| View source on GitHub |
RunConfig
This class specifies the configurations for an Estimator
run.
tf.compat.v1.estimator.RunConfig
tf.compat.v2.estimator.RunConfig
__init__
__init__( model_dir=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_steps=_USE_DEFAULT, save_checkpoints_secs=_USE_DEFAULT, session_config=None, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, log_step_count_steps=100, train_distribute=None, device_fn=None, protocol=None, eval_distribute=None, experimental_distribute=None, experimental_max_worker_delay_secs=None, session_creation_timeout_secs=7200 )
Constructs a RunConfig.
All distributed training related properties cluster_spec
, is_chief
,
master
, num_worker_replicas
, num_ps_replicas
, task_id
, and
task_type
are set based on the TF_CONFIG
environment variable, if the
pertinent information is present. The TF_CONFIG
environment variable is a
JSON object with attributes: cluster
and task
.
cluster
is a JSON serialized version of ClusterSpec
's Python dict from
server_lib.py
, mapping task types (usually one of the TaskType
enums) to
a list of task addresses.
task
has two attributes: type
and index
, where type
can be any of
the task types in cluster
. When TF_CONFIG
contains said information,
the following properties are set on this class:
cluster_spec
is parsed from TF_CONFIG['cluster']
. Defaults to {}. If
present, must have one and only one node in the chief
attribute of
cluster_spec
.task_type
is set to TF_CONFIG['task']['type']
. Must set if
cluster_spec
is present; must be worker
(the default value) if
cluster_spec
is not set.task_id
is set to TF_CONFIG['task']['index']
. Must set if
cluster_spec
is present; must be 0 (the default value) if
cluster_spec
is not set.master
is determined by looking up task_type
and task_id
in the
cluster_spec
. Defaults to ''.num_ps_replicas
is set by counting the number of nodes listed
in the ps
attribute of cluster_spec
. Defaults to 0.num_worker_replicas
is set by counting the number of nodes listed
in the worker
and chief
attributes of cluster_spec
. Defaults to 1.is_chief
is determined based on task_type
and cluster
.There is a special node with task_type
as evaluator
, which is not part
of the (training) cluster_spec
. It handles the distributed evaluation job.
 If save_checkpoints_steps
or save_checkpoints_secs
is set,
keep_checkpoint_max
might need to be adjusted accordingly, especially in
distributed training. For example, setting save_checkpoints_secs
as 60
without adjusting keep_checkpoint_max
(defaults to 5) leads to situation
that checkpoint would be garbage collected after 5 minutes. In distributed
training, the evaluation job starts asynchronously and might fail to load or
find the checkpoint due to race condition.
model_dir
: directory where model parameters, graph, etc are saved. If
PathLike
object, the path will be resolved. If None
, will use a
default value set by the Estimator.tf_random_seed
: Random seed for TensorFlow initializers.
Setting this value allows consistency between reruns.save_summary_steps
: Save summaries every this many steps.save_checkpoints_steps
: Save checkpoints every this many steps. Can not be
specified with save_checkpoints_secs
.save_checkpoints_secs
: Save checkpoints every this many seconds. Can not
be specified with save_checkpoints_steps
. Defaults to 600 seconds if
both save_checkpoints_steps
and save_checkpoints_secs
are not set
in constructor. If both save_checkpoints_steps
and
save_checkpoints_secs
are None
, then checkpoints are disabled.session_config
: a ConfigProto used to set session parameters, or None
.keep_checkpoint_max
: The maximum number of recent checkpoint files to
keep. As new files are created, older files are deleted. If None
or 0,
all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent
checkpoint files are kept.)keep_checkpoint_every_n_hours
: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables
the feature.log_step_count_steps
: The frequency, in number of global steps, that the
global step and the loss will be logged during training. Also controls
the frequency that the global steps / s will be logged (and written to
summary) during training.train_distribute
: An optional instance of tf.distribute.Strategy
.
If specified, then Estimator will distribute the user's model during
training, according to the policy specified by that strategy. Setting
experimental_distribute.train_distribute
is preferred.device_fn
: A callable invoked for every Operation
that takes the
Operation
and returns the device string. If None
, defaults to
the device function returned by tf.train.replica_device_setter
with round-robin strategy.protocol
: An optional argument which specifies the protocol used when
starting server. None
means default to grpc.eval_distribute
: An optional instance of tf.distribute.Strategy
.
If specified, then Estimator will distribute the user's model during
evaluation, according to the policy specified by that strategy.
Setting experimental_distribute.eval_distribute
is preferred.experimental_distribute
: An optional
tf.contrib.distribute.DistributeConfig
object specifying
DistributionStrategy-related configuration. The train_distribute
and
eval_distribute
can be passed as parameters to RunConfig
or set in
experimental_distribute
but not both.experimental_max_worker_delay_secs
: An optional integer
specifying the maximum time a worker should wait before starting.
By default, workers are started at staggered times, with each worker
being delayed by up to 60 seconds. This is intended to reduce the risk
of divergence, which can occur when many workers simultaneously update
the weights of a randomly initialized model. Users who warm-start their
models and train them for short durations (a few minutes or less) should
consider reducing this default to improve training times.session_creation_timeout_secs
: Max time workers should wait for a session
to become available (on initialization or when recovering a session)
with MonitoredTrainingSession. Defaults to 7200 seconds, but users may
want to set a lower value to detect problems with variable / session
(re)-initialization more quickly.ValueError
: If both save_checkpoints_steps
and save_checkpoints_secs
are set.cluster_spec
device_fn
Returns the device_fn.
If device_fn is not None
, it overrides the default
device function used in Estimator
.
Otherwise the default one is used.
eval_distribute
Optional tf.distribute.Strategy
for evaluation.
evaluation_master
experimental_max_worker_delay_secs
global_id_in_cluster
The global id in the training cluster.
All global ids in the training cluster are assigned from an increasing sequence of consecutive integers. The first id is 0.
Note: Task id (the property field task_id
) is tracking the index of the
node among all nodes with the SAME task type. For example, given the cluster
definition as follows:
cluster = {'chief': ['host0:2222'], 'ps': ['host1:2222', 'host2:2222'], 'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
Nodes with task type worker
can have id 0, 1, 2. Nodes with task type
ps
can have id, 0, 1. So, task_id
is not unique, but the pair
(task_type
, task_id
) can uniquely determine a node in the cluster.
Global id, i.e., this field, is tracking the index of the node among ALL nodes in the cluster. It is uniquely assigned. For example, for the cluster spec given above, the global ids are assigned as:
task_type | task_id | global_id -------------------------------- chief | 0 | 0 worker | 0 | 1 worker | 1 | 2 worker | 2 | 3 ps | 0 | 4 ps | 1 | 5
An integer id.
is_chief
keep_checkpoint_every_n_hours
keep_checkpoint_max
log_step_count_steps
master
model_dir
num_ps_replicas
num_worker_replicas
protocol
Returns the optional protocol value.
save_checkpoints_secs
save_checkpoints_steps
save_summary_steps
service
Returns the platform defined (in TF_CONFIG) service dict.
session_config
session_creation_timeout_secs
task_id
task_type
tf_random_seed
train_distribute
Optional tf.distribute.Strategy
for training.
replace
replace(**kwargs)
Returns a new instance of RunConfig
replacing specified properties.
Only the properties in the following list are allowed to be replaced:
model_dir
,tf_random_seed
,save_summary_steps
,save_checkpoints_steps
,save_checkpoints_secs
,session_config
,keep_checkpoint_max
,keep_checkpoint_every_n_hours
,log_step_count_steps
,train_distribute
,device_fn
,protocol
.eval_distribute
,experimental_distribute
,experimental_max_worker_delay_secs
,In addition, either save_checkpoints_steps
or save_checkpoints_secs
can be set (should not be both).
**kwargs
: keyword named properties with new values.ValueError
: If any property name in kwargs
does not exist or is not
allowed to be replaced, or both save_checkpoints_steps
and
save_checkpoints_secs
are set.a new instance of RunConfig
.


