What is EEG?
EEG stands for Electroencephalography, and is a non-invasive method that records electrical activity of the brain.  EEG recordings in this study are not intended to be diagnostic and do not replace a clinical EEG recording.
Are there risks associated with EEG?
There are no risks associated with EEG recording procedure.
What will my child do in the EEG room?
Your child will spend about 1.5 hours in the EEG room, including preparation time and breaks. Your child will watch a child-friendly cartoon and play a set of short computer games while we record his or her brain activity.
There are no risks associated with EEG recording procedure.
How do I know that my child will be comfortable during EEG?
Before we begin, we explain the EEG process to your child. No portion of the EEG should be uncomfortable. If, at any point, your child is uncomfortable, he or she can discontinue participation. We take many breaks during EEG, where your child will be able to stretch and eat snacks.
What is MRI?
MRI stands for Magnetic Resonance Imaging. MRI is a non-invasive imaging method that uses a strong magnetic field and radio waves to make images of your brain. No X-rays or ionizing radiation are used in the MRI.  The MRI scans are not intended to be diagnostic and do not replace a clinical MRI scan.
Are there risks of MRI?
When you and your child arrive to our facilities, you will be greeted at the door by one of our researchers. We will briefly explain the day’s activities and ask you to sign a few pieces of paper, stating that you understand and agree to the voluntary research participation. Then, your child will be lead to a room by a researcher to begin participation! Participation includes, MRI , EEG , and other games. You will be lead to a comfortable waiting room, where we have many magazines, games, and refreshment options. After your child’s participation is finished, he or she will be rewarded with a small gift, and you will receive monetary compensation for your time.
What will my child do in the MRI?
You child will be asked to lie in our MRI machine for approximately 45 minutes but definitely not longer than 60 minutes. During each session, your child will be asked to keep his/her head still while MRI scans are completed.
During the experiment, your child will hear repetitive tapping noises from the machine and he/she will wear earplugs to reduce the noise. During certain scanning sequences, your child may also feel the table vibrate as part of its normal method of operation. Your child can watch a video of his/her choice inside the scanner to help lying quietly in the machine while scanning. Your child can bring his/her video or choose one from our selection of children DVDs such as Toy Story, Frozen etc.
What if my child gets scared?
 If you choose to stay in the room, you will be offered earplugs to reduce the noise of the MRI scanner which produces loud thumping and humming noises during imaging.
Additionally, participation in all aspects of this study is voluntary. While we would love for every child to participate in the MRI scan, we are understanding of every child’s needs and wishes. If your child decides, at any point before or during the scan, that he or she does not want to participate anymore, we will remove them from the scanner immediately. A call button is provided such that your child may have the scan stopped at any time during the study.
How do I know that my child will be comfortable in the MRI?
Before we enter the MRI scanning room, we explain very thoroughly what the MRI experience will look, feel, and sound like. Your child will practice in a mock model of MRI machine, where we will make sure he or she feels completely comfortable and understands the process. During the scan, your child will be provided with blankets, pillows, and stuffed toys if desired. For the entire duration of the scan, your child will be able to talk to us and we will be able to talk to them. If your child wishes, we can stay in the scanner room with them.
What else should I know about MRI?
 Any loose metallic object in your child’s body or in his/her pockets has to be removed because it will be attracted by the magnet and it will fly out of his/her pockets. This includes: key, jewelry, money clips, paper clips, safety pins, hairpins and barrettes. If you choose to stay inside the MRI scanning room during the session with your child, you will be asked to remove all metal and electronic objects prior to entering the MRI room. In addition, objects such as watches, credit cards and hearing aids could be damaged in the presence of magnetic field.


When mitmproxy receives a connection destined for an SSL-protected service, it freezes the connection before reading its request data, and makes a connection to the upstream server to "sniff" the contents of its SSL certificate. The information gained - the __Common Name__ and __Subject Alternative Names__ - is then used to generate the interception certificate, which is sent to the client so the connection can continue. This rather intricate little dance lets us seamlessly generate correct certificates even if the client has specifed only an IP address rather than the hostname. It also means that we don't need to sniff additional data to generate certs in transparent mode. Upstream cert sniffing is on by default, and can optionally be turned off.
| command-line | --no-upstream-cert |
|---|---|
| mitmproxy shortcut | o then U |


TEME
Welcome
How long is your Menstural cycle ?
How Long is your Period ?
When Did you last get the period
Cancel
Done


--- layout: post title: Shiny new Subversion and Trac cluster categories: - general - edge - horizon - releases author: Jeremy Kemper published: true date: 2007-02-10 08:08:00.000000000 +00:00 ---
You’ve all noticed the excruciating Rails svn updates and Trac molasses in the last couple of weeks. Following the release of Rails 1.2 we thoroughly overwhelmed our development server, no small feat for a hefty dual Xeon. Congratulations, all, for your hearty Rails appetite! Your sustained Mbps say more than words possibly could.
Our friends at TextDrive have stepped up once again to keep Rails development running smoothly and your production apps deploying predictably. Please give a warm welcome to our new development cluster, a load-balanced crew of SunFires and Thumpers hosting Trac at dev.rubyonrails.org and Subversion at svn.rubyonrails.org.
Subversion will remain available at the old dev URL so you needn’t touch your live apps. Feel free to migrate to the new URL at your own speed.


