| com.google.android.gms.games.quest.Quests |
Entry point for Quest functionality.
| Nested Classes | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Quests.AcceptQuestResult | Result delivered when accepting a quest. | ||||||||||
| Quests.ClaimMilestoneResult | Result delivered when claiming a milestone. | ||||||||||
| Quests.LoadQuestsResult | Result delivered when quest data has been loaded. |
| Fields | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| SELECT_ALL_QUESTS | Array of all the valid state selector constants. |
| Public Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Asynchronously accept the quest.
| |||||||||||
Asynchronously claim the milestone.
| |||||||||||
Get an intent to view a single quest.
| |||||||||||
Get an intent to view a list of quests.
| |||||||||||
Asynchronously load the quest data for the currently signed-in player and game into
a single result, including only quests in the provided state array.
| |||||||||||
Asynchronously load single quest data for the currently signed-in player and game.
| |||||||||||
Register a listener to listen for updates on
Quest instances.
| |||||||||||
Display a popup based on the current state of the quest.
| |||||||||||
Unregisters this client's quest update listener, if any.
|
Constant used to add quests in the STATE_ACCEPTED
state to the QuestBuffer
returned by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
.
Constant used to add quests in the STATE_COMPLETED
state to the QuestBuffer
returned by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
.
Constant used to add quests in the STATE_COMPLETED
state that have at least
one milestone in the STATE_COMPLETED_NOT_CLAIMED
state to the QuestBuffer
returned by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
. This is a subset of
SELECT_COMPLETED
, so the two criteria do not both need to be included to get
all quests.
Constant used to add quests in the STATE_ACCEPTED
state that have been marked
as ending soon isEndingSoon()
returned by load(GoogleApiClient, int[], int, boolean)
and
getQuestsIntent(GoogleApiClient, int[])
. This is a subset of STATE_ACCEPTED
, so the two
criteria do not both need to be included to get all quests.
Constant used to add quests in the STATE_EXPIRED
state to the QuestBuffer
returned by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
.
Constant used to add quests in the STATE_FAILED
state to the QuestBuffer
returned by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
.
Constant used to add quests in the STATE_OPEN
state to the QuestBuffer returned
by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
.
Constant used to add quests in the STATE_FAILED
state with a
getLastUpdatedTimestamp()
no older than 7 days ago. In simple terms, it will
include quests that the player attempted, but failed in the last 7 days. Use with the
load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
API endpoints. This is a subset of
STATE_FAILED
.
Constant used to add quests in the STATE_UPCOMING
state to the QuestBuffer
returned by load(GoogleApiClient, int[], int, boolean)
and getQuestsIntent(GoogleApiClient, int[])
.
Sort requests with the quests ending soon first.
Sort requests with the recently updated quests first.
Array of all the valid state selector constants.
Asynchronously accept the quest. This enters the player into an STATE_OPEN
quest and will start tracking their progress towards the milestone goal.
API
SCOPE_GAMES
| apiClient | The GoogleApiClient to
service the call. |
|---|---|
| questId | The quest ID of the quest to accept. |
PendingResult
to access the data
when available.
Asynchronously claim the milestone. This method informs Google Play Games services that the
player has completed the milestone on this device, and prevents the milestone from being
claimed on another device by the same user. If the method call returns
STATUS_OK
, your app should reward the player based on the
CompletionRewardData of the milestone.
API
SCOPE_GAMES
| apiClient | The GoogleApiClient to service the
call. |
|---|---|
| questId | The parent quest ID. |
| milestoneId | The ID of the milestone to claim. |
PendingResult
to access the data when
available.
Get an intent to view a single quest. Note that this must be invoked using
startActivityForResult(Intent, int)
so that the identity of the
calling package can be established.
(GoogleApiClient, int[])
, the only difference being this activity displays a single quest
rather than a list of quests. See
getQuestsIntent(GoogleApiClient, int[])
for usage details and behavior.
Required API: API
SCOPE_GAMES
| apiClient | The GoogleApiClient to service the call. |
|---|---|
| questId | The ID of the quest to display. |
Intent
that can be started to view the quest.
Get an intent to view a list of quests. Note that this must be invoked using
startActivityForResult(Intent, int)
so that the identity of the
calling package can be established.
accept(GoogleApiClient, String)
will be called. If the quest is successfully
accepted, the activity will close with RESULT_OK
, and the data
intent will contain a parcelable object in EXTRA_QUEST
indicating the accepted
quest.
If the user clicks the "Claim Reward" button on a quest, the activity will close with
RESULT_OK
, and the data intent will contain a parcelable object in
EXTRA_QUEST
indicating the quest the user would like to claim a reward for.
The caller is responsible for examining the state of this returned quest. If the quest state
is STATE_COMPLETED
, the caller should invoke
claim(GoogleApiClient, String, String)
. If the claim succeeds, the caller
should then give the reward to the player.
If the user canceled, the result will be RESULT_CANCELED
.
Required API: API
SCOPE_GAMES
| apiClient | The GoogleApiClient to service the call. |
|---|---|
| questSelectors | Array of quest selectors to include in the list of quests. Valid values
include: SELECT_UPCOMING , SELECT_OPEN , SELECT_ACCEPTED ,
SELECT_ENDING_SOON , SELECT_COMPLETED ,
SELECT_COMPLETED_UNCLAIMED , SELECT_EXPIRED ,
SELECT_FAILED , or SELECT_RECENTLY_FAILED .
Use SELECT_ALL_QUESTS to view all quests. |
Intent
that can be started to view the list of quests.
Asynchronously load the quest data for the currently signed-in player and game into a single result, including only quests in the provided state array.
Required API:API
SCOPE_GAMES
| apiClient | The GoogleApiClient to
service the call. |
|---|---|
| questSelectors | An array of quest selectors to indicate which quest states to include.
Valid values include:
SELECT_UPCOMING , SELECT_OPEN ,
SELECT_ACCEPTED , SELECT_ENDING_SOON ,
SELECT_COMPLETED , SELECT_COMPLETED_UNCLAIMED ,
SELECT_EXPIRED , SELECT_FAILED , or SELECT_RECENTLY_FAILED .
Use SELECT_ALL_QUESTS to view all quests. |
| sortOrder | The sort order to use for sorting the quests. Must be either
SORT_ORDER_ENDING_SOON_FIRST or SORT_ORDER_RECENTLY_UPDATED_FIRST . |
| forceReload | If true, this call will clear any locally cached data and attempt to fetch the latest data from the server. This would commonly be used for something like a user-initiated refresh. Normally, this should be set to false to gain advantages of data caching. |
PendingResult
to access the data
when available.
Asynchronously load single quest data for the currently signed-in player and game.
Required API:API
SCOPE_GAMES
| apiClient | The GoogleApiClient to
service the call. |
|---|---|
| forceReload | If true, this call will clear any locally cached data and attempt to fetch the latest data from the server. This would commonly be used for something like a user-initiated refresh. Normally, this should be set to false to gain advantages of data caching. |
| questIds | The IDs of quests to retrieve. |
PendingResult
to access the data
when available.
Register a listener to listen for updates on Quest
instances.
API
SCOPE_GAMES
| apiClient | The GoogleApiClient to service the call. |
|---|---|
| listener | The listener that is called when the quest state changes. |
Display a popup based on the current state of the quest. Popups are only displayed for quests
in STATE_ACCEPTED
. If the quest is in any other state, no popup will be
displayed. Call this from the onAccepted callbacks to show a popup for accepting a quest.
accept(GoogleApiClient, String)
returns).
Required API: API
SCOPE_GAMES
| apiClient | The GoogleApiClient to service the call. |
|---|---|
| questId | The ID of the quest to fetch assets from for the popup (name and images). |
Unregisters this client's quest update listener, if any.
Required API:API
SCOPE_GAMES
| apiClient | The GoogleApiClient to service the call.
|
|---|


General
Emulation
Crawling
Checks
Injection
Evasion
Protocol
Advanced
Enable application checks
%dynamic_checks%
%dynamic_injection_checks%
OK
Cancel


| Field | Required | Type | Description |
|---|---|---|---|
geocoded_waypoints |
required | Array<DirectionsGeocodedWaypoint> |
Contains an array with details about the geocoding of origin, destination and waypoints. Elements in the geocoded_waypoints array correspond, by their zero-based position, to the origin, the waypoints in the order they are specified, and the destination. These details will not be present for waypoints specified as textual latitude/longitude values if the service returns no results. This is because such waypoints are only reverse geocoded to obtain their representative address after a route has been found. An empty JSON object will occupy the corresponding places in the geocoded_waypoints array. See DirectionsGeocodedWaypoint for more information. |
routes |
required | Array<DirectionsRoute> |
Contains an array of routes from the origin to the destination. See Routes below. Routes consist of nested Legs and Steps. See DirectionsRoute for more information. |
status |
required | DirectionsStatus |
Contains the status of the request, and may contain debugging information to help you track down why the request failed. See DirectionsStatus for more information. |
available_travel_modes |
optional | Array<string> |
Contains an array of available travel modes. This field is returned when a request specifies a travel mode and gets no results. The array contains the available travel modes in the countries of the given set of waypoints. This field is not returned if one or more of the waypoints are 'via waypoints'. |
error_message |
optional | string |
When the service returns a status code other than |
Generated from the OpenAPI specification. Edit Report bug


Prizes
Grand Prize
Grand Champion
Awarded to the best darned game we could find.
- The Grand Prize: an all expense paid trip to San Francisco for GDC 2013!
- Get the red carpet treatment! Roundtrip airfare, 7 nights of hotel accomodations in San Francisco, two all-access tickets to GDC 2013 -- which includes entry to the Indie Game Developer Conference -- and more
- Dinner with EA's award-winning game developer Michael John
- A tour of Mozilla's headquarters in Mountain View and lunch with the Mozilla games team
- The chance to be featured in the Firefox Marketplace
- The opportunity to show your game to the awesome folks at Chillingo for a chance to have it published on their network
- Promotion of your game in Game On Gallery and across Mozilla networks
- Nvidia GeForce GTX690
- One year membership to top industry associations IGDA and UKIE
- Special Edition Mozilla Swag. Leave room in your luggage. Trust us.
Category Prizes
Best Hackable Game (Non Stop Hacking)
Awarded to the games with the highest "hackability" score. Games that let their players remix mechanics, fork code or use assets from the web in order for them to create their very own version of an existing game.
Best Learning Game (Head of Class)
Awarded to the games that deepen an understanding of a concept or help a player to master a set of skills.
Best Multi-Device Game (Mobileet)
Awarded to the games that use the power of the mobile phones and cross-device play to enhance assymetric gameplay and location based mechanics.