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|
Neonode zForce(TM) SDK
|
Go to the source code of this file.
Classes | |
| struct | CartesianCoordinates |
| Struct representing a coordinate. More... | |
| struct | TouchActiveArea |
| This structure represents the Touch Active Area of a sensor. More... | |
| struct | ReverseTouchActiveArea |
| This structure represents whether the Touch Active Area coordinates are reversed or not. More... | |
Enumerations | |
| enum | DeviceType { None = 0, Platform = 1, Sensor = 2, Core = 4, Air = 8, Plus = 16, Lighting = 32 } |
| Enum describing the different device types that exists. | |
| enum | MessageType { EnableMessageType, DisableMessageType, OperationModesMessageType, ResolutionMessageType, TouchActiveAreaMessageType, TouchMessageType, DetectedObjectSizeRestrictionMessageType, NumberOfTrackedObjectsMessageType, FingerFrequencyMessageType, IdleFrequencyMessageType, ReverseTouchActiveAreaMessageType, McuUniqueIdentifierMessageType, OffsetMessageType, HidDisplaySizeMessageType, FlipXYMessageType, ReflectiveEdgeFilterMessageType, MergeTouchesMessageType, TouchModeMessageType, HighestValidMessageType = TouchModeMessageType } |
| Enum describing the different message types that exists. | |
| enum | MessageGroup { Request, Response, Notification, HighestValidMessageGroup = Notification } |
| Enum describing the different groups a message can belong to. | |
| enum | MessageAction { GetAction, SetAction } |
| Enum describing the action of a message. More... | |
| enum | OperationModes { NoOperationMode = 0, DetectionMode = 1, SignalsMode = 2, LedLevelsMode = 4, DetectionHidMode = 8, GesturesMode = 16, HighestValidOperationMode = (GesturesMode * 2) - 1 } |
| Enum describing the OperationModes that a sensor can operate in. More... | |
| enum | TouchEvent { DownEvent, MoveEvent, UpEvent, InvalidEvent, GhostEvent } |
| Enum describing what state a touch can be in. More... | |
| enum | TouchOrientation { PortraitOrientation, LandscapeOrientation } |
| Enum describing what orientation touches are generated in. More... | |
| enum | TouchModes { NormalTouchMode, ClickOnTouchTouchMode } |
| Enum describing what touch mode a device can be in. More... | |
| typedef struct CartesianCoordinates CartesianCoordinates |
| typedef struct Connection Connection |
| typedef struct CoreDevice CoreDevice |
| typedef enum DeviceType DeviceType |
| typedef struct DisableMessage DisableMessage |
| typedef struct EnableMessage EnableMessage |
| typedef struct FingerFrequencyMessage FingerFrequencyMessage |
| typedef struct FlipXYMessage FlipXYMessage |
| typedef struct HidDisplaySizeMessage HidDisplaySizeMessage |
| typedef struct IdleFrequencyMessage IdleFrequencyMessage |
| typedef struct LightingDevice LightingDevice |
| typedef struct McuUniqueIdentifierMessage McuUniqueIdentifierMessage |
| typedef struct MergeTouchesMessage MergeTouchesMessage |
| typedef enum MessageAction MessageAction |
| typedef enum MessageGroup MessageGroup |
| typedef enum MessageType MessageType |
| typedef struct NumberOfTrackedObjectsMessage NumberOfTrackedObjectsMessage |
| typedef struct OffsetMessage OffsetMessage |
| typedef enum OperationModes OperationModes |
| typedef struct OperationModesMessage OperationModesMessage |
| typedef struct PlatformDevice PlatformDevice |
| typedef struct PlusDevice PlusDevice |
| typedef struct ProtocolEntry ProtocolEntry |
| typedef struct ReflectiveEdgeFilterMessage ReflectiveEdgeFilterMessage |
| typedef struct ResolutionMessage ResolutionMessage |
| typedef struct ReverseTouchActiveArea ReverseTouchActiveArea |
| typedef struct ReverseTouchActiveAreaMessage ReverseTouchActiveAreaMessage |
| typedef struct SensorDevice SensorDevice |
| typedef struct StreamingDataFrame StreamingDataFrame |
| typedef struct TouchActiveArea TouchActiveArea |
| typedef struct TouchActiveAreaMessage TouchActiveAreaMessage |
| typedef enum TouchEvent TouchEvent |
| typedef struct TouchMessage TouchMessage |
| typedef struct TouchModeMessage TouchModeMessage |
| typedef enum TouchModes TouchModes |
| typedef enum TouchOrientation TouchOrientation |
| typedef struct TransportEntry TransportEntry |
| enum MessageAction |
| enum OperationModes |
Enum describing the OperationModes that a sensor can operate in.
| enum TouchEvent |
Enum describing what state a touch can be in.
| Enumerator | |
|---|---|
| DownEvent |
New Touch object detected. |
| MoveEvent |
The Touch object is moving. |
| UpEvent |
The Touch object is no longer detected. |
| InvalidEvent |
Invalid Touch event reported by Device. |
| GhostEvent |
Ghost touch detected. |
| enum TouchModes |
| enum TouchOrientation |


| cache |
Class Coverage Distribution |
Class Complexity |
Top Project Risks |
Least Tested Methods
|
| Generated by PHP_CodeCoverage 1.0.4 using PHP 5.3.5 and PHPUnit 3.5.14 at Sun Nov 6 20:11:31 PST 2011. |


SENSELESS
A Film Review by James Berardinelli
RATING: ** OUT OF ****
United States, 1998 U.S. Release Date: 2/20/98 (wide) Running Length: 1:28 MPAA Classification: R (Profanity, sexual situations) Theatrical Aspect Ratio: 1.85:1
Cast: Marlon Wayans, David Spade, Brad Dourif, Matthew Lillard, Tamara Taylor, Rip Torn Director: Penelope Spheeris Producers: Eric L. Gold, David Hoberman Screenplay: Greg Erb, Craig Mazin Cinematography: Daryn Okada Music: Yello U.S. Distributor: Dimension Films
SENSELESS is a completely generic comedy that is only occasionally enlivened by the on-screen antics of Marlon Wayans. Yet, while Wayans has the energy and malleable features to match those of Jim Carrey (whom he appears to be impersonating), he isn't endowed with quite as forceful a screen presence. Of course, it doesn't help that he isn't given much to work with. The screenplay is dumb, and director Penelope Spheeris, who has ridden her WAYNE'S WORLD success through a series of mediocre movies, can't seem to find a stable tone. She seems to be striving for something more sophisticated than mere physical comedy, but her attempts never reach fruition. In fact, there are isolated instances when SENSELESS almost seems grim.
The screenplay does nothing with a potentially-interesting premise. Darryl Witherspoon (Wayans) is one of the hardest working men on the Stratford University campus. Because he sends money home to support his mother and siblings, he has to work about three different jobs to make ends meet. So, when the medical department advertises for human guinea pigs to test out their latest serum -- a wonder drug that magnifies the effectiveness of all five senses -- Darryl is first in line to collect the $3000 offered for being a subject. But, although Darryl's first reactions to "Protocol 563" are negative (extreme itching, being bothered by seemingly-unobtrusive sounds, etc.), he soon begins to reap the benefits of having enhanced senses. He turns into a leading candidate for a position at the Smythe Bates corporation, becomes the protégé of one of Smythe Bates' top men, Randall Tyson (Rip Torn), is transformed into a hockey star, and starts a romantic relationship with a fellow student, Janice (Tamara Taylor). All of this doesn't sit well with Darryl's chief detractor, Scott Butler (David Spade), who is determined to do anything to undermine his rival's success.
For the most part, SENSELESS falls into the category of "underdog makes good." The autopilot script follows a well-known trajectory: someone gains confidence in himself because of an outside agent, then, once that agent has been removed, finds that his true strength comes from within. In a case like this, when there's not much to be found in how the plot is structured, the movie's success depends on whether it offers enough laughs to camouflage its deficiencies. Unfortunately, while SENSELESS can boast a few funny sequences, they're too few and far apart to allow me to describe this film as anything better than mundane.
Marlon Wayans' performance is uneven. On some occasions, such as when he's running around buck naked on a city street, he is hilarious, but there are times when he's more bland than funny. And the sporadic attempts to copy Jim Carrey didn't do anything for me. Surprisingly, Wayans is perhaps most effective when working through the romantic subplot. He and Tamara Taylor develop a nice rapport that isn't completely reliant upon sexual tension. David Spade, despite being given second billing, is pretty much a nonentity. Aside from an occasional low key put-down, Spade doesn't have much to do -- as a villain, he's decidedly unimpressive. Supporting performers include SCREAM's Matthew Lillard as Darryl's excessively-pierced roommate and Brad Dourif as the doctor in charge of the Protocol 563 experiment.
There's really not a lot more to say about this film. While the amusement level isn't high, the familiarity level is, but at least the movie is short enough that boredom never sets in. Those who prefer not to be challenged by motion pictures, and who are willing to endure the same regurgitated material time-after-time, may enjoy SENSELESS. For everyone else, the handful of good jokes aren't enough to justify ninety minutes in a theater.
Copyright 1998 James Berardinelli
- James Berardinelli e-mail<EMAIL_ADDRESS>Now with more than 1300 reviews... The ReelViews web site: http://movie-reviews.colossus.net/
"No art passes our conscience in the way film does, and goes directly to our feelings, deep down into the dark rooms of our souls"
- Ingmar Bergman
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


--- layout: post title: "Really achieving your childhood dreams" date: 2007-10-05 comments: false ---


Solution Installation and Feature Activation
1. The PeopleZen Setup Package
The PeopleZen setup package (the
roxority_PeopleZen.zip file) is a zipped archive containing:
- Deploy.exe — a graphical user interface (GUI) setup wizard for installing
and deploying, or retracting and uninstalling PeopleZen to and from a SharePoint
server farm, to be run by a farm administrator user on the "primary"
SharePoint farm or web front-end server — in a multi-server farm environment,
that is the server hosting Central Administration.
- docs — a folder containing this product documentation in Microsoft
Compiled HTML (CHM) and plain HTML formats.
- Additional supporting files that are required for Deploy.exe to run
and must neither be modified nor removed.
2. Installing the PeopleZen Solution Package
Run Deploy.exe (under user account credentials added to the Farm Administrators
group in Central Administration, on the "primary" SharePoint farm
or web front-end server — in a multi-server farm environment, that is the
server hosting Central Administration) and when the Welcome screen
is displayed, click Next to start a series of system checks. To proceed with
the installation, all system checks need to succeed:
Click Next and on the following screen, select the Web Applications to deploy
PeopleZen to. You can also de-select those Site Collections you do not
want the setup wizard, upon successful deployment, to activate the Web Part
Feature immediately for you on.
Note: even when PeopleZen is deployed in a Web Application, before
you can add and start using the PeopleZen User Profiles Web Part on
Web Part Pages inside a web site, the PeopleZen User Profiles Web Part Feature
needs to be activated for the parent Site Collection, as described in the
Feature Activation section below. For now, the setup wizard performs these
activations for you, depending on your selections.
Click Next to start the installation and wait for the deployment jobs and
Feature activation to complete. This process can take up to a few minutes.
After deployment and Feature activation have completed, review the installation
log:
Once you have successfully completed the setup wizard, you can start using the PeopleZen
User Profiles Web Part on Web Part Pages inside the previously selected Site
Collections immediately.
3. Activating the Site Collection Feature
The PeopleZen User Profiles Web Part is a SharePoint Site Collection-scoped
Feature, which needs to be 'activated' on each Site Collection it
is to be used in.
- Start from the Web Part Page you want to use the Web Part on. Select Site Settings
from the Site Actions menu.
- The Site Settings page contains the Site Collection Administration
section. In this section, click the Site collection features link. If it
doesn't exist, click the Go to top level site settings link in the Site
Collection Administration section instead, and on the root web's Site Settings
page now displayed, click the Site Collection Features link inside the Site
Collection Administration section.
- The Site Collection Features page should contain a row titled PeopleZen
Web Part.
- If it does, click the Activate button to make the Web Part available
to all web sites, Web Part galleries and Web Part Pages inside this site collection;
or click the Deactivate button to disable the Web Part throughout this site
collection.
- Note: if you do deactivate the Site Collection Feature, existing PeopleZen
Web Parts will continue to work: the feature activation status only controls whether
the Web Part is available inside your Site Web Part Galleries.
- If the row doesn't exist, the PeopleZen solution package was not
yet deployed to this web application. On the System Settings page (Operations
in SharePoint 2007) in the Central Administration, click Manage Farm Solutions
(Solution management in SharePoint 2007). On the Solution Management
page, click roxority_peoplezen_xyz.wsp (the xyz part will be different
depending on your SharePoint version) to open the Solution Properties page.
Click Deploy Solution, then select this Site Collection's parent web
application and click OK. (The deployment is delegated to a background job
scheduled to run either at the date and time specified, or as soon as possible.
Once started, it may take this job up to a few minutes to finish deployment.)


