If there is space, it can help to add a few cards to disrupt your opponent. These might be to help with certain match ups that would otherwise be difficult for your deck or more general cards to buy you an extra turn in any match up. There are several different ways you can disrupt your opponent (or they can disrupt you) and this guide will go through some of the best cards in the Standard format for disruption.
You don't want to make it easy for your opponent to setup their Pokémon and attack you. Changing their active Pokémon on them can help you take a quick knock out on a weak supporting Pokémon or stall for a few turns with one of their Pokémon which doesn't yet have the energy to attack. {{ 'B0128VBONK'|affiliate_link('Lysandre', page='detail') }} is a staple supporter with the ability to bring up any benched Pokémon to the active. Most decks include 2-3 copies. {{ 'B01MTGK8V8'|affiliate_link('Pokémon Catcher', page='detail') }} is an item with the same effect but requires heads on a coin flip. {{ 'B00S5LN2LK'|affiliate_link('Escape Rope', page='detail') }} allows you and your opponent to change their active but your opponent goes first. This can be useful for both getting something out of the way or bringing up something which isn't ready yet. {{ 'B01IUAFGEG'|affiliate_link('Captivating Poke Puff', page='detail') }} doesn't impact the active Pokémon but it can force your opponent to bench a Pokémon. This can be very disruptive for Pokémon such as Shaymin-EX or Hoopa-EX which have abilities that activate when they are played to the bench normally but won't activate this way.
{{ display_examples([('B0128VBONK', 'Lysandre'), ('B01MTGK8V8', 'Catcher'), ('B00S5LN2LK', 'Rope'), ('B01IUAFGEG', 'Poke Puff')]) }}{{ 'B0128VBALG'|affiliate_link('Hex Maniac', page='detail') }} is a disruptive supporter which stops all Pokémon abilities for a turn. This can slow down damage you opponent can do with their attacks next turn or slow down their ability to setup without supporting Pokémon. {{ 'B00S5LNG2K'|affiliate_link('Silent Lab', page='detail') }} is a stadium with a similar effect but it only impacts basic Pokémon. Also since it's a stadium your opponent has the opportunity to replace it with their own stadium, if they have one. But it will last as long as it's in play. {{ 'B016VKC9UG'|affiliate_link('Parallel City', page='detail') }} is another disruptive stadium with two choices. Either it can limit your opponent's bench size to only three Pokémon or it can reduce damage from grass, fire, and water Pokémon by 20. Be careful though because one side faces your opponent and the other faces you. Still either side can be valuable depending the match up.
{{ display_examples([('B0128VBALG', 'Hex'), ('B00S5LNG2K', 'Lab'), ('B016VKC9UG', 'Parallel') ]) }}While it doesn't exactly disrupt the Pokémon themselves, {{ 'B072BGM813'|affiliate_link('Field Blower', page='detail') }} can remove tools attached to them. This can be helpful for removing Fighting Fury Belt, Choice Band, Exp Share, and Float Stone that your opponent has setup.
{{ display_examples([('B072BGM813', 'Field Blower'), ]) }}Since you can only attach a single energy per turn, losing energy off of the board can set really set you back and cause you to miss a turn of attacking. Decks that can accelerate energy from the discard pile or have energy recovery can still bounce back but other decks can simply run out of energy with enough disruption. {{ 'B01NB0E4RO'|affiliate_link('Crushing Hammer', page='detail') }} can remove any energy attached to any Pokémon but it requires flipping heads. {{ 'B00OZ35BJ8'|affiliate_link('Enhanced Hammer', page='detail') }} doesn't require a coin flip but it can only discard special energy so it won't be useful in every matchup. {{ 'B01BZE98NO'|affiliate_link('Team Flare Grunt', page='detail') }} is a supporter which discards any energy but only from the active Pokémon.
{{ display_examples([('B01NB0E4RO', 'Crushing'), ('B00OZ35BJ8', 'Enhanced'), ('B01BZE98NO', 'Flare Grunt') ]) }}There are a number of different Pokémon which discard energy with attacks. Of those, {{ 'B01AWTP2B0'|affiliate_link('Jirachi (XY67)', page='detail') }} is the most easily included into decks because it has an attack for a single colorless energy which can discard special energy. {{ 'B01M4QKHYJ'|affiliate_link('Raticate (Evolutions)', page='detail') }} has two single-energy colorless attacks. The first discards any energy and the second does more damage for the amount of special energy in the discard. Since it's a stage one, it takes up more spaces in the deck and since it's a Pokémon it can be knocked out and give up a prize.
{{ display_examples([('B01AWTP2B0', 'Jirachi'), ('B01M4QKHYJ', 'Raticate')]) }}So far this guide has focused on cards that are already active in play, but you can also disrupt your opponent's hand and their ability to play cards as well. {{ 'B01EU44SKO'|affiliate_link('N', page='detail') }} is a supporter played for early game draw support and late in the game it can disrupt your opponent if they've taken most of their prizes. {{ 'B0128VAIT6'|affiliate_link('Ace Trainer', page='detail') }} has as similar disruptive effect giving the player that is ahead only three cards instead of six but since it can't be played if you're ahead or tied on prizes, N is generally favored.
{{ display_examples([('B01EU44SKO', 'N'), ('B0128VAIT6', 'Ace Trainer'), ]) }}{{ 'B06VSJMR3R'|affiliate_link('Gumshoos-GX (Sun & Moon)', page='detail') }} has the ability "Search the Premises" which allows you to look at your opponent's hand once per turn. This can synergize well with trainers such as {{ 'B01MUJ8TOH'|affiliate_link('Team Skull Grunt', page='detail') }} and {{ 'B01IUAFGEG'|affiliate_link('Captivating Poke Puff', page='detail') }} to force them to discard energy or bench Pokémon early. When there is a stadium in play and your opponent has a low hand size, {{ 'B01B3LEY6K'|affiliate_link('Delinquent', page='detail') }} can force them to get rid of all of their cards. This will leave them hoping to draw something quickly to let them get more cards.
{{ display_examples([('B06VSJMR3R', 'Gumshoos'), ('B01MUJ8TOH', 'Skull'), ('B01IUAFGEG', 'Poke Puff'), ('B01B3LEY6K', 'Delinquent'), ]) }}Along with these trainers, there are Pokémon with hand-disruptive attacks or abilities. {{ 'B0128V8WDK'|affiliate_link('Vileplume (Ancient Origins)', page='detail') }} has the "Irriating Pollen" ability which stops both players from playing any item cards. {{ 'B0128VEJN2'|affiliate_link('Giratina-EX (Ancient Origins)', page='detail') }} has the attack "Chaos Wheel" which prevents your opponent from playing any tools, special energy, or stadiums on their next turn. As long as you can keep the attack going, the lock remains in place. This can lock in a stadium of your choice or hurt decks which rely on special energy for energy acceleration.
{{ display_examples([('B0128V8WDK', 'Vileplume'), ('B0128VEJN2', 'Giratina'), ]) }}So far this guide has looked at cards to disrupt cards in play, either in the hand or on the table. There are some disruption cards which allow you discard directly from the deck so your opponent never has the chance to draw them. Without sufficient recycle cards this can leave your opponent without important cards. Of course if your opponent can't draw a card to start their turn then they lose the game. {{ 'B016VKAIMC'|affiliate_link('Houndoom-EX (BREAKthrough)', page='detail') }} and {{ 'B00S5LMUDG'|affiliate_link('Bunnelby (Primal Clash)', page='detail') }} each have attacks which allow you to discard up to two cards per turn. Neither of these do any damage so they are usually only played in deck which don't intend to take any prizes and instead try to run their opponent out of cards. {{ 'B01EU455NI'|affiliate_link('Team Rocket\'s Handiwork', page='detail') }} is a supporter with the same effect. It can discard up to four cards based on coin flips.
{{ display_examples([('B016VKAIMC', 'Houndoom'), ('B00S5LMUDG', 'Bunnelby'), ('B01EU455NI', 'Team Rocket'), ]) }}Adding element of disruption to a deck can help swing a game or two. Unless your deck is going for full disruption, you don't want to add too much and hurt the consistency of your own setup. Many decks find a space for a couple disruption cards to buy an extra turn of attacking, taking a knock out on an important Pokémon, or taking the last prize on an easy target.