CHAPTER 41D
STATE RISK MANAGEMENT AND INSURANCE
ADMINISTRATION
Section
41D-1 Definitions
41D-2 Responsibilities of the comptroller
41D-3 Adjustment of claims against the State
41D-4 State risk management revolving fund
41D-5 Investment of fund
41D-6 Fund advancement; dissolution
41D-7 Risk management
41D-8 Insurance on public vehicles
41D-8.5 Insurance for indemnification
41D-9 Construction


Head Conference Chair
Finance Chair
Logistics Chair
Marketing Chair
Programming Chair
Website Chair
Finance Planning Team
Logistics Planning Team
Marketing Planning Team
Programming Planning Team
Website Planning Team


Rezolver
Rezolver is a fast, efficient, extensible, open source IOC container supporting .Net Core. This site contains API documentation and developer guides for getting the most out of it.
Features
Linked topics provide high level overviews and examples in our developer guide - click on them to find out more! (*)
- Constructor Injection
- Multiple constructors supported
- 'Intelligent' constructor discovery based on registered services
- Named argument binding
- Parameters with default arguments are supported
- Member injection (extensible)
- Open generics (with specific closed generics taking precedence)
- Decorators (non-generic and generic)
- Enumerables (empty enumerables returned by default)
- Child containers (overriding registrations in one container with those of another)
- Child registration containers (lower-level overriding of registrations for similar but sibling containers)
- Hierarchical lifetime scoping
- Delegates and Expressions as factories, with argument injection
- Explicit resolving supported inside factory/expression bodies
- Singletons
- Objects as services
- Scoped objects (i.e. 'singleton per scope')
- No 'prepare' phase - you can register targets in a container after you start using it
- Note - services which have already been used cannot yet be replaced
- Extensible compiler framework
- Expression tree compiler used by default
- Integration with Microsoft.Extensions.DependencyInjection and Microsoft.AspNetCore.Hosting via nuget packages
- Extensible configuration framework (still in development)
- Json configuration (still in development)
* If a topic isn't linked, it's probably because we're still working on the documentation!


Name: {{animal.species}}
Age: {{animal.age}}
Diet: {{animal.diet}}
Location: {{animal.location}}
Number of Caretakers: {{animal.caretakers}}
Sex: {{animal.sex}}
Likes: {{animal.likes}}
Dislikes: {{animal.dislikes}}
Name: {{animal.species}}
Age: {{animal.age}}
Diet: {{animal.diet}}
Location: {{animal.location}}
Number of Caretakers: {{animal.caretakers}}
Sex: {{animal.sex}}
Likes: {{animal.likes}}
Dislikes: {{animal.dislikes}}
Name: {{animal.species}}
Age: {{animal.age}}
Diet: {{animal.diet}}
Location: {{animal.location}}
Number of Caretakers: {{animal.caretakers}}
Sex: {{animal.sex}}
Likes: {{animal.likes}}
Dislikes: {{animal.dislikes}}


Click on the map to create a draggable marker. Display heatmaps on the map by picking from 'Pick Heatmap Layers!' Change cities by selectin from 'Pick a City.'
Note: If the heatmaps are not displaying, change your browser's security settings.