Scope
Info
Includes
Exludes
Save
Example:
*://2ch.hk/*
*4chan.org/*
http://boards.4channel.org/*
*://8ch.net/test/*
...
Info...


What fraction of this circle is shaded red?
NUM_1 / DEN_1
Count the red slices for the numerator.
There plural( "is", NUM_1 ) plural( NUM_1, "red slice").
Count the total slices (\color{#e00}{\text{Red Slices}}
+ \color{#999}{\text{Gray Slices}}
) for the denominator.
There are DEN_1 slices total.
So your answer should be \dfrac{\color{#e00}{NUM_1}}{\color{#666}{\text{DEN_1}}}
.
What fraction of the bar is shaded red?
NUM_1 / DEN_1
Count the red blocks for the numerator.
There plural( "is", NUM_1 ) plural( NUM_1, "red block").
Count the total blocks (\color{#e00}{\text{Red Blocks}}
+ \color{#999}{\text{Gray Blocks}}
) for the denominator.
There are DEN_1 blocks total.
So your answer should be \dfrac{\color{#e00}{NUM_1}}{\color{#666}{\text{DEN_1}}}
.


SciNet is a High Performance Computing consortium based at the University of Toronto which operates Canada's largest supercomputer with 30,000 cores and supports researchers in a wide variety of disciplines from across the country.


M26 Mass - Better Battlelog Forums #1564
Sitemap
25. March 2012 18:04
Topicstarter
Guest
M26 Mass is not listed in the shotgun section. this should be fixed
25. March 2012 19:52
BrainFooLong
Administrator
Thanks. Is fixed in the next update.


--- section: contribute layout: docs title: Contributing to this site authors: - Sam Smith pagination: next: name: Editing a page link: /contribute/editing/ ---
Thank you for your interest in in helping to build The Open Data Handbook. We warmly welcome comments, corrections and additions, as well as suggestions for additional sections and areas to examine. For general discussion about the Handbook, please get in touch. To jump in with improvements and additions, read on.
In order to contribute, you need a little insight of how things work under the hood. We’re not going to go into too much detail here, but the are three components you need some understanding of.
GitHub is a web-based repository hosting service, which amongst other things offers revision control and source code management via a web-based graphical interface.
Any changes you wish to make, whether they be edits to an existing page, or creating a new one, will most likely be done via the Github website (it is also possible to download and edit the files on your local machine, instructions for this method will be added in the future). All the files for this site can be browsed and edited the Github website. You will need to sign up for a (free) Github account. For full instructions, see Editing a page.
Jekyll is a static site generator, which allows us to host websites based on our GitHub repositories. Jekyll takes the content, renders Markdown, and produces a complete, static website ready to be viewed on the web.
All you really need to know about Jekyll is the method it uses to include metadata (ie. page title). Each page needs to start with a section it calls Front Matter, containing the page title. An example is provided in the Adding a page section.
Markdown is a markup language with plain text formatting, designed so that it can be converted to HTML. Markdown can be used to create rich text using a plain text editor.
Markdown is your key to formatting the text your provide for this site. By learning a few intuitive rules you’ll be able to ensure your text is formatted with headings, list, quotes etc, without writing any HTML. For examples, head to the Markdown section.


Psalm 111
1 Praise Yahweh.
I will give thanks to Yahweh with my whole heart
in the assembly of the upright, in their gathering.
2 The works of Yahweh are great,
eagerly awaited by all those who desire them.
3 His work is majestic and glorious,
and his righteousness endures forever.
4 He does wonderful things that will be remembered;
Yahweh is gracious and merciful.
5 He gives food to his faithful followers.
He will always call to mind his covenant.
6 He showed his powerful works to his people
in giving them the inheritance of the nations.
7 The works of his hands are trustworthy and just;
all his instructions are reliable.
8 They are established forever,
to be observed faithfully and properly.
9 He gave victory to his people;
he ordained his covenant forever;
holy and awesome is his name.
10 To honor Yahweh is the beginning of wisdom;
those who carry out his instructions have good understanding.
His praise endures forever.


NASA unveils stunning photo of Geminids Meteor Shower ahead of peak this weekend
NASA's Astronomy Picture of the Day is this stunning photo of the Geminids Meteor Shower, which was snapped in Chile in 2013
by Shivali Best, https://www.facebook.com/shivalibestjournalist/While a meteor shower is stunning to behold, a new photo of the Geminids Meteor Shower is even more exceptional, thanks to a special appearance from Jupiter .
Photographer Yuri Beletsky snapped the photo in Chile back in 2013, but NASA has now featured it as its Astronomy Picture of the Day.
In the photo, the meteors all appear to be streaming out from the same point in the sky.
NASA explained: “When the Earth crosses a stream of Sun-orbiting meteors, these meteors appear to come from the direction of the stream - with the directional point called the radiant.
“An example occurs every mid-December for the Geminids meteor shower, as apparent in the featured image.
“Recorded near the shower's peak in 2013, the featured skyscape captures Gemini's shooting stars in a four-hour composite from the dark skies of the Las Campanas Observatory in Chile.”
The foreground of the photo features the du Pont Telescope, as well as the SWOPE telescope, as well as a beautiful setting sun.
Meanwhile, the skies also feature Jupiter seen as the bright spot near centre of the image, the central band of the Milky Way, seen vertically on the left of the image, and the Orion Nebula on the far left.
NASA added: “Dust swept up from the orbit of active asteroid 3200 Phaethon, Gemini's meteors enter the atmosphere traveling at about 22 kilometers per second.”
This year’s Geminids meteor shower will peak this weekend, providing you with the perfect opportunity to see a shooting star yourself!


Swedish Quiz 2 - Numbers
Welcome to the Swedish numbers quiz! You will be asked a question, and you must select the correct answer.
Welcome to the Swedish numbers quiz! You will be asked a question, and you must select the correct answer.
Question 1. What is the Swedish word for the number "two (2)"?.
Question 2. What is the Swedish word for the number "twelve (12) "?.
Question 3. What is the Swedish word for the number "fourty-two (42)"?.
Question 4. What is the Swedish word for the number "eighty-nine (89)"?.
Question 1: The correct answer was 1 - Två.
Question 2: The correct answer was 4 - Tolv.
Question 3: The correct answer was 1 - Fyrtiovå.
Question 4: The correct answer was 3 - Åttionio.
You answered all Swedish number questions correctly, well done!


Final Week!
It's been 10 weeks already!? Went by quite fast then you're not paying attention to days flying by. Over the couse we did accomplish a good amount and learned some valuable information especually about Google Analytics. Unlike those old clickers you might see on older websites GA go a lot farther than that in tracking users. Although we only scratched the surface, we gained a general understanding on how it works, and learned how to impliment our own goals and conduct experiments on our sites.
Analytics and Goals
My goal was to see if I could get users to arrive at a certain page from clicking on a link on my home page. It was actually two different events I set up so I could see if one was clicked on more than the other. One Icon took users to a page showing a video about web accessability and the other took them to a dancing Pikachu. I decided on this goal because I wanted to see if using icons as links instead of words would work just as well as getting users to trigger an event.
Experiment Testing
For the last stretch of things I conducted A/B experiments on my web page testing how making slight changes would affect the conversions my page would get. I used Google's optimization feature to get things up and running, it was quite easy to do so.
You can see below that the changes I made were not major but having them in different locations could alter the resutls each version could get.
Unsurprisingly the original ended up getting more clicks than the variant, I'm guessing it's because the original has the icon in a more natural place.
It's a simple change but it does what it needs to by helping me see which version will be utilized more. Initially I kept getting and error when I tried to verify my variantions that said it didn't detect the script, but after a while I realized that it was my ad blocker that was causing the issue. Everything was running just fine but on my end it seemed that there was a problem.
Conclusion
Google Analytics is an extremly valuable tool when gathering data about you website and its users. If there was more time I would have like to conduct larger and more complex testing just to learn a few more things and see what GA can really do.
Now that I have a general understanding on how to use GA I can being incorporating it into my future projects because the information gained cam help drive up data gathering needed to grow your website..


Hi guys. Could you plz check if download link for firefox is working because for me it isn't. I get this alert: add-on could not be installed because of connection failure. Can you help plz?
Maybe you can try that solution (deactivating avast for the download if you have one).
Seems like that avast blocks mostly all addon's that not directly coming from the mozilla amo store.
Greetz
Topicstarter
ErmiN
Thanks, works now. It was Avast.
Post edited 1 x times, last by
BrainFooLong
Administrator
BrainFooLong
Administrator
ErmiN wrote:
Thanks, works now. It was Avast.
Weird, i must take a look at avast what exactly the problem is.
The thing is that our addon is officially signed by mozilla amo store and our website use secure ssl certs for https - Both are things that guarantee good security and quality.


