ----------------------------------- }


Our mission
Doing business like this takes much more effort than doing your own business at home, and on top of that there's the curse of travelling, worries about making train connections, bad and irregular food, contact with different people all the time so that you can never get to know anyone or become friendly with them.
Members
Build a team
And it's a funny sort of business to be sitting up there at your desk, talking down at your subordinates from up there, especially when you have to go right up close because the boss is hard of hearing. Well, there's still some hope; once I've got the money together to pay off my parents' debt to him - another five or six years I suppose - that's definitely what I'll do.
Doing business like this takes much more effort than doing your own business at home, and on top of that there's the curse of travelling, worries about making train connections, bad and irregular food, contact with different people all the time so that you can never get to know anyone or become friendly with them.
One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin. He lay on his armour-like back, and if he lifted his head a little he could see his brown belly, slightly domed and divided by arches into stiff sections. The bedding was hardly able to cover it and seemed ready to slide off any moment. His many legs, pitifully thin compared with the size of the rest of him, waved about helplessly as he looked. "What's happened to me?" he thought.
Pricing
And it's a funny sort of business to be sitting up there at your desk, talking down at your subordinates from up there, especially when you have to go right up close because the boss is hard of hearing. Well, there's still some hope; once I've got the money together to pay off my parents' debt to him - another five or six years I suppose - that's definitely what I'll do.
Doing business like this takes much more effort than doing your own business at home, and on top of that there's the curse of travelling, worries about making train connections, bad and irregular food, contact with different people all the time so that you can never get to know anyone or become friendly with them.
One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin. He lay on his armour-like back, and if he lifted his head a little he could see his brown belly, slightly domed and divided by arches into stiff sections. The bedding was hardly able to cover it and seemed ready to slide off any moment. His many legs, pitifully thin compared with the size of the rest of him, waved about helplessly as he looked. "What's happened to me?" he thought.


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Electrostatic Potential Energy
Electrostatic potential energy has a similar form to gravitational potential energy. It is Coulomb's law, but the radius is not squared.
You can think of the electrostatic energy as the work to move two charges to a distance, r, from each other.
$$ U_{e} = \frac{k_{e}q_{1}q_{2}}{r} $$
\(U_e\) = electrostatic potential energy [J, Joules, kg m²/s²]
\(k_e\) = 8.987 × 109 = Coulomb's constant [N m²/C²]
\(q\) = charge [C, Coulomb]
\(r\) = distance between the center of each charge [m, meters]
Only valid for stationary point charges.
Like all energy, electrostatic potential energy is a scalar, but it can go negative.
answer
When the distance increases the energy decreases. As the distance approaches infinity the energy approaches zero.
solution
$$\text{n = nano} = 10^{-9} \quad \quad \text{m = milli} = 10^{-3}$$ $$ U_{e} = \frac{k_{e}q_{1}q_{2}}{r} $$ $$ U_{e} = \frac{(8.987 \times 10^{9}) (200 \times 10^{-9})(-200 \times 10^{-9})}{5 \times 10^{-3}} $$ $$ U_{e} = -0.0719 \, \mathrm{J}$$The system has -0.0719J of energy. To separate the objects we will have to cancel out that energy, so it will take 0.0719J.
solution
$$ U_{e} = \frac{k_{e}q_{1}q_{2}}{r} $$ $$ r = \frac{k_{e}q_{1}q_{2}}{U_{e}} $$ $$ r = \frac{(8.987 \times 10^{9})(1 \times 10^{-3})(1 \times 10^{-3})}{200} $$ $$ r = \frac{8.987 \times 10^{3}}{200} $$ $$ r = 44.935 \, \mathrm{m} $$Moving two 1 C charges from 4 meters to 2 meters apart?
Moving a 1 C and a -1 C charge from 5 meters to 100 meters apart?
solution
$$ U_{e} = \frac{k_{e}q_{1}q_{2}}{r} $$$$ \Delta U_{e} = \text{final - initial} $$ $$ \Delta U_{e} = \frac{k_{e}(1)(1)}{2} - \frac{k_{e}(1)(1)}{4} $$ $$ \Delta U_{e} = 0.5k_{e} - 0.25k_{e} $$ $$ \Delta U_{e} = 0.25k_{e} $$
$$ \Delta U_{e} = \text{final - initial} $$ $$ \Delta U_{e} = \frac{k_{e}(1)(-1)}{100} - \frac{k_{e}(1)(-1)}{5} $$ $$ \Delta U_{e} = -0.01k_{e} + 0.20k_{e} $$ $$ \Delta U_{e} = 0.19k_{e}$$
$$\text{4 m to 2 m takes slightly more energy}$$
Chemistry is most accurately described by quantum mechanics, but chemical bonds and chemical reactions can be loosely explained with electrostatic potential energy. Let's see how far classical physics will take us.
Na: Sodium
1 valence electron
mass = 3.8 x 10-26 kg
ionic radius = 227 x 10-12 m
Cl: Chlorine
7 valence electrons
mass = 5.9 x 10-26 kg
ionic radius = 175 x 10-12 m
strategy
In nonionic atoms the numbers of electrons and protons are equal so the electrostatic forces are balanced to zero. When NaCl ionically bond one electron leaves Na and joins Cl. Na gains a +1 charge and Cl gains a -1 charge. The unbalanced charges produce an attractive force.
solution
$$ U_{e} = \frac{k_{e}q_{1}q_{2}}{r} $$ $$ U_{e} = \frac{(8.987 \times 10^{9})(1.602 \times 10^{-19})(-1.602 \times 10^{-19})}{402 \times 10^{-12}} $$ $$ U_{e} = -5.737 \times 10^{-19} \, \mathrm{J}$$Our answer is negative because it would take added positive energy to get the ions to separate.
The measured NaCl dissociation energy is slightly higher at -6.82 x 10-19 J. Coulomb's law isn't perfect, but it is a close approximation.
Electric Potential
Electric potential is mathematically similar to electric fields. Electric fields are the force per unit charge, and electric potential is the energy per unit charge.
$$ V = \frac{U_e}{q}$$You can think of electric potential as the energy needed to bring a +1C test charge from very far away to a distance, r, from the other charge.
$$V = \frac{k_{e}q}{r} $$
\(V\) = electric potential [V, volts, J/C]
\(k_e\) = 8.987 × 109 = Coulomb's constant [N m²/C²]
\(q\) = charge [C, Coulomb]
\(r\) = distance between the center of each charge [m, meters]
Electric potential has units of volts, a unit that shows up again when dealing with electric circuits.
charges ≈ Here is a Coulomb's law simulation of electric potential as a scalar field. The mountains indicate positive potential, and the valleys indicate negative potential.
Click the simulations a few times to push the charges around. These simulations help develop an vague intuition for electric potential, but they don't capture the nuances of quantum mechanics.
Let's estimate the voltage needed for a 1 nanometer resolution. We can start by finding the energy to potentially bring an electron very close to another electron. What is the potential at 1 nanometer from an electron?
solution
$$ V = \frac{k_{e}q}{r} $$ $$ V = \frac{(8.987 \times 10^{9})(-1.6 \times 10^{-19})}{10^{-9}} $$ $$ V = -1.440\, \mathrm{volts}$$What is the potential at 0.001 nm from a single electron?
solution
$$ V = \frac{(8.987 \times 10^{9})(-1.6 \times 10^{-19})}{10^{-12}} $$ $$ V = -1440 \, \mathrm{volts}$$solution
$$ V = \frac{\color{red}{k_{e}q_{1}}}{\color{red}{r}} $$ $$ U_{e} = \frac{ {\color{red}{k_{e}q_{1} } }{q_2}}{\color{red}{r}} $$ $$ U_{e} = V q $$ $$ U_{e} = (10)(4 \times 10^{-6}) $$ $$ U_{e} = 40 \times 10^{-6} \, \mathrm{J} $$Question: Are the electrons the peaks or the valleys?
answer
Since electrons are negative, they are have a lower potential. They are the valleys. The protons are the peaks.
In this simulation the protons also don't move.
answer
Since electrons are negative, they will move towards a positive potential. Positive potential is shown as higher up in the simulation.