© Copyright 1991-2008 by the University of Washington. Written by Joseph Felsenstein. Permission is granted to copy this document provided that no fee is charged for it and that this copyright notice is not removed.
Seqboot is a general bootstrapping and data set translation tool. It is intended to allow you to generate multiple data sets that are resampled versions of the input data set. Since almost all programs in the package can analyze these multiple data sets, this allows almost anything in this package to be bootstrapped, jackknifed, or permuted. Seqboot can handle molecular sequences, binary characters, restriction sites, or gene frequencies. It can also convert data sets between Sequential and Interleaved format, and into the NEXUS format or into a new XML sequence alignment format.
To carry out a bootstrap (or jackknife, or permutation test) with some method in the package, you may need to use three programs. First, you need to run Seqboot to take the original data set and produce a large number of bootstrapped or jackknifed data sets (somewhere between 100 and 1000 is usually adequate). Then you need to find the phylogeny estimate for each of these, using the particular method of interest. For example, if you were using Dnapars you would first run Seqboot and make a file with 100 bootstrapped data sets. Then you would give this file the proper name to have it be the input file for Dnapars. Running Dnapars with the M (Multiple Data Sets) menu choice and informing it to expect 100 data sets, you would generate a big output file as well as a treefile with the trees from the 100 data sets. This treefile could be renamed so that it would serve as the input for Consense. When Consense is run the majority rule consensus tree will result, showing the outcome of the analysis.
This may sound tedious, but the run of Consense is fast, and that of Seqboot is fairly fast, so that it will not actually take any longer than a run of a single bootstrap program with the same original data and the same number of replicates. This is not very hard and allows bootstrapping or jackknifing on many of the methods in this package. The same steps are necessary with all of them. Doing things this way some of the intermediate files (the tree file from the Dnapars run, for example) can be used to summarize the results of the bootstrap in other ways than the majority rule consensus method does.
If you are using the Distance Matrix programs, you will have to add one extra step to this, calculating distance matrices from each of the replicate data sets, using Dnadist or Gendist. So (for example) you would run Seqboot, then run Dnadist using the output of Seqboot as its input, then run (say) Neighbor using the output of Dnadist as its input, and then run Consense using the tree file from Neighbor as its input.
The resampling methods available are:
|
Andrew Rambaut's BEAST XML format |
http://evolve.zoo.ox.ac.uk/beast/introXML.html and http://evolve.zoo.ox.ac.uk/beast/reference/index.html
|
A format for alignments. There is also a format for phylogenies described there. |
| MSAML |
http://xml.coverpages.org/msaml-desc-dec.html
|
Defined by Paul Gordon of University of Calgary. See his big list of molecular biology XML projects. |
| BSML |
http://www.bsml.org/resources/default.asp
|
Bioinformatic Sequence Markup Language includes a multiple sequence alignment XML format |
The data input file is of standard form for molecular sequences (either in interleaved or sequential form), restriction sites, gene frequencies, or binary morphological characters.
When the program runs it first asks you for a random number seed. This should be an integer greater than zero (and probably less than 32767) and which is of the form 4n+1, that is, it leaves a remainder of 1 when divided by 4. This can be judged by looking at the last two digits of the integer (for instance 7651 is not of form 4n+1 as 51, when divided by 4, leaves the remainder 3). The random number seed is used to start the random number generator. If the randum number seed is not odd, the program will request it again. Any odd number can be used, but may result in a random number sequence that repeats itself after less than the full one billion numbers. Usually this is not a problem. As the random numbers appear to be unpredictable, there is no such thing as a "good" seed -- the numbers produced from one seed are statistically indistinguishable from those produced by another, and it is not true that the numbers produced from one seed (say 4533) are similar to those produced from a nearby seed (say 4537).
Then the program shows you a menu to allow you to choose options. The menu looks like this:
Bootstrapping algorithm, version 3.69 Settings for this run: D Sequence, Morph, Rest., Gene Freqs? Molecular sequences J Bootstrap, Jackknife, Permute, Rewrite? Bootstrap % Regular or altered sampling fraction? regular B Block size for block-bootstrapping? 1 (regular bootstrap) R How many replicates? 100 W Read weights of characters? No C Read categories of sites? No S Write out data sets or just weights? Data sets I Input sequences interleaved? Yes 0 Terminal type (IBM PC, ANSI, none)? ANSI 1 Print out the data at start of run No 2 Print indications of progress of run Yes Y to accept these or type the letter for one to change |
The user selects options by typing one of the letters in the left column, and continues to do so until all options are correctly set. Then the program can be run by typing Y.
It is important to select the correct data type (the D selection). Each time D is typed the program will change data type, proceeding successively through Molecular Sequences, Discrete Morphological Characters, Restriction Sites, and Gene Frequencies. Some of these will cause additional entries to appear in the menu. If Molecular Sequences or Restriction Sites settings are chosen the I (Interleaved) option appears in the menu (and as Molecular Sequences are also the default, it therefore appears in the first menu). It is the usual I option discussed in the Molecular Sequences document file and in the main documentation files for the package, and is on by default.
If the Restriction Sites option is chosen the menu option E appears, which asks whether the input file contains a third number on the first line of the file, for the number of restriction enzymes used to detect these sites. This is necessary because data sets for Restml need this third number, but other programs do not, and Seqboot needs to know what to expect.
If the Gene Frequencies option is chosen a menu option A appears which allows the user to specify that all alleles at each locus are in the input file. The default setting is that one allele is absent at each locus. Note that for sampling methods such as the bootstrap and jackknife, whole loci are sampled, not individual alleles.
The J option allows the user to select Bootstrapping, Delete-Half-Jackknifing, the Archie-Faith permutation of species within characters, permutation of character order, shuffling character order separately within each species, or Rewriting. It changes successively among these each time J is typed.
The P menu option appears if the data are molecular sequences and the J option is used to choose the Rewrite option. It gives you the choice between our normal PHYLIP format or a new (and nonstandard) XML sequence alignment format. This encloses the alignment between <ALIGNMENT> ... </ALIGNMENT> tags. Each sequence between <SEQUENCE> ... </SEQUENCE> tags, has a TYPE attribute of the sequence which is either "dna", "rna" or "protein". This is set by default to "dna" but can be changed by the user in an S Sequence type menu option. Each sequence has its name, enclosed between <NAME> ... </NAME> tags, and the data itself, enclosed between <DATA> ... </DATA> tags. The XML option is not available unless the data are molecular sequences. It is a new format -- no programs yet read it. In other cases the P menu option does not appear and the PHYLIP output format is assumed. Here is a simple example of this XML sequence alignment format, for the (silly) data set used in our main documentation file:
<alignment> <sequence type="dna"> <name>Archaeopt</name> <data>CGATGCTTAC CGC</data> </sequence> <sequence type="dna"> <name>Hesperorni</name> <data>CGTTACTCGT TGT</data> </sequence> <sequence type="dna"> <name>Baluchithe</name> <data>TAATGTTAAT TGT</data> </sequence> <sequence type="dna"> <name>B. virgini</name> <data>TAATGTTCGT TGT</data> </sequence> <sequence type="dna"> <name>Brontosaur</name> <data>CAAAACCCAT CAT</data> </sequence> <sequence type="dna"> <name>B.subtilis</name> <data>GGCAGCCAAT CAC</data> </sequence> </alignment> |
For the gene frequencies and restriction sites data types, this Rewrite option does not change the data set. The option will be useful mostly to write the data out in a standard format, in cases where the input file is messy-looking.
The B option selects the Block Bootstrap. When you select option B the program will ask you to enter the block length. When the block length is 1, this means that we are doing regular bootstrapping rather than block-bootstrapping.
The % option allows the user control over what fraction of the characters are sampled in the bootstrap and jackknife methods. Normally the bootstrap samples a number of times equal to the number of characters, and the jackknife samples half that number. This option permits you to specify a smaller fraction of characters to be sampled. Note that doing so is "statistically incorrect", but it is available here for whatever other purposes you may have in mind. Note that the fraction you will be asked to enter is the fraction of characters sampled, not the fraction left out. If you specify 100 as the fraction of sites retained and are using the jackknife, the data set will simply be rewritten. Note (as mentioned below) that this can be used together with the W (Weights) option to rewrite a data set while omitting a particular set of sites.
The R option allows the user to set the number of replicate data sets. This defaults to 100. Most statisticians would be happiest with 1000 to 10,000 replicates in a bootstrap, but 100 gives a rough picture. You will have to decide this based on how long a running time of the tree programs you are willing to tolerate. (The time needed to do the sampling in this program is not much of an issue).
The W (Weights) option allows weights to be read from a file whose default name is "weights". The weights follow the format described in the main documentation file. Weights can only be 0 or 1, and act to select the characters (or sites) that will be used in the resampling, the others being ignored and always omitted from the output data sets. If you use W together with the S (just weights) option, you write a file of weights (whose default name is "outweights"). In that file, any character whose original weight is 0 will have weight 0, the other weights varying according to the resampling. Note that if you write out data sets rather than weights (not using the S option), this output weights file is not written, as the characters are written different numbers of times in the data output file. Note that with restriction sites, the weights are not used by some of the programs. Writing out files of weights will not be helpful with those programs. For the moment, with all gene frequencies programs the weights are also not used.
Note that it is possible to use Seqboot to rewrite a data set while omitting certain sites. This can be done, not with the rewrite choice in option J, but with its jackknife choice. Choose the delete-half jackknife, but then use the % option to set the fraction of sites sampled to 100%. Also use the W option to read a set of weights that select which sites to retain (those with weights 1 instead of 0). Use the R option to set the number of replicates to 1. The program will write one data set, with all the sites that have weights 1, in order.
The C (Categories) option can be used with molecular sequence programs to allow assignment of sites or amino acid positions to user-defined rate categories. The assignment of rates to sites is then made by reading a file whose default name is "categories". It should contain a string of digits 1 through 9. A new line or a blank can occur after any character in this string. Thus the categories file might look like this:
122231111122411155<PHONE_NUMBER>444
The only use of the Categories information in Seqboot is that they are sampled along with the sites (or amino acid positions) and are written out onto a file whose default name is "outcategories", which has one set of categories information for each bootstrap or jackknife replicate.
In the discrete characters data type, three more options appear in the menu. These are the N (aNcestors), X (miXture of methods), and F (Factors) options. They may be useful with the program Mix, which allows input of ancestors information and information specifying the mixture of parsimony methods to be used. Factors information is also read and used by programs Move, Dolmove, and Clique, in calculating how many multistate characters are compatible with a tree. The mixture, ancestors, and factors information for the characters are specified in input files whose default names are "ancestors", "mixture", and "factors". Seqboot produces output files that properly reflect what the resampling implies for these files. The corresponding output files have default file names "outancestors", "outmixture", and "outfactors".
For futher description of the mixture, ancestors, and factors file formats and contents see the Discrete Characters Programs documentation file.
The S option is a particularly important one. It is used whether to produce multiple output files or multiple weights. If your data set is large, a file with (say) 1000 such data sets can be very large and may use up too much space on your system. If you choose the S option, the program will instead produce a weights file with multiple sets of weights. The default name of this file is "outweights". Except for some programs that cannot handle multiple sets of weights, PHYLIP programs have an M (multiple data sets) option that asks the user whether to use multiple data sets or multiple sets of weights. If the latter is selected when running those programs, they read one data set, but analyze it multiple times, each time reading a new set of weights. As both bootstrapping and jackknifing can be thought of as reweighting the characters, this accomplishes the same thing (the multiple weights option is not available for the various kinds of permutation). As the file with multiple sets of weights is much smaller than a file with multiple data sets, this can be an attractive way to save file space. When multiple sets of weights are chosen, they reflect the sampling as well as any set of weights that was read in, so that you can use Seqboot's W option as well.
The 0 (Terminal type) option is the usual one.
Often runs of distance programs, or of phylogeny programs, on large numbers of bootstrap replicates are very time-consuming. If you have multiple computers, you can save time by splitting up these runs among multiple machines. For example, if you have 1000 replicate data sets (or weights) from bootstrapping, you could divide these into ten files of 100 data sets (or you could simply use Seqboot ten times with different random number seeds). If these are run on ten separate computers, the execution time is speeded up by as much as a factor of 10. Each input file of 100 data sets results in an output tree file. These can be concatenated end-to-end using a word processor program or using a command such as the Unix/Linux cat command. Make sure that these files are not turned into Microsoft Word format when this is done. The consensus tree program Consense will hande the concatenated tree file properly.
If a distance matrix method is being used, you can also produce the distance matrices on different machines, and concatenate them end-to-end to produce an input file of distance matrices for Fitch, Kitsch, or Neighbor. This is particularly relevant for Neighbor, which in most cases makes trees more quickly than the distance matrices can be produced.
The data files read by Seqboot are the standard ones for the various kinds of data. For molecular sequences the sequences may be either interleaved or sequential, and similarly for restriction sites. Restriction sites data may either have or not have the third argument, the number of restriction enzymes used. Discrete morphological characters are always assumed to be in sequential format. Gene frequencies data start with the number of species and the number of loci, and then follow that by a line with the number of alleles at each locus. The data for each locus may either have one entry for each allele, or omit one allele at each locus. The details of the formats are given in the main documentation file, and in the documentation files for the groups of programs.
The output file will contain the data sets generated by the resampling process. Note that, when Gene Frequencies data is used or when Discrete Morphological characters with the Factors option are used, the number of characters in each data set may vary. It may also vary if there are an odd number of characters or sites and the Delete-Half-Jackknife resampling method is used, for then there will be a 50% chance of choosing (n+1)/2 characters and a 50% chance of choosing (n-1)/2 characters.
The Factors option causes the characters to be resampled together. If (say) three adjacent characters all have the same factor, so that they all are understood to be recoding one multistate character, they will be resampled together as a group.
The numerical options 1 and 2 in the menu also affect the output file. If 1 is chosen (it is off by default) the program will print the original input data set on the output file before the resampled data sets. I cannot actually see why anyone would want to do this. Option 2 toggles the feature (on by default) that prints out up to 20 times during the resampling process a notification that the program has completed a certain number of data sets. Thus if 100 resampled data sets are being produced, every 5 data sets a line is printed saying which data set has just been completed. This option should be turned off if the program is running in background and silence is desirable. At the end of execution the program will always (whatever the setting of option 2) print a couple of lines saying that output has been written to the output file.
The program runs moderately quickly, though more slowly when the Permutation resampling method is used than with the others.
5 6 Alpha AACAAC Beta AACCCC Gamma ACCAAC Delta CCACCA Epsilon CCAAAC |
(If Replicates are set to 10 and seed to 4333)
5 6 Alpha ACAAAC Beta ACCCCC Gamma ACAAAC Delta CACCCA Epsilon CAAAAC 5 6 Alpha AAAACC Beta AACCCC Gamma CCAACC Delta CCCCAA Epsilon CCAACC 5 6 Alpha ACAAAC Beta ACCCCC Gamma CCAAAC Delta CACCCA Epsilon CAAAAC 5 6 Alpha ACCAAA Beta ACCCCC Gamma ACCAAA Delta CAACCC Epsilon CAAAAA 5 6 Alpha ACAAAC Beta ACCCCC Gamma ACAAAC Delta CACCCA Epsilon CAAAAC 5 6 Alpha AAAACA Beta AAAACC Gamma AAACCA Delta CCCCAC Epsilon CCCCAA 5 6 Alpha AAACCC Beta CCCCCC Gamma AAACCC Delta CCCAAA Epsilon AAACCC 5 6 Alpha AAAACC Beta AACCCC Gamma AAAACC Delta CCCCAA Epsilon CCAACC 5 6 Alpha AAAAAC Beta AACCCC Gamma CCAAAC Delta CCCCCA Epsilon CCAAAC 5 6 Alpha AACCAC Beta AACCCC Gamma AACCAC Delta CCAACA Epsilon CCAAAC |


I write my thoughts and experiences here.
!!! Coming Soon !!!
Let me explain you what my project is all about!


nips nips2007 nips2007-214 nips2007-214-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Manfred Opper, Guido Sanguinetti
Abstract: Markov jump processes play an important role in a large number of application domains. However, realistic systems are analytically intractable and they have traditionally been analysed using simulation based techniques, which do not provide a framework for statistical inference. We propose a mean ﬁeld approximation to perform posterior inference and parameter estimation. The approximation allows a practical solution to the inference problem, while still retaining a good degree of accuracy. We illustrate our approach on two biologically motivated systems.
[1] Harley H. McAdams and Adam Arkin. Stochastic mechanisms in gene expression. Proceedings of the National Academy of Sciences USA, 94:814–819, 1997.
[2] Long Cai, Nir Friedman, and X. Sunney Xie. Stochastic protein expression in individual cells at the single molecule level. Nature, 440:580–586, 2006.
[3] Yoshito Masamizu, Toshiyuki Ohtsuka, Yoshiki Takashima, Hiroki Nagahara, Yoshiko Takenaka, Kenichi Yoshikawa, Hitoshi Okamura, and Ryoichiro Kageyama. Real-time imaging of the somite segmentation clock: revelation of unstable oscillators in the individual presomitic mesoderm cells. Proceedings of the National Academy of Sciences USA, 103:1313–1318, 2006.
[4] Daniel T. Gillespie. Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry, 81(25):2340–2361, 1977.
[5] Eric Mjolsness and Guy Yosiphon. Stochastic process semantics for dynamical grammars. to appear in Annals of Mathematics and Artiﬁcial Intelligence, 2006.
[6] Richard J. Boys, Darren J. Wilkinson, and Thomas B. L. Kirkwood. Bayesian inference for a discretely observed stochastic kinetic model. available from http://www.staff.ncl.ac.uk/d.j.wilkinson/pub.html, 2004.
[7] Manfred Opper and David Saad (editors). Advanced Mean Field Methods. MIT press, Cambridge,MA, 2001.
[8] Cedric Archambeau, Dan Cornford, Manfred Opper, and John Shawe-Taylor. Gaussian process approximations of stochastic differential equations. Journal of Machine Learning Research Workshop and Conference Proceedings, 1(1):1–16, 2007.
[9] Manfred Opper and David Haussler. Bounds for predictive errors in the statistical mechanics of supervised learning. Physical Review Letters, 75:3772–3775, 1995.
[10] Uri Alon. An introduction to systems biology. Chapman and Hall, London, 2006.
[11] Andrew Golightly and Darren J. Wilkinson. Bayesian inference for stochastic kinetic models using a diffusion approximation. Biometrics, 61(3):781–788, 2005. 8


sudoprofiles
/
com.sudoplatform.sudoprofiles
/
ApiErrorCode
/
POLICY_ERROR
POLICY_ERROR
POLICY_ERROR
The API call failed due to the user having insufficient entitlements.