A
-
Used by Gracenote Three-Line-Solution (TLS) for classical music. In most cases, a classical album’s title is comprised of the composer(s) and work(s) that are featured on the product, which yields a single entity in the album name display. However, for albums that have formal titles (unlike composers and works), the title is listed as it is on the product. General title example: Beethoven: Violin Concerto. Formal title example: The Best Of Baroque Music.
-
An Activity Object is one of the building blocks of an Android application. An Activity provides the application with context to render to the screen. An Android application typically has at least one Activity object.
-
Used by Gracenote Three-Line-Solution (TLS) for classical music. A consistent format is used for listing a recording artist(s)—by soloist(s), conductor, and ensemble(s)—which yields a single entity in the artist name display. For example: Hilary Hahn; David Zinman: Baltimore Symphony Orchestra
C
-
Each customer receives a unique Client ID string from Gracenote. This string uniquely identifies each application and lets Gracenote deliver the specific features for which the application is licensed.
The Client ID string has the following format:<PHONE_NUMBER>23456789012312. The first part is a six-digit Client ID, and the second part is a 17-digit Client ID Tag.
-
Used by Gracenote Three-Line-Solution (TLS) for classical music. The composer(s) that are featured on an album are listed by their last name in the album title (where applicable). In the examples noted below, the composer is Beethoven.
-
A Contributor refers to any person who plays a role in an AV Work. Actors, Directors, Producers, Narrators, and Crew are all consider a Contributor. Popular recurring Characters such as Batman, Harry Potter, or Spider-man are also considered Contributors in Video Explore.
-
A credit lists the contribution of a person (or occasionally a company, such as a record label) to a recording. Generally, a credit consists of:
The name of the person or company.
The role the person played on the recording (an instrument played, or another role such as composer or producer).
The tracks affected by the contribution.
A set of optional notes (such as “plays courtesy of Capitol records”).
See Role
F
-
Term used to encompass a particular media stream's characteristics and attributes; this is metadata and information accessed from processing a media stream. For example, when submitting an Album's Track, this includes information such as fingerprint, mood, and tempo metadata (Attributes).
-
A unique representation of a sample of audio that can be used to identify a track.
G
-
A selection rule for determining which tracks to add to a playlist.
-
Short for playlist generator. This is a term used to indicate that an artist has at least one of the extended metadata descriptors populated. The metadata may or may not be complete, and the artist text may or may not be locked or certified. See also: extended metadata, playlist optimized artist.
-
A categorization of a musical composition characterized by a particular style.
L
-
A module available in the Desktop and Auto products that allows applications to access and present enriched content related to media that has been identified using identification features.
M
-
A manual playlist is a playlist that the user has created by manually selecting tracks and a play order. An auto-playlist is a playlist generated automatically by software. This distinction only describes the initial creation of the playlist; once created and saved, all that matters to the end-user is whether the playlist is static (and usually editable) or dynamic (and non-editable). All manual playlists are static; the track contents do not change unless the end-user edits the playlist. An auto-playlist may be static or dynamic.
-
Data about data. For example, metadata such as the artist, title, and other information about a piece of digital audio such as a song recording.
-
Track-level perceptual descriptor of a piece of music, using emotional terminology that a typical listener might use to describe the audio track; includes hierarchical categories of increasing granularity. See Sonic Attributes.
-
A mechanism for quickly generating playlists from a user’s music collection based on their similarity to a designated seed track, album, or artist.
-
During CD recognition, the case where a single TOC from a CD may have more than one exact match in the MDB. This is quite rare, but can happen. For example with CDs that have only one track, it is possible that two of these one-track CDs may have exactly the same length (the exact same number of frames). There is no way to resolve these cases automatically as there is no other information on the CD to distinguish between them. So the user must be presented with a dialog box to allow a choice between the alternatives. See also: frame, GN media database, TOC.
-
Enables MusicID recognition for identifying CDs, digital music files and streaming audio and delivers relevant metadata such as track titles, artist names, album names, and genres. Also provides library organization and direct lookup features.
-
A feature of the MusicID product that identifies digital music files using a combination of waveform analysis technology, text hints and/or text lookups.
P
-
Pulse Coded Modulation is the process of converting an analog in a sequence of digital numbers. The accuracy of conversion is directly affected by the sampling frequency and the bit-depth. The sampling frequency defines how often the current level of the audio signal is read and converted to a digital number. The bit-depth defines the size of the digital number. The higher the frequency and bit-depth, the more accurate the conversion.
-
PCM data generated from an audio signal.
-
A set of tracks from a user’s music collection, generated according to the criteria and limits defined by a playlist generator.
-
Popularity is a relative value indicating how often metadata for a track, album, artist and so on is accessed when compared to others of the same type. Gracenote’s statistical information about the popularity of an album or track, based on aggregate (non-user-specific) lookup history maintained by Gracenote servers. Note that there’s a slight difference between track popularity and album popularity statistics. Track popularity information identifies the most popular tracks on an album, based on text lookups. Album popularity identifies the most frequently looked up albums, either locally by the end-user, or globally across all Gracenote users. See Rating and Ranking
R
-
For Playlist, ranking refers to any criteria used to order songs within a playlist. So a playlist definition (playlist generator) may rank songs in terms of rating values, or may rank in terms of some other field, such as last-played date, bit rate, etc.
-
Rating is a value assigned by a user for the songs in his or her collection. See Popularity and Ranking.
-
A role is the musical instrument a contributor plays on a recording. Roles can also be more general, such as composer, producer, or engineer. Gracenote has a specific list of supported roles, and these are broken into role categories, such as string instruments, brass instruments. See Credit.
S
-
Used by playlist definitions to generate a new playlist of songs that are related in some way, or similar, to a certain artist, album, or track.This is a term used to indicate that an artist has at least one of the extended metadata descriptors populated. The metadata may or may not be complete, and the artist text may or may not be locked or certified. See also: extended metadata, playlist optimized artist, playlist-related terms.
-
The Gracenote Service provides two metadata fields that describe the sonic attributes of an audio track. These fields, mood and tempo, are track-level descriptors that capture the unique characteristics of a specific recording. Mood is a perceptual descriptor of a piece of music, using emotional terminology that a typical listener might use to describe the audio track. Tempo is a description of the overall perceived speed or pace of the music. The Gracenote mood and tempo descriptor systems include hierarchical categories of increasing granularity, from very broad parent categories to more specific child categories.
T
-
The track attribute used by a generation criterion for selecting tracks to include in a generated playlist.
-
Track-level descriptor of the overall perceived speed or pace of the music; includes hierarchical categories of increasing granularity. See Sonic Attributes.
-
GNSDK support for classical music metadata. Gracenote TLS provides the four basic classical music metadata components—composer, album name, artist name, and track name.
-
See Three-Line-Solution.
-
Table of Contents. An area on CDs, DVDs, and Blu-ray discs that describes the unique track layout of the disc.
-
Used by Gracenote Three-Line-Solution (TLS) for classical music. A consistent format is used for listing a track title—composer, work title, and (where applicable) movement title—which yields a single entity in the track name display. For example: Beethoven: Violin Concerto In D, Op. 61 – 1. Allegro Ma Non Troppo


Money Play. How should Red play 62?
pips Red=158 White=175
(CorrectAnswer= 0/0, ErrorScore= 0)
The first inclination is to hit with the 6 and then look for a 2. However, any hitting play is an error and some of them are blunders.
Red is ahead in the race and outboarded so logic says he should race. White has only seven checkers in the attack zone. All of this points to a running play. 22/14 is reasonable but slightly better is 22/16, 13/11. This keeps the rear checkers in communication and there is meaningful triplication of fours.
Not an immediately obvious play but once you see 22/16, 13/11 the more you appreciate it.
Red is ahead in the race and outboarded so logic says he should race. White has only seven checkers in the attack zone. All of this points to a running play. 22/14 is reasonable but slightly better is 22/16, 13/11. This keeps the rear checkers in communication and there is meaningful triplication of fours.
Not an immediately obvious play but once you see 22/16, 13/11 the more you appreciate it.
1. Cubeful 2-ply 8/5 Eq.: -0.356 0.405 0.103 0.002 - 0.595 0.139 0.005 2. Cubeful 2-ply 13/11 6/5 Eq.: -0.418 (-0.062) 0.405 0.101 0.003 - 0.595 0.175 0.009 3. Cubeful 2-ply 13/10 Eq.: -0.421 (-0.065) 0.389 0.092 0.002 - 0.611 0.139 0.004 4. Cubeful 2-ply 10/9 8/6 Eq.: -0.451 (-0.095) 0.383 0.092 0.002 - 0.617 0.143 0.005 5. Cubeful 2-ply 10/8 6/5 Eq.: -0.457 (-0.101) 0.379 0.087 0.002 - 0.621 0.133 0.004


Flat navigation
Sometimes viewers don’t understand the whole concept of vertical navigation for nested slides.
A solution would be to limit your presentation to horizontal slides only, but you might still want to use ‘chapters’.
Simplemenu has a solution for that. Make your presentation in a flat structure (no nested sections) and turn the flat
option to true
. Then, if a data-name is set for a slide, any following slides will keep that menu name.
Of course, you can set it to stop inheriting the name as well.
Set the flat option to true
:
…
Reveal.initialize({
simplemenu: {
flat: true
},
plugins: [ Simplemenu ]
Build your presentation in a flat way (no nested slides).
Then give your sections a data-name
:

//...

//... The 'Chapter 1' menu-item will be active for this slide

//... The 'Chapter 1' menu-item will be active for this slide

Any slides following a slide with a data-name will keep that menu name.
To stop inheriting the previous slide menu name, start a new named section, or add data-sm="false"
to your slide.

//...

//... The 'Chapter 1' menu-item will be active for this slide

//... No menu-item will be active for this slide


My Faculty
Dashboard
Departments
Settings
Exit
Type of page
Menu
Terms
Department menu
Department page


Class AuthorizationResponse
An authorization response.
Implements
Inherited Members
Namespace: JetBlack.MessageBus.Messages
Assembly: JetBlack.MessageBus.Messages.dll
Syntax
public class AuthorizationResponse : Message, IEquatable<AuthorizationResponse>
Constructors
| Improve this Doc View SourceAuthorizationResponse(Guid, String, String, Boolean, ISet<Int32>)
Construct an authorization response.
Declaration
public AuthorizationResponse(Guid clientId, string feed, string topic, bool isAuthorizationRequired, ISet<int> entitlements)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Guid | clientId | The client id. |
| System.String | feed | The feed name. |
| System.String | topic | The topic name |
| System.Boolean | isAuthorizationRequired | Indicates whether entitlements are required. |
| System.Collections.Generic.ISet<System.Int32> | entitlements | The entitlements. |
Properties
| Improve this Doc View SourceClientId
The client id.
Declaration
public Guid ClientId { get; }
Property Value
| Type | Description |
|---|---|
| System.Guid |
Entitlements
The entitlements for an authorized feed/topic.
Declaration
public ISet<int> Entitlements { get; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.ISet<System.Int32> |
Feed
The name of the feed.
Declaration
public string Feed { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
IsAuthorizationRequired
If true then authorization is required, so entitlements should be used.
Declaration
public bool IsAuthorizationRequired { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Topic
The name of the topic.
Declaration
public string Topic { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Methods
| Improve this Doc View SourceEquals(AuthorizationResponse)
Tests for equality.
Declaration
public bool Equals(AuthorizationResponse other)
Parameters
| Type | Name | Description |
|---|---|---|
| AuthorizationResponse | other | Thje message to test. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the messages are equal. |
Equals(Object)
Test for equality.
Declaration
public override bool Equals(object obj)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Object | obj | The object to compare. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the objects are equal. |
Overrides
GetHashCode()
Returns the hash code for this instance.
Declaration
public override int GetHashCode()
Returns
| Type | Description |
|---|---|
| System.Int32 | A 32-bit signed integer hash code. |
Overrides
ReadBody(DataReader)
Read the message body.
Declaration
public static AuthorizationResponse ReadBody(DataReader reader)
Parameters
| Type | Name | Description |
|---|---|---|
| DataReader | reader | The reader. |
Returns
| Type | Description |
|---|---|
| AuthorizationResponse | The authorization response. |
ToString()
Converts the value of the current object to it's equivalent string representation.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String | A string representation of the current object. |
Overrides
| Improve this Doc View SourceWrite(DataWriter)
Write a message.
Declaration
public override DataWriter Write(DataWriter writer)
Parameters
| Type | Name | Description |
|---|---|---|
| DataWriter | writer | The message writer. |
Returns
| Type | Description |
|---|---|
| DataWriter | The message writer. |


photo_camera
Cameras
No cameras found
{{ formatName(camera.name) }}
{{ formatName(camera.name) }}
Attendance
save_alt
No scans yet
Date:
Time:
{{ scan.content }}
Project by A.Adarsh(17281A0525) under guidance of N.Raghu Asst.prof in CSE.


This module is dedicated to the development of a Database for frequency-based time-series (Waveforms), low-frequency time-series (numerics), annotations and metadata about the data source.
Why not using an already existing Database for that? Actually, pancarte-db is constructed on top of two other existing databases (see the schema bellow) to create a unique hybrid database capable of:
- real-time insertions
- fast GET with filtering (historical view on the pancarte Visualisation WebApp)
- big-data analysis with Hadoop (real-time statistics, data science, machine learning)
Source code: github.com/EDS-APHP/pancarte-db
The interaction with the Pancarte DB is done via the HTTP REST API.
Annotations and metadata are stored inside what we called a "Mutable Store", it is in fact a RDBMS.
Waveforms and numerics are stored inside what we called an "Immutable Store", it is in fact a columnar file format such as Parquet, ORC, or even Apache Phoenix.