Raw php, MySQL,Bootstrap template are used to build the project. It was a freelancing project developed in 2017. Source link
Close ProjectIt is a windows game. Developed in C# at 2016. Random colors will fall from upper side. Player only have to score the green color boxes. Source Link
Close ProjectLorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
Close ProjectAn agriculture based application developed in DIU App Contest. It is an android app created on Java and XML, Webview also used. App link.
Close ProjectLorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
Close ProjectLorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
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For any item that you want to buy. We've almost got it all for you.
Conveniently and with affordable price.
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Something here!


Hero Page
This is the Hero page intro content.
@@include('./_includes/head.html', { "title": "Grids Page", "description": "Description for the Grids page", "keywords": "grids grids grids" })
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The hero sections content uses flexbox for vertical centering.


"Falling Hard for a Dead Drunk" THE DOORS A film review by Ann Hodgins Copyright 1991 Ann Hodgins
I wrote this title after seeing THE DOORS IN EUROPE, a documentary of the original Doors' performances. That movie made a worshipper out of me. Then I was brought down hard by NO ONE HERE GETS OUT ALIVE, the biography: Jim made so many deadly errors it's a wonder he didn't die sooner. But although the book killed my hero worship it did not kill my love for the guy. But what difference does it make. No woman could have broken through to Jim. All my love's in vain. Total failure. No blame. Jim talked of women as "meager food for souls for God." What woman could win out in the face of such a total negation?
So I was prepared to find Oliver Stone's DOORS a grueling experience. As a great admirer of Stone's artistry I was afraid that he would successfully revive the past, the feeling and meaning of it. I was 17 in 68, and if he was successful Stone could make me relive my feelings. I was prepared for that but it didn't happen. Instead I was able to compose this review in my head during most of the movie. The music was there, and the fashions and all, but nothing touched me as it should have.
Perhaps the problem was that Jim Morrison was not just a guy in the crowd that a mere camera could capture. Jim was a soul who was doomed to be the spirit of his times. He was racked on the extremes of those times. He was born into a proto-typical American family -- his mother a neglected nag and his father Mr. Megadeath, a nuclear war expert in the army. He appeared to be normal and conforming, like every other boy did. But he caught a spirit from those Indians. He passed puberty just as Rock and Roll was being born. He came of drafting age during the War in Vietnam. He came to consciousness just as LSD was invented. He discovered love during the Summer of Love. He briefly became a father when the red tide of abortion reached its peak.
In himself he was the times and he did his best to work out those terrible internal/external conflicts and make something positive emerge from the struggle into his poetry and to give that good thing to others. That's why I love his soul, because he was the anti-Hitler. He fought harder than his father who was the youngest general in the navy. Jim's personal struggle took him farther faster.
"Don't underestimate the audience," Jim said. "They want the sacred." Stone's movie fails to deliver. One can hardly blame Stone for that. Maybe Jim's soul was just too big to capture. Or maybe I just expected too much after viewing THE DOORS IN EUROPE. Or maybe it was because I'm a woman looking back at a time of life and a time in history when men and women just didn't see the same things. I can find myself in the movie only as a rag of Indian silk, a boney body and a hank of hair. My experience is locked on the wrong side of those heavily made-up eyes.
Maybe I should not have expected Stone to revive those times for me, but actually I think that the movie missed all the points. Things didn't ring true or have the impact they should have had. For instance, in the scene where Jim tells his wife to kill their baby, I can't believe that Patricia replied to that by criticizing Jim's recent weight gain! That is not how the scene could have been or how it is described in NO ONE HERE GETS OUT ALIVE.
Everything in the movie was like that for me -- just not right. So I left the theatre competely unmoved and unable to write the review I wanted to write. I suggest seeing the documentary and reading the poetry.
a.h.
.
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


USD/JPY: FOMC minutes as expected, USD/JPY steady despite trade-deal headline downside risks
by Ross J Burland- FOMC minutes highlights: Most officials viewed rates as well collaborated after Oct cut; Downside risks elevated.
- USD/JPY steady on the release of the minutes and focus is back to trade deal between US and China.
USD/JPY has been steady on the back of the Federal Open Market Committee minutes which had been expected to signal that policy is on hold for now, barring a material reassessment in the outlook. We had already had a glance at the mood at the Federal Reserve as per Chair Powell’s recent rhetoric. Traders were scrutinising the minutes today for any light that could have been shed on what exactly would qualify as a “material reassessment,” as well as how diversified voters were with respect to the rate cut. Markets were also looking for any updates on the policy review.
Highlights of the minutes
- Most judged level now appropriate barring a 'material' reassessment of the outlook.
- 'A couple' said Fed should reinforce statement with communications that another rate cut unlikely without signs of a 'significant slowdown'.
- Many said rate cut warranted due to global weakness and trade uncertainty.
- Some favored keeping rates steady and argued outlook was favorable and inflation expected to rise.
- A couple supported rate cut but said it was a close call.
- Several concerned some banks had reduced capital buffers when the should be rising.
- Discussed that risks to the economic outlook remained tilted to the downside.
Markets are on red alert
Elsewhere, markets are on red alert and will remain sensitive to trade headlines. Most recently, in light of the latest trade deal headlines, risk sentiment has been soured by news that a Phase One US-china trade deal may not be completed this year, a headline courtesy of Reuters.
Phase One' US-China trade deal may not be completed this year – Reuters
Indeed, with what appears to be a less dovish backdrop from central banks on the whole, such geopolitical themes associated trade and also the bill on Hong Kong human rights whereby the US Senate on Tuesday unanimously passed the "Hong Kong Human Rights and Democracy Act", overtly taken sides with HK rioters, risk sentiment is on thin ice – that's bullish for the Yen.
USD/JPY levels:
More to come...
USD/JPY
| Overview | |
|---|---|
| Today last price | 108.48 |
| Today Daily Change | -0.07 |
| Today Daily Change % | -0.06 |
| Today daily open | 108.55 |
| Trends | |
|---|---|
| Daily SMA20 | 108.77 |
| Daily SMA50 | 108.26 |
| Daily SMA100 | 107.7 |
| Daily SMA200 | 108.99 |
| Levels | |
|---|---|
| Previous Daily High | 108.84 |
| Previous Daily Low | 108.45 |
| Previous Weekly High | 109.3 |
| Previous Weekly Low | 108.23 |
| Previous Monthly High | 109.29 |
| Previous Monthly Low | 106.48 |
| Daily Fibonacci 38.2% | 108.6 |
| Daily Fibonacci 61.8% | 108.69 |
| Daily Pivot Point S1 | 108.39 |
| Daily Pivot Point S2 | 108.22 |
| Daily Pivot Point S3 | 108 |
| Daily Pivot Point R1 | 108.78 |
| Daily Pivot Point R2 | 109 |
| Daily Pivot Point R3 | 109.17 |