---
layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 144837PRreviews/280951223extendtheAPIoffinmap.html
---
The archive URL format has changed. Please find this page at:
https://leanprover-community.github.io/archive/stream/144837-PR-reviews/topic/.231223.20extend.20the.20API.20of.20.60finmap.60.html


Title :sport challenges to motivate people to exercise
Acronym :Challenji
Long term objectives
- Create neural network to suggest challenges based on performance
- Show progress of spesific person and suggest custom made challenges
- Organise events for people to compete in different sports exercises
- Connection with devices like running meters for better evaluation
Short term objectives
- Finish design of webapp
- Create database with users and challenges
- Authentication using ldap
- Create leaderboards with scores
Related projects
Nike Challenge
Links git and vm
Git
Challenji webapp


{% extends "layout/base.html" %} {% load i18n %} {% block content %}
{% trans "INSTANCE" %}
{% trans "This website is an instance of the Hawkpost software project (see more details about it below). For your information here are a few details about the people responsible for this particular website:" %}
{% trans "Main Administrator" %}: {{ admin_name }}
{% trans "Contact" %}: {{ admin_email }}
{% if description %}{% trans "Instance description" %}: {{ description }}
{% endif %}{% trans "Version" %}: {{ version }}
{% trans "HAWKPOST" %}
{% trans "For many years pigeons were used to send private messages to people. But today, to send private and confidential information a pigeon is not enough. We upgraded the pigeon for a hawk - it's stronger, faster and will protect all of your messages better." %}
{% trans "We know that sometimes it's difficult to explain how to encrypt messages or how to install that software to protect confidential information. That's why we've created Hawkpost - a place where people can send you sensible information in an easy and secure way." %}
{% trans "This project was made at Whitesmith's Hackathon during our company retreat. The aim of this hackathon was to launch a side-project in 24h." %}
{% blocktrans %}You can read more about the project in our FAQ at the bottom of our landing page.{% endblocktrans %}
{% trans "WHITESMITH" %}
{% blocktrans %}Whitesmith is a web and mobile product development studio founded in 2013 and based in Coimbra (Portugal) and London. From Service Oriented Architecture and Internet of Things to Web & Mobile apps, we converge into solving real problems with great user experience. We are more than simple designers & developers - we are an empowered team of problem solvers.{% endblocktrans %}


Reptile-inator
Cool side
Hot side
Temperature
???°F
???°F
Surface Temperature
???°F
Humidity
???%
???%
Heater:
???
Time:
???
Click to edit the configuration.


August 9
There remains therefore a rest to the people of God....Let us labor therefore to enter into that rest, lest any man fall after the same example of unbelief. Hebrews 4:9, 11
God's rest. Considering the hectic, busy lives you and I live that sounds like a pretty good thing to have, doesn't it? But exactly what is that rest? And how do we enter it?
The third and fourth chapters of Hebrews compares God's rest with the children of Israel taking possession of the Promised Land.  All they had to do was go in and possess it. But something kept them from it: unbelief and disobedience.
As believers, we, too, have the opportunity to enter a Promised Land of abundance and peace. A land where we can rest from our struggles and enjoy the victory of God. To enter it we must do what the children of Israel failed to do. We must simply trust God and obey His voice.
How do you come to that place of trust and obedience? By getting to know your Father. By spending time fellowshiping with Him in prayer and in the Word. That is the labor that will bring you into His rest!
I'll never forget when I first discovered that. I had been learning the principles of faith, striving hard to do them. It seemed back then that keeping doubt and unbelief out of my heart was difficult. Then, one day, I began to labor to know the Father instead of just knowing about Him.
When I did that, He began to reveal Himself to me. He gave me glimpses of His heart, His nature and His love. As He revealed to me how much He wanted to do for His children, it changed my striving into peace, my doubt into trust, my fear into bold obedience. It enabled me to enter His rest.
Get to know your Father. Work at it. Make it your "labor." He has a Promised Land of rest that is waiting for you!
Scripture Reading: Hebrews 4:1-11


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Maggie Coblentz is a food designer pursuing a master’s in Industrial Design at the Rhode Island School of Design. She is currently a researcher at Massachusetts Institute of Technology CoLab


/** * @ignore * base event object for custom and dom event. * @author<EMAIL_ADDRESS>*/ KISSY.add('event/base/object', function (S, undefined) { var FALSE_FN = function f_f() { return false; }, TRUE_FN = function t_f() { return true; }; /** * @class KISSY.Event.Object * @private * KISSY 's base event object for custom and dom event. */ function EventObject() { var self = this; self.timeStamp = S.now(); /** * target * @property target * @member KISSY.Event.Object */ self.target = undefined; /** * currentTarget * @property currentTarget * @member KISSY.Event.Object */ self.currentTarget = undefined; /** * current event type * @property type * @type {String} * @member KISSY.Event.Object */ } EventObject.prototype = { constructor: EventObject, /** * Flag for preventDefault that is modified during fire event. if it is true, the default behavior for this event will be executed. * @method * @member KISSY.Event.Object */ isDefaultPrevented: FALSE_FN, /** * Flag for stopPropagation that is modified during fire event. true means to stop propagation to bubble targets. * @method * @member KISSY.Event.Object */ isPropagationStopped: FALSE_FN, /** * Flag for stopImmediatePropagation that is modified during fire event. true means to stop propagation to bubble targets and other listener. * @method * @member KISSY.Event.Object */ isImmediatePropagationStopped: FALSE_FN, /** * Prevents the event's default behavior * @member KISSY.Event.Object */ preventDefault: function () { this.isDefaultPrevented = TRUE_FN; }, /** * Stops the propagation to the next bubble target * @member KISSY.Event.Object */ stopPropagation: function () { this.isPropagationStopped = TRUE_FN; }, /** * Stops the propagation to the next bubble target and * prevents any additional listeners from being executed * on the current target. * @member KISSY.Event.Object */ stopImmediatePropagation: function () { var self = this; self.isImmediatePropagationStopped = TRUE_FN; // fixed 1.2 // call stopPropagation implicitly self.stopPropagation(); }, /** * Stops the event propagation and prevents the default * event behavior. * @param {Boolean} [immediate] if true additional listeners on the current target will not be executed * @member KISSY.Event.Object */ halt: function (immediate) { var self = this; if (immediate) { self.stopImmediatePropagation(); } else { self.stopPropagation(); } self.preventDefault(); } }; return EventObject; });


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Make steak extra special with Mary Berry's classic peppercorn sauce.
Using your hands, flatten the steaks to a thickness of about 1½cm/¾in. Season them well with salt and pepper.
Heat a large frying pan or griddle pan over a high heat until very hot. Spread the butter directly onto the steak and fry for 2 minutes on each side. Cover loosely with foil, rest and keep warm.
For the sauce, add the stock to a saucepan, bring to the boil and cook until reduced by half. Add the garlic, brandy, Worcestershire sauce, mustard, cream and peppercorns. Stir and bring to the boil over a high heat. Season with salt and cook until it reaches the consistency of pouring cream. Stir in the parsley.
Divide the steaks between plates, pour over the sauce and serve.


Twin sisters set to make a million with '£12.99 boob job' backed by celebrities
Stars including Gemma Collins and Vicky Pattison have praised Samantha and Charlotte Ryder's boob tape invention, which can support a range of cup sizes
by Vickie Scullard, Anna YeTwin sisters claim a nifty boob tape creation that promises the same results as a surgical breast lift is set to make a million pounds.
The average boob job costs more than £6,000, so it's not really an option for those on a budget.
But twins Samantha and Charlotte Ryder, 28, from Stockport have come up with an invention that will offer women perkier breasts - for as little as £12.99.
The sisters say their product Lift & Shape Tape from Perky Pear is not overly sticky nor uncomfortable and will not fall off due to sweat, reports Manchester Evening News .
And most importantly, they say it works for a range of boob sizes, from cups A to G.
The sisters' boob stickers have already been praised by a whole host of celebrities, including This Morning host Holly Willoughby, Geordie Shore's Vicky Pattison and Towie star Gemma Collins. They are also being sold on popular online retailers Pretty Little Thing and Boohoo.
Samantha said: “I have always had naturally big boobs.
"Aged 13, I was already a C-cup.
"When you’re that young and see older guys leering over you, it’s not nice, and my boobs kept growing, making some clothes impossible to wear comfortably.
“But it wasn’t until 2016 that I realised I needed a bit of help with lift.
"I was newly single and started going out more often and at that time low cut tops were in fashion.
“I hadn’t worn anything like that for a long time and my boobs had changed in that time, so I felt like I couldn’t wear them without some serious support.”
Samantha, who has a background in design, said she struggled to find any suitable boob tape for her ample E-cups, so she decided to make her own.
She said: “Nothing that was on the market at the time worked. I tried everything.
“I had tried so many things before I made my own - I even used duct tape, which was really painful.
"Especially if you’re putting tape over tape over tape.
"I didn’t want a hack, I needed a solution.
“My grandma used to be a physio and she had some strong tape and my brother hurt his knee.
"I remember looking at it and thinking, that would work perfectly for my boobs."
She continued: “Using my grandmother’s tape I cut it into a shape that I thought would look good,” she explained.
“I wanted something sweat proof, so made sure there are gaps in the adhesive so that sweat doesn’t build up under the adhesive, and the tape stays on until you want to take it off.
“I came up with the perfect shape that cups and fully lifts and is rounded, with no pointy corners.
"Then, in 2017 I started looking for manufacturers.”
The company is a family effort, with sister Charlotte joining three months after the Perky Pear tape was launched. Their mum then joined after working as a manager for 20 years.
Samantha said: “At first, I was working in my mum’s living room and was inundated with orders.
“Now we have four offices, a team of 11 and 40 staff in the factory.
"It’s mad to think that I started out making this for myself and now we’re set to break a million by the end of the year.”
The brand is the first cotton-based breast lift tape which offers the same results as a surgical breast lift. Samantha has described the tape as a "flat pack boob job".
She explained: “Some might only see it as a tape but it’s so much more than that.
“I know the struggles myself - it can help change your whole wardrobe.
"When you put it on your posture and silhouette changes, it gives the wearer confidence.
“We have a 47 per cent returning customer rate, and that proves that they love it.
"They’re very loyal to us.
“We have a lot of customers saying that they feel different since using it.
"We say it’s like a flat pack boob job."
Geordie Shore star Vicky Pattison has made an Instagram video praising the tape. The Only Way is Essex (TOWIE) star Gemma Collins has also posted on social media about how happy she is with the sisters' creation.
The brand is continuing to grow and will launch in the US next year after a cash injection from American investors.
Samantha said: "We have already hit the million mark and next year we will have grown 500 per cent organically.
"Since the investment, from the US directors the brand has grown massively.
“Looking ahead, we want to extend the range a bit.
"We currently have black and nude colours but quite a lot of customers are requesting a brown shade.
“We’re not trying to make skin colours as such, but we appreciate that some people would prefer a more natural shade dependent on their skin colour.”
Speaking about what it is like to work with her sister, Samantha said: “We do bicker from time to time but we’re best friends.
“One of the great things about having a twin is, when people go into business and say they wish they could split themselves in two so they get more done, well I can do that.
“It’s both our business, I don’t think it could have grown in the way it has without the two of us.”