--- layout: article publication-date: 2016-10-16 title: Metamaterial Mechanisms authors: - Alexandra Ion - Johannes Frohnhofen - Ludwig Wall - Robert Kovacs - Mirela Alistar - Jack Lindsay - Pedro Lopes - Hsiang-Ting Chen - Patrick Baudisch venue: In Proceedings of UIST'16. Tokyo, Japan, October 16 – 19, 2016 type: - Conference - Full Paper - Peer-reviewed awards: - Best Paper Honorable Mention (top 2%) tags: - Metamaterial - Mechanism - Fabrication - 3D printing video-thumb: "https://youtu.be/lsTiWYSfPck" video: "https://youtu.be/lsTiWYSfPck" image: teaser.jpg pdf: 2016UIST-Metamaterial-Mechanisms-authors-copy.pdf doi: https://dl.acm.org/doi/10.1145/2984511.2984540 external-urls: - https://github.com/jfrohnhofen/metamaterial-mechanisms - https://jfrohnhofen.github.io/metamaterial-mechanisms/ - https://www.thingiverse.com/hassoplattnerinstitute_hci/collections/metamaterial-mechanisms - https://youtu.be/y3_cKICnUvg - https://drive.google.com/uc?export=download&id=17KI8VWj0shfBb8MsBnCYkIzhwXamV4Av external-names: - source code - try our editor - 3D models - talk - press images (113 MB) external-types: - git-code - deploy - link - video - download page-subtitle: "So far, metamaterials were understood as materials—we want to think of them as machines." ---
Recently, researchers started to engineer not only the outer shape of objects, but also their internal microstructure. Such objects, typically based on 3D cell grids, are also known as metamaterials. Metamaterials have been used,for example, to create materials with soft and hard regions.
So far, metamaterials were understood as materials—we want to think of them as machines. We demonstrate metamaterial objects that perform a mechanical function. Such metamaterial mechanisms consist of a single block of material the cells of which play together in a well-defined way in order to achieve macroscopic movement. Our metamaterial door latch, for example, transforms the rotary movement of its handle into a linear motion of the latch.
Our metamaterial Jansen walker consists of a single block of cells—that can walk. The key element behind our metamaterial mechanisms is a specialized type of cell, the only ability of which is to shear.
In order to allow users to create metamaterial mechanisms efficiently we implemented a specialized 3D editor. It allows users to place different types of cells, including the shear cell, thereby allowing users to add mechanical functionality to their objects. To help users verify their designs during editing, our editor allows users to apply forces and simulates how the object deforms in response.


The image collection is a major hurdle for OpenSeadragon. It allows arbitrarily large sets of tile sources, of mixed types, to be used to easily visualize the group as 'wall' or 'grid'. Many core features are available for preview now, though the
No doubt this makes OpenSeadragon into a whole new platform for deepzoom image visualization.
Just a simple row of tile sources visualized as a collection.
OpenSeadragon({ ... collectionMode: true, collectionRows: 1, collectionTileSize: 1024, collectionTileMargin: 256, ... });
Multiple rows of tile sources visualized as a collection.
OpenSeadragon({ ... collectionMode: true, collectionRows: 2, collectionTileSize: 1024, collectionTileMargin: 256, ... });
Multiple columns of tile sources visualized as a collection.
OpenSeadragon({ ... collectionMode: true, collectionRows: 2, collectionTileSize: 1024, collectionTileMargin: 256, collectionLayout: 'vertical', ... });


This vignette introduces the mashr
software for someone who is familiar with the basic idea in Urbut et al., which we strongly recommend reading before proceeding.
In this first vignette we use only the “canonical” covariance matries to simplify presentation. However, an important aspect of mashr
is the use of data-driven covariance matrices that are learned from the data, so when you are done here you should look at the data-driven vignette and the non-canonical matrix vignette. If you think you may have correlations in your data measurements then also look at how to incorporate correlations.
This package implements the “multivariate adaptive shrinkage” (mash) method from Urbut et al..
Mash is a method for dealing with large numbers of tests in many (e.g. dozens) different conditions. Here “conditions” can mean many different things: it could be different tissues (as in Urbut et al), or different time points, or different treatments, or even different phenotypes (think of testing multiple phenotypes at many genetic variants in a GWAS).
There are essentially four steps to a mash analysis
Here we go through an example that illustrates each of these steps in turn. In this example we use the same (simulated) data at each step. In complex practical applications – especially very large applications – one might want to use different subsets of the data at different steps. For example, one might only want to extract posterior summaries (step 4) for a subset of the tests. We will deal with such complications in later vignettes. For now, we note the one crucial rule:
The Crucial Rule: Step 3 (fitting the mash
model) must be performed with either all the tests you performed, or – if that causes computational challenges – a large random subset of tests.
In particular, you must not use only a set of selected “significant” or “strong” tests in step 3. This is because mash
uses step 3 to learn about the null signal in the data, as well as the non-null signal. In particular, in settings where most tests are null or nearly-null, this is the point where mash
learns this, and consequently “shrinks” (“corrects”) the posterior estimates towards 0. (Effectively this step is analogous to a “multiple testing correction” step.)
First we simulate some data for illustration.
library(ashr)
library(mashr)
set.seed(1)
simdata = simple_sims(500,5,1)
This simulation routine creates a dataset with 5 conditions, and four different types of effect: null, independent among conditions, condition-specific in condition 1, and shared (equal effects in all conditions). It creates 500 effects of each type for a total of 2000 effects.
To run mash
you need data consisting of a matrix of effects (Bhat
) and a matrix of standard errors (Shat
), for \(J\) effects (rows) in \(R\) conditions (columns).
[If you have only access to \(Z\) scores, you can set Bhat
to the Z scores, and set Shat
to be the matrix with all 1s].
The simulation above created both these matrices for us (in simdata$Bhat
and simdata$Shat
). To get these ready for applying mash
you must first use mash_set_data
to create a data object with those two pieces of information:
data = mash_set_data(simdata$Bhat, simdata$Shat)
There are two types of covariance matrix you can use in mash
: “canonical” and “data-driven”. The canonical ones are very easy to set up and so we use those here for illustration. However, in applications you will likely also want to use data-driven matrices, and this is an important feature of mash
. See the data-driven vignette for more details on how to do this.
The function to set up canonical covariance matries is cov_canonical
. The following sets up canonical covariances in U.c
(we used .c
to indicate canonical), which is a named list of matrices.
U.c = cov_canonical(data)
print(names(U.c))
# [1] "identity" "condition_1" "condition_2" "condition_3"
# [5] "condition_4" "condition_5" "equal_effects" "simple_het_1"
# [9] "simple_het_2" "simple_het_3"
Having set up the data and covariance matrices you are ready to fit the model using the mash
function:
m.c = mash(data, U.c)
# - Computing 2000 x 151 likelihood matrix.
# - Likelihood calculations took 0.05 seconds.
# - Fitting model with 151 mixture components.
# - Model fitting took 163.73 seconds.
# - Computing posterior matrices.
# - Computation allocated took 0.10 seconds.
This can take a little time. What this does is to fit a mixture model to the data, estimating the mixture proportions. Specifically the model is that the true effects follow a mixture of multivariate normal distributions: \(B \sim \sum_k \sum_l \pi_{kl} N(0, \omega_l U_k)\) where the \(\omega_l\) are scaling factors set by the “grid” parameter in mash
and the \(U_k\) are the covariance matrices (here specified by U.c
).
Remember the Crucial Rule! This step must be peformed using all the tests (or a large random subset), because this is where mash
learns that many tests are null and corrects for it.
You can extract estimates (posterior means and posterior standard deviations) and measures of significance (local false sign rates) using functions like get_pm
(posterior mean), get_psd
(posteriore standard deviation) and get_lfsr
(local false sign rate):
head(get_lfsr(m.c))
# condition_1 condition_2 condition_3 condition_4 condition_5
# effect_1 0.7058731 0.6550167 0.6823369 0.6702674 0.7028355
# effect_2 0.6741736 0.6068573 0.6918971 0.5786526 0.6047107
# effect_3 0.6937052 0.6234818 0.6818834 0.7244426 0.7367592
# effect_4 0.7098717 0.7123390 0.6704936 0.7315070 0.7077594
# effect_5 0.7176814 0.6829167 0.7368497 0.7359613 0.7333636
# effect_6 0.7051578 0.6050047 0.6984023 0.5944263 0.6731436
head(get_pm(m.c))
# condition_1 condition_2 condition_3 condition_4 condition_5
# effect_1 0.03167247 -0.09682315 -0.063257585 0.0880400766 -0.036108241
# effect_2 -0.02754472 -0.13133115 -0.015955629 -0.1754886124 -0.135294626
# effect_3 -0.04826380 0.15185915 -0.072980287 0.0176872821 -0.001938137
# effect_4 -0.02804483 0.02248847 0.074639681 0.0015987191 0.028993822
# effect_5 0.01592609 -0.06100623 0.004101135 -0.0009937698 -0.002082982
# effect_6 -0.03349555 0.15579085 -0.052166453 0.1758214687 0.059850874
head(get_psd(m.c))
# condition_1 condition_2 condition_3 condition_4 condition_5
# effect_1 0.3275706 0.3646973 0.3190687 0.3747228 0.2995326
# effect_2 0.3481994 0.3731738 0.3353865 0.4250630 0.3764221
# effect_3 0.3344089 0.4561742 0.3425730 0.2900998 0.2905154
# effect_4 0.3170211 0.2775196 0.3203516 0.2721282 0.2780047
# effect_5 0.2975991 0.3142275 0.2666648 0.2655621 0.2691009
# effect_6 0.3662871 0.4250229 0.3664854 0.4497329 0.3278061
Each of these are \(J \times R\) matrices.
Use get_significant_results
to find the indices of effects that are “significant”, which here means they have lfsr less than t in at least one condition, where t is a threshold you specify (default 0.05). The output is ordered from most significant to least significant.
head(get_significant_results(m.c))
# effect_1800 effect_1812 effect_1857 effect_1953 effect_1915 effect_1713
# 1800 1812 1857 1953 1915 1713
print(length(get_significant_results(m.c)))
# [1] 138
You can also get the significant results in just a subset of conditions. For example
print(head(get_significant_results(m.c, conditions=1)))
# effect_1812 effect_1726 effect_1501 effect_1993 effect_1800 effect_1953
# 1812 1726 1501 1993 1800 1953
Use get_loglik
to find the log-likelihood of the fit (this will only be useful when you have other fits to compare it with!)
print(get_loglik(m.c))
# [1] -16192.62
Use get_estimated_pi
to extract the estimates of the mixture proportions for different types of covariance matrix:
print(get_estimated_pi(m.c))
# null identity condition_1 condition_2 condition_3
# 0.02557146 0.08721613 0.11577348 0.09028063 0.08347747
# condition_4 condition_5 equal_effects simple_het_1 simple_het_2
# 0.08349696 0.08789045 0.12852902 0.09153850 0.09801499
# simple_het_3
# 0.10821026
barplot(get_estimated_pi(m.c),las = 2)
Here we can see most of the mass is on the null, identity, singletons_1
(which corresponds to effects that are specific to condition 1) and equal_effects
. This reassuringly matches the way that these data were generated.
The following produces a meta-plot based on the posterior means and posterior variances of an effect. Here we look at the most significant result.
mash_plot_meta(m.c,get_significant_results(m.c)[1])
print(sessionInfo())
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.6
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] mashr_<IP_ADDRESS>1 ashr_2.2-35
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.1 knitr_1.20 magrittr_1.5
# [4] MASS_7.3-48 doParallel_1.0.11 pscl_1.5.2
# [7] SQUAREM_2017.10-1 lattice_0.20-35 foreach_1.4.4
# [10] plyr_1.8.4 stringr_1.3.1 tools_3.4.3
# [13] parallel_3.4.3 grid_3.4.3 rmeta_3.0
# [16] htmltools_0.3.6 iterators_1.0.9 assertthat_0.2.0
# [19] abind_1.4-5 yaml_2.2.0 rprojroot_1.3-2
# [22] digest_0.6.17 mixsqp_0.1-116 Matrix_1.2-12
# [25] codetools_0.2-15 evaluate_0.11 rmarkdown_1.10
# [28] stringi_1.2.4 compiler_3.4.3 backports_1.1.2
# [31] mvtnorm_1.0-8 truncnorm_1.0-8