pymatgen.phonon.bandstructure module¶
-
class
PhononBandStructure
(qpoints, frequencies, lattice, nac_frequencies=None, eigendisplacements=None, nac_eigendisplacements=None, labels_dict=None, coords_are_cartesian=False, structure=None)[source]¶ Bases:
monty.json.MSONable
This is the most generic phonon band structure data possible it’s defined by a list of qpoints + frequencies for each of them. Additional information may be given for frequencies at Gamma, where non-analytical contribution may be taken into account.
Parameters: - qpoints – list of qpoint as numpy arrays, in frac_coords of the given lattice by default
- frequencies – list of phonon frequencies in THz as a numpy array with shape (3*len(structure), len(qpoints)). The First index of the array refers to the band and the second to the index of the qpoint.
- lattice – The reciprocal lattice as a pymatgen Lattice object. Pymatgen uses the physics convention of reciprocal lattice vectors WITH a 2*pi coefficient.
- nac_frequencies – Frequencies with non-analytical contributions at Gamma in THz. A list of tuples. The first element of each tuple should be a list defining the direction (not necessarily a versor, will be normalized internally). The second element containing the 3*len(structure) phonon frequencies with non-analytical correction for that direction.
- eigendisplacements – the phonon eigendisplacements associated to the frequencies in cartesian coordinates. A numpy array of complex numbers with shape (3*len(structure), len(qpoints), len(structure), 3). he First index of the array refers to the band, the second to the index of the qpoint, the third to the atom in the structure and the fourth to the cartesian coordinates.
- nac_eigendisplacements – the phonon eigendisplacements associated to the non-analytical frequencies in nac_frequencies in cartesian coordinates. A list of tuples. The first element of each tuple should be a list defining the direction. The second element containing a numpy array of complex numbers with shape (3*len(structure), len(structure), 3).
- labels_dict – (dict) of {} this links a qpoint (in frac coords or cartesian coordinates depending on the coords) to a label.
- coords_are_cartesian – Whether the qpoint coordinates are cartesian.
- structure – The crystal structure (as a pymatgen Structure object) associated with the band structure. This is needed if we provide projections to the band structure
-
asr_breaking
(tol_eigendisplacements=1e-05)[source]¶ Returns the breaking of the acoustic sum rule for the three acoustic modes, if Gamma is present. None otherwise. If eigendisplacements are available they are used to determine the acoustic modes: selects the bands corresponding to the eigendisplacements that represent to a translation within tol_eigendisplacements. If these are not identified or eigendisplacements are missing the first 3 modes will be used (indices [0:3]).
-
get_nac_eigendisplacements_along_dir
(direction)[source]¶ Returns the nac_eigendisplacements for the given direction (not necessarily a versor). None if the direction is not present or nac_eigendisplacements has not been calculated.
Parameters: direction – the direction as a list of 3 elements Returns: the eigendisplacements as a numpy array of complex numbers with shape (3*len(structure), len(structure), 3). None if not found.
-
get_nac_frequencies_along_dir
(direction)[source]¶ Returns the nac_frequencies for the given direction (not necessarily a versor). None if the direction is not present or nac_frequencies has not been calculated.
Parameters: direction – the direction as a list of 3 elements Returns: the frequencies as a numpy array o(3*len(structure), len(qpoints)). None if not found.
-
has_eigendisplacements
¶ True if eigendisplacements are present.
-
has_nac
¶ True if nac_frequencies are present.
-
class
PhononBandStructureSymmLine
(qpoints, frequencies, lattice, has_nac=False, eigendisplacements=None, labels_dict=None, coords_are_cartesian=False, structure=None)[source]¶ Bases:
pymatgen.phonon.bandstructure.PhononBandStructure
This object stores phonon band structures along selected (symmetry) lines in the Brillouin zone. We call the different symmetry lines (ex: Gamma to Z) “branches”.
Parameters: - qpoints – list of qpoints as numpy arrays, in frac_coords of the given lattice by default
- frequencies – list of phonon frequencies in eV as a numpy array with shape (3*len(structure), len(qpoints))
- lattice – The reciprocal lattice as a pymatgen Lattice object. Pymatgen uses the physics convention of reciprocal lattice vectors WITH a 2*pi coefficient
- has_nac – specify if the band structure has been produced taking into account non-analytical corrections at Gamma. If True frequenciens at Gamma from diffent directions will be stored in naf. Default False.
- eigendisplacements – the phonon eigendisplacements associated to the frequencies in cartesian coordinates. A numpy array of complex numbers with shape (3*len(structure), len(qpoints), len(structure), 3). he First index of the array refers to the band, the second to the index of the qpoint, the third to the atom in the structure and the fourth to the cartesian coordinates.
- labels_dict – (dict) of {} this links a qpoint (in frac coords or cartesian coordinates depending on the coords) to a label.
- coords_are_cartesian – Whether the qpoint coordinates are cartesian.
- structure – The crystal structure (as a pymatgen Structure object) associated with the band structure. This is needed if we provide projections to the band structure
-
as_phononwebsite
()[source]¶ Return a dictionary with the phononwebsite format: http://henriquemiranda.github.io/phononwebsite
-
get_branch
(index)[source]¶ Returns in what branch(es) is the qpoint. There can be several branches.
Parameters: index – the qpoint index Returns: A list of dictionaries [{“name”,”start_index”,”end_index”,”index”}] indicating all branches in which the qpoint is. It takes into account the fact that one qpoint (e.g., Gamma) can be in several branches
-
get_equivalent_qpoints
(index)[source]¶ Returns the list of qpoint indices equivalent (meaning they are the same frac coords) to the given one.
Parameters: index – the qpoint index Returns: a list of equivalent indices TODO: now it uses the label we might want to use coordinates instead (in case there was a mislabel)
-
write_phononwebsite
(filename)[source]¶ Write a json file for the phononwebsite: http://henriquemiranda.github.io/phononwebsite
-
eigenvectors_from_displacements
(disp, masses)[source]¶ Calculate the eigenvectors from the atomic displacements


What to watch on Netflix Canada in December 2019
NOW critics pick the best TV shows and movies coming to Netflix Canada, including Marriage Story, The Witcher, Tiffany Haddish: Black Mitzvah and You season 2
by NOW StaffWHAT WE CAN’T WAIT TO WATCH
The Witcher
As the world searches for the next Game Of Thrones, Netflix throws down this lavish sword-and-sorcery series – based on the work of Polish author Andrzej Sapkowski – starring Henry Cavill as Geralt of Rivia, a golden-eyed, white-haired monster hunter who travels the mystical kingdom of The Continent slaying beasts – while also starting to wonder whose will he truly serves. The streaming service is so committed to the show that it has already ordered a second season, and it’s rolling out the episodes of season one weekly, for that extra prestige feel. December 20
Tiffany Haddish: Black Mitzvah
Typically, Jewish girls and boys have their bat mitzvahs and bar mitzvahs when they turn 13. But Tiffany Haddish is celebrating her coming of age on her 40th birthday. The comedian recently learned about her Jewish heritage – her dad was an Eritrean Jew – and wanted to get in touch with her roots. In this stand-up special, Haddish reflects on her life, from being homeless and raising herself, to the time Beyoncé gave her a jumpsuit. No doubt, Haddish’s got chutzpah. December 3
6 Underground
What happens when self-aware action hero Ryan Reynolds teams up with decidedly un-self-aware action director Michael Bay? We’re about to find out in 6 Underground, which appears to be an international explosions-and-shooting picture revolving around an elite team of super-awesome crime fighters who operate with impunity because they’re all dead. We assume it’s just a technical thing, like that they’ve been declared dead to keep their families safe, but if it turns out they’re ghosts or zombies fighting crime, that could be even cooler. December 13
John Mulaney And The Sack Lunch Bunch
Not content with being one of the best regarded stand-ups in America, a delightful voice actor (in Big Mouth and Spider-Man: Into The Spider-Verse) and the wild card that makes his episodes of Documentary Now! really pop, John Mulaney decided he wanted to make a contemporary version of those empowering TV specials he watched as a kid, like Free To Be... You And Me. So Netflix let him, and now it’s here: a sketches-and-songs variety show with Mulaney, children and celebrity guest stars like David Byrne, Natasha Lyonne, Annaleigh Ashford, Richard Kind, André De Shields, Shereen Pimentel and Mysterio himself, Jake Gyllenhaal. We’re listening. December 24
Lost In Space (season 2)
You may recall that the first season of Netflix’s expensive upgrade of the fondly remembered 60s sci-fi series ended with our heroes finally getting lost in space – drawn into a wormhole, specifically, to meet the alien species that created that mysterious Robot. What happens now? More thrilling interstellar adventures, which our codependent family (Toby Stephens, Molly Parker, Taylor Russell, Mina Sundwall and Maxwell Jenkins) will presumably survive by the skin of their collective teeth, while Ignacio Serricchio’s ace pilot Don West tries to help and Parker Posey’s Dr. Smith does her chaotic-neutral thing. It’s what the people want. Read our season 1 review here. December 24
You (season 2)
Just in time for prime bingeing time – i.e. the days before and after Christmas when everyone is permanently in pajamas and robes – the second season of psychological thriller You is back. In the second season, Joe is laying low in California, dealing with his past and searching for love. Although high-brow TV watchers might write this series off as romanticizing stalking, as our reviewer said of the first season, it’s actually a smart satire that skewers modern culture. Read our season 1 review here. December 26
SOLID BETS
Marriage Story
After ripping people’s hearts out during its all-too-brief theatrical release at the TIFF Bell Lightbox, Noah Baumbach’s devastating study of a dissolving couple may not be as powerful when viewers are able to pause it and go for a walk to shake off the emotional weight. But Adam Driver and Scarlett Johansson’s fully committed performances will pull you along just the same, and the streaming format allows us to immediately revisit key scenes, and marvel at the incredible work Julie Hagerty, Merritt Wever, Ray Liotta, Alan Alda and a never-better Laura Dern are doing in the margins. Read our review here. December 6
The Shape Of Water
Guillermo del Toro’s lyrical Cold War monster movie plays like a cross between Amélie and Creature From The Black Lagoon, as a mute cleaner (Sally Hawkins) working in a secret government facility befriends an amphibious biped (Doug Jones) and decides to spring him from captivity. There aren’t many filmmakers operating at this scale who put as much trust in their audience as del Toro does – and this time, that trust was rewarded with four Oscars, including best picture and best director. And even though it’s playing Baltimore, Toronto’s never looked so good. Read our review here. December 1
Titanic
If the last time you watched Titanic was on VHS, when it was split over two tapes, you’re in luck: it’s finally coming to Netflix. During your re-watch, gush over young Leo and Kate, giggle at the nude drawing scene (“Draw me like one of your French girls”), get overly angry that Rose didn’t move over a couple inches on that damn floating door and then sing along to My Heart Will Go On. December 1
List of new titles available in December by date:
TV SHOWS
December 1
Eastsiders (season 4)
December 2
Miraculous: Tales Of Ladybug & Cat Noir (season 3, part 1)
Team Kaylie (part 2)
December 3
Tiffany Haddish: Black Mitzvah
December 4
The Blacklist (season 6)
Los Briceño
Magic For Humans (season 2)
December 5
Apache: La vida de Carlos Tevez
Greenleaf (season 4)
Home For Christmas
December 6
Astronomy Club: The Sketch Show
The Chosen One (season 2)
The Confession Killer
Fuller House (season 5 )
Glow Up
Spirit Riding Free: The Spirit Of Christmas
Teasing Master Takagi-san (season 2)
Three Days Of Christmas
Triad Princess
Virgin River
December 8
RuPaul’s Drag Race Holi-Slay Spectacular
December 9
A Family Reunion Christmas
Shameless (U.S.) (season 9)
December 10
Michelle Wolf: Joke Show
December 12
Jack Whitehall: Christmas With My Father
December 14
RuPaul’s Drag Race: All Stars (season 4)
December 15
Northern Rescue (season 1)
Star Trek
December 17
Ronny Chieng: Asian Comedian Destroys America!
December 18
Don’t F**k With Cats: Hunting An Internet Killer
Soundtrack
December 19
Ultraviolet (season 2)
Twice Upon A Time
December 20
The Witcher
December 24
Carole & Tuesday (part 2)
Crash Landing On You
John Mulaney & The Sack Lunch Bunch
Lost In Space (season 2)
Terrace House: Tokyo 2019-2020 (part 2)
December 26
Le Bazar de la Charité
Fast & Furious Spy Racers
You (season 2)
December 27
The Gift
Kevin Hart: Don’t F**k This Up
December 30
The Disastrous Life Of Saiki K.: Reawakened
December 31
The Degenerates (season 2)
The Neighbor
Suits (season 9)
Yanxi Palace: Princess Adventures
MOVIES
December 1
American Made
A Cinderella Story: Christmas Wish
Crocodile Dundee
Dead Kids
Dinner For Schmucks
The Duchess
Gladiator
The Greatest Showman
Look Who’s Talking Too
The Lovely Bones
Steel Magnolias
The Terminal
Terminator Genisys
Titanic
December 3
Especial de Natal Porta dos Fundos: A Primeira Tentação de Cristo
December 4
About a Boy
Baby Mama
The Bone Collector
Charlie Wilson’s War
Cinderella Man
How The Grinch Stole Christmas
Jarhead
Let’s Dance
Lock, Stock And Two Smoking Barrels
Notting Hill
Savages
December 5
A Christmas Prince: The Royal Baby
Jumanji: Welcome To The Jungle
V Wars
December 6
December 8
Light Of My Life
December 10
12 Strong
December 13
6 Underground
December 19
After The Raid
December 20
The Two Popes
December 24
Como caído del cielo
December 25
December 26
The App
December 28
Hot Gimmick: Girl Meets Boy
December 30
Alexa & Katie (season 3)
December 31
Made Of Honor
LAST CALL
TV series and movies leaving Netflix this month.
December 2
December 14
The Good Wife (seasons 1-6)
December 17
The Fall (seasons 1-3)