Classes
The following classes are available globally.
-
Helper class to contain a polygon geometry for use in
-[TGMarker polygon]
.The polygon winding order and internal polygons must be set according to the GeoJSON specification.
See moreDeclaration
Objective-C
@interface TGGeoPolygon : NSObject
Swift
class TGGeoPolygon : NSObject
-
Helper class to contain a polyline geometry for use in
-[TGMarker polyline]
.Set the geometry of a marker to a polyline along the given coordinates; _coordinates is a pointer to a sequence of _count LngLats; markers can have their geometry set multiple times with possibly different geometry types; returns true if the marker ID was found and successfully updated, otherwise returns false.
See moreDeclaration
Objective-C
@interface TGGeoPolyline : NSObject
Swift
class TGGeoPolyline : NSObject
-
A configurable http handler used in a
TGMapViewController
.TGMapViewController
provides a default http handler with the following configuration:- cache location:
/tangram_cache
- cache memory capacity: 4Mb
- cache disk capacity: 30Mb
To change this configuration, create a new http handler and set it to the map view with
See more-[TGMapViewController httpHandler]
.Declaration
Objective-C
@interface TGHttpHandler : NSObject
Swift
class TGHttpHandler : NSObject
- cache location:
-
Data structure holding the result of a label selection that occured on the map view.
See
See more-[TGMapViewController pickLabelAt:]
and[TGMapViewDelegate mapView:didSelectLabel:atScreenPosition:]
.Declaration
Objective-C
@interface TGLabelPickResult : NSObject
Swift
class TGLabelPickResult : NSObject
-
A
TGMapData
is a convenience class to display point, polygons or polylines from a dynamic data layer. The data layer will be styled according to the scene file using the provided data layer name.In your stylesheet, add a layer with the name of the data layer you want to add in your client application:
layers: mz_route_line_transit: data: { source: mz_route_line_transit } draw: polylines: color: function() { return feature.color || '#06a6d4'; } order: 500 width: 10px
In your implementation, to add a polyline fitting under the
mz_route_line_transit
layer:
See more// Create a data layer in the TGMapViewController mapView var dataLayer = mapView.addDataLayer(name: "mz_Route_line_transit"); var line = TGGeoPolyline() // Add some coordinates to the polyline line.add(latlon: TGGeoPointMake(longitude0, latitude0)) line.add(latlon: TGGeoPointMake(longitude1, latitude1)) // Set the data properties var properties = ["type": "line", "color": "#D2655F"] // Add the line to the data layer dataLayer.add(polyline: line, properties: properties);
Declaration
Objective-C
@interface TGMapData : NSObject
Swift
class TGMapData : NSObject
-
TGMapViewController
is a flexible and customizable map view managing the lifecycle of a GLKView component. This view provides default gesture handlers for tap, double tap, long press, pan, pinch, rotate and shove.The public interface provides dynamic map marker placement, change of camera view settings, and map description changes through scene updates.
This view uses scenes descibed by the Tangram YAML scene syntax allowing you to fully customize your map description using your own data. Some pre-made basemap styles can be found here using Mapzen data sources.
To use basemap styles you can sign up for an API key and load it through your application:
let sceneURL = "https://mapzen.com/carto/walkabout-style-more-labels/walkabout-style-more-labels.yaml"; view.loadSceneFile(sceneURL, sceneUpdates: [ TGSceneUpdate(path: "sources.mapzen.url_params", value: "{ api_key: \(YOUR_API_KEY) }") ]);
See moreNote
All the screen positions used in this inteface are in logical pixel or drawing coordinate system (based on aUIKit
coordinate system); which is independent of the phone pixel density. Refer the Apple documentation Coordinate Systems and Drawing in iOS for more informations.Declaration
Objective-C
@interface TGMapViewController : GLKViewController <UIGestureRecognizerDelegate>
Swift
class TGMapViewController
-
Marker interface that makes you able to add icons, polylines and polygons to the map view.
The marker style should defined using the Tangram YAML syntax.
See moreDeclaration
Objective-C
@interface TGMarker : NSObject
Swift
class TGMarker : NSObject
-
Data structure holding the result of a marker selection that occured on the map view.
See
See more-[TGMapViewController pickMarkerAt:]
and[TGMapViewDelegate mapView:didSelectMarker:atScreenPosition:]
.Declaration
Objective-C
@interface TGMarkerPickResult : NSObject
Swift
class TGMarkerPickResult : NSObject
-
Represents a data structure to specify a YAML path and the corresponding value for a Tangram scene update.
Example to update an API key for a source defined like this in your stylesheet:
sources: osm: type: MVT url: https://tile.mapzen.com/mapzen/vector/v1/all/{z}/{x}/{y}.mvt max_zoom: 16 url_params: api_key: vector-tiles-tyHL4AY
See moreview.queueSceneUpdates(sceneUpdates: [ TGSceneUpdate(path: "sources.osm.url_params", value: "{ api_key: \(YOUR_API_KEY) }") ]) view.applySceneUpdates()
Declaration
Objective-C
@interface TGSceneUpdate : NSObject
Swift
class TGSceneUpdate : NSObject


like1.1M
KEETHNAA
Keethnaa is not your typical heroine. She struggles with her confidence, her morals, and life in general by not only holding herself to high standards, but her men as well. Enter two less-than-perfect men: one who may be the Prince Charming she’s been waiting for to deliver a fairy tale ending, the other a brash reality check who does NOT believe in happily-ever-after. What will Keethnaa do? Will she sacrifice her standards? Or keep searching for that mythical white knight who may or may not exist in the first place? Funny, messy and sexy, Edith’s journey is relatable to anyone who’s ever left their heart on the battlefield of the dating world.
like5.4M
After an unexpected family tragedy, a reclusive high school student is forced to leave his home -- only to face something much scarier: a reality where monsters are trying to wipe out humanity. Now he must fight alongside a handful of reluctant heroes to try and save the world before it’s too late.
like19.3M
Having a boyfriend who's a full foot taller than you might seem adorable at first, but it usually just ends up causing a whole bunch of minor inconveniences. Follow Fishball as she navigates the Malaysian life with her 6'5" geeky boyfriend.
like3.4M
He’s a dark, uber-powerful god-level demon…who’s also really helpful around the house. Welcome to the unique world of Ghost Wife, where an average high school girl can go to sleep with normal teenage problems, only to wake up and find herself married to an over-protective demon with a creepy smile and an appetite for live cats.
like903,993
Meet Gwendolyn – living proof that princesses don’t always have it all. See, although she lives in a castle and her father is the King, Gwendolyn isn’t like a movie princess, or even a fairly-tale princess. She’s got a big heart, but isn’t particularly attractive and doesn’t have a Disney-quality singing voice. But one night, she accidentally stumbles upon the twisted world of the Cursed Princess Club, and her life will never be the same. Hexed and cast out, the ladies of the club are just the people Gwendolyn needs to show her that just because she doesn’t “fit the mold” does not mean she’s any less of a princess.
like12.6M
What do you desire? Money and wealth? Honor and pride? Authority and power? Revenge? Or something that transcends them all?


--- layout: post esbma_id: 1478 date: 2003-08-29 12:00:00 keywords: cornsharp title: Profiler=Good, RichTextBox=Bad ---
Call me a fan of the Ants Profiler from Red Gate Software.
Our top priority at SourceGear right now is making Vault faster. I don't get to do much coding these days, so I decided to get my hands dirty and pitch in. Several of the other folks here have been using Ants to profile Vault, so I thought I would give it a try. The tool was extremely smooth. I installed it with no problems. I didn't read a word of the documentation. I launched the profiler and told it to profile the Vault GUI client EXE. I did my test and exited the Vault client. Ants showed me the results.
The results browser is cool. I cruised around the various functions and examined the timings. Soon I discovered one of those problems which profilers were born to find: The .NET RichTextBox class is really, really slow.
We use a RichTextBox to display user messages in a pane at the bottom of the Vault client window. I suppose any plain text box would suffice, but we do use a little bit of color for highlighting errors in red. Anyway, the profiler was showing that Vault spends an obscene amount of time showing user progress messages. My test case involved the retrieval of 6,000 files from a repository, which means I was adding 6,000 messages to that RichTextBox.
At first I suspected string concatenation. The code to append messages looked something like this:
AppendText("["+msg.When+"] "+msg.Message+"\r\n");
So I changed it to eliminate the string concatenation:
AppendText("[");
AppendText(msg.When);
AppendText("] ");
AppendText(msg.Message);
AppendText("\r\n");
This made no real difference in performance, but the profiler results became even more useful. Ants can show how much time is being spent on each line of code. When I broke things up, Ants reported that the first four calls to AppendText were fast, and the fifth one was slow. Then I realized what's going on. When you append a CRLF to the RichTextBox, it's re-running its internal layout algorithm.
RichTextBox is obviously overkill for the way we are using it. It's not like we need full justification or embedded tables in our user messages pane, but we're paying for these features in performance.
The problem was easily fixed. Instead of calling AppendText() on every single message, I batched up the non-urgent messages into groups so we could append them with a single call. Much faster, no lost functionality, everybody will be happier.