Overview
lua-CoatPersistent is an Object-Relational Mapping for
lua-Coat.
It is built over the modules LuaSQL
and Dado.
It could support all database engine which has a driver in LuaSQL.
Lineage
lua-CoatPersistent is a Lua port of Coat::Persistent, a Perl module, which is inspired by Ruby on Rails Active Record.
Status
lua-CoatPersistent is in early stage.
It was developed for Lua 5.1 & 5.2.
Documentation
After these HTML files, there are many unit tests in test/.
And finally, there are the code in src/ where you could check the availability of a feature.
Download
lua-CoatPersistent source can be downloaded from GitHub.
Installation
lua-CoatPersistent is available via LuaRocks:
luarocks install lua-coatpersistent
or manually, with:
make install
Test
The test suite requires the module lua-TestMore and LuaSQL-SQLite3.
make test
Copyright and License
Copyright © 2010-2013 François Perrad
This library is licensed under the terms of the MIT/X11 license, like Lua itself.


created: 01/20/02; small additions 02/23/02, 02/17/03
This chapter introduces a new graphical component—radio buttons—and a new layout manager, BorderLayout.
BorderLayout
Box
How many stations should your radio be tuned to at one time?


Work
|
XPeng Motors
Guangzhou, China
Big Data Architect - platform team
Jun 2021 - Present
-
Built a real-time autonomous driving data processing platform that provides comprehensive
data processing services for domestic and international collection vehicles and user
vehicles. The platform handles various types of data, including images, videos, LiDAR, and
Canbus, processing over 60TB of data on average per day while ensuring processing time is
less than 12 hours. The platform efficiently handles data ingestion, cleaning, filtering,
and anonymization from multiple data sources, sensors, and scenarios, ensuring downstream
users can quickly access accurate and compliant data. Committed to providing efficient and
reliable data processing solutions for users.
-
The data collected from collection-vehicles serves as cold-start data for the perception
team to establish a foundational model. On the other hand, user-vehicles remotely trigger
events when they encounter corner cases or failures of NGP(Navigate on Autopilot) that
require user intervention. These trigger events capture the scene at that precise moment and
encompass images, videos, LiDAR, Canbus data, text logs, and DDS data results from the
vehicle
model output. The recorded data is then transmitted to the cloud server.
Within the cloud, the data processing platform injects this data into a simulation system,
enabling the perception team to reconstruct and comprehend the scenarios. This process
effectively tackles the long-tail effect of perception models and significantly contributes
to enhancing KPIs
-
Built a cloud-based pipeline utilizing Unreal Engine (UE) for the generation of synthetic
data. This pipeline efficiently generates a substantial amount of relevant datasets,
focusing on specialized corner cases. By complementing real-world collected data, it
effectively enhances model KPIs while simultaneously reducing the overall costs associated
with data collection.
-
Developed the company's first image and video data anonymization system, implemented
both on the cloud and vehicle side, and applied it to data collection tasks both
domestically and internationally. This system plays a crucial role in protecting user
privacy by ensuring sensitive data undergoes appropriate handling and protection(1 patent,
company's quality project award)
-
By leveraging CDC(Change Data Capture) technology, aggregate data from various business
systems, including data pipelines, map POIs(Points of Interest), annotation platforms, and
full-stack testing management, ensuring compatibility with multiple data types. We then
analyze
and refine the aggregated data to provide users with valuable insights.
-
As a Technical Lead, I have been involved in designing parts of the data flow architecture,
optimizing data quality, and reducing costs. My role includes project delivery, coordinating
team resources, and minimizing technical debt.
- Keywords: Autonomous Driving Big Data Processing
Grab
Singapore
Big Data Architect - traffic team
Jun 2019 - Jun 2021
-
Established a real-time geospatial traffic flow platform that covers over 100 cities
in Southeast Asia by integrating real-time location data from drivers and delivery riders.
This platform provides real-time traffic conditions, facilitates optimal route planning,
estimates arrival time (ETA), and offers fare prediction services for our ride-hailing and
food delivery core businesses
-
Performed a major reconstruction of the real-time traffic computation system by
modularizing and separating each component. Built up the capability to quickly
integrate third-party map data sources, such as Google Maps, Here Maps, and SKT Maps.
Additionally, implemented the entire continuous delivery process based on
Kubernetes, ensuring efficient and streamlined deployment of updates and new features
-
By decoupling offline data from real-time computation through source modularization, the
system's complexity and operational costs were reduced. This implementation introduced
real-time aggregation of vehicle GPS data and watermark calculations, leading to enhanced
ETA accuracy and fair billing
- Keywords: Traffic Flow Big Data Processing
Tencent
Shenzhen, China
Big Data Engineer - platform team
May 2018 - Jun 2019
-
Introduced automated sharding and indexing with hierarchical splitting, resulting in a 30%
improvement in log indexing speed for the game log retrieval platform, facilitating faster
data flow for user data
-
Designed a unified interface framework that enables routing, migration, hierarchical
structuring, scaling, monitoring, and fault recovery of game data across different database
components, reducing user complexity
-
Developed a management system for multiple database components, enhancing the efficiency of
automated database management and operations
-
Open-sourced a relatively independent and comprehensive Elasticsearch automation monitoring
dashboard
- Keywords: Gaming Big Data Storage
JD.com
Beijing, China
Big Data Engineer - ads quality team
Jul 2015 - Apr 2018
-
Designed an intelligent advertising bidding platform that incorporates a Multi-Touch
Attribution(MTA) model and confidence intervals, addressing the issue of sparse advertising
data across different dimensions. This platform enables advertisers to easily and
intelligently place advertisements, resulting in a 5% increase in Return on Investment(ROI)
for both the platform's internal and external pages(1 patent, company's gold project award)
-
Implemented a multi-dimensional valuation system that helps advertisers achieve automatic
differentiated bidding across various channels, allowing for precise allocation of their
advertising budgets.
-
Built a product retrieval system that integrates with WeChat's social users, achieving a
monthly Gross Merchandise Volume(GMV) of millions and attracting new users.
- Keywords: Advertising Big Data Retrieval
|


—— Web Design
Interactive Design
Hi! I'm John Snow.
A Product Designer & Developer focussing on interactive experiences, currently polishing skills in machine learning.
Read Full BioProjects Done:
1 3
Interactive Design
Projects Done:
1 3


How to Run Tests Silently and Query the Results
When you run a test suite using runtests, the results are summarized in the Command Window. This example shows you how to run a test suite so that nothing prints to the Command Window, and it shows you how to write a program to automatically determine the results of running the test suite.
There are four steps to follow.
1. Construct a TestSuite object. In this example we'll use the fromPwd method of the TestSuite class to construct a test suite using all the test cases found in the examples_general directory.
cd examples_general
suite = TestSuite.fromPwd();
You can look up information about the individual test cases.
suite.TestComponents{1}
ans = TestSuite handle Properties: TestComponents: {[1x1 TestUsingTestCase] [1x1 TestUsingTestCase]} Name: 'TestUsingTestCase' Location: 'C:\Work\matlab_xunit\doc\examples_general\TestUsingTestCase.m'
You can see above that the first test component in the test suite is itself another test suite, which contains the test cases defined by the M-file named TestUsingTestCase. Here's what one of these individual test cases looks like:
suite.TestComponents{1}.TestComponents{1}
ans = TestUsingTestCase handle Properties: fh: [] MethodName: 'testPointer' Name: 'testPointer' Location: 'C:\Work\matlab_xunit\doc\examples_general\TestUsingTestCase.m'
2. Construct a TestLogger object. This object can receive notifications about what happens when a test suite is executed.
logger = TestRunLogger;
3. Call the run method of the TestSuite object, passing it the logger.
suite.run(logger);
The TestLogger object can now be queried to determine what happened during the test.
logger
logger = TestRunLogger handle Properties: Log: {1x34 cell} NumFailures: 1 NumErrors: 1 NumTestCases: 8 Faults: [1x2 struct]
There were eight test cases run (logger.NumTestCases), resulting in one test failure and one test error. Detailed information about what went wrong can be found in logger.Faults.
logger.Faults(1)
ans = Type: 'failure' TestCase: [1x1 FunctionHandleTestCase] Exception: [1x1 MException]
logger.Faults(2)
ans = Type: 'error' TestCase: [1x1 FunctionHandleTestCase] Exception: [1x1 MException]
You can drill further to determine the names of the failing tests, as well as the complete stack trace associated with each failure.
logger.Faults(1).TestCase
ans = FunctionHandleTestCase handle Properties: MethodName: 'runTestCase' Name: 'testSinPi' Location: 'C:\Work\matlab_xunit\doc\examples_general\testBadSinTest.m'
logger.Faults(1).Exception.stack(1)
ans = file: 'C:\Work\matlab_xunit\doc\examples_general\testBadSinTest.m' name: 'testSinPi' line: 7
logger.Faults(1).Exception.stack(2)
ans = file: 'C:\Work\matlab_xunit\xunit\FunctionHandleTestCase.m' name: 'FunctionHandleTestCase.runTestCase' line: 112


This Campaign
Op-Ed
Press Release
Website
Videos
Infographics
Photos
Social Media
Broadcast
Print & Online