class: center, middle ### W4995 Applied Machine Learning # Recap & Summary 04/27/20 Andreas C. Müller ??? Only gonna talk about stuff for final, so not including first 11 lectures. Also: this lecture will not be comprehensive, there will be things in the exam not in this lecture. FIXME t-sne plot FIXME CASH what does it stand for again FIXME word2vec for document representations via averages --- class: middle # Model inspection ??? # FIXME post-hoc vs interpretable model --- # Types of explanations ## Explain "kind of model" - How would the model change if this feature was dropped? - Doesn't explain this particular fitted model ##Explain model globally - How does the output depend on the input? - Often: some form of marginals ## Explain model locally - Why did it classify this point this way? - Explanation could look like a "global" one but be different for each point. - "What is the minimum change to classify it differently?" ??? Marginals: how does the prediction change as function of a particular Features? --- # Permutation importance Idea: measure marginal influence of one feature ```python def permutation_importance(est, X, y, n_bootstrap=100): baseline_score = estimator.score(X, y) for f_idx in range(X.shape[1]): for b_idx in range(n_bootstrap): X_new = X.copy() X_new[:, f_idx] = np.random.shuffle(X[:, f_idx]) feature_score = estimator.score(X_new, y) scores[f_idx, b_idx] = baseline_score - feature_score ``` --- # Partial Dependence Plots .center[ ![:scale 70%](images/feat_impo_part_dep.png) ] ??? And so this is what the partner dependence plots look like. So this is the most important feature, the second most important feature and so on. This is sort of the marginal contribution of each feature. Basically, in each tree, you're summing out the contribution of all the other features, and you look only at what is the contribution of this particular feature. In general, this would be hard to compute. For tree-based models you can compute is a very efficient way because you can basically just sum up over the whole space by traversing the tree. Here with increased room size, the price increases and you can see how it increases and you can see that there's like a big step function here. And you can see that for increased LSTAT, the price decreases. For the others, there aren’t any drastic effect. There seems to be some threshold for NOX. And something funky going on DIS. The question is, so I said for random forest, you can't find out the direction of which way the feature influences. But that was for the feature importance. So the feature importance tells the impurity decrease. So here I also look at the feature importance and they're just numbers so they don't give you the direction. For both gradient boosting and random forest, you can look at partial dependency plot, and they will give you a non-parametric model of how a single feature influences it. Unfortunately, it’s not available in scikit-learn. You could do the same thing for random forests. Because in the end, the way they predict is quite similar. --- # Feature selection ## Why Select Features? - Avoid overfitting (?) - Faster prediction and training - Less storage for model and dataset - More interpretable model ??? There's a couple of reasons why you might want to feature selection. One is to avoid overfitting and get a better model. In practice, I have rarely seen that happen. It's not usually what I would try to do to increase performance. If I’m only interested in performance I probably would not try to do automatic feature selection unless I think only a very small subset of my feature is actually important. There's a lot of increasing performance just by selecting only important features. What I think is more commonly, the reason to do automatic feature selection is you want to shrink your model to make faster predictions, to train your model faster, to store fewer data and possibly to collect fewer data. If you're collecting the data or to feature from some online process, maybe it means you need fewer features, you need to store less information. I think actually the top reason to do feature selection is to have a more interpretable model. If your model is smaller, if your model has 5 features instead of 500, it's probably much easier for you to grasp what the model does. If you can have a model that is as good with way fewer features, it will be much easier to explain and most people will be happy with it. --- class: center, middle # AutoML --- # Formulating model-selection as Hyperparameter Optimization - One big search, many conditional Hyper-Parameters - Categorical, integer, continuous, conditional `$$\Lambda^* = \arg\max_\Lambda f(\Lambda)$$` Parameters $\Lambda$, model-evaluation $f$. ??? FIXME what does cash stand for? General optimization of unknown, non-differentiable f, possibly no-smooth. NP Hard in general. Function f is very slow to evaluate - think training a neural net for a week. --- # Approaches ## Random Search ## Bayesian Optimization, SMBO ## Successive Halving --- # Dimensionality Reduction --- class:split-40 # PCA .left-column[ `$$\large\max\limits_{u_1 \in R^p, \| u_1 \| = 1} \text{var}(Xu_1)$$` `$$\large\max\limits_{u_1 \in R^p, \| u_1 \| = 1} u_1^T \text{cov} (X) u_1$$` ] .smaller.right-column[ .center[ ![:scale 90%](images/pca-intuition.png) ] ] ??? So second way to describe PCA is using Reconstruction Error. I can look at how well this reconstruction here matches the input space. And if I want a reconstruction X’, that is the best possible in the least squares sense, so that is the square norm. If I look at the square norm of X – X’, then this is minimized exactly when I'm using X’, X’ is the PCA reduction. This is like written in a slightly different way.  But this problem is exactly solved by basically rotating using PCA direction, dropping the data and then rotating back. Maximizing the variance is the same thing as minimizing the Reconstruction Error, given a fixed number of dimensions. Find directions of maximum variance. (Find projection (onto one vector) that maximizes the variance observed in the data.) Subtract projection onto u1, iterate to find more components. Only well-defined up to sign / direction of arrow! --- # Manifold Learning ![:scale 100%](images/manifold-learning-structure.png) ??? Manifold learning is basically a class of algorithms that are the much more complicated transformation of the data. A manifold is a geometric structure in higher dimensions. So the idea is that you have low dimensional space, and sort of embedded into a high dimensional space. The classical example is this Swiss roll. Here, this is sort of a two-dimensional data set that was embedded in a weird way in the three-dimensional data set. And you hope to recover the structure of the data. And the structure of the data here would be the 2D structure of the layouts with Swiss roll. So PCA can't do that, because PCA is only linear. So if you would project this down with two dimensions, it's going to smooch all of these together and the blue points will be very close to the red points, if it projects down this axis, for example. And that's sort of not really what the geometry of the data is like. So for this, you would need nonlinear transformations of the input space that can sort of try to capture the structure in the data. Learn underlying “manifold” structure, use for dimensionality reduction. --- .smaller[```python from sklearn.manifold import TSNE from sklearn.datasets import load_digits digits = load_digits() X = digits.data / 16. X_tsne = TSNE().fit_transform(X) X_pca = PCA(n_components=2).fit_transform(X) ```] .left-column[![:scale 95%](images/pca-digits.png)] -- .right-column[![:scale 95%](images/tsne-digits.png)] ??? Now I want to show you some of the outcomes. This is a PCA visualization of digit dataset, so both PCA and peacenik are completely unsupervised. This is just doing PCA with two components, first principle, and second principal component, and then I color it by the class labels. PCA obviously doesn’t anything about the class labels, I just put them in here so we can see what's happening. You can see that sort of some of these classes is pretty well separated, others are like, kind of smooshed together. This is in t-SNE, when I first saw this I thought this was pretty impressive, because this Algorithm is completely unsupervised, but it still manages to mostly group nearly all of these classes in very compact clusters. In this completely unsupervised method, I could sort of draw boundaries by hand in 2D from the original 64-dimensional space and I could group the data. This 2D view can help me understand the data. --- # Clustering - Data Exploration - Are there coherent groups ? - How many groups are there ? -- - Data Partitioning - Divide data by group before further processing -- - Unsupervised feature extraction - Derive features from clusters or cluster distances -- - Evaluation and parameter tuning - Quantitative measures of limited use - Usually qualitative measures used - Best: downstream tasks - What is your goal? ??? So why would we want to use a clustering algorithm? The main usage that I've come across is exporation of the data and understanding the structure of the data. Are there similar groups within the data? What are these? How many are there? You can also use clustering to summarize or compress the data by representing each group by a single prototype, say the center. You can also use clustering if you want to divide your data, for example if you want to know different parts of the dataset behave fundamentally differently, so you could for example cluster a dataset, and then apply, say, a linear model, to each cluster. That would create a more powerfull model than just learning a linear model on the whole dataset. Another use is unsupervised feature extraction. You can also use clustering to derive a different representation of the data. An often cited application of clustering is customer segmentation, though I'd say data exploration or feature extraction are more common. --- # K-Means algorithm .wide-left-column[ .center[ ![:scale 100%](images/kmeans.png) ] ] .narrow-right-column[ .smaller[ - Pick number of clusters k. - Pick k random points as “cluster center” - While cluster centers change: 1. Assign each data point to it’s closest cluster center 2. Recompute cluster centers as the mean of the assigned points. ] ] ??? The algorithm proceeds like this: you pick the number of clusters k, then you randomly pick k data points from the data, and declare those as cluster centers. Then, you label each data point according to which cluster center it is closest to. Finally, you recompute the cluster centers as the means of the clusters, and you iterate. The algorithm always converges, which is when the assignment of the points to the clusters doesn't change any more. Often you might want to not wait until full convergence, but just stop if the cluster centers don't move much between iterations. So you can see the green cluster center moving towards the red here until it has captured all the points in this group. Questions? Who has seen this before? There's a couple of nice things about this algorithm; It's very easy to write down and understand. Each cluster is represented by a center, which makes things simple, and you can reassign any new point you observe to a cluster by just computing the distances to all the cluster centers. So in this case, if we build a model on some data that we collected, we can easily apply this clustering to new data points. That's not the case for all clustering algorithms. --- # NMF
.center[ ![:scale 80%](images/nmf.png) ] ??? In NMF, we compute X as a product of W and H. W stands for weights, and H stands for hidden representation. We want to find W and H so that their product is as close as possible to X. .center[ PCA (ordered by projection, not eigenvalue) ![:scale 70%](images/pca_projection.png) ] .center[ NMF (ordered by hidden representation) ![:scale 70%](images/nmf_hid_rep.png) ] ??? Here's an example of what this looks like in practice. Here we have NMF and PCA. This is the digit dataset, so the images are 28x28 grayscale images. I have these the digits, 0 and 1, and I tried to express them as a linear combination of the PCA components and as a linear combination of the NMF components. Here, I ordered them by which component is the most important for each of these data points. For both, 0 and 1, the most active component is this one, which is a guess either 0 or 1, depending on whether it's positive or negative. You can see that neither of these latter components looks a hardly like a 0 or a 1. What you’re seeing here is the cancellation effect taking place. The idea with NMF is that in NMF, hopefully, you got something that’s more interpretable in particular, since everything is positive. Usually, we can't actually look at our data this easily and so being able to interpret the component might be quite important. Often, people use this for gene analysis, for example, where you have very high dimensional vectors, and if you want to see which genes act together and so having these positive combinations really helps with interpretability. --- # Text Data: Bag of Words .center[