|
.NET Framework | |||||||||
| PREV TYPE NEXT TYPE | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONST | PROP | METHOD | OP | EVENT | DETAIL: FIELD | CONST | PROP | METHOD | OP | EVENT |
Class to manage the text resource for the Velocity Runtime. *
| Field Summary | |
|---|---|
| NVelocity.Runtime.Resource.ResourceCache |
globalCache
Object implementing ResourceCache to be our resource manager's Resource cache. |
| int |
RESOURCE_CONTENT
A static content resource. |
| int |
RESOURCE_TEMPLATE
A template resources. |
| System.Collections.ArrayList |
resourceLoaders
The List of templateLoaders that the Runtime will use to locate the InputStream source of a template. |
| NVelocity.Runtime.RuntimeServices | rsvc |
| Constructor Summary | |
|---|---|
|
ResourceManagerImpl() Initializes a new instance of the ResourceManagerImpl class. |
| Method Summary | |
|---|---|
| string |
getLoaderNameForResource(string resourceName) Determines is a template exists, and returns name of the loader that provides it. This is a slightly less hokey way to support the Velocity.templateExists() utility method, which was broken when per-template encoding was introduced. We can revisit this. * |
| NVelocity.Runtime.Resource.Resource |
getResource(string resourceName, int resourceType, string encoding) Gets the named resource. Returned class type corresponds to specified type (i.e. Templateto RESOURCE_TEMPLATE). * |
| NVelocity.Runtime.Resource.Resource |
getResource(string resourceName, int resourceType) Gets the named resource. Returned class type corresponds to specified type (i.e. Templateto RESOURCE_TEMPLATE). * |
| void |
initialize(NVelocity.Runtime.RuntimeServices rs) Initialize the ResourceManager. It is assumed that assembleSourceInitializers() has been called before this is run. |
| NVelocity.Runtime.Resource.Resource |
loadResource(string resourceName, int resourceType, string encoding) Loads a resource from the current set of resource loaders |
| void |
refreshResource(NVelocity.Runtime.Resource.Resource resource, string encoding) Takes an existing resource, and 'refreshes' it. This generally means that the source of the resource is checked for changes according to some cache/check algorithm and if the resource changed, then the resource data is reloaded and re-parsed. * |
| Methods inherited from class System.Object |
|---|
| Equals, Finalize, GetHashCode, GetType, MemberwiseClone, ToString |
| Field Detail |
|---|
protected internal NVelocity.Runtime.Resource.ResourceCache globalCache
Object implementing ResourceCache to be our resource manager's Resource cache.
public int RESOURCE_CONTENT
A static content resource.
public int RESOURCE_TEMPLATE
A template resources.
protected internal System.Collections.ArrayList resourceLoaders
The List of templateLoaders that the Runtime will use to locate the InputStream source of a template.
protected internal NVelocity.Runtime.RuntimeServices rsvc
| Constructor Detail |
|---|
public ResourceManagerImpl()
Initializes a new instance of the ResourceManagerImpl class.
| Method Detail |
|---|
public string getLoaderNameForResource(string resourceName)
Determines is a template exists, and returns name of the loader that provides it. This is a slightly less hokey way to support the Velocity.templateExists() utility method, which was broken when per-template encoding was introduced. We can revisit this. *
Parameters:
resourceName
- Name of template or content resource
public NVelocity.Runtime.Resource.Resource getResource(string resourceName, int resourceType, string encoding)
Gets the named resource. Returned class type corresponds to specified type (i.e.
Templateto
RESOURCE_TEMPLATE). *
Parameters:
resourceName
- The name of the resource to retrieve.
resourceType
- The type of resource (RESOURCE_TEMPLATE,
RESOURCE_CONTENT, etc.).
encoding
- The character encoding to use.
public NVelocity.Runtime.Resource.Resource getResource(string resourceName, int resourceType)
Gets the named resource. Returned class type corresponds to specified type (i.e.
Templateto
RESOURCE_TEMPLATE). *
Parameters:
resourceName
- The name of the resource to retrieve.
resourceType
- The type of resource (RESOURCE_TEMPLATE,
RESOURCE_CONTENT, etc.).
public void initialize(NVelocity.Runtime.RuntimeServices rs)
Initialize the ResourceManager. It is assumed that assembleSourceInitializers() has been called before this is run.
protected internal NVelocity.Runtime.Resource.Resource loadResource(string resourceName, int resourceType, string encoding)
Loads a resource from the current set of resource loaders
Parameters:
resourceName
- The name of the resource to retrieve.resourceType
- The type of resource (RESOURCE_TEMPLATE,
RESOURCE_CONTENT, etc.).
encoding
- The character encoding to use.protected internal void refreshResource(NVelocity.Runtime.Resource.Resource resource, string encoding)
Takes an existing resource, and 'refreshes' it. This generally means that the source of the resource is checked for changes according to some cache/check algorithm and if the resource changed, then the resource data is reloaded and re-parsed. *
Parameters:
resource
- resource to refresh
*
@throws ResourceNotFoundException if template not found
from current source for this Resource
@throws ParseErrorException if template cannot be parsed due
to syntax (or other) error.
@throws Exception if a problem in parse
|
.NET Framework | |||||||||
| PREV TYPE NEXT TYPE | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONST | PROP | METHOD | OP | EVENT | DETAIL: FIELD | CONST | PROP | METHOD | OP | EVENT |


A responsive site is a web page that changes automatically depending on the user's device and screen size. If your website is not responsive, it will take away from the user experience, which is the exact opposite of what you want to do. If a visitor to your site has to zoom into the text because its too small or scroll right on the screen because they can't read a full sentence, then that person's experience will be frustrating and they're less likely to spend time on your website.
The mobile first design is the concept to build a website specifically mobile friendly in both size and design, and then go on to produce it for larger screens like tablets and desktops. This ensures that all of the key information is available on screen and that it is not overcrowded. If we were to design our web page off desktop dimensions, we would have to cut a lot out of our mobile view. Also, something to note, more people are viewing web content on their mobile devices compared to laptops and desktops, so it makes sense to first make your website mobile friendly.
Frameworks are templates of code, such as HTML and CSS, that allow a simpler approach to coding as you can just fill it in and have a website in a short amount of time. The pros of using framework are that there are great web designs out there and they are easily accessible and encouraged. Alongside that, you don't have to fully copy a webpage design! You can copy it and then change it to make it your own. The cons are it allows you to get a bit lazy in terms of creativity and making something brand new. It is good practice to build your code yourself and can make it easier to identify faults if you start having problems with your webpage.
The wireframe method is using rows and columns to put each element of our webpage exactly where we want it to go. We use it because it helps us visualise what our website will look like.
Just understanding the whole concept really. I feel like its something I do need to keep revising and practising. It is another thing that the more I do it, the better I'll get.


We are excited to announce the availability of PyTorch 1.8. This release is composed of more than 3,000 commits since 1.7. It includes major updates and new features for compilation, code optimization, frontend APIs for scientific computing, and AMD ROCm support through binaries that are available via pytorch.org. It also provides improved features for large-scale training for pipeline and model parallelism, and gradient compression. A few of the highlights include:
- Support for doing python to python functional transformations via
torch.fx
; - Added or stabilized APIs to support FFTs (
torch.fft
), Linear Algebra functions (torch.linalg
), added support for autograd for complex tensors and updates to improve performance for calculating hessians and jacobians; and - Significant updates and improvements to distributed training including: Improved NCCL reliability; Pipeline parallelism support; RPC profiling; and support for communication hooks adding gradient compression. See the full release notes here.
Along with 1.8, we are also releasing major updates to PyTorch libraries including TorchCSPRNG, TorchVision, TorchText and TorchAudio. For more on the library releases, see the post here. As previously noted, features in PyTorch releases are classified as Stable, Beta and Prototype. You can learn more about the definitions in the post here.
New and Updated APIs
The PyTorch 1.8 release brings a host of new and updated API surfaces ranging from additional APIs for NumPy compatibility, also support for ways to improve and scale your code for performance at both inference and training time. Here is a brief summary of the major features coming in this release:
[Stable] Torch.fft
support for high performance NumPy style FFTs
As part of PyTorch’s goal to support scientific computing, we have invested in improving our FFT support and with PyTorch 1.8, we are releasing the torch.fft
module. This module implements the same functions as NumPy’s np.fft
module, but with support for hardware acceleration and autograd.
- See this blog post for more details
- Documentation
[Beta] Support for NumPy style linear algebra functions via torch.linalg
The torch.linalg
module, modeled after NumPy’s np.linalg module, brings NumPy-style support for common linear algebra operations including Cholesky decompositions, determinants, eigenvalues and many others.
[Beta] Python code Transformations with FX
FX allows you to write transformations of the form transform(input_module : nn.Module)
-> nn.Module
, where you can feed in a Module
instance and get a transformed Module
instance out of it.
This kind of functionality is applicable in many scenarios. For example, the FX-based Graph Mode Quantization product is releasing as a prototype contemporaneously with FX. Graph Mode Quantization automates the process of quantizing a neural net and does so by leveraging FX’s program capture, analysis and transformation facilities. We are also developing many other transformation products with FX and we are excited to share this powerful toolkit with the community.
Because FX transforms consume and produce nn.Module instances, they can be used within many existing PyTorch workflows. This includes workflows that, for example, train in Python then deploy via TorchScript.
You can read more about FX in the official documentation. You can also find several examples of program transformations implemented using torch.fx
here. We are constantly improving FX and invite you to share any feedback you have about the toolkit on the forums or issue tracker.
We’d like to acknowledge TorchScript tracing, Apache MXNet hybridize, and more recently JAX as influences for program acquisition via tracing. We’d also like to acknowledge Caffe2, JAX, and TensorFlow as inspiration for the value of simple, directed dataflow graph program representations and transformations over those representations.
Distributed Training
The PyTorch 1.8 release added a number of new features as well as improvements to reliability and usability. Concretely, support for: Stable level async error/timeout handling was added to improve NCCL reliability; and stable support for RPC based profiling. Additionally, we have added support for pipeline parallelism as well as gradient compression through the use of communication hooks in DDP. Details are below:
[Beta] Pipeline Parallelism
As machine learning models continue to grow in size, traditional Distributed DataParallel (DDP) training no longer scales as these models don’t fit on a single GPU device. The new pipeline parallelism feature provides an easy to use PyTorch API to leverage pipeline parallelism as part of your training loop.
[Beta] DDP Communication Hook
The DDP communication hook is a generic interface to control how to communicate gradients across workers by overriding the vanilla allreduce in DistributedDataParallel. A few built-in communication hooks are provided including PowerSGD, and users can easily apply any of these hooks to optimize communication. Additionally, the communication hook interface can also support user-defined communication strategies for more advanced use cases.
Additional Prototype Features for Distributed Training
In addition to the major stable and beta distributed training features in this release, we also have a number of prototype features available in our nightlies to try out and provide feedback. We have linked in the draft docs below for reference:
- (Prototype) ZeroRedundancyOptimizer - Based on and in partnership with the Microsoft DeepSpeed team, this feature helps reduce per-process memory footprint by sharding optimizer states across all participating processes in the
ProcessGroup
gang. Refer to this documentation for more details. - (Prototype) Process Group NCCL Send/Recv - The NCCL send/recv API was introduced in v2.7 and this feature adds support for it in NCCL process groups. This feature will provide an option for users to implement collective operations at Python layer instead of C++ layer. Refer to this documentation and code examples to learn more.
- (Prototype) CUDA-support in RPC using TensorPipe - This feature should bring consequent speed improvements for users of PyTorch RPC with multiple-GPU machines, as TensorPipe will automatically leverage NVLink when available, and avoid costly copies to and from host memory when exchanging GPU tensors between processes. When not on the same machine, TensorPipe will fall back to copying the tensor to host memory and sending it as a regular CPU tensor. This will also improve the user experience as users will be able to treat GPU tensors like regular CPU tensors in their code. Refer to this documentation for more details.
- (Prototype) Remote Module - This feature allows users to operate a module on a remote worker like using a local module, where the RPCs are transparent to the user. In the past, this functionality was implemented in an ad-hoc way and overall this feature will improve the usability of model parallelism on PyTorch. Refer to this documentation for more details.
PyTorch Mobile
Support for PyTorch Mobile is expanding with a new set of tutorials to help new users launch models on-device quicker and give existing users a tool to get more out of our framework. These include:
Our new demo apps also include examples of image segmentation, object detection, neural machine translation, question answering, and vision transformers. They are available on both iOS and Android:
In addition to performance improvements on CPU for MobileNetV3 and other models, we also revamped our Android GPU backend prototype for broader models coverage and faster inferencing:
Lastly, we are launching the PyTorch Mobile Lite Interpreter as a prototype feature in this release. The Lite Interpreter allows users to reduce the runtime binary size. Please try these out and send us your feedback on the PyTorch Forums. All our latest updates can be found on the PyTorch Mobile page
[Prototype] PyTorch Mobile Lite Interpreter
PyTorch Lite Interpreter is a streamlined version of the PyTorch runtime that can execute PyTorch programs in resource constrained devices, with reduced binary size footprint. This prototype feature reduces binary sizes by up to 70% compared to the current on-device runtime in the current release.
Performance Optimization
In 1.8, we are releasing the support for benchmark utils to enable users to better monitor performance. We are also opening up a new automated quantization API. See the details below:
(Beta) Benchmark utils
Benchmark utils allows users to take accurate performance measurements, and provides composable tools to help with both benchmark formulation and post processing. This expected to be helpful for contributors to PyTorch to quickly understand how their contributions are impacting PyTorch performance.
Example:
from torch.utils.benchmark import Timer
results = []
for num_threads in [1, 2, 4]:
timer = Timer(
stmt="torch.add(x, y, out=out)",
setup="""
n = 1024
x = torch.ones((n, n))
y = torch.ones((n, 1))
out = torch.empty((n, n))
""",
num_threads=num_threads,
)
results.append(timer.blocked_autorange(min_run_time=5))
print(
f"{num_threads} thread{'s' if num_threads > 1 else ' ':<4}"
f"{results[-1].median * 1e6:>4.0f} us " +
(f"({results[0].median / results[-1].median:.1f}x)" if num_threads > 1 else '')
)
1 thread 376 us
2 threads 189 us (2.0x)
4 threads 99 us (3.8x)
(Prototype) FX Graph Mode Quantization
FX Graph Mode Quantization is the new automated quantization API in PyTorch. It improves upon Eager Mode Quantization by adding support for functionals and automating the quantization process, although people might need to refactor the model to make the model compatible with FX Graph Mode Quantization (symbolically traceable with torch.fx
).
- Documentation
- Tutorials:
Hardware Support
[Beta] Ability to Extend the PyTorch Dispatcher for a new backend in C++
In PyTorch 1.8, you can now create new out-of-tree devices that live outside the pytorch/pytorch
repo. The tutorial linked below shows how to register your device and keep it in sync with native PyTorch devices.
[Beta] AMD GPU Binaries Now Available
Starting in PyTorch 1.8, we have added support for ROCm wheels providing an easy onboarding to using AMD GPUs. You can simply go to the standard PyTorch installation selector and choose ROCm as an installation option and execute the provided command.
Thanks for reading, and if you are excited about these updates and want to participate in the future of PyTorch, we encourage you to join the discussion forums and open GitHub issues.
Cheers!
Team PyTorch