Copyright © 2011 Mark R. Greenstreet
Authors: Mark R. Greenstreet (mrg@cs.ubc.ca).
| arity/1 | Return the arity of Fun . |
| cumsum/1 |
Return a list where the N th element of
the result is the sum of the first N elements of
List . |
| cumsum/2 |
Return a list where the N th element of
the result is the Acc0 plus the sum of the first
N elements of List . |
| cut/2 | Break L into M, roughly equal length segments. |
| deal/2 | Deal the elements of M around the M lists of List_of_M_lists . |
| index/2 | Return the position of the first occurrence of Key
in List . |
| intervals/3 | Divide [Lo, Hi] into N roughly intervals of roughly equal size. |
| logsteps/2 |
For each M in Mant and each E in
Exp , M*10E is an element of
the return list. |
| mod/2 | like the rem BIF but result has same sign as M . |
| msg_dump/1 | Print the pending messages of a process -- for debugging. |
| msg_dump/2 | Print the pending messages of a process -- for debugging. |
| neighbours/1 | Return a list of tuples of all adjacent pairs of elements of list. |
| pow/2 | N raised to the M th power. |
| print/1 | Equivalent to print(Term, 100). |
| print/2 | Print an erlang term on standard out. |
| rlist/1 | Equivalent to rlist(N, 10). |
| rlist/2 | Return a list of N random elements. |
| rlist/3 | Return a list of N elements. |
| sync/1 | Equivalent to sync(P, infinity). |
| sync/2 | Synchronize with process P . |
| sync/3 | Equivalent to sync(P, infinity, SndToken, RcvToken). |
| sync/4 | Synchronize with process P . |
arity(F::Fun) -> integer()
Return the arity of Fun
.
If Fun
is not a function, then the atom
'false'
is returned.
cumsum(L::List) -> [number()]
Return a list where the N
th element of
the result is the sum of the first N
elements of
List
. Corresponds to the cumsum function from
Matlab.
cumsum(Acc0, L::List) -> [number()]
Return a list where the N
th element of
the result is the Acc0
plus the sum of the first
N
elements of List
.
cut(L, M) -> List_of_M_lists
Break L into M, roughly equal length segments. Parameters:
L
: a list.M
: a positive integer -- the number of segments to create from L
.M
can be a non-empty list, in which case,
length(M)
is used as the number of segments to make.
List_of_M_lists
: Each of the M
segments has a length of length(L) div M
or
(length(L) div M) + 1
.
cut([0, 1, 2, 3, 4], 3) -> [[0], [1,2], [3,4]]
See also: deal/2.
deal(L, M) -> List_of_M_lists
Deal the elements of M
around the M
lists of List_of_M_lists
.
Parameters:
L
: a list.M
: a positive integer -- the number dealt lists to create from L
.M
can be a non-empty list, in which case,
length(M)
is used as the number of new lists to make.
List_of_M_lists
: The elements of L
are
'dealt' in a round-robin fashion in to the M
lists
of List_of_M_lists
.
hd(hd(List_of_M_lists)) = hd(L)
hd(lists:nth(I, List_of_M_lists)) = lists:nth(I, L)
for I
between 1 and M
inclusive.
lists:nth((N div M)lists:nth((N rem M)+1, List_of_M_lists))
= lists:nth(N+1, L)
,
for N
between 0 and length(L)-1
inclusive.
deal([0, 1, 2, 3, 4], 3) -> [[0,3], [1,4], [2]]
See also: cut/2.
index(Key, List) -> integer() | false
Return the position of the first occurrence of Key
in List
.
If List
has no element that matches Key
,
then false is returned.
intervals(Lo, Hi, N) -> List
Divide [Lo, Hi] into N roughly intervals of roughly equal size.
Lo
and Hi
must be integers or floats.N
must be an integer.List
is a list of intervals. Each interval is
a tuple, {Lo_i, Hi_i}
where
Lo_i
is the lower bound for the
ith
interval, and
Hi_i
is the upper bound.
Lo
and Hi
are both integers,
then intervals in List
have integer bounds.
Lo
or Hi
is a float,
then intervals in List
have floating
point numbers for their bounds.
Lo
or Hi
is an integer
whose magnitude is too large to represent as a float and the
other is a float, then intervals(Lo, Hi, N)
will
produce a 'bad argument in an arithmetic expression' error.
misc:intervals(1,100,7) ->
[{1,15},{15,29},{29,43},{43,57},{57,71},{71,85},{85,100}]
misc:intervals(1.0,100,7) ->
[{1.00,15.1429},{15.1429,29.2857},{29.2857,43.4286},{43.4286,57.5714},{57.5714,71.7143},{71.7143,85.8571},{85.8571,100}]
logsteps(Mant, Exp) -> [number()]
For each M
in Mant
and each E
in
Exp
, M*10E
is an element of
the return list.
Example: logsteps([1,2,5], lists:seq(-1,2) -> [0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500].
logsteps
can be handy for generating a sequence of
problem sizes for run-time measurements.
mod(N, M) -> rQ
like the rem
BIF but result has same sign as M
.
N
and M
must be integers or floats.N
and M
are both integers, then the result is an integer.N
or M
is a float, then the result is a float.{Lo_i, Hi_i}
where
M == 0
then Q = N
.M /= 0
then Q IN [0, M)
.M
is not 0, then N - Q
is a multiple of M
.msg_dump(Patterns) -> failed
Print the pending messages of a process -- for debugging.
msg_dump(Patterns)
is equivalent to
msg_dump(nobody, Patterns)
.
msg_dump(Who, Patterns) -> failed
Print the pending messages of a process -- for debugging.
msg_dump
prints an error message where each line
is prefixed by the process pid and a colon. It states that a
receive operation timed out, prints the list of patterns, and
then prints all pending messages for the process.
Who
is a string, then the first line printed is
"$PID: receive timed out in $Who"
$PID$
is the PID of this process,
and $Who
is the string from Who
.Who
is the atom 'nobody'
,
then the first line printed is
"$PID: receive timed out
$PID$
is the PID of this process.Patterns
is the list of strings that describe what
receive
was looking for.
msg_dump
returns the atom 'failed'
neighbours(T::List) -> TupleList
Return a list of tuples of all adjacent pairs of elements of list.
Example: neighbours([1,2,3,5,7,11]) -> [{1,2},{2,3},{3,5},{5,7},{7,11}].
If List
is empty or a singleton, then TupleList
is empty.
pow(N::number(), M::number()) -> number()
N
raised to the M
th power.
M
is an integer, then NM
is computed using the Russian Peasant algorithm with
N
N
is an integer.
Furthermore, it avoids an exception when
N < 0
.
M
must be a float, in which case
NM
is computed using
math:pow/2
.
N =0
and M < 0
,
then the atom 'undefined'
is returned.
If N < 0
and M
is a float, then
an arithmetic exception occurs. Likewise, there is an exception when
M
is a float and the NM
produces a floating point overflow.
print(Term) -> ok
Equivalent to print(Term, 100).
print(Term, N) -> ok
Print an erlang term on standard out.
Unlike the erlang shell, print always prints a list of integers as a list of integers.
Thus, print("hello world"
is equivalent to
io:format("~s", "[104,101,108,108,111,32,119,111,114,108,100]")
.
The point behind this is to prevent lists that really are supposed to be lists of
integers from printing as strings just because every element of the list had a value
in the range 32..127.
N
gives (roughly) the number of terms to print. This means that if
you ask to print a really big term, erlang won't be spewing stuff for several hours.
If you want it to print the whole thing, use the atom 'infinity'
for
N
.
rlist(N) -> List
Equivalent to rlist(N, 10).
rlist(N, M) -> List
Return a list of N
random elements. If
M
is an integer, then the values are chosen
uniformly from 1..M
.M
is an floating point number, then the values are chosen
uniformly from [0, M).rlist(N, M, State0) -> {List, State1}
Return a list of N
elements. If
M
is an integer, then the values are chosen
uniformly from 1..M
.M
is an floating point number, then the values are chosen
uniformly from [0, M).State0
is the starting state for the pseudo-random number generator.
rlist
returns the random list and the state for the PRNG at the end of
generating the list.
sync(P) -> ok | exit | failed
Equivalent to sync(P, infinity).
sync(P, TimeOut) -> ok | failed
Synchronize with process P
.
This process sends a 'sync' message to P
, and
P
should call this function to send a 'sync'
message to us. When we receive the message from P
we continue. This ensures that both processes have made it to
the point of their sync
calls before either proceeds.
sync(P, SndToken, RcvToken) -> ok | termR() | failed
Equivalent to sync(P, infinity, SndToken, RcvToken).
sync(P, TimeOut, SndToken, RcvToken) -> ok | termR() | failed
Synchronize with process P
.
This process sends SndToken
to P
, and
P
should respond by making a call to one of the versions
of sync
with this process as the target process.
RcvToken
, we return it.'ok'
.'failed'
.SndToken
is the empty tuple, {}
, then
the default msg:sync
token is sent. Likewise, if
RcvToken
is the empty tuple, then we only look to receive
the default msg:sync
token.
sync
ensures that this process
and process P
have both reached the synchronization point
before either proceeds.
Generated by EDoc, Jan 25 2017, 12:13:15.


Brendan Kim
Skills
Staying up really late at night for no legitmate reason
Binge watching random YouTube videos
Social media expert specifically Instagram and SnapChat


Using Microsoft photoshop, illustrator, PDIP, and Garment printer, my objective for rotation 3 was to design and print out my own T-shirt. For additional projects, I had to create a 12x12 chipboard which I am going to use for my next rotation's jigsaw puzzle. I also had to print either an image on a golf ball, a phone case, or print a Paper Mache box top. I ended up creating a mache box top.
This is my finished product!
I decided to put our dorm pictures on my T-shirt. People in North Hall are always energetic and they are like my family. I made this T-shirt in the hopes of this shirt being our dorm shirts for this year. I first had to design the photo by using Microsoft photoshop and then importing the picture into Corel Draw. Then, I had to export the photo into the USB drive and plug it into the Brother GT-3 machine. In order to print the photo clearly on the t-shirt, we first had to heat the t-shirt and flatten it out on the machine so that the photo is printed correctly. After the printing process, it was important to re-heat the t-shirt as the photo might get ruined if you rub it right after the product is finished.
This is my finished product!
For my next assignment, I had to print a 12x12 chipboard using the UV printer along with Fusion 360. I decided to print out a 12x12 image of my favorite hockey team, the Chicago Blackhawks. Printing the image itself was not too hard. I simply had to resize the image so that it could be printed within 12 by 12 inches then export the .jpeg file of the image that I wanted to print. I simply plugged in the USB drive into the UV printer and the image was printed on the chipboard.
This is my finished product!
For my final project of the rotation, I decided to print out an image of the Blackhawks winning the Stanley Cup in 2015. Creating the mache box top was similar to the process for creating the 12x12 chipboard. All I had to do was to resize the image that I wanted to print to fit the dimensions of the box top. Then, I had to export the image file and plug the USB drive into the UV printer just like how I did for creating my chipboard.
By my third rotation, we had the opportunity to design our own T-shirts through using Microsoft Illustrator and Photoshop. It was important that we heated the T-shirt before printing the picture on it, since the picture does not come out well if it is wrinkled. Regarding the UV printer, there were some limitations in terms of printing certain pictures to certain objects. Since LEF-20 UV printer is using UV rays, anything that blocks the UV light cannot be printed on. One crucial thing that I learned was that we should not be printing out copyrighted materials. It was fascinating how we can print the pictures that we took or got from the internet, and apply them to any daily objects, such as t-shirts, box tops, or even on cardboard.


If you need an app or website I would love to work with you whatever your idea maybe. Lets Build it together.