Common lilac bushes (Syringa vulgaris) are deciduous shrubs that bloom in late spring. 'Wedgewood Blue' is an example of a cultivar of this plant. Syringa is a member of the olive family, along with other such ornamental plants as ash trees, forsythia shrubs, and privet shrubs.
Botanical description: A small perennial, evergreen shrub, growing up to 2 m. The stem is stiff and varied, the leaves are dark green oval opposite, the flowers are clustered, bright white apical that appears in spring until late autumn, and the fruits are small balloons do not appear much, and the roots are locally spread and dense, and the growth rate of the shrub is slow to medium, stops growing almost completely in the winter And it grows significantly in the summer if it has water and sunlight. Plant tolerance to local environmental conditions, jelly grows well under local environmental conditions, and tolerates harsh environmental factors well in terms of high temperature to 45 degrees Celsius, wind and drought, but it has little tolerance to salinity, and is suitable for rich, well-drained loamy soils and is vulnerable to infestation by scale insects and bugs. Mealy and whitefly.


Here is where the new changes made from the repository!
This is the first release of SilentWeb! Release 1.0


Apicurio Registry content rules
This chapter introduces the optional rules used to govern registry content and provides details on the available rule configuration:
Govern registry content using rules
To govern the evolution of registry content, you can configure optional rules for artifact content added to the registry. All configured global rules or artifact rules must pass before a new artifact version can be uploaded to the registry. Configured artifact rules override any configured global rules.
The goal of these rules is to prevent invalid content from being added to the registry. For example, content can be invalid for the following reasons:
-
Invalid syntax for a given artifact type (for example,
AVRO
orPROTOBUF
) -
Valid syntax, but semantics violate a specification
-
Incompatibility, when new content includes breaking changes relative to the current artifact version
You can add these optional content rules using the Apicurio Registry web console, REST API commands, or a Java client application.
When rules are applied
Rules are applied only when content is added to the registry. This includes the following REST operations:
-
Adding an artifact
-
Updating an artifact
-
Adding an artifact version
If a rule is violated, Apicurio Registry returns an HTTP error. The response body includes the violated rule and a message showing what went wrong.
| If no rules are configured for an artifact, the set of currently configured global rules are applied, if any. |
How rules work
Each rule has a name and optional configuration information. The registry storage maintains the list of rules for each artifact and the list of global rules. Each rule in the list consists of a name and a set of configuration properties, which are specific to the rule implementation.
A rule is provided with the content of the current version of the artifact (if one exists) and the new version of the artifact being added. The rule implementation returns true or false depending on whether the artifact passes the rule. If not, the registry reports the reason why in an HTTP error response. Some rules might not use the previous version of the content. For example, compatibility rules use previous versions, but syntax or semantic validity rules do not.
For more details, see Apicurio Registry artifact and rule reference.
Content rule configuration
You can configure rules individually for each artifact, as well as globally. Apicurio Registry applies the rules configured for the specific artifact. If no rules are configured at that level, Apicurio Registry applies the globally configured rules. If no global rules are configured, no rules are applied.
Configure artifact rules
You can configure artifact rules using the Apicurio Registry web console or REST API. For details, see the following:
Configure global rules
You can configure global rules in several ways:
-
Use the
/rules
operations in the REST API -
Use the Apicurio Registry web console
-
Set default global rules using Apicurio Registry application properties
You can configure Apicurio Registry at the application level to enable or disable global rules. You can configure default global rules at installation time without post-install configuration using the following application property format:
registry.rules.global.<ruleName>
The following rule names are currently supported:
-
compatibility
-
validity
The value of the application property must be a valid configuration option that is specific to the rule being configured. The following table shows the valid values for each rule:
| Rule | Value |
|---|---|
Validity |
|
|
|
|
|
Compatibility |
|
|
|
|
|
|
|
|
|
|
|
|
You can configure these application properties as Java system properties or include them in the Quarkus
application.properties file. For more details, see the Quarkus documentation.
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class: center, middle, inverse, title-slide # Lecture 6 ## Preference Heterogeneity with Mixture Distributions ### Tyler Ransom ### ECON 6343, University of Oklahoma --- # Attribution Many of these slides are based on slides written by Peter Arcidiacono. I use them with his permission. These slides also heavily follow Chapters 6 and 14 of Train (2009) --- # Plan for the day 1. Preference Heterogeneity 2. Mixed Logit 3. Finite Mixture Models 4. The EM algorithm --- # Preference Heterogeneity - So far, we have only looked at models where all agents have identical preferences - Mathematically, `\(\beta_{RedBus}\)` does not vary across agents - Implies everyone has same price elasticity, etc. - But in real life, we know people have different values, interests, and preferences - Failure to account for this heterogeneity will result in a misleading model - e.g. lowering a product's price likely won't induce purchasing from some customers --- # Observable preference heterogeneity - One solution to the homogeneity problem is to add interaction terms - Suppose we have a 2-option transportation model: `\begin{align*} u_{i,bus}&=\beta_1 X_i + \gamma Z_1\\ u_{i,car}&=\beta_2 X_i + \gamma Z_2 \end{align*}` - We could introduce heterogeneity in `\(\gamma\)` by interacting `\(Z_j\)` with `\(X_i\)`: `\begin{align*} u_{i,bus}&=\beta_1 X_i + \widetilde{\gamma} Z_1 X_i\\ u_{i,car}&=\beta_2 X_i + \widetilde{\gamma} Z_2 X_i \end{align*}` - Now a change in `\(Z_j\)` will have a heterogeneous impact on utility depending on `\(X_i\)` - e.g. those w/diff. income `\((X_i)\)` may be more/less sensitive to changes in price `\((Z_j)\)` --- # Unobservable preference heterogeneity - Observable preference heterogeneity can be useful - But many dimensions of preferences are likely unobserved - In this case, we need to "interact" `\(Z\)` with something unobserved - One way to do this is to assume that `\(\beta\)` or `\(\gamma\)` varies across people - Assume some distribution (e.g. Normal), called the .hi[mixing distribution] - Then integrate this out of the likelihood function --- # Mixed Logit likelihood function - Assume, e.g. `\(\gamma_i \sim F\)` with pdf `\(f\)` and distributional parameters `\(\mu\)` and `\(\sigma\)` - Then the logit choice probabilities become `\begin{align*} P_{ij}\left(X,Z;\beta,\gamma\right)&= \int\frac{\exp\left(X_{i}\left(\beta_{j}-\beta_{J}\right)+\gamma\left(Z_{ij}-Z_{iJ}\right)\right)}{\sum_k \exp\left(X_{i}\left(\beta_{k}-\beta_{J}\right)+\gamma\left(Z_{ik}-Z_{iJ}\right)\right)}f\left(\gamma;\mu,\sigma\right)d\gamma \end{align*}` - Annoyance: the log likelihood now has an integral inside the log! .smaller[ `\begin{align*} \ell\left(X,Z;\beta,\gamma,\mu,\sigma\right)&=\sum_{i=1}^N \log\left\{\int\prod_{j}\left[\frac{\exp\left(X_{i}\left(\beta_{j}-\beta_{J}\right)+\gamma\left(Z_{ij}-Z_{iJ}\right)\right)}{\sum_k \exp\left(X_{i}\left(\beta_{k}-\beta_{J}\right)+\gamma\left(Z_{ik}-Z_{iJ}\right)\right)}\right]^{d_{ij}}f\left(\gamma;\mu,\sigma\right)d\gamma\right\} \end{align*}` ] --- # Common mixing distributions - Normal - Log-normal - Uniform - Triangular - Can also go crazy and specify a multivariate normal - This would allow, e.g. heterogeneity in `\(\gamma\)` to be correlated with `\(\beta\)` --- # Mixed Logit estimation - With the integral inside the log, estimation of the mixed logit is intensive - To estimate the likelihood function, need to numerically approximate the integral - The most common way of doing this is .hi[quadrature] - Another common way of doing this is by .hi[simulation] (Monte Carlo integration) - I'll walk you through how to do this in this week's problem set --- # Finite Mixture Distributions - Another option to mixed logit is to assume the mixing distribution is discrete - We assume we have missing variable that has finite support and is independent from the other variables - Let `\(\pi_s\)` denote the probability of being in the `\(s\)`th unobserved group - Integrating out over the unobserved groups then yields the following log likelihood: `\begin{align*} \ell\left(X,Z;\beta,\gamma,\pi\right)=&\sum_{i=1}^N \log\left\{\sum_{s}\prod_{j}\left[\frac{\exp\left(X_{i}\left(\beta_{j}-\beta_{J}\right)+\gamma_{s}\left(Z_{ij}-Z_{iJ}\right)\right)}{\sum_k \exp\left(X_{i}\left(\beta_{k}-\beta_{J}\right)+\gamma_{s}\left(Z_{ik}-Z_{iJ}\right)\right)}\right]^{d_{ij}}\right\}\\ \end{align*}` --- # Mixture Distributions and Panel Data - With panel data, mixture dist. allows for .hi[permanent unobserved heterogeneity] - Here the unobs. variable is fixed over time and indep. of the covariates at `\(t=1\)` - The log likelihood function for the finite mixture case is then: `\begin{align*} \ell\left(X,Z;\beta,\gamma,\pi\right)=&\sum_{i=1}^N \log\left\{\sum_{s}\prod_{t}\prod_{j}\left[\frac{\exp\left(X_{it}\left(\beta_{j}-\beta_{J}\right)+\gamma_{s}\left(Z_{ijt}-Z_{iJt}\right)\right)}{\sum_k \exp\left(X_{it}\left(\beta_{k}-\beta_{J}\right)+\gamma_{s}\left(Z_{ikt}-Z_{iJt}\right)\right)}\right]^{d_{ijt}}\right\} \end{align*}` - And for the mixed logit case is: .smaller[ `\begin{align*} \ell\left(X,Z;\beta,\gamma,\mu,\sigma\right)=&\sum_{i=1}^N \log\left\{\int\prod_{t}\prod_{j}\left[\frac{\exp\left(X_{it}\left(\beta_{j}-\beta_{J}\right)+\gamma\left(Z_{ijt}-Z_{iJt}\right)\right)}{\sum_k \exp\left(X_{it}\left(\beta_{k}-\beta_{J}\right)+\gamma\left(Z_{ikt}-Z_{iJt}\right)\right)}\right]^{d_{ijt}}f\left(\gamma;\mu,\sigma\right)d\gamma\right\}\\ \end{align*}` ] --- # Dynamic Selection - Often, we want to link the choices to other outcomes: - labor force participation and earnings - market entry and profits - If individuals choose to participate in the labor market based upon unobserved wages, our estimates of the returns to participating will be biased - Mixture distributions provide an alternative way of controlling for selection - .hi[Assumption:] no selection problem once we control for the unobserved variable --- # Dynamic Selection - Let `\(Y_{1t}\)` denote the choice and `\(Y_{2t}\)` denote the outcome - The assumption on the previous slide means the joint likelihood is separable: `\begin{align*} \mathcal{L}(Y_{1t},Y_{2t}|X_{1t},X_{2t},\alpha_1,\alpha_2,s)&=\mathcal{L}(Y_{1t}|Y_{2t},X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s)\\ &=\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s) \end{align*}` where `\(s\)` is the unobserved type --- # Estimation in Stages - Suppose `\(s\)` was observed - There'd be no selection problem as long as we could condition on `\(s\)` and `\(X_{1t}\)` - The log likelihood function is: `\begin{align*} \ell=&\sum_{i}\sum_t \ell_1(Y_{1t}|X_{1t},\alpha_1,s)+\ell_2(Y_{2t}|X_{2t},\alpha_2,s) \end{align*}` - Estimation could proceed in stages: 1. Estimate `\(\alpha_2\)` using only `\(\ell_2\)` 2. Taking the estimate of `\(\alpha_2\)` as given, estimate `\(\alpha_1\)` using `\(\ell_1\)` --- # Non-separable means no stages - When `\(s\)` is unobserved, however, the log likelihood function is not additively separable: `\begin{align*} \ell=&\sum_i\log\left(\sum_s\pi_s\prod_t\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s)\right) \end{align*}` where `\(\mathcal{L}\)` is a likelihood function - Makes sense: if there is a selection problem, we can't estimate one part of the problem without considering what is happening in the other part --- # The EM Algorithm - We can get additive separability of the finite mixture model with the .hi[EM algorithm] - EM stands for "Expectation-Maximization" - The algorithm iterates on two steps: - E-step: estimate parameters having to do with the mixing distribution (i.e. the `\(\pi\)`'s) - M-step: pretend you observe the unobserved variable and estimate - The EM algorithm is used in other applications to fill in missing data - In this case, the missing data is the permanent unobserved heterogeneity --- # The EM Algorithm (Continued) - With the EM algorithm, the non-separable likelihood function `\begin{align*} \ell=&\sum_i\log\left(\sum_s\pi_s\prod_t\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s)\right) \end{align*}` can be written in a form that is separable: `\begin{align*} \ell=&\sum_i\sum_s q_{is}\sum_t\ell_1\left(Y_{1t}|X_{1t},\alpha_1,s\right)+\ell_2\left(Y_{2t}|X_{2t},\alpha_2,s)\right) \end{align*}` where `\(q_{is}\)` is the probability that `\(i\)` belongs to group `\(s\)` - `\(q_{is}\)` satisfies `\(\pi_s = \frac{1}{N}\sum_{i}q_{is}\)` --- # Estimation in stages again - We can now estimate the model in stages because of the restoration of separability - The only twist is that we need to .hi[weight] by the `\(q\)`'s in each estimation stage - Stage 1 of M-step: estimate `\(\ell(Y_{1t}|X_{1t},\alpha_1,s)\)` weighting by the `\(q\)`'s - Stage 2 of M-step: estimate `\(\ell(Y_{2t}|X_{1t},\alpha_1,s)\)` weighting by the `\(q\)`'s - E-step: update the `\(q\)`'s by calculating `\begin{align*} q_{is}=&\frac{\pi_s\prod_t\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s)}{\sum_m\pi_m\prod_t\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,m)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,m)} \end{align*}` - Iterate on E and M steps until the `\(q\)`'s converge (Arcidiacono and Jones, 2003) --- # To Recap - Why are we doing all of this difficult work? - Because preference heterogeneity allows for a more credible structural model - Introducing preference heterogeneity can make the model intractible - Discretizing the distribution of heterogeneity and using the EM algorithm can help --- # References .smaller[ Arcidiacono, P. and J. B. Jones (2003). "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm". In: _Econometrica_ 71.3, pp. 933-946. DOI: [10.1111/1468-0262.00431](https://doi.org/10.1111%2F1468-0262.00431). Train, K. (2009). _Discrete Choice Methods with Simulation_. 2nd ed. Cambridge; New York: Cambridge University Press. ISBN:<PHONE_NUMBER>555. ]