![:scale 85%](images/bag_of_words.png) ] ??? The most common approach that people use for this is bag of words, which is a very simple approach. We start with a string which would be your whole document. The first step is to tokenize it, which is basically breaking it up the document into words. And then, you built a vocabulary over all documents over all words. So you look over all the movie reviews, or all the emails or the text you have, and you collect all the tokens which are representing the words. Finally, using the vocabulary, you create a group representation. The length of your feature vector will be the size of the vocabulary. For each word in the vocabulary, you count how often this word appears in the string that you want to encode. Most of the words in the English language don't appear in a sentence. So most of these entries will be 0. But the word ‘ants’ appears once, so increment the count for ‘ants’ by 1 and so on. So I'll have 6 non-zero entries and all the other entries will be 0. So this is what we call like a very sparse vector so most entries of this vector are 0. When coding this, we’ll use a sparse matrix encoding. The idea of sparse matrix encoding is that you only store the non-zero entries. So basically, we will only store the 1s, so storing the string as a bag of word will cost us a nearly 6 floating point numbers. --- # N-grams: Beyond single words - Bag of words completely removes word order. - "didn't love" and "love" are very different! -- .center[ ![:scale 80%](images/single_words.png) ] ??? Because there's a big difference between ‘didn't love’ and ‘love’ and the default settings in the count vectorizer cannot distinguish between the two because somewhere along the document, it could appear ‘don't’ and you don't know if it's in front of ‘love’, or if it’s ‘love, don't hate’ or ‘don't love hate’, since they basically have the same representation. The idea behind N-grams is you look at pairs of words that appear next to each other. Unigrams looks at single words, bigrams look at pairs of two words next to each other, and trigrams looks at three words next to each other, and so on. Here instead of splitting it up into single tokens, we can split it up into pairs of tokens allowing me to have a little bit of context around each of the words. - N-grams: tuples of consecutive words --- # Word Embeddings .left-column[ ![:scale 120%](images/skip_gram_net_arch.png) .smallest[http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/] ] -- .right-column[ ![:scale 100%](images/cbow_skipgram_1.png) ] ??? Here’s the architecture that we will be using for this. It's kind of like neural network only, this is completely linear. In the left-hand side, we have the input layer with one hot encoding services in V-dimensional. V represents the size of vocabulary. And basically, only one of them will be 1 and the others will be 0. Then we have a weight matrix connecting it to the n-dimensional hidden layer. From there, we have multiple outputs that correspond to the previous word, previous 2 words, and previous 3 words and so on. And then the next word, next 2 words, next 3 words and so on. Each output is, again, one hot encoding, which allows you to predict each possible version of vocabulary. These layers here are very large in size. Behind the hidden layer is basically a logistic regression. So this is just a linear classifier with a softmax activation. So each of these Ws will try to predict what the words are before and after.  The reason why you have these two matrix multiplication is basically you want to create a bottleneck so if you had a very wide vector, you could do one matrix multiplication, basically, you want this matrix to have a lower rank. So you want to compress a representation to something small. Each row of W corresponds to a single word. We train this whole thing in a supervised fashion to minimize the log loss. But we’re only interested in is getting weights w. --- # Analogues and Relationships .center[ ![:scale 100%](images/king_queen.png) ] .smaller[ Answer “King is to Kings as Queen is to ?”:
Find closest vector to vec(“Queen”) + (vec(“Kings”) - vec(“King”)) ] .smallest[
Mikolov et. al. Linguistic Regularities in Continuous Space Word Representations (2013)
] ??? You can use these vectors to find relationships. So people found that if they look at the difference between men, and women, it looks very similar to the difference between uncle and aunt or king and queen. They also found that, for example, the difference between King and kings is the same as between queen and Queens. If you want to ask a question, what is to Queen as kings are to King? You can take vector representation of Queen and add the vector representation of kings, and subtract the vector representation of King. And if you look at the closest point to that, you'll find it's going to be queens. People did this with a lot of different sort of semantic pairs. --- # Neural Networks .center[ ![:scale 50%](images/nn_basic_arch.png) ] `$ h(x) = f(W_1x+b_1) $` `$ o(x) = g(W_2h(x) + b_2) $` ??? So now let's look at what a neural network is. It's very similar to the logistic regression, only applied several times. So here I drew three inputs x, from which we compute weighted sums. But now there is not only a single output, but we compute several intermediate outputs, the hidden units. Each of the hidden units is a weighted sum of the inputs, using different weights. Each hidden unit corresponds to an inner product with a weight vector, so all together they correspond to multiplying the input by a matrix, here a 3 by 4 matrix. We also add a bias for each of the hidden units, so a vector of dimension 4 for all of them. Then, we basically repeat the process and compute again weighted sums of the hidden units, to arrive at the outputs. Here this would correspond to multiplying by a 4 by 2 matrix, and adding a vector of dimension 2. Then we could apply for example a logistic sigmoid or softmax to get a classification output. So we basically do two matrix multiplications. If that was all, we could simplify this by just multiplying the matrices together into a single matrix, and we would just have a linear model. But the interesting part is that in the hidden layer, after we compute the weighted sum, we apply a non-linear function, often called activation function or just nonlinearity. That is what makes this whole function non-linear, and allows us to express much more interesting relationships. You can think of this as doing logistic regression with learning non-linear basis functions. The process is written here in formulas, we take the input x, multiply with a matrix W, add a bias b, and apply a non-linearity f, to get h. Then we multipy h by another matrix W', add a bias b' and apply a non-linearity g. This looks a bit like the word2vec we had last time, though here it's really very important to have the non-linear activation functions. Wha we want to learn from data are the weights and biases, so in this case a 3x4 matrix, a vector of dim 4, a 2x4 matrix and a vector of dim 2. Each of these steps of computation is usually called a layer. Here we have an input layer, one hidden layer, and an output layer. The hidden layer is called hidden because the computation is not actually part of the result, it's just internal to the algorithm. Though I'm drawing all these things in a similar way, don't confuse them with graphical models, as I drew them for latent dirichlet allocation. All these nodes here are completely deterministic functions, and the graph just illustrates very simple computations. --- #Convolution Neural Networks .center[ ![:scale 100%](images/CNET1.png) ] - Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner: Gradient-based learning applied to document recognition ??? Here is the architecture of an early convolutional net form 1998. The basic architecture in current networks is still the same. You can multiple layers of convolutions and resampling operations. You start convolving the image, which extracts local features. Each convolutions creates new “feature maps” that serve as input to later convolutions. To allow more global operations, after the convolutions the image resolution is changed. Back then it was subsampling, today it is max-pooling. So you end up with more and more feature maps with lower and lower resolution. At the end, you have some fully connected layers to do the classificattion. --- # Tricks for learning Deep Nets --- # Drop-out Regularization .center[ ![:scale 75%](images/dropout_reg.png) ] ??? For each sample, during training, you actually remove certain hidden nodes. So basically, you just X out the activations. You do this every time you trade over a training sample anew. So, this will be different for every example. The idea at the very high level is to add noise into the training procedure so that you basically make it harder to overfit. Since you drop out these parts, it gets much harder for the model to learn the training set by heart because you always remove different information. The goal is that you then learn weights that are more robust and since you're trying to prevent overfitting that possibly generalizes better. The dropout rate is often pretty high, sometimes it 50%, which means you set 50% of your units to zero. This is only during learning. You only want to add noise in the learning process. When you want to do predictions, you use all the weights but down weight them by the drop out rate. Here, we're not only perturbing the input image, but we're also perturbing the hidden units in a very particular way. As it turns out that actually works much better than just adding noise to the input. -- .smallest[ - https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - Rate often as high as .5, i.e. 50% of units set to zero! - Predictions: use all weights, down-weight by rate ] ??? --- #Batch Normalization .center[ ![:scale 80%](images/batch_norm.png) ] .smallest[ [Ioffe, Szegedy: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167) ] ??? Another relatively recent advance in neural networks is batch normalization. The idea is that neural networks learn best when the input is zero mean and unit variance. We can scale the data to get that. But each layer inside a neural network is itself a neural network with inputs given by the previous layer. And that output might have much larger or smaller scale (depending on the activation function). Batch normalization re-normalizes the activations for a layer for each batch during training (as the distribution over activation changes). The avoids saturation when using saturating functions. To keep the expressive power of the model, additional scale and shift parameters are learned that are applied after the per-batch normalization. --- # Problem ![:scale 90%](images/resnet-no-deep-nets.png) ??? We can't fit deep networks well - not even on training set! "vanishing gradient problem" - was motivation for relu, but not solved yet. The deeper the network gets, usually the performance gets better. But if you make your network too deep, then you can't learn it anymore. This is on CIFAR-10, which is a relatively small dataset. But if you try to learn a 56-layer convolutional, you cannot even optimize it on the training set. So basically, it's not that we can't generalize, we can't optimize. So these are universal approximators, so ideally, we should be able to overfit completely the training set. But here, if we make it too deep, we cannot overfit the training set anymore. It's kind of a bad thing. Because we can’t really optimize the problem. So this is sort of connected to this problem of vanishing gradient that it's very hard to backpropagate the error through a very deep net because basically, the idea is that the further you get from the output, the gradients become less and less informative. We talked about RELU units, which sort of helped to make this a little bit better. Without RELU units, you had like 4 or 5 layers, with RELU units, you have like 20 layers. But if you do 56 layers, it's not going to work anymore. Even it's not going to work anymore, even on the training set. So this has been like a big problem. And it has a surprisingly simple solution, which is the RES-NET layer. --- class: center # Solution: Residual Neural Networks ![:scale 60%](images/residual-layer.png) `$$y = F(x, \{W_i\}) + x \quad \text{ for same size layers}$$` -- `$$y = F(x, \{W_i\}) + W_sx \quad \text{ for different size layers}$$` ??? instead of learning a function. learn the difference to identity. if sizes different, add linear projection. Here's how the residual layer looks like. And the idea is, let's say you have a bunch of weight layers, instead of learning a function, we learn how the function is different from the identity. So you're not trying to model the whole relationship between x and y, you want to model how is y different from x. In practice, what happens is you have multiple weight layers, usually like 2, and you have skip connection that gives the identity from before these layers to after these layers. So basically, if you set these weights all to zero, you have a pass-through layer. And this allows information to be back-propagated much more easily because you have all these identity matrices, so something always gets backpropagated. So this obviously only works if y and x have the same shape. So in CNNs, often the convolutional layers have sort of the same shape. But then you also have max-pooling layers. And so what you can do is, instead of having the identity, you use a linear transformation. And this way, the gradients can propagate better. So just seems like a very simple idea but people have tried it before and it really made a big difference. --- class: center, middle ![:scale 25%](images/resnet-architecture.png) ??? dotted lines are linear projection, others are identity. MTG19, which a state of the art neural network before and these are all the layers. Next to it is a 34 layered, convolutional neural network. And then is the residual network. For each pair of two layers, you add in a skip connection. This black arrow is just an identity mapping because things are the same size. If there's pooling, there's this dashed arrow, which is a linear transformation. But basically, they're sort of a pathway that allows you to project the identity from the very beginning to the very end. Or from the output signal you get back towards the beginning. --- # Practical Considerations for Neural Nets --- class: middle # Questions ?