--- layout: post status: publish published: true title: "分享一首音乐, Home Again" author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 596 wordpress_url: http://leaskh.wordpress.com/2006/03/11/%e5%88%86%e4%ba%ab%e4%b8%80%e9%a6%96%e9%9f%b3%e4%b9%90-home-again date: '2006-03-11 05:28:19 +0800' date_gmt: '2006-03-11 05:28:19 +0800' categories: - Music tags: [] comments: - id: 239 author: renee author_email: '' author_url: '' date: '2006-03-11 15:55:32 +0800' date_gmt: '2006-03-11 15:55:32 +0800' content: |- good song~Home Again by Vonda Shepard ---


Bulk upload of clients from EXCEL file
This page is a placeholder, not part of final product. But the underlying API works!
Actions to perform:
- Download template file
- Open it by Excel, Open Office Calc or other compatible CSV editors
- Enter your data
- Save or export file, the format have to be csv
- Upload file


JavaScript unit test file
This file tests invoices.js.
| Status | Invoice Date | ID | Client Name | Amount Paid | Invoice Total |
|---|---|---|---|---|---|
| Open | 08 Oct 2008 | 1048 | Euro Client | €0.00 | €3,750.00 |
| EUR €3,750.00 | |||||
| Status | Invoice Date | ID | Client Name | Amount Paid | Invoice Total |
| Open | 08 Oct 2008 | 1047 | GBP Client | £0.00 | £4,275.00 |
| GBP £4,275.00 | |||||
| Status | Invoice Date | ID | Client Name | Amount Paid | Invoice Total |
| Open | 10 Oct 2008 | 1046 | USD Client | $0.00 | $20,425.00 |
| $13,500.00 | USD $50,132.90 |


Abstract
This document was prepared by reviewing version 1.3 of the Intelligent Personal Assistant Architecture Report and extracting requirements for the architecture that were implied by the report. This was done in order to have a standalone list of architecture requirements. The headings in this document correspond to the headings in the architecture report. Sections of the Architecture document which are not referenced here were not considered to contain any requirements. The terms "MUST", "MAY" and "SHOULD" are used in this document as defined in IETF RFC 2119.
Status of This Document
This specification was published by the
Voice Interaction Community Group.
It is not a W3C Standard nor is it on the W3C Standards Track.
Please note that under the
W3C Community Contributor License Agreement (CLA) there is a limited opt-out and other conditions apply. Learn more about W3C Community and Business Groups.
1. Introduction
- Intelligent Personal Assistants (IPA's) MUST be able to provide general purpose information
- Specialized virtual assistants MUST be able to provide enterprise-specific information
- Specialized virtual assistants MAY be able to provide non-enterprise-specific information
- IPA's SHOULD be able to perform transactions
- Specialized assistants MUST be able to interoperate with general IPA's
- IPA's SHOULD be able to execute operations in a user's environment
- IPA's MUST be able to interact with users through voice or text or both.
2. Problem Statement
-
IPA's MAY be able to transfer a partially completed task to another IPA
3. Architecture
- IPA's MAY include a Client layer
- IPA's MUST include a Dialog layer
- IPA's MAY include an API/Data layer
- Components MAY be shifted to other layers as needed
- The architecture SHOULD support question answering and information retrieval applications
- The architecture SHOULD support executing local services to accomplish tasks
- The architecture SHOULD support executing remote services to accomplish tasks
- The architecture MUST support dynamically adding local and remote services or knowledge sources.
- It MAY be possible to forward requests from one IPA to another with the same architecture
- It MAY be possible to forward requests or partial requests from one IPA to another with the same architecture, omitting the client layer
- IPA extensions MAY be selected from a standardized marketplace
3.1 Client Layer
3.1.2 User Input and System Output
- The Client layer MAY include a microphone
- The Client layer MAY include a means for text input
- The Client layer MAY include a speaker
- The Client layer MAY include a display
- Additional (non-speech) output modalities MAY be employed to render output or to capture input
3.1.3 IPA Client
- The IPA Client MUST allow activation and deactivation by means of a Client Activation Strategy.
- IPA Clients MAY also capture input via text and output text
- IPA Clients MAY also capture input from various modality recognizers
- IPA Clients MAY also capture contextual information, e.g., location, time, environmental sounds or other inputs that it obtains from Local Data Providers
- An IPA Client MAY also receive commands to be executed locally in the Local Services.
- An IPA Client MAY also receive multimodal output to be rendered by a respective modality synthesizer
- IPA Clients MAY reference a session identifier.
<IP_ADDRESS> Client Activation Strategy
- The IPA Client MUST be activated with a Client Activation Strategy
- The Client Activation Strategy MAY be push-to-talk
- The Client Activation Strategy MAY be hotword
- The Client Activation Strategy MAY be triggered by an interpreted text string (either from audio or text)
- The Client Activation Strategy MAY be a change in environment
- The Client Activation Strategy MAY be triggered by a script or environmental condition
- The Client Activation Strategy MAY be a different strategy not enumerated here
<IP_ADDRESS> Local Service Registry
- The IPA Client MUST include a Local Service Registry
- The Local Service Registry MUST maintain a list of Local Services
- The Local Service Registry MUST maintain a list of Local Data Providers
Dialog Layer
3.2.1 IPA Service
- The IPA Client MUST forward audio data and metadata (if any) to the ASR
- The IPA Service MUST forward text data and metadata (if any) to the NLU
- The IPA Service MUST forward audio output output from the TTS to the IPA Client if supported
- The IPA Service MUST forward multimodal output from the Dialog Manager to the Client if supported
- The IPA Service MUST forward text output from the NLG to the IPA Client if supported
3.2.2 ASR
- The ASR MUST generate one or more recognition hypotheses from voice input that it receives from the IPA Service
- The ASR MAY associate recognition hypotheses with confidence scores
- The ASR MUST forward the recognition hypotheses to the NLU
- The ASR MAY update the History with the recognition hypotheses
3.2.3 NLU
- The NLU MUST extract textual interpretations from text strings (either from audio or text)
- The NLU MAY extract multiple interpretations from input text strings (either from audio or text)
- The NLU MUST be able to interpret input Core Intent Sets
- The NLU SHOULD be able to interpret utterances that are a combination of activation strategies and commands.
- The NLU make make use of the Data Provider to access local or external data
- The NLU MAY make use of the Context to check for complementary information such as information in the history or knowledge
- The NLU MUST forward the semantic interpretation of the input to the Dialog Manager >
- The NLU MAY associate statistical confidences with interpretations
- The NLU MAY extract emotion or sentiment from text strings either from audio or text)
3.2.4 Dialog Manager
- The Dialog Manager MUST recognize when the user goals are changed
- The Dialog Manager SHOULD confirm when the user goals are changed
- The Dialog Manager MAY consider ongoing workflows that must not be interrupted when the user switches goals.
- The Dialog Manager SHOULD update the History with dialog moves
- The Dialog Manager SHOULD determine the next dialog move
- based on internal considerations
- based on output from other components in the same dialog system
- based on output from other agents (IPA services)
- how the Dialog Manager determines the next move is outside the scope of these requirements
- The Dialog Manager SHOULD make use of the TTS to generate audio data to be rendered on the IPA Client
- The Dialog Manager MAY provide commands to be executed by the IPA Client or the External Services
3.2.5 Context
- The Context MAY make use of the Local Service Registry to include external knowledge from Local Data Providers
- The Context MAY make use of the External Service Registry to include external knowledge from Data Providers
- The Context MAY provide external knowledge temporarily to the Knowledge Graph to be considered in reasoning
<IP_ADDRESS> History
- The Dialog History MAY store the past dialog events per user
3.3 API's/Data Layer
- The Provider Selection Service MAY receive input from the Dialog Manager to query data from Data Providers
- The Provider Selection Service MAY receive input from the Dialog Manager to execute External Services
- If the Provider Selection Service is called by the Dialog Manager with a preselected identifier of an IPA provider, it MUST use the preselected provider
- If the Provider Selection Service is not called with a preselected identifier of an IPA provider, the Provider Selection Service MUST follow a Provider Selection Strategy to determine those IPA Providers that are best suited to answer the request


Add class .btn-group-*
to size all buttons in a button group.
To span the entire width of the screen, use the .btn-group-justified
class
For <button>
elements, you must wrap each button in a .btn-group
class
Nest button groups to create drop-down menu:
To split button dropdowns, once you have created the button, you must create another button with class
dropdown-toggle
, data-toggle dropdown
and add
within <button>


☺ Yellow hands
Here you can choose whatever you are willing to provide for the god children and our aged god parents!
mail
Email
vpn_key
Password
Signin
send
Not a yellow hand yet?click signup
Signup


An implementation of an algorithm family for continuous optimization called memetic algorithms with local search chains (MA-LS-Chains). Memetic algorithms are hybridizations of genetic algorithms with local search methods. They are especially suited for continuous optimization.
| Version: | 0.2-4 |
| Depends: | Rcpp (≥ 0.9.10) |
| LinkingTo: | Rcpp |
| Suggests: | inline |
| Published: | 2018-04-29 |
| Author: | Christoph Bergmeir [aut, cre, cph], José M. Benítez [ths], Daniel Molina [aut, cph], Robert Davies [ctb, cph] (Developer of the matrix library newmat which partly ships with this package), Dirk Eddelbuettel [ctb, cph] (Developer of RcppDE from which code was used in evaluate.h), Nikolaus Hansen [ctb, cph] (Author of the original cmaes implementation that ships with the package) |
| Maintainer: | Christoph Bergmeir <c.bergmeir at decsai.ugr.es> |
| License: | GPL-3 | file LICENSE |
| NeedsCompilation: | yes |
| Citation: | Rmalschains citation info |
| Materials: | ChangeLog |
| In views: | MachineLearning, Optimization |
| CRAN checks: | Rmalschains results |
| Reference manual: | Rmalschains.pdf |
| Package source: | Rmalschains_0.2-4.tar.gz |
| Windows binaries: | r-devel: Rmalschains_0.2-4.zip, r-release: Rmalschains_0.2-4.zip, r-oldrel: Rmalschains_0.2-4.zip |
| OS X binaries: | r-release: Rmalschains_0.2-4.tgz, r-oldrel: Rmalschains_0.2-4.tgz |
| Old sources: | Rmalschains archive |
| Reverse imports: | hybridEnsemble |
| Reverse suggests: | airGR, MSCMT |
Please use the canonical form https://CRAN.R-project.org/package=Rmalschains to link to this page.


--- layout: post title: "Internship at Wiesendanger/Vedmedenko Research Group, University of Hamburg" subtitle: "Computational Simulation of Magnetic Domain Behaviour" date: 2019-12-12 background: '/img/posts/01.jpg' ---
An important task in theoretical physics and quantum mechanics is the generalization of different concepts and principles, such as magnetic domains and the formation of domain walls in ferromagnets and ferrimagnets in the field of micromagnetism. However, understanding ferromagnetic materials on a macroscopic scale allows to relay key information in a concise and engaging way to a deeper comprehension about the characteristics such as stripe domains and perpendicular magneto-crystalline anisotropy which enables the possibility to develop magnetic devices and therefore realizing spintronic applications such as energetically efficient information and communication technologies.