activity extract anti-arthritic Pharm Res rats used arthritis Indian induced Int Sci plants plant Med effects Ethnopharmacol anti-inflammatory treatment effect Pharmacol activities Biol antioxidant review extracts potential leaves mice Chem traditional Linn Linn. Anti-inflammatory Singh medicinal various Anti-arthritic Kim Lee formulation herbal {border-style Evaluation Gupta Lantana properties Kumar bark camara ethanolic inflammatory px;} vitro #ffffff;} Alstonia India Phyllanthus adjuvant doses experimental male model possess study wistar Food chronic constituents inflammation mg/kg parts toxicity Medicinal Patel active amarus disease isolated roots seeds Asian Boerhaavia Sharma Terminalia arthritic arthritis. collagen-induced data Toxicol commonly drugs essential like models polyherbal root significant Exp Table Xanthium activity. acute albino cell diabetic evaluation reported rheumatoid scholaris traditionally useful using Boswellia Effect Vitex Zhang acid adjuvant-induced aqueous concluded diffusa human juice medicines negundo patients pomegranate species studies Aristolochia Studies Wang agents cells chemical medicine oil rat rheumatism rheumatism, sativum strumarium therapeutic use (.%) Bull Clin India, Moreover, Pomegranate Screening Sida action aerial certain chebula dose fractions information inhibiting levels mainly major oxidative pain paw potent release rheumatism. skin Afr Antibacterial CFA Coriandrum Effects Eur Herbal Nat Plants Ruta Saussurea Traditionally, Trigonella analgesic anti-diabetic anti-inflammatory, antibacterial biological cancer conclusion, high known leaf possesses px; sappan serum shows stress studied wood {font-family Adv Agric Caesalpinia Chinese Mazumder Mishra Planta Punica Trop acid, animals antiarthritic asthma, complete cough, countries cytokines damage diarrhea, foenum font-size freund's fruit fruits granatum graveolens gum hepatoprotective inhibits mentioned methanolic oils rats. reduction research showed vivo Antimicrobial Biomed Cinnamomum Immunopharmacol Inhibitory Nair Pac Patil Phytother Prod Rao Shah Southern TNF-α Tandon Technol Thus, anti-fungal associated clinical contains double; fenugreek formulations height herb immune include included includes indigenous inhibition injury lappa liver longum mechanism myocardial natural new pharmacological present resin rhombifolia serrata stem tree uses wound Altern Antiarthritic Antioxidant Arch Aviram Ayurveda BMC Chen Choi Curcuma Figure Han Hepatoprotective Khan Pharmacological Phytochemical Piper Protective Thai Traditional Unani administered ailments anti-bacterial anticancer antimicrobial article blood body border-color border-width brain cannabidiol changes compared compounds consumption crude cultivated cure destruction disease, diuretic, edema expectorant, expression extracted fever, flowers form fraction healing helpful investigation lipid mediators murine native oil, phytochemical plant, prevention proliferation proved reduced rhizomes screening shown solid; swelling transaminase treat tropical virus zeylanicum {font-style {font-weight (L.). Acad Acta Acute Ali Antidiabetic Arthritis Arulmozhi Assoc Bani Basu Biochem Biotechnol Br. Cell Complement Das Drugs Glycyrrhiza Helen III India. Inflamm Integr Kang Kuttan Lett Liu Mehrotra Microbiol Natl New Nutr Physiol Phytomedicine Rev Rohilla Soc Sri Srivastava Ther Tripathi agent ancient anti-cancer anti-microbial antifungal antipyretic antirheumatic attenuates autoimmune bacterial benefits bold carried cellular cinnamon combination common constituent courier; cytotoxic development diseases diseases. distributed distribution dosage drug efficacy em; enzymes ethanol evaluated factor families flavonoids, folk formaldehyde gathered given graecum grows hydroalcoholic infections infections, inflammation, infusion joint medical membrane methanol mg/ observed oxide plants. polyphenolic prepared production profile profile, pt;} published reduces resistance response revealed risks sesquiterpene shrub synovial ulcers, volatile


Provides a variety of introspective-type support functions for things
like call tips and command auto completion.
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getAutoCompleteList(command='
' ,
locals=None,
includeMagic=1,
includeSingle=1,
includeDouble=1,
sortCaseSensitive=False)
Return list of auto-completion options for command. |
source code
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getAttributeNames(object,
includeMagic=1,
includeSingle=1,
includeDouble=1,
sortCaseSensitive=False)
Return list of unique attributes, including inherited, for object. |
source code
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getCallTip(command='
' ,
locals=None)
For a command, return a tuple of object name, argspec, tip text. |
source code
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getRoot(command,
terminator=None)
Return the rightmost root portion of an arbitrary Python command. |
source code
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getTokens(command)
Return list of token tuples for command. |
source code
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rtrimTerminus(command,
terminator=None)
Return command minus anything that follows the final terminator. |
source code
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getBaseObject(object)
Return base object and dropSelf indicator for an object. |
source code
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getConstructor(object)
Return constructor for class object, or None if there isn't one. |
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Dr. Norman Borlaugh
The Man who Saved a Billion Lives
Dr. Norman Borlaug, third from the left, trains biologists in Mexico on how to increase wheat yields - part of his life-long war on hunger.
Here's a time line of Dr. Borlaug's life:
- 1914 - Born in Cresco, Iowa
- 1933 - Leaves his family's farm to attend the University of Minnesota, thanks to a Depression era program known as the "National Youth Administration"
- Has to stop school and save up more money. Works in the Civilian Conservation Corps, helping starving Americans. "I saw how food changed them", he said. "All of this left scars on me."
"Borlaug's life and achievement are testimony to the far-reaching contribution that one man's towering intellect, persistence and scientific vision can make to human peace and progress."
-- Indian Prime Minister Manmohan Singh
If you have time, you should read more about this incredible human being on his Wikipedia entry.