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Durant and Dressler: A tale of two Rider legends (Rob's Green Time Machine)
Durant and Weston Dressler were both low-key acquisitions by the Roughriders who went on to become superstars.
by Regina Leader-PostRob Vanstone compares the careers of Darian Durant and Weston Dressler, both low-key acquisitions by the Roughriders who went on to become superstars.
Rob’s Green Time Machine is a video series where Leader-Post sports columnist Rob Vanstone looks back at some of the most glorious, painful and quirky moments in Rider history.
Stay tuned for more Rob’s Green Time Machine videos by subscribing to our YouTube channel.
Previous episodes
Looking back on the Roughriders’ 2013 Grey Cup win
The top 10 Labour Day Classics
How the Riders have benefitted from other CFL teams folding
The Roughriders’ first Grey Cup win
O.J. Simpson’s connections to the Roughriders
Glen Suitor outrage and the 1989 Grey Cup
Ron Lancaster’s legendary last game


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AP Top News at 7:26 a.m. EST
by EndPlayIn last days, al-Baghdadi sought safety in shrinking domain
Elections in 4 states offer tests of 2020 voter enthusiasm
Former ambassador says she was warned to 'watch my back'
Iran to fuel centrifuges in new step away from nuclear deal
Lawyer: Trump's phone records back details of woman's claim
Trial of Trump crony Roger Stone promises political drama
APNewsbreak: Arpaio aides ignored order to halt sweeps
Hong Kong leader says she has Xi's backing to tackle unrest
Anti-police violence surges in the tough suburbs of Paris
Prescott throws for 3 TDs as Cowboys beat Giants again