{% extends "layout.html" %} {% block body %}
Thank you for participating in the Capital Pro Bono Honor Roll.
If you need to edit your entry, please visit https://capitalprobono.org/email_edit and you can receive an email with a personalized link to editing your entry.
In the meantime, if you have any difficulty, please contact<EMAIL_ADDRESS>Thank you again!
{% endblock %}


Hey dear friends, you can type your friend's name or your name to explore some secrets here.
Contact me This is a homework for zengularity.Courage, innovation and hard working are the key words of my career. When meet difficulties I will not easily give up. I always come up with new ideas and have strong ability to learn new things.
A new day begin when the sun rises, I am a positive and optimistic person, I do have confidence with my future. Want to discovery more? Click the picture below redirect to Linkedin


5-year-old brings heroin to school, father pleads not guilty
by The Associated PressHOLYOKE, Mass. (AP) -- A Massachusetts man has pleaded not guilty to drug charges after authorities said his 5-year-old son brought heroin to school and told his teacher that when he tastes the powder he becomes Spider-Man.
The Daily Hampshire Gazette reports that 29-year-old Benny Garcia, of Holyoke, was arraigned Friday in Holyoke District Court. He faces charges of drug possession and reckless endangerment of a child.
 Authorities say the boy was taken to a hospital but was unharmed.
Police searching Garcia's home say they found more than 200 bags of heroin and cocaine.
A judge ordered Garcia to be held without bail until a Nov. 20 hearing.


Welcome to Dynibatch’s documentation¶
Dynibatch is a Python library dedicated to providing mini-batches of audio data to machine learning algorithms.
It has been designed to deal with the following issues:
Reproducibility: given a dataset (data + train/valid/test split + labels) and some parameters (e.g. segment size and overlap, audio feature to be used…), an experiment should be easily reproducible. Dynibatch allows to keep all this information in dedicated objects and a configuration file.
Big data: because some datasets are huge, Dynibatch keeps a low memory footprint by generating mini-batches on the fly. To avoid recomputing at every epoch the data needed to generate the mini-batches, it can be cached in the disk.
Label management: labels are typically provided either per file (i.e. one label for the whole audio file) or per chunk (i.e. one label for an audio chunk, delimited by a start time and an end time). In either case, Dynibatch automatically maps the labels provided with the dataset to one label per segment, where a segment is one fixed-size observation (see [the tutorial](examples/tutorial.ipynb) for more details). Labels are not mandatory, so that unsupervised algorithms can be run.
Usability: with a given config file, generating mini-batches is as easy as
mb_gen = MiniBatchGen.from_config(config) mb_gen_e = mb_gen.execute(with_targets=True) # get the first mini-batch data, targets = next(mb_gen_e)


Rush Hour (1998) * * * A movie review by Serdar Yegulalp Copyright 1998 by Serdar Yegulalp
I seem to be in a minority of one, but Chris Tucker's outrageous mannerisms are beginning to grow on me. When he appeared in THE FIFTH ELEMENT as an over-the-top radio DJ, my senses misfired: my brain wasn't properly able to compute what I was seeing until I relaxed and had a good time.
The same thing applies to RUSH HOUR, a sprightly cop-buddy movie that works by being cheerful and engaging instead of cruel and mean-spirited.  It is also the best of the Jackie Chan vehicles to be produced in English so far -- it outstrips MR. NICE GUY by a fair margin, since that movie was little more than a weak clothesline for various action pieces. RUSH HOUR is funner and more sustained, and puts Chan up against someone who is a terrific foil.
The plot is standard-issue. A Chinese diplomat's daughter is kidnapped by a gang of criminals. The diplomat wants to enlist the help of Lee, an old cop friend of his from pre-1998 Hong Kong days. The FBI refuses to allow this, so they have the LAPD send over one of their own to "distract" him.
Carter (Tucker) catches on fast to the sleazy runaround he's been given, and tells Lee off: He doesn't want to babysit a cop; he wants to go out there and find these slimeballs. Funny thing is, Lee feels the same way, and before you know it, they're up to their elbows in suspects and explosives. All of this is, of course, sustained and interleaved with Tucker's wild-man motormouthing (which gets funnier and funnier the more you watch it) and Jackie's typically remarkable stuntwork.
What makes all of this stand out is primarily the tone and attitude. Most movies in this genre tend to be cold-blooded and unpleasant; this one's very much a romp, with good timing and pacing, and a lot of good laughs. I'm betting Jackie himself is the main reason for that, since he's professed repeatedly that he wants to make more family-oriented movies rather than John Woo-style blast-fests. Barely anyone gets shot, and most of the martial-arts stunts are about timing, object juggling, or Three Stooges-style slapstick. (There is one stunt involving an antique vase that's vintage Jackie -- although the way the stunt climaxes is a bit of a cheat.)
There is also the byplay between Chan and Tucker, much of which seems improvised (or at least minimally scripted). At one point during a stakeout, Edwin Starr's "War" comes on the radio, and Jackie attempts to sing along -- only to have Tucker coach him on how to sing with soul. In turn, Jackie teaches Tucker how to do the snatch-the-other-guy's-gun trick (and the payoff for THAT gag is a howl).
My main criterion for a comedy is simple: Did I laugh? I walked into RUSH HOUR in a terrible mood, and laughed all the way through. I don't think you can reasonably expect much more than that.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


?
Back
Save & Reboot
WiFi setup
Connect to existing network
Network name (SSID, empty to not connect):
Network password:
Static IP (leave at <IP_ADDRESS> for DHCP):
.
.
.
Static gateway:
.
.
.
Static subnet mask:
.
.
.
mDNS address (leave empty for no mDNS):
http://
.local
Client IP:
Not connected
Configure Access Point
AP SSID (leave empty for no AP):
Hide AP name:
AP password (leave empty for open):
Access Point WiFi channel:
AP opens:
No connection after boot
Disconnected
Always
Never (not recommended)
AP IP:
Not active
Back
Save & Reboot


Neuroplasticity is the measurement of the brain's capacity to improve.
Spoilers its seemingly endless.The benefits of neuroplasticity are that it shows that your brain can improve even after its fully developed.
And I think it's pretty obvious as to how that would affect your studies, but in case it isn't it (since I didn't give a full explanation) if you keep practicing something you'll get better at it.I’m sure that you've heard similar sayings.
Growth mindset is the belief that you still have room to grow and reach new heights in terms of ye mind.
And simply believing you can grow, or at least being open to the idea that you can improve and overcome a difficult task greatly helps learning capacity and rate. How will I integrate this info into my journey of improvement? I’ll not look down on myself as much and go forward with the thought of when i finish the course and not if I finish.


Due to popular movies such as Jurassic Parka and The Land Before Time, many of the general public have a perceived notion of what a velociraptor would look like.
But according to new scientific research, velociraptors had feathers and are related by ancestry to chickens.


A MIDSUMMER NIGHT'S DREAM
Directed by William Dieterle and Max Reinhardt Running Time: 132 minutes Originally Released: 1935
Reviewed by E. Benjamin Kelsey
* * * (out of four)
Reviewing a movie that came out in the 30's can be a difficult task. First off, to fairly view the film, one must attempt to transport themselves back in time. If you critique the film using today's standards, most films would get zero stars. Technology has come a very long way in the past sixty years, and even acting and writing styles have changed. Keeping that in mind, I tried to the best of my ability to watch A MIDSUMMER NIGHT'S DREAM objectionably, forgetting for two hours that when it was released, my GRANDparents hadn't even married yet.
MIDSUMMER is, of course, based on the William Shakespeare play by the same name. It's just one of about 17 or so versions of the play that have been done for movie or TV. This version stars, most notably, James Cagney as Bottom, the actor who winds up with a donkey head, and a young Mickey Rooney, playing the mischievous sprite Puck. It's a love story, granted a bizarre and comedic love story, focusing on four characters: Lysander (Dick Powell), Demetrius (Ross Alexander), Hermia (Olivia De Havilland), and Helena (Jean Muir). Lysander and Hermia are in love, but Demetrius also loves the beautiful Hermia. This is heartbreaking to sweet, unloved Helena, who has loved Demetrius for quite some time without the returning of such feelings.
Off in the forest dwells the fairies. Oberon (Victor Jory), king of the fairies, instructs his servant Puck to make Demetrius fall in love with Helena so that all may be well. In a case of mistaken identity, Puck actually has Lysander falling in love with Helena. Eventually he gets Demetrius to fall in love with Helena too! So here we have four characters. The men are fighting over a woman still, just a different one.   (Not Shakespeare's words, but mine).
Another little story is going on during all this. Puck loves to cause trouble.  Bottom is part of an acting group that are going to put on a play for the marriage of Theseus (Ian Hunter) and Hippolyta (Verree Teasdale). Then Oberon has Puck putt a spell on Titania (Anita Louise), the queen of the fairies, to have her fall in love with Bottom so he can still the Indian baby she has stolen. Confusing enough? Well, it was for me too, and I'm not completely sure if I even have all of this right, but when you're watching the film, it flows well enough that you don't have to get too concerned.
A MIDSUMMER NIGHT'S DREAM is actually quite good, and, not that I really know, but the effects seemed pretty good for coming from the 1930's. It is difficult a lot of times to tell what the people are saying, and it's very confusing at first, but when you get into it, it's fun. The last half an hour or so got kind of slow and boring, but the middle of the film makes it worthwhile. James Cagney is great, and Dick Powell is quite fun in a sappy way. The most annoying thing here by far is Mickey Rooney. It's hard not to cringe every time he squeals or shrivels up his face. He's almost unbearable to watch and is definitely the biggest complaint about the film.
Over sixty years old, this film might be hard to come by. I don't really know how popular a version this is. Just to make note, this film was nominated for the Best Picture Oscar in 1936, but didn't win. It did pick up two other Oscars - for film editing and cinematography. This is a recommendable film, but if you're really one for modern technology you'll probably find it unbearable. Especially Mickey Rooney!!!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


<bookid>
The <bookid> element contains the publisher's identification information for the book, such as part number, edition number and ISBN number.
Category: Bookmap metadata elements


{% extends admin_base_template %} {% block head %} {% endblock %} {% block body %}
Note, these users will not be allowed to use the Altmetrics service until their accounts have been approved.


| Category: functors | Component type: concept |
| First argument type | The type of the Strict Weak Ordering's first argument. |
| Second argument type | The type of the Strict Weak Ordering's second argument. The first argument type and second argument type must be the same. |
| Result type | The type returned when the Strict Weak Ordering is called. The result type must be convertible to bool. |
| F | A type that is a model of Strict Weak Ordering |
| X | The type of Strict Weak Ordering's arguments. |
| f | Object of type F |
| x, y, z | Object of type X |
| Name | Expression | Precondition | Semantics | Postcondition |
|---|---|---|---|---|
| Function call | f(x, y) | The ordered pair (x,y) is in the domain of f | Returns true if x precedes y, and false otherwise | The result is either true or false |
| Irreflexivity | f(x, x) must be false. |
| Antisymmetry | f(x, y) implies !f(y, x) |
| Transitivity | f(x, y) and f(y, z) imply f(x, z). |
| Transitivity of equivalence | Equivalence (as defined above) is transitive: if x is equivalent to y and y is equivalent to z, then x is equivalent to z. (This implies that equivalence does in fact satisfy the mathematical definition of an equivalence relation.) [1] |
[1] The first three axioms, irreflexivity, antisymmetry, and transitivity, are the definition of a partial ordering; transitivity of equivalence is required by the definition of a strict weak ordering. A total ordering is one that satisfies an even stronger condition: equivalence must be the same as equality.