Username: The Username must be 3-20 characters long and consist of alphanumeric characters only (and _- ÄÖÜ äöü ß).
Password: The Password must consist of Upper- and Lowercase characters and digits. Minumum length is 5. Only some special characters are allowed: -_,.;:!?&()?+ยง%=">
Retype Password: Enter the same Password again to prevent typing issues.


We can do event coverage which involves video coverage and photos. The events that can be covered include the weddings, giveaways, graduation ceremonies
We can take different kinds of photo shoots forexample individual, family
Events are considered to beone of the most strategic marketing and communication tools by companies of all sizes. From corporate entertainment and corporate events to business events and team building, companies create events to help themcommunicate with clients and staff. Deciding to hold a corporate event is just the beginning and to most companies itis a very time consuming and daunting task; to us it is our next challenge. weofferurshering services, event lighting and decoration.
Fox Images is an innovative, cutting-edge events management and media production company.
Fox images comprises a passionate team of media professionals who are dedicated to producing high quality projects. With our experienced and qualified video editors, digital effects artists, audio experts, and strategic public relations proffessionals, Fox Images provides you with economical professional excellence. Most importantly, we love what we do.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley type a scrambled it to make a type specimen book. It has survived not only five centuries,
We aim to provide our contumers with professional media services a competitive prices
If you have the vision, we have the ability to make others see it and respond to it.
Nyerere Avenue, Kabale municipality
<EMAIL_ADDRESS>+256 772 820041
Mon - Sat: 08 Am - 17 Pm


Trader Galladucci:
Homesick, huh? The roads around Giran are dangerous, be careful on your way home. You can use the Scroll of Escape to return if you choose. Just know that since it's a specialty item, you can
use it only once
!


Vegas – Backgrounds and Slideshows
Vegas is a jQuery/Zepto plugin to add beautiful backgrounds and Slideshows to DOM elements.
Vegas is a jQuery/Zepto plugin to add beautiful backgrounds and Slideshows to DOM elements.
JQuery lightSlider is a lightweight responsive Content slider with carousel thumbnails navigation
A simple jquery plugin for creating image comparison sliders


To setup this example, you will need to have Admin access to your Jive Cloud instance. Simply click on the Add-Ons action in the Satellite Navigation in the Upper-Right Hand Corner.
Then click on Analytics Services. Generate a Client ID and Secret, and then copy those values into the jiveclientconfiguration.json
For more information about the CloudAlytics API, please see the documentation


---
layout: post
title: Test Plan-Thread-sample information using Jmeter Functions
date: '2013-05-23T01:59:00.002+06:00'
author: Shantonu Sarker
tags:
- jmeter
modified_time: '2013-06-30T04:25:57.823+06:00'
blogger_id: tag:blogger.com,1999:blog-2902524569242410076.post-8493983778368220886
blogger_orig_url: https://shantonusarker.blogspot.com/2013/05/test-plan-thread-sample-information.html
---
In this following article we are going to see how can we retrieve current Test Plan , Thread and sample information. These are static, so they are rarely used.
-To get name of current running Test Plan
${__TestPlanName}
-Only used in function that calls the full test plan.
-To get number if thread currently running
${__threadNum}
-It is independent thread wise.
-not useful for test plan
-To know the current sampler name
${__samplerName()}
-Not useful for test plan
-Sometime it can be used with header manager/cookie manager to get value from current sample
Thanks..:)


Pathanamthitta district is known as the Headquarters of Pilgrimage Tourism, of the Kerala State. Situated near the Western Ghats and bordered by the hills, Pathanamthitta district is a treat to eyes with its vast unending stretches of forests, rivers and rural landscapes.
A natural waterfall with captivating surroundings is situated on the banks of the River Pamba. The fall is 60 to 100 ft deep which pours down on a rocky bed. The place is a favourite picnic spot among locals as well as tourists.
One of the most beautiful places in India, Gavi is a small village tucked between tea estates and wide and dense forests. A unique project in itself, the place is a model of eco-tourism site bringing to the world its spellbinding abundance in flora and fauna. Gavi is situated some 14 km away from the famed Periyar Tiger Reserve and is heaven on Earth, quite literally
Kaviyoor Mahadeva Temple is an ancient temple located on a small hilltop at Kaviyoor, Pathanamthitta District. Dedicated to Lord Shiva and Goddess Parvati, this temple is one of the most popular and significant Shiva Temple in Kerala.
This place is considered to be the equivalent to an elephant's cage. The reason being is that the main attraction of the region is a massive wooden cage where elephant are trained.


[VisAPI Index] [VisAPI Hierarchy]
Attribute factory specification. More...
#include <VisApi/VisApi.h>
Inherits: visObject
This class is used to instance new attributes of a specific type. It can be passed to other objects that need to create new attribute instances. It holds the dimension of the attribute. Special implementations exist for every attribute type.
Return vector dimension.
Returns new instance of attribute.
| Kdoc |


We archived this page because it's out of date
You can either
View the item you were looking for in the UK Government Web Archive
or
Visit more recent guidance at https://www.gov.uk/browse/housing-local-services/planning-permission
You can either
View the item you were looking for in the UK Government Web Archive
or
Visit more recent guidance at https://www.gov.uk/browse/housing-local-services/planning-permission


KSh 19,999
Impressively thin and light, the HP EliteBook 8440 empowers users to create, connect, and collaborate, using enterprise-class performance technology that helps keep you productive in and out of the office. Combine high performance technology and long battery life with Windows 10 Pro, 7th Gen Intel® Core™ i5-7500U processor, Drive performance with 4GB DDR4 memory, 500 GB Hard Disk Drive and dual storage options for your most demanding business applications and fast access to data.
| Description | Specification |
|---|---|
| SKU | HP246EL102JBNNAFAMZ |
| Weight | 1.48 kgs |
| Display size | (L x W x H cm) 33.8 x 23.7 x 1.89 |
| RAM | (GB) 4 |
| Memory size | (GB) 500 |
| Processor | Intel Core i5 |
| Operating System | no OS |
| Product Warranty | 6 Months |
| Screen Technology | LED |


2015 Team Chevy Wins
+73
Watch Live Streaming From SEMA Las Vegas 2015 |
All Things Bela
Bespoke: Bella's Truly Unique Interior
Praise From Prominent Publications
Debut At SEMA 2015
Wallpaper Bella Edition
Bella Gets Boosted
Bella Gets Framed
News
Vehicles
Parts
Racing
Events
Downloads
Media
Photos
Video
Portals
SEMA 2015
COPO
LSX