Order List
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Message
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Please select express company
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Common Reference
Here you can find all methods and variables common for every BridgeJS solution.
! Important -> All methods should be called on PayEngine object (e.g PayEngine.desiredMethod(parameters)).
setPublishableKey
Identifies the merchant against the Bridge API, based on the given publishableKey (Merchant ID).
Signature
setPublishableKey (publishableKey)
Arguments
| Name |
Mandatory |
Type |
Default Value |
Description |
| publishableKey |
YES |
String |
N/A |
The MerchantID given to every user on registration in Merchant Center (can be found in My Account section) |
Error object
Can be returned in any callback function as the error parameter.
Signature
{
error: {General error message},
status: {General status code},
message: {
properties: [
key: {Error details key},
code: {Error details code},
message: {Error details message}
]
}
}
Error codes
| Code |
Description |
| 202 |
Not fully loaded |
| 400 |
Wrong type |
| 404 |
Not found |
| 408 |
Timed out |
| 412 |
Precondition failed |
| 4121 |
Field is required |
| 4122 |
Property should not be empty if present |
| 4123 |
Value shaould be numeric |
| 4124 |
Value shaould be alphanumeric |
| 4125 |
Credit card type unrecognizable |
| 4126 |
Invalid length |
| 4127 |
Invalid credit card number |
| 4128 |
Invalid cvc length |
| 4129 |
Invalid expiry month |
| 41210 |
Invalid expiry month format |
| 41211 |
Invalid expiry year |
| 41212 |
Invalid expiry year format |
| 41213 |
Country not supported |
| 41214 |
Invalid iban length |
| 41215 |
Invalid iban |
| 41216 |
Invalid bic length |
| 41217 |
Bic country not supported |
| 41218 |
Property should not be undefined |
User variables
For convenience you can access the userVariables object under PayEngine
PayEngine.userVariables.optionsKeys.cardNumberStyle
PayEngine.userVariables.optionsKeys.verificationStyle
PayEngine.userVariables.optionsKeys.cardNumberPlaceholder
PayEngine.userVariables.optionsKeys.verificationPlaceholder
PayEngine.userVariables.paymentInstrumenTypes.creditcard
PayEngine.userVariables.paymentInstrumenTypes.sepa
PayEngine.userVariables.iframeTypes.cardNumber
PayEngine.userVariables.iframeTypes.verification
PayEngine.userVariables.fieldEvents.blur
PayEngine.userVariables.fieldEvents.enter
PayEngine.userVariables.fieldEvents.focus
PayEngine.userVariables.fieldEvents.input
PayEngine.userVariables.fieldEvents.mouseOver
PayEngine.userVariables.fieldEvents.mouseOut
PayEngine.userVariables.fieldEvents.shiftTab
PayEngine.userVariables.fieldEvents.tab


Popper
Here's a little
demoooooooo
of Popper
Absolutely positioned box
Sometimes these can be a real
pain


Description: Bind an event handler to the "unload" JavaScript event.
handler(eventObject): Function() A function to execute when the event is triggered.
eventData: Object A map of data that will be passed to the event handler.
handler(eventObject): Function() A function to execute each time the event is triggered.
This method is a shortcut for .bind('unload', handler)
.
The unload
event is sent to the window
element when the user navigates away from the page. This could mean one of many things. The user could have clicked on a link to leave the page, or typed in a new URL in the address bar. The forward and back buttons will trigger the event. Closing the browser window will cause the event to be triggered. Even a page reload will first create an unload
event.
The exact handling of the
unload
event has varied from version to version of browsers. For example, some versions of Firefox trigger the event when a link is followed, but not when the window is closed. In practical usage, behavior should be tested on all supported browsers, and contrasted with the proprietarybeforeunload
event.
Any unload
event handler should be bound to the window
object:
$(window).unload(function() {
alert('Handler for .unload() called.');
});
After this code executes, the alert will be displayed whenever the browser leaves the current page.
It is not possible to cancel the unload
event with .preventDefault()
. This event is available so that scripts can perform cleanup when the user leaves the page.
$(window).unload( function () { alert("Bye now!"); } );


About Me
|
|
|
Hey, I am Avni Agarwal. I am pursing B.Tech in CS with Artifical Intelligence. I'm passionate about coding and designing. I discovered my love for coding in 7th grade. Since then I've been working hard to learn new skills. For me coding is like art, I love it as much as I to paint. I want to do things that stand out, my goal in life is to create new technologies that can help people and inspire them too.
I'm interested in full-stack development and AI. As of now, I can work well with full-stack web development, cross-browser compatibility, technical debt, database management, AWS and troubleshooting. I can convert designs into webpages, create their backends and host them online.
I may know only a drop of code in the gigantic technology ocean, but I'm trying my best to learn more each day and yes one day I may succeed in creating something outstanding.
|
|


Mexican Software Engineer strongly experienced in (but not limited to) Unix-like systems.
A natural troubleshooter and team player, always looking for new challenges that can help me and my team grow up in every aspect and build strong relationships to perform above expectations and deliver an outstanding service.
Passionated for systems automation and always looking for new tools and ways to improve processes. Embracing Cloud technologies in an effort to have more stable and flexible infrastructures to provide a better experience to the end users.
Working for Daimler providing support for all Mercedes Benz autos in Americas and Asia continents.
Main skills: Linux (Red Hat, Oracle), Bash scripting, Jenkins, Springboot, Postgresql, Jira, Kibana.
Administration and improving of servers infrastructure as well as migrating it to the cloud (AWS).
Main skills: Linux (Debian, RedHat, CentOS), Apache Tomcat, Postfix, Bash, Amazon Web Services (EC2, S3).
Supporting and creating new modules for inhouse eCommerce system used by hundreds of newspapers all around the world.
Main skills: Java, JSP, Spring, IntellijIdea, Postgresql, HTML, CSS.
In charge of creating web app for compliance department.
Complete redesign of the corporate website.
Main skills: PHP, MySQL, HTML, CSS
I write in my personal blog.
I love cinema: Letterboxd.
I make music: Bandcamp + YouTube.
I take photos: Flickr.