Justin Nelson - Lead Developer @ Self Esteem Brands
#HealthyCode
/ˈpær əˌdaɪm, -dɪm/
a framework containing the basic assumptions, ways of thinking, and methodology that are commonly accepted by members of a scientific community
The way you think code should look
Outcome: Change the way you think code should look
var client = new HttpClient();
// 1. Fetch the questions
var result = client.Get("http://stackoverflow.com/api/questions/tagged/csharp");
var isSuccess = result.IsSuccessStatusCode;
if (!isSuccess) return null;
var data = result.Data;
var parsed = JsonConvert.Parse<List<Question>>(data);
// 2. Filter the questions
var popular = new List<Question>();
foreach(var q in parsed)
{
if (q.Score > 250) popular.Add(q);
}
// 3. Sort the questions
popular.Sort(new QuestionScoreComparer());
// 4. Print the titles
foreach(var q in popular)
{
Console.WriteLine(q.Title);
}
class StackOverflowQuestionGetter
{
private static HttpClient client = new HttpClient();
private List<Question> _questions;
public List<Question> Questions
{
get
{
if (_questions == null)
{
// 1. Fetch the questions
var result = client.Get("http://stackoverflow.com/api/questions/tagged/csharp");
if (!result.IsSuccessStatusCode) throw new Exception();
_questions = JsonConvert.Parse<List<Question>>(result.Data);
}
return _questions;
}
}
public void PrintPopularQuestions()
{
var popular = PopularQuestions().ToList();
// 3. Sort the questions
popular.Sort(new QuestionScoreComparer());
// 4. Print the titles
foreach(var q in popular)
{
Console.WriteLine(q.Title);
}
}
// 2. Filter the questions
private IEnumerable<Question> PopularQuestions()
{
foreach(var q in Questions)
{
if (q.Score > 250) yield return q;
}
}
}
var client = new HttpClient();
// 1. Fetch the questions
var result = client.Get("http://stackoverflow.com/api/questions/tagged/csharp");
if (!result.IsSuccessStatusCode) throw new Exception();
JsonConvert.Parse<List<Question>>(result.Data)
.Where(q => q.Score > 250) // 2. Filter the questions
.OrderBy(q => q.Score) // 3. Sort the questions
.ToList()
.ForEach(q => Console.WriteLine(q.Title)); // 4. Print the titles
Many ways to write the same code
Some are subjectively better 😉
There are objective differences
Ordered statments that change a program's state to a desired result
int a = 6;
int b = 8;
int a2 = a * a;
int b2 = b * b;
int c2 = a2 + b2;
double c = Math.sqrt(c2); // c = 10
Encapsulate logic and data into 'classes'
class Incrementor
@value = 0
def increment
@value = @value + 1
end
def value
return @value
end
end
Tell a computer 'what' to do, not 'how' to do it
SELECT something FROM somewhere WHERE it looks like this
Utilizes functions to contain state and logic
(defun fatorial (n)
(cond
((= n 1) 1)
(t (* n (fatorial (- n 1))))))
Programmers are not as smart as we'd like to think
Code that declares its intent serves 2 purposes - computation and documentation
Do more with less
Functional code is not magic
let questions = [{ title: '...', userId: '...'}, ... ];
let wordyQuestionsByAsker = {};
for(var ix = 0; ix < questions.length; ix++) {
let question = questions[ix];
if (question.title.length > 280) {
if (!wordyQuestionsByAsker[question.userId]) {
wordyQuestionsByAsker[question.userId] = [];
}
wordyQuestionsByAsker[question.userId].push(question);
}
}
let questions = [{ title: '...', userId: '...'}, ... ];
let wordyQuestionsByAsker = questions
.filter(question => question.title.length > 280)
.groupBy(question => question.userId);
// `predicate` is a function that takes one value and
// returns true or false
function filter(predicate) {
let result = [];
for(var ix = 0; ix < this.length; ix++) {
if (predicate(this[ix])) result.push(this[ix]);
}
return result;
}
// `groupSelector` is a function that takes one value and
// computes a 'key' that will be used to group the value
function groupBy(groupSelector) {
let result = {};
for(var ix = 0; ix < this.length; ix++) {
let groupKey = groupSelector(this[ix]);
if (!result[groupKey]) result[groupKey] = [];
result[groupKey].push(this[ix]);
}
return result;
}
There are many ways to write code. Choose the way that best suites your application.
Questions?


If you use assistive technology (such as a screen reader) and need a
version of this document in a more accessible format, please email
<EMAIL_ADDRESS>.
Please tell us what format you need. It will help us if you say what assistive technology you use.
Details
Requirements and advice for institutions directly recruiting 14- to 16-year-olds in the 2015 to 2016 academic year
Information for institutions directly recruiting 14- to 16-year-old students in 2015 to 2016 academic year.
Institutions intending to deliver the programme in the 2015 to 2016 academic year


| Home | Trees | Indices | Help |
|
|---|
|
|
Container object and Object instance factory.
If your account has the feature enabled, containers can be publically shared over a global content delivery network.
| Instance Methods | |||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
| bool |
|
||
| str |
|
||
| str |
|
||
| str |
|
||
| Object |
|
||
| ObjectResults |
|
||
| Object |
|
||
| list({"name":"...", "hash":..., "size":..., "type":...}) |
|
||
| list(str) |
|
||
|
|||
|
|||
|
|||
|
Inherited from |
| Instance Variables | |
| str |
name the container's name (generally treated as read-only) |
| bool |
cdn_log_retention retention of the logs in the container. |
| number |
cdn_ttl the time-to-live of the CDN's public cache of this container (cached, use make_public to alter) |
| number |
object_count the number of objects in this container (cached) |
| number |
size_used the sum of the sizes of all objects in this container (cached) |
| Properties | |
|
Inherited from |
| Method Details |
Containers will rarely if ever need to be instantiated directly by the user. Instead, use the create_container, get_container, list_containers and other methods on a valid Connection object.
|
Update Container Metadata >>> metadata = {'x-container-meta-foo' : 'bar'} >>> container.update_metadata(metadata)
|
Enable static web for this Container >>> container.enable_static_web('index.html', 'error.html', True, 'style.css')
|
Disable static web for this Container
>>> container.disable_static_web()
|
Enable object versioning on this container >>> container.enable_object_versioning('container_i_want_versions_to_go_to')
|
Disable object versioning on this container
>>> container.disable_object_versioning()
|
Either publishes the current container to the CDN or updates its CDN attributes. Requires CDN be enabled on the account. >>> container.make_public(ttl=604800) # expire in 1 week
|
Disables CDN access to this container. It may continue to be available until its TTL expires.
>>> container.make_private()
|
Purge Edge cache for all object inside of this container. You will be notified by email if one is provided when the job completes. >>><EMAIL_ADDRESS>or >>> container.purge_from_cdn("user<EMAIL_ADDRESS>or
>>> container.purge_from_cdn()
|
Enable CDN log retention on the container. If enabled logs will be periodically (at unpredictable intervals) compressed and uploaded to a ".CDN_ACCESS_LOGS" container in the form of "container_name/YYYY/MM/DD/HH/XXXX.gz". Requires CDN be enabled on the account.
>>> container.log_retention(True)
|
Returns a boolean indicating whether or not this container is publically accessible via the CDN. >>> container.is_public() False >>> container.make_public() >>> container.is_public() True
|
Return the URI for this container, if it is publically accessible via the CDN. >>> connection['container1'].public_uri() 'http://c00061.cdn.cloudfiles.rackspacecloud.com'
|
Return the SSL URI for this container, if it is publically accessible via the CDN. >>> connection['container1'].public_ssl_uri() 'https://c61.ssl.cf0.rackcdn.com'
|
Return the Streaming URI for this container, if it is publically accessible via the CDN. >>> connection['container1'].public_ssl_uri() 'https://c61.stream.rackcdn.com'
|
Return an Object instance, creating it if necessary. When passed the name of an existing object, this method will return an instance of that object, otherwise it will create a new one. >>> container.create_object('new_object') <cloudfiles.storage_object.Object object at 0xb778366c> >>> obj = container.create_object('new_object') >>> obj.name 'new_object'
|
Return a result set of all Objects in the Container. Keyword arguments are treated as HTTP query parameters and can be used to limit the result set (see the API documentation). >>> container.get_objects(limit=2) ObjectResults: 2 objects >>> for obj in container.get_objects(): ... print obj.name new_object old_object
|
Return an Object instance for an existing storage object. If an object with a name matching object_name does not exist then a NoSuchObject exception is raised. >>> obj = container.get_object('old_object') >>> obj.name 'old_object'
|
Return information about all objects in the Container. Keyword arguments are treated as HTTP query parameters and can be used limit the result set (see the API documentation). >>> conn['container1'].list_objects_info(limit=2) [{u'bytes': 4820, u'content_type': u'application/octet-stream', u'hash': u'db8b55400b91ce34d800e126e37886f8', u'last_modified': u'2008-11-05T00:56:00.406565', u'name': u'new_object'}, {u'bytes': 1896, u'content_type': u'application/octet-stream', u'hash': u'1b49df63db7bc97cd2a10e391e102d4b', u'last_modified': u'2008-11-05T00:56:27.508729', u'name': u'old_object'}]
|
Return names of all Objects in the Container. Keyword arguments are treated as HTTP query parameters and can be used to limit the result set (see the API documentation). >>> container.list_objects() ['new_object', 'old_object']
|
str(x)
|
Permanently remove a storage object. >>> container.list_objects() ['new_object', 'old_object'] >>> container.delete_object('old_object') >>> container.list_objects() ['new_object']
|
| Instance Variable Details |
namethe container's name (generally treated as read-only)
|
| Home | Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Fri Jun 1 13:14:14 2012 | http://epydoc.sourceforge.net |


--- title: Protease information keywords: fastai sidebar: home_sidebar nb_path: "nbs/proteolytic_cleavage.ipynb" ---
A numba compatible dictionary that stores different regular expressions for proteolytic enzymes. The dictionary is identical to the one in AlphaPept and was largely taken from the Pyteomics website which in turn derived the rules are from expasy.
protease_dict["trypsin"]


TechFire
Dashboard
Constants
FileScope
Reconnecting to robot...
RPM Adjustment: {{robot.shooterRPMAdjustment}}
Distance: {{robot.visionDistance}}
Shooter: {{robot.shooterMode}} Setpoint: {{robot.shooterSetpoint}}
Drivetrain: {{robot.drivetrainMode}}
Hopper: {{robot.hopperMode}}
Gear Holder: {{robot.gearHolderMode}}
Climber: {{robot.climberMode}}