Fortnite has a variety of different weapons, these include:
An assualt rifle:Common
Fact File
DPS: 176
Damage: 32
Magazine size: 30
A Tactical Shotgun:Uncommon
Fact File
DPS: 100
Damage: 67
Magazine size: 8
And a Bolt-action sniper:Rare
Fact File
DPS: 35
Damage: 105
Magazine size: 1


--- layout: post title: A step at a time date: 2010-05-12 04:00:46.000000000 -07:00 type: post parent_id: '0' published: true password: '' status: publish categories: - Random Thoughts tags: [] meta: _edit_last: '1965023' author: login: paradoxofmatrix email<EMAIL_ADDRESS>display_name: paradoxofmatrix first_name: '' last_name: '' ---
don't look back for fear of guilt or complacency
never look infront for fear of discouragement or illusory ascendancy
just think of the step u r about to take
about the change it would bring ,
the impact it would make
no matter how dark the past has been
or how bleak the future might seem
immerse in the present, with this solemn vow
to always be in this moment
in this limitless expanse of here and now


New data: Comment: Change rate to: Visual graph configuration: Show all points Show steppy data lines Show rosy points Show moving average Show aura Hide y-axis labels0 Show title with statistics Show hashtags Goal configuration (change with CAUTION!): yaw dir kyoom odom monotone aggday: Right click to download BB file |
Graph Legend:
Datapoints: Your actual reported data. The color shows
where you are with respect to your yellow brick road.
Yellow Brick Road: Keep your dots on the
right side of the bright red line!
Dark Green or Gray: More than a week to derail, Green: Derailing within a week, Blue: Derailing in 2 days, Orange: Derailing in 1 day, Red: On the wrong side of the road!
Akrasia Horizon: This line is a week away from now. You can
only make changes in the steepness of your road beyond the akrasia
horizon.
Optimistic projection: An optimistic view of progress,
i.e., a view of your data through rose-colored glasses. It shows
your inexorable march towards your goal. If you're bouncing
around it just shows you flat. When you go down (or whichever
direction your goal is), it shows you that right away.
Moving average: This is an exponentially weighted moving
average with smoothing constant 0.25. It shows the overall trend
of your data, but lagged. If most of your daily numbers are below
this trend line then you are inexorably dragging it down.
Polynomial fit trend: Think of this as a very thick line
charting your progress or an aura around your actual
data. (Technically it's a polynomial fit of your data.) It gives
you a sense of your true trend.
Yellow guidelines: These thin yellow lines indicate the
good side of the yellow brick road. The thicker line demarcates 7
days of safety buffer. (If you keep your safety buffer at 7 days
or higher then you're immune to being stung. If anything comes
up, just flatten your yellow brick road. It will take effect
after the 7-day akrasia horizon but since you have that much
safety buffer, you're golden!)
Amounts Due By Day
|


PyRadio: Command line internet radio player.
[Back to Build Instructions] | [Back to README]
What? A linux console application on Windows?
Yes, sure. Why not?
PyRadio is a python script after all, and python does run on Windows. So, with a little bit of tweaking of the code, it is possible.
This page will guide you through the process of installing, updating and running PyRadio on Windows.
First of all, let me tell you that if you are still running Windows XP, you can just stop reading right now; it won’t happen…
PyRadio on Linux (its main target platform) can use any of three players MPV, MPlayer and VLC; and this is also the case on Windows!
MPV is the preferred player for Windows and will be automatically installed on a fresh PyRadio installation.
Installing MPlayer takes a couple of extra steps, and you may find that some streams (e.g. m3u8) may not be playable. Furthermore, special care has to be taken in order to be able to save the volume of the player.
If you are on Windows 7 and using MPlayer, you will not be able to use profiles; it seems the use of profiles is not supported.
VLC is much easier to install, but song titles’ updating may not be 100% consistent (if any). If this is not a deal breaker for you, then just go on and use VLC as PyRadio’s player.