2. Section Message Data
on page Message Data
,
Section C++ Datatypes
on page C++ Datatypes
, and
Annex Defined Values and Handles
on page Defined Values and Handles
.
MPI_LONG_LONG_INT, MPI_LONG_LONG (as synonym), MPI_UNSIGNED_LONG_LONG,
MPI_SIGNED_CHAR,
and MPI_WCHAR are moved from optional to official and they are therefore
defined for all three language bindings.
3. Section Return Status
on page Return Status
.
MPI_GET_COUNT with zero-length datatypes:
The value returned as the count argument of
MPI_GET_COUNT for a datatype of length zero where zero bytes
have been transferred is zero. If the number of bytes transferred is
greater than zero, MPI_UNDEFINED is returned.
High-quality implementations should raise an error when a keyval
that was created by a call to MPI_XXX_CREATE_KEYVAL is
used with an object of the wrong type with a call to
MPI_YYY_GET_ATTR, MPI_YYY_SET_ATTR, MPI_YYY_DELETE_ATTR, or
MPI_YYY_FREE_KEYVAL. To do so, it is necessary to maintain, with
each keyval, information on the type of the associated user
function.
( End of advice to implementors.)
Performance implications of using multiple edges or a non-symmetric
adjacency matrix are not defined. The definition of a node-neighbor
edge does not imply a direction of the communication.
( End of advice to users.)
Whether the errorcode is returned from the executable or
from the MPI process startup mechanism (e.g., mpiexec), is an aspect of
quality of the MPI library but not mandatory.
( End of advice to users.)
Where possible, a high-quality implementation will
try to return the errorcode from the MPI process startup mechanism (e.g.
mpiexec or singleton init).
( End of advice to implementors.)
An application may often repeat a call to
MPI_TYPE_CREATE_F90_xxxx with the same combination of
( xxxx, p, r).
The application is not allowed to free the returned predefined, unnamed
datatype handles. To prevent the creation of a potentially huge amount of
handles, the MPI implementation should return the same datatype handle for
the same ( REAL/COMPLEX/INTEGER, p, r) combination.
Checking for the
combination ( p, r) in the preceding call to
MPI_TYPE_CREATE_F90_xxxx and
using a hash-table to find formerly generated handles should limit the
overhead of finding a previously generated datatype with same combination
of ( xxxx, p, r).
( End of advice to implementors.)
4. Section Derived Datatypes
on page Derived Datatypes
.
General rule about derived datatypes:
Most datatype constructors have replication count or block length arguments.
Allowed values are
non-negative
integers. If the value is zero, no elements are
generated in the type map and there is no effect on datatype bounds or
extent.
5. Section Canonical MPI_PACK and MPI_UNPACK
on page Canonical MPI_PACK and MPI_UNPACK
.
MPI_BYTE should be used to send and receive data that is packed
using MPI_PACK_EXTERNAL.
6. Section All-Reduce
on page All-Reduce
.
If comm is an intercommunicator in MPI_ALLREDUCE, then
both groups should
provide count and datatype arguments that specify the same type
signature
(i.e., it is not necessary that both groups provide the same count value).
7. Section Group Accessors
on page Group Accessors
.
MPI_GROUP_TRANSLATE_RANKS and MPI_PROC_NULL:
MPI_PROC_NULL is a valid rank for input to
MPI_GROUP_TRANSLATE_RANKS, which returns
MPI_PROC_NULL as the translated rank.
8. Section Caching
on page Caching
.
About the attribute caching functions:
Advice
to implementors.
9. Section Naming Objects
on page Naming Objects
.
In MPI_COMM_GET_NAME:
In C, a null character is additionally stored at name[resultlen].
resultlen cannot be larger then
MPI_MAX_OBJECT_NAME-1.
In Fortran, name is padded on the right with blank characters.
resultlen cannot be larger then
MPI_MAX_OBJECT_NAME.
10. Section Overview of the Functions
on page Overview of the Functions
.
About MPI_GRAPH_CREATE and MPI_CART_CREATE:
All input arguments must have identical values on all processes
of the group of comm_old.
11. Section Cartesian Constructor
on page Cartesian Constructor
.
In MPI_CART_CREATE:
If ndims is zero then a zero-dimensional Cartesian topology is created. The
call is erroneous if it specifies a grid that is larger than the group size or if
ndims is negative.
12. Section General (Graph) Constructor
on page General (Graph) Constructor
.
In MPI_GRAPH_CREATE:
If the graph is empty, i.e., nnodes == 0,
then MPI_COMM_NULL is returned in all processes.
13. Section General (Graph) Constructor
on page General (Graph) Constructor
.
In MPI_GRAPH_CREATE:
A single process is allowed to be defined multiple times in the list of
neighbors of a process (i.e., there may be multiple edges between two
processes). A process is also allowed to be a neighbor to itself (i.e., a self
loop in the graph). The adjacency matrix is allowed to be non-symmetric.
Advice to users.
14. Section Topology Inquiry Functions
on page Topology Inquiry Functions
.
In MPI_CARTDIM_GET and MPI_CART_GET:
If comm is associated with a zero-dimensional Cartesian topology,
MPI_CARTDIM_GET returns ndims=0 and MPI_CART_GET will keep
all output arguments unchanged.
15. Section Topology Inquiry Functions
on page Topology Inquiry Functions
.
In MPI_CART_RANK:
If comm is associated with a zero-dimensional Cartesian topology,
coord is not significant and 0 is returned in rank.
16. Section Topology Inquiry Functions
on page Topology Inquiry Functions
.
In MPI_CART_COORDS:
If comm is associated with a zero-dimensional Cartesian topology,
coords will be unchanged.
17. Section Cartesian Shift Coordinates
on page Cartesian Shift Coordinates
.
In MPI_CART_SHIFT:
It is erroneous to call MPI_CART_SHIFT with a direction that is either
negative or greater than or equal to the number of dimensions in the Cartesian
communicator. This implies that it is erroneous to call MPI_CART_SHIFT with a
comm that is associated with a zero-dimensional Cartesian topology.
18. Section Partitioning of Cartesian structures
on page Partitioning of Cartesian structures
.
In MPI_CART_SUB:
If all entries in remain_dims are false or comm is already associated
with a zero-dimensional Cartesian topology then newcomm is associated
with a zero-dimensional Cartesian topology.
19. Section Version Inquiries
on page Version Inquiries
.
The subversion number changed from 0 to 1.
20. Section Environmental Inquiries
on page Environmental Inquiries
.
In MPI_GET_PROCESSOR_NAME:
In C, a null character is additionally stored at name[resultlen].
resultlen cannot be larger then MPI_MAX_PROCESSOR_NAME-1.
In Fortran, name is padded on the right with blank characters.
resultlen cannot be larger then MPI_MAX_PROCESSOR_NAME.
21. Section Error Handling
on page Error Handling
.
MPI_ {COMM,WIN,FILE }_GET_ERRHANDLER behave as if a
new error handler object is created.
That is, once the error handler is no longer needed,
MPI_ERRHANDLER_FREE should be called with the error handler returned
from MPI_ERRHANDLER_GET or
MPI_ {COMM,WIN,FILE }_GET_ERRHANDLER
to mark the error handler for deallocation.
This provides behavior similar to that of MPI_COMM_GROUP and
MPI_GROUP_FREE.
22. Section Startup
on page Startup
, see explanations to MPI_FINALIZE.
MPI_FINALIZE is collective over all connected processes.
If no processes were spawned, accepted or connected then this means
over MPI_COMM_WORLD; otherwise it is collective over the
union of all processes that have been and continue to be connected,
as explained in Section Releasing Connections
on page Releasing Connections
.
23. Section Startup
on page Startup
.
About MPI_ABORT:
Advice to users.
Advice
to implementors.
24. Section The Info Object
on page The Info Object
.
An implementation must support info objects as caches for arbitrary ( key,
value) pairs, regardless of whether it recognizes the key. Each function that
takes hints in the form of an MPI_Info must be prepared to ignore any key it
does not recognize. This description of info objects does not attempt to
define how a particular function should react if it recognizes a key but not the
associated value.
MPI_INFO_GET_NKEYS, MPI_INFO_GET_NTHKEY,
MPI_INFO_GET_VALUELEN, and MPI_INFO_GET
must retain all ( key, value)
pairs so that layered functionality can also use the Info object.
25.
Section Communication Calls
on page Communication Calls
.
MPI_PROC_NULL is a valid target rank in the MPI RMA calls
MPI_ACCUMULATE, MPI_GET, and MPI_PUT.
The effect is the same as for MPI_PROC_NULL in MPI point-to-point
communication.
See also item Changes from Version 2.0 to Version 2.1
in this list.
26.
Section Communication Calls
on page Communication Calls
.
After any RMA operation with rank MPI_PROC_NULL, it is still necessary to
finish the RMA epoch with the synchronization method that started the epoch.
See also item Changes from Version 2.0 to Version 2.1
in this list.
27. Section Accumulate Functions
on page Accumulate Functions
.
MPI_REPLACE
in MPI_ACCUMULATE,
like the other predefined operations,
is defined only for the predefined MPI datatypes.
28. Section File Info
on page File Info
.
About MPI_FILE_SET_VIEW and MPI_FILE_SET_INFO:
When an info object that specifies a subset of valid hints is passed to
MPI_FILE_SET_VIEW or MPI_FILE_SET_INFO,
there will be no effect on
previously set or defaulted hints that the info does not specify.
29. Section File Info
on page File Info
.
About MPI_FILE_GET_INFO:
If no hint exists
for the file associated with fh,
a handle to a newly created info object is returned that
contains no key/value pair.
30. Section File Views
on page File Views
.
If a file does not have the mode MPI_MODE_SEQUENTIAL,
then MPI_DISPLACEMENT_CURRENT is invalid as disp in
MPI_FILE_SET_VIEW.
31. Section External Data Representation: ``external32''
on page External Data Representation: ``external32''
.
The bias of 16 byte doubles was defined with 10383. The correct value is 16383.
32. Section Class Member Functions for MPI
on page Class Member Functions for MPI
.
In the example in this section,
the buffer should be declared as const void* buf.
33. Section Additional Support for Fortran Numeric Intrinsic Types
on page Additional Support for Fortran Numeric Intrinsic Types
.
About MPI_TYPE_CREATE_F90_xxxx:
Advice
to implementors.
34. Section Defined Constants
on page Defined Constants
.
MPI_BOTTOM is defined as
void * const MPI::BOTTOM.


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"ETH-BAAL "with Baal, a king of Sidon (B.C. 940-908), father of Jezebel, who was the wife of Ahab (1 Kings 16:31). He is said to have been also a priest of Astarte, whose worship was closely allied to that of Baal, and this may account for his daughter’s zeal in promoting idolatry in Israel. This marriage of Ahab was most fatal to both Israel and Judah. Dido, the founder of Carthage, was his granddaughter.
"ETH-BAAL "with Baal, a king of Sidon (B.C. 940-908), father of Jezebel, who was the wife of Ahab (1 Kings 16:31). He is said to have been also a priest of Astarte, whose worship was closely allied to that of Baal, and this may account for his daughter’s zeal in promoting idolatry in Israel.
This marriage of Ahab was most fatal to both Israel and Judah. Dido, the founder of Carthage, was his granddaughter.


Many vehicles, including tricycles, were also burnt as a result of the fire that followed the crash, while many people sustained varying degrees of injury.
Our correspondent, who visited the scene, gathered that the driver of the petrol-laden truck lost control around the NNPC filling station as the vehicle suffered a brake failure.
Eyewitnesses stated that the tanker rammed into four tricycles, two motorcycles, three cars and a bus, spilled its content on them and the ensuing fire consumed them.
However, men of the state fire service rushed to the scene to extinguish the fire.
Most of the filling stations in the area were closed, while men of the fire service were strategically positioned to prevent the spread of the fire.
Officials of the Federal Road Safety Corps were also on the ground to evacuate the corpses of the deceased, while those who were injured were rushed to hospitals.
According to the FRSC, the crash occurred around 8.50am at the Felele area along the Lokoja-Abuja Expressway as a result of brake failure on the part of a white DAF petrol-laden tanker with number plate JEG 141 XA.
Seventeen persons were said to be involved in the accident, out of which eight were killed, while seven were injured.
The victims were taken to the Federal Medical Centre and the Specialist Hospital, Lokoja, by the FRSC.
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DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
Millions invested to create more school places and improve facilities for children with special educational needs and disabilities (SEND).
A £215 million funding boost to transform the lives of thousands of children with special educational needs and disabilities, by increasing school capacity and making it easier for them to access good school places, has today (4 March 2017) been announced by Edward Timpson, Minister for Vulnerable Children and Families.
Every local council’s allocation is at least £500,000 to enable them to expand and improve their special educational needs and disabilities ( SEND ) provision , with more than half receiving at least £1 million.
Councils will be free to invest the funding as they see fit to help children and young people with education, health and care plans to get a high-quality education.
The investment can used in mainstream schools, including:
It could be used, for example, to build new specialised classrooms for children with emotional, social and mental health difficulties, expand existing classrooms to increase their size for those using mobility aids, purchase mobility equipment and even create new storage facilities for wheelchairs.
Minister for Vulnerable Children and Families, Edward Timpson, said:
This government is determined to build a country that works for everyone - a country where every child has an equal opportunity to reach their full potential regardless of their background, and any challenges they may face.
We’ve already made the biggest changes for pupils with special educational needs and disabilities in a generation, but we want to go further and build on that success.
Our multi-million pound investment will enable local councils to build new classrooms and improve facilities for pupils, ensuring that no child is left behind.
Councils will be expected to consult with local parents, carers, schools, and others on how their funding should be used. They will be required to publish a short plan showing how they will spend the funding.
This new fund follows the £23 million that has already been recently allocated to local authorities to support them to review their provision for children and young people with SEN and disabilities and make strategic plans to get the best outcomes.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 4 March 2017


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This function is the same as MPI_TYPE_INDEXED except that the blocklength is the same for all blocks. There are many codes using indirect addressing arising from unstructured grids where the blocksize is always 1 (gather/scatter). The following convenience function allows for constant blocksize and arbitrary displacements.
| MPI_TYPE_CREATE_INDEXED_BLOCK(count, blocklength, array_of_displacements, oldtype, newtype) | |
| IN count | length of array of displacements (integer) |
| IN blocklength | size of block (integer) |
| IN array_of_displacements | array of displacements (array of integer) |
| IN oldtype | old datatype (handle) |
| OUT newtype | new datatype (handle) |
int MPI_Type_create_indexed_block(int count, int blocklength, int array_of_displacements[], MPI_Datatype oldtype, MPI_Datatype *newtype)
MPI_TYPE_CREATE_INDEXED_BLOCK(COUNT, BLOCKLENGTH, ARRAY_OF_DISPLACEMENTS, OLDTYPE, NEWTYPE, IERROR)
INTEGER COUNT, BLOCKLENGTH, ARRAY_OF_DISPLACEMENTS(*), OLDTYPE, NEWTYPE, IERROR
MPI::Datatype MPI::Datatype::Create_indexed_block( int count, int blocklength, const int array_of_displacements[]) const