Hints: Dock-able Windows
- Use Ctrl+Tab to show window selector
- Hover the mouse over the auto-hidden buttons to view window preview
- Use Auto-Hide button to toggle the auto-hide mode of the window
- Window (Right click mouse) to display the context menu
Menu Shortcut Keys
- New = Ctrl-N
- Open = Ctrl-O
- Save = Ctrl-S
- SaveAs = Ctrl-Shift
- Exit = Alt-F4
- Undo = Ctrl-Z
- Redo = Ctrl-Y
- SelectAll = Ctrl-A
- UnSelectAll = Ctrl-Shift
- BringToFront = Ctrl-F
- SendToBack = Ctrl-Shift
- Delete = Delete
- Cut = Ctrl-X
- Copy = Ctrl-C
- Paste = Ctrl-V
- Help = Ctrl-F1
- Operation_Pocket = Alt-P
- Operation_Inside = Alt-I
- Operation_Outside = Alt-O
WorkArea Shortcut Keys
- Select Multiple = Alt-Click
- Zoom In/Out = Ctrl-MouseWheel


| 28 | A 360-DEGREE 4K×2K PANORAMIC VIDEO RECORDING OVER SMART-PHONES |
Tsu-Ming Liu*, Mediatek Inc.; Chi-Cheng Ju |
| 55 |
CREATIVE COLOR DESIGNS FOR PRODUCT PACKAGING | Ying Li*, IBM T. J. Watson Research
|
| 56 | Flexible Media Transport Framework for Android | Sándor Molnár*, BME; Péter Megyesi, BME; Szilárd Solymos, ; Zsolt
Kramer, BME; Zoltán Móczár, BME |
| 70 | Machine learning-Based Behavior Recognition System for a Basketball
Player Using Multiple Kinect Cameras | Wei-Yuan Kuo, National Central University; Chien-Hao Kuo*, National
Central University; Shih-Wei Sun, ; Pao-Chi Chang, National Central University;
Ying-Ting Chen, Taipei National University of the Arts |
| 205 | Automated Recording Platform for High Quality Short Scientific Video
Abstract | Samuel Toba*, American Chemical Society; Russell Silber; Michael David |


Evaluates an expression on scored documents.
| def google.appengine.api.search.stub.expression_evaluator.ExpressionEvaluator.__init__ |
( |
|
self, |
|
|
|
document, |
|
|
|
inverted_index, |
|
|
|
is_sort_expression = False |
|
) |
| |
Constructor.
Args:
document: The ScoredDocument to evaluate the expression for.
inverted_index: The search index (used for snippeting).
is_sort_expression: The flag indicates if this is a sort expression. Some
operations (such as COUNT) are not supported in sort expressions.
| def google.appengine.api.search.stub.expression_evaluator.ExpressionEvaluator.Evaluate |
( |
|
self, |
|
|
|
expression |
|
) |
| |
Evaluates the expression for a document and attaches the result.
Args:
expression: The Expression protobuffer object.
| def google.appengine.api.search.stub.expression_evaluator.ExpressionEvaluator.ValueOf |
( |
|
self, |
|
|
|
expression, |
|
|
|
default_value = None , |
|
|
|
return_type = None |
|
) |
| |
Returns the value of an expression on a document.
Args:
expression: The expression string.
default_value: The value to return if the expression cannot be evaluated.
return_type: The type the expression should evaluate to. Used to create
multiple sorts for ambiguous expressions. If None, the expression
evaluates to the inferred type or first type of a field it encounters in
a document.
Returns:
The value of the expression on the evaluator's document, or default_value
if the expression cannot be evaluated on the document.
Raises:
ExpressionEvaluationError: sort expression cannot be evaluated
because the expression or default value is malformed. Callers of
ValueOf should catch and return error to user in response.
QueryExpressionEvaluationError: same as ExpressionEvaluationError but
these errors should return query as error status to users.
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/api/search/stub/expression_evaluator.py


概要信息
详细信息 h5 不只包括 html新增的标签 还包括 css3 js语法
跑马灯
scrollamount
alex is learning html
alex is learning html
alex is learning html
this is 江南 course
alex is learning html direction="right"
alex is learning html loop="1"
behavior="slide"
behavior="alternate"


Elasticsearch
You Know, for Search
Elasticsearch presentation 09/14 @ Web-Dev-BBQ Stuttgart/Germany
Markus Wanjura
Head of IT & Development
@ develop4edu GmbH Stuttgart
Let's start!
One of the most common requirement for a modern web application!
▾
Search.
Does the following dialog sound familiar to you?
Customer:
I need a search for my shop?
Me:
Easy! Let's use SQL!
SELECT * FROM product
WHERE title LIKE '%foobar%'
ORDER BY price DESC
LIMIT 1000;
Me:
Customer:
- Can you handle filters?
- How about fuzzy search?
- I need autocompletition?
- Can you show result counts on filters?
- Marketing asked to boost that product XY for a while!
- What about sorting and pagination?
- I want a "Did you mean?" function.
- Does it scale?
- Ops team asked for failover, replication and clustering!
- It has to be blazing fast!
- Could you...
Me: ...
What kind of technology can handle that?
Lucene
- formally: Apache Lucene™
- Open-source search engine.
Do i want to integrate Lucene directly into my webapp?
And manage ALL by my self?
No!
If you don't have to, you won't!
Ok. Got it!
Is there an enterprise ready search server based on lucene?
Elasticsearch
vs.
Apache Solr
I'll keep this part short.
We used a Solr 3.x for a couple of year's.
It was a bit painful and slow development.
It woulnd't be fair to compare a Solr 3.x with an Elasticsearch 1.x.
Solr 4.10 and Elasticsearch 1.3 are equal competitors
In our infrastructure scaleability is key!
So we decided to move to Elasticsearch.
Our evaluation keypoints were:
- Elasticsearch is the more modern and faster growing ecosystem
- It is designed to be distributed from the ground up
- Friendlier to teams which are used to REST APIs and JSON
- We already use Logstash+Kibana for webserver log aggregation
(with an index amount of hundreds of gigabyte) - Big scale portals like GitHub, XING, SoundCloud already use it
If your ecosystem supports Solr more and scaleability is not your biggest keypoint,
I think you'll be very happy with either Solr or Elasticsearch.
You'll find a very good Feature Smackdown at http://solr-vs-elasticsearch.com/
Let's move on
and have some fun with Elasticsearch.
Local installation
(Mac)
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ brew install elasticsearch
$ elasticsearch start
(Windows untested)
Download zip from elasticsearch.org...
c:\elasticsearch-{version}bin>service install
Installing service : "elasticsearch-service-x64"
Using JAVA_HOME (64-bit): "c:\jvm\jdk1.7"
The service 'elasticsearch-service-x64' has been installed.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/setup-service-win.html
DONE!
Open your browser with http://localhost:9200/
Put some data to our index
curl -XPUT 'http://<IP_ADDRESS>:9200/blog/post/1' -d '{
"user": "mwanjura",
"postDate": "2014-09-10",
"body": "Search is hard. Search should be easy." ,
"title": "On search"
}'
curl -XPUT 'http://<IP_ADDRESS>:9200/blog/post/2' -d '{
"user": "mwanjura",
"postDate": "2014-08-12",
"body": "Distribution is hard. Distribution should be easy." ,
"title": "On distributed search"
}'
curl -XPUT 'http://<IP_ADDRESS>:9200/blog/post/3' -d '{
"user": "mwanjura",
"postDate": "2014-07-10",
"body": "We need a new search" ,
"title": "Better search"
}'
Result
Index mapping is dynamically created for you
{"_index":"blog","_type":"post","_id":"1","_version":1,"created":true}
{"_index":"blog","_type":"post","_id":"2","_version":1,"created":true}
{"_index":"blog","_type":"post","_id":"3","_version":1,"created":true}
Index mapping
curl -XGET 'http://<IP_ADDRESS>:9200/blog/_mapping?pretty=true'
Result
{
"blog" : {
"mappings" : {
"post" : {
"properties" : {
"body" : { "type" : "string" },
"postDate" : { "type" : "date", "format" : "dateOptionalTime" },
"title" : { "type" : "string" },
"user" : { "type" : "string" }
}
}
}
}
}
Get some specific data by id
curl -XGET 'http://<IP_ADDRESS>:9200/blog/post/2?pretty=true'
Result
{
"_index" : "blog",
"_type" : "post",
"_id" : "2",
"_version" : 1,
"found" : true,
}
"_source":{ "user": "mwanjura", "postDate": ...}
Basic search by date
curl -XGET 'http://<IP_ADDRESS>:9200/blog/_search?pretty=true' -d '
{
"query" : {
"range" : {
"postDate" : { "from" : "2014-08-10", "to" : "2014-12-12" }
}
}
}'
Result
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 15,
"successful": 15,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "blog",
"_type": "post",
"_id": "1",
"_score": 1,
"_source": {
"user": "mwanjura",
"postDate": "2014-09-10",
"body": "Search is hard. Search should be easy.",
"title": "On search"
}
},
{
"_index": "blog",
"_type": "post",
"_id": "2",
"_score": 1,
"_source": {
"user": "mwanjura",
"postDate": "2014-08-12",
"body": "Distribution is hard. Distribution should be easy.",
"title": "On distributed search"
}
}
]
}
}
GUI's
You don't want to use curl on terminal the whole time?
http://elastichammer.exploringelasticsearch.com/


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--- layout: post status: publish published: true title: Movies author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 396 wordpress_url: http://winterdom.com/2005/01/movies date: '2005-01-02 19:25:25 +0000' date_gmt: '2005-01-02 19:25:25 +0000' categories: - Personal tags: [] comments: [] ---
Yesterday, my girlfriend and I went to the movies and saw Hero, with Jet Li. I really enjoyed the movie, and the scenery on it is just beatiful. We also saw the incredibles last week... really fun as well :)


nips nips2004 nips2004-191 nips2004-191-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Oliver Williams, Andrew Blake, Roberto Cipolla
Abstract: There has been substantial progress in the past decade in the development of object classiﬁers for images, for example of faces, humans and vehicles. Here we address the problem of contaminations (e.g. occlusion, shadows) in test images which have not explicitly been encountered in training data. The Variational Ising Classiﬁer (VIC) algorithm models contamination as a mask (a ﬁeld of binary variables) with a strong spatial coherence prior. Variational inference is used to marginalize over contamination and obtain robust classiﬁcation. In this way the VIC approach can turn a kernel classiﬁer for clean data into one that can tolerate contamination, without any speciﬁc training on contaminated positives. 1
[1] E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. Proc. Conf. Computer Vision and Pattern Recognition, pages 130– 136, 1997.
[2] H.A. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Transactions on Pattern Alaysis and Machine Intelligence, 20(1):23–38, 1998.
[3] S. Romdhani, P. Torr, B. Sch¨ lkopf, and A. Blake. Computationally efﬁcient face o detection. In Proc. Int. Conf. on Computer Vision, volume 2, pages 524–531, 2001.
[4] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. Conf. Computer Vision and Pattern Recognition, 2001.
[5] J. MacCormick and A. Blake. Spatial dependence in the observation of visual contours. In Proc. European Conf. on Computer Vision, pages 765–781, 1998.
[6] M.E. Tipping. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211–244, 2001.
[7] R. Kindermann and J.L. Snell. Markov Random Fields and Their Applications. American Mathematical Society, 1980.
[8] CBCL face database #1. MIT Center For Biological and Computation Learning: http://www.ai.mit.edu/projects/cbcl.
[9] B. Sch¨ lkopf and A. Smola. Learning with Kernels: Support Vector Machines, Rego ularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). MIT Press, 2001.
[10] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6(6):721–741, 1984.
[11] T. Jaakkola. Tutorial on variational approximation methods. In Advanced Mean Field Methods: Theory and Practice. MIT Press, 2000.
[12] T. Cover and J. Thomas. Elements of Information Theory. John Wiley & Sons, 1991.
[13] L. Saul, T. Jaakkola, and M. Jordan. Mean ﬁeld theory for sigmoid belief networks. Journal of Artiﬁcial Intelligence Research, 4:61–76, 1996.
[14] A.K. Jain. Fundamentals of Digital Image Processing. System Sciences. PrenticeHall, New Jersey, 1989.
[15] R. Lienhart and J. Maydt. An extended set of Haar-like features for rapid object detection. In Proc. IEEE ICIP, volume 1, pages 900–903, 2002.