Having said that, let us proceed with the installation.
The installation consists of three steps:
If you don’t already have Python installed, just get to its Windows Downloads page and download one of the 3.x releases.
A tip as to which Python version to choose is to go to zephyrproject-rtos /windows-curses github page and check the latest supported version.
Here’s how you do that: the page contains some folders named pyXY (XY is the python version the folder corresponds to). Make a note of the largest XY number; this is the latest Python version supported, and this is the one you should download. At the time of writing this, version 3.9 was the latest supported one (folder py39), even though Python 3.10 had already been released.
When the download is done, run its setup and select “Custom Installation” so that you can “Add Python to environment variables”. You can refer to the following image to see the relevant setting.
Either if you have just installed Python or you already have it installed, you need to verify that its executable is in the PATH (i.e. Python can be executed from a console by typing”python”).
So, go ahead and open a console (the command is cmd) and type python.
If you get something similar to the following snippet, you are good to go.
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)] on win Type "help", "copyright", "credits" or "license" for more information. >>>
If the command could not be found, you have to run the installation again, select “Modify” and set the “Add Python to environment variables” option. You can refer to the following image to see the relevant setting.
Note: If you don’t have the setup file of the original Python installation, you will have to download it from Python’s Windows Downloads. In case you want to upgrade to the latest version, you must uninstall the one currently installed, beforehand.
7-Zip Windows archiver is needed for MPV’s installation. PyRadio will try to download and run the installation, if 7-Zip is not already installed.
As already stated MPV will be automatically installed on a fresh installation.
If the user decides to install a different player, he should have the following in mind:
| MPV | MPlayer | VLC | |
|---|---|---|---|
| Pros | Fully functional | Fully functional | Plays almost all streams |
| Cons | - | May not play all streams (e.g. m3u8) | Titles update is not consistent (if any) |
PyRadio provides a helper function to install, update and uninstall MPV and MPlayer.
This function will be part of the installation procedure, when PyRadio detects that this is a fresh installation.
After you have installed or updated PyRadio, you cas still enable this function, by executing PyRadio and pressing “F8”. Then PyRadio will terminate and you will be presented with a screen similar to the following one:
Reading config... Reading playlist... Please select an action: 1. Update MPV 2. Install MPlayer 3. Uninstall MPV Note: VLC media player is already installed Press 1, 2, 3 or q to Cancel:
Press any of the numbers presented to you to execute an action, or press “q” to exit.
Note: If you already have installed any of these players for a pre <IP_ADDRESS> installation, you should:
Note: If at some point and for whatever reason you want to perform a manual player installation, just refer to the relevant instructions for MPV, or the relevant instructions for MPlayer.
If VLC is your selection, just go and get it and install it as any other Windows program.
As long as you install it to its default location (e.g “C:\Program Files\VideoLAN\VLC” or “C:\Program Files (x86)\VideoLAN\VLC”) Pyradio will be able to detect and use it.
At last!
You are ready to install PyRadio!
So here’s how you do it: Right click on this link and use your browser “Save as” menu entry to save the file in your home folder as install.py.
Finally, open a console (press the Win key, type cmd and press ENTER).
Then type:
cd %USERPROFILE% python install.py
If you have just installed Python, you will probably end up with the following message and the installation script will terminate.
PyRadio has installed all required python modules. In order for them to be properly loaded, the installation script has to be executed afresh. Please execute the installation script again, like so: python install.py
This is perfectly normal; some Python modules have been installed, but in order to be used by the installation script, it has to be executed one more time. So you just type (again):
python install.py
in order to complete the installation.
If the installation is successful, you will get something similar to the following image:
PyRadio has been installed preforming a “user installation”, which means that the program is available to your current user only.
Furthermore, if the WARNING shown in the previous image has been shown to you, the executable of the program is not in your PATH (you cannot just open a console and type “pyradio” to execute it; you have to use the Icon/Shortcut created on your Desktop to do that).
Now, you can just call it a day; you can run PyRadio from its Desktop Shortcut.
If you want to be able to run it from a terminal, you have to add the path shown to you to your PATH variable.
Note: If you are having a problem finding the path to add to your PATH variable, just copy the one found after right-clicking on the PyRadio icon on your Desktop and selection “Properties”, or follow the instructions shown at Getting the path to pyradio.exe.
Finally, please keep in mind that, if you upgrade your Python version, you will have to update PyRadio’s path in your PATH variable (since you will have to reinstall PyRadio anyway).
In case one has not added the “Scripts” path to the PATH variable, but has to have the path to the executable of the program (in order to execute it from a console, for example), one can just execute PyRadio and press “F9”. Then the following info will be displayed:
After PyRadio terminates, the following will be displayed:
Reading config... Reading playlist... PyRadio EXE files: System: %PROGRAMFILES%\Python310\Scripts\pyradio.exe User: %APPDATA%\Python\Python310\Scripts\pyradio.exe Press any key to exit...
In this example, both a “System” and a “User” path to PyRadio executable is displayed.
This would be the case after installing a version newer than <IP_ADDRESS> while a version older than <IP_ADDRESS> is already installed.
If this is your case, please follow the instructions found in “Removing an old-style installation”.
If you want to use the Title Logging feature, and your Country/Region is anything different than English, chances are you will have to correctly set your locale for non-unicode applications, which is something you should be doing regardless. Otherwise, the titles’ log file may contain unreadable characters.
If you want to go on and set your locale for non-unicode applications, just refer to the following image.
The instructions work for Windows 7 up to 11.
After the installation is completed, there will be some files left on your system, which you may want to remove. These are:
You can safely delete these files.
If you need to have PyRadio’s sources, you can just get them from the “tmp-pyradio” folder.
PyRadio will inform you when a new release is available and ask you to go on with the update.
If you answer “y” to the question asked, PyRadio will terminate after creating an update batch file and opening Windows Explorer to its location.
You just double click on the batch file (called update.bat) to go on with the update.
In any case, you can perform the update at any time, using the command:
pyradio -U
Note: If PyRadio is not in your PATH, you will have to use the full path to it to execute the previous command. Just right-click Pyradio’s icon on your Desktop and copy the command found there, or follow the instructions shown at Getting the path to pyradio.exe. Once you have it, paste it on a console, add a “-U” and you are good to go.
If you are on a pre 0.8.9 release and want to update PyRadio, just follow the installation instructions, but add the “–force” command line parameter to the installation command.
So, instead of
python install.py
do a
python install.py --force
If you have an old-style installation (an installation done with a version up to <IP_ADDRESS>), you should remove the installed files which are not used anymore, they just clutter your system.
Just execute PyRadio and press “F7”.
PyRadio will search you system and put all the files that should be removed in a BAT file, which it will execute. Just make sure no other apps are open at the time.
To uninstall PyRadio you just press “F10” and confirm the action.
Then PyRadio will terminate and an “Explorer Window” will open containing a BAT file called uninstall.bat.
Just double-click on it to complete the procedure.
When a bug is found, please do report it by opening an issue at github.
In you report you should, at the very least, state your pyradio version and python version.
It would be really useful to include %USERPROFILE%/pyradio.log in your report.
To create it, enter the following commands in a terminal:
cd %USERPROFILE% del pyradio.log pyradio -d
Note: If PyRadio is not in your PATH, you will have to use the full path to it to execute the previous command. Just right-click Pyradio’s icon on your Desktop and copy the command found there, or follow the instructions shown at Getting the path to pyradio.exe. Once you have it, paste it on a console, add a “-d” and you are good to go.
Then try to reproduce the bug and exit PyRadio.
Finally, include the file produced in your report.


Hi {{.User}}, application {{.ClientID}} wants access resources on your behalf and to:
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This is multiline text that has a
long description of about how the text is really long
and a link.
Secondary Inner Buttons
disabled left icon buttons
Dark right icon buttons
icon only buttons default
ion-list-header right icon/text button large
ion-list-header left clear button small
ion-list-header right clear button


LoadImage()
语法
Result = LoadImage(#Image, Filename$ [, Flags])概要
Load the specified image from a file.
参数
#Image A number to identify the loaded image. #PB_Any can be specified to auto-generate this number. Filename$ The name of the file to load. If the filename does not include a full path, it is interpreted relative to the current directory. Flags (可选) This parameter currently has no meaning. If it is specified, 0 should be used for future compatibility.
返回值
Returns nonzero if the image was loaded successfully and zero if the image could not be loaded. If #PB_Any was specified as the #Image parameter then the auto-generated number is returned on success.
备注
The limit for the image size that can be handled depends on the operating system and the available amount of memory. If enough memory is available, then images up to at least 8192x8192 are can be handled by all operating systems supported by PureBasic.
When an image is loaded, it is converted either in 24-bit (if the image depth is less or equal to 24-bit) or in 32-bit (if the image has an alpha-channel). A loaded image can be freed by using the FreeImage() function.
The image can be in BMP, icon (.ico, only on Windows) or any other format supported by the ImagePlugin library. The following functions can be used to enable automatically more image formats:
UseJPEGImageDecoder()
UseJPEG2000ImageDecoder()
UsePNGImageDecoder()
UseTIFFImageDecoder()
UseTGAImageDecoder()
You can use the several other functions for acting with the loaded image:
StartDrawing() with ImageOutput() to draw on the loaded image
StartVectorDrawing() with ImageVectorOutput() to draw on the created image using vector drawing
CopyImage() to create another image from the actual one
GrabImage() to create another image from a given area of the actual one
DrawImage() with ImageID() to draw the image on actual output channel.
DrawAlphaImage() with ImageID() to draw the image (with alpha channel) on actual output channel.
ImageGadget() for displaying image on an application window
ButtonImageGadget() for creating an image button on an application window
参阅
CreateImage(), CatchImage(), CopyImage(), GrabImage(), ImagePlugin library
已支持操作系统
所有


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onEnter
ControllableCCLayer
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ControllableCCLayer
open
CC3Camera::open()
CC3OpenGLES11Engine::open()
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