@@include('common/head.html')
The areas of assistance in the spring network include stress, natural disasters, various events, major loss, disability and other factors caused by psychological trauma, psychological trauma of the psychological, legal, medical aid technology platform for the psychological trauma of the needs of those who have The help of the relevant skills provides a bridge of interworking
"Spring wind project" is the first public welfare project in mainland China
The areas of aid of the spring air net (www.858.org.cn) include stress, natural disasters, various events, major losses, disability and psychological trauma, which is psychological trauma...


SFAccountManager Class Reference
| Inherits from | NSObject |
| Declared in | SFAccountManager.h |
Tasks
-
+ sharedInstance
Returns the singleton instance of this class for the default account.
-
+ sharedInstanceForAccount:
Returns the singleton instance of this class for the given account.
-
+ logoutSettingEnabled
Wheher or not the Logout app setting is enabled.
-
+ ensureAccountDefaultsExist
Makes sure that the app-level configuration for the login host has been populated with the current value in the app settings of the app.
-
+ loginHost
The configured login host for the app.
-
+ setLoginHost:
Sets a new value for the login host for the app.
-
+ updateLoginHost
Synchronizes the app-level login host setting with the value in app settings.
-
+ clientId
The OAuth client ID of the app.
-
+ setClientId:
Sets a new value for the app’s OAuth client ID.
-
+ redirectUri
The app’s OAuth redirect URI.
-
+ setRedirectUri:
Sets a new value for the app’s OAuth redirect URI.
-
+ scopes
The OAuth scopes associated with the app.
-
+ setScopes:
Sets a new value for the OAuth scopes associated with the app.
-
– clearAccountState:
Clears the account state of the given account (i.e. clears credentials, coordinator instances, etc.
-
– mobilePinPolicyConfigured
Whether or not there is a mobile pin code policy configured for this app.
-
accountIdentifier
The account identifier for a given account manager instance.
property -
coordinator
The OAuth Coordinator associated with this account.
property -
idCoordinator
The Identity Coordinator associated with this account.
property -
credentials
The auth credentials maintained for this app.
property -
idData
The Identity data associated with this account.
property
Properties
accountIdentifier
The account identifier for a given account manager instance.
@property (nonatomic, readonly) NSString *accountIdentifier
Discussion
The account identifier for a given account manager instance.
Declared In
SFAccountManager.h
coordinator
The OAuth Coordinator associated with this account.
@property (nonatomic, retain) SFOAuthCoordinator *coordinator
Discussion
The OAuth Coordinator associated with this account.
Declared In
SFAccountManager.h
credentials
The auth credentials maintained for this app.
@property (nonatomic, retain) SFOAuthCredentials *credentials
Discussion
The auth credentials maintained for this app.
Declared In
SFAccountManager.h
Class Methods
clientId
The OAuth client ID of the app.
+ (NSString *)clientId
Return Value
The OAuth client ID of the app.
Declared In
SFAccountManager.h
ensureAccountDefaultsExist
Makes sure that the app-level configuration for the login host has been populated with the current value in the app settings of the app.
+ (void)ensureAccountDefaultsExist
Discussion
Makes sure that the app-level configuration for the login host has been populated with the current value in the app settings of the app.
Declared In
SFAccountManager.h
loginHost
The configured login host for the app.
+ (NSString *)loginHost
Return Value
The configured login host for the app.
Declared In
SFAccountManager.h
logoutSettingEnabled
Wheher or not the Logout app setting is enabled.
+ (BOOL)logoutSettingEnabled
Return Value
YES if so, NO if not.
Discussion
Wheher or not the Logout app setting is enabled.
Declared In
SFAccountManager.h
redirectUri
The app’s OAuth redirect URI.
+ (NSString *)redirectUri
Return Value
The app’s OAuth redirect URI.
Declared In
SFAccountManager.h
scopes
The OAuth scopes associated with the app.
+ (NSSet *)scopes
Return Value
The OAuth scopes associated with the app.
Declared In
SFAccountManager.h
setClientId:
Sets a new value for the app’s OAuth client ID.
+ (void)setClientId:(NSString *)newClientId
Parameters
- newClientId
The new value for the client ID.
Discussion
Sets a new value for the app’s OAuth client ID.
Declared In
SFAccountManager.h
setLoginHost:
Sets a new value for the login host for the app.
+ (void)setLoginHost:(NSString *)newLoginHost
Parameters
- newLoginHost
The new host value to set.
Discussion
Sets a new value for the login host for the app.
Declared In
SFAccountManager.h
setRedirectUri:
Sets a new value for the app’s OAuth redirect URI.
+ (void)setRedirectUri:(NSString *)newRedirectUri
Parameters
- newRedirectUri
The new value for the app’s OAuth redirect URI.
Discussion
Sets a new value for the app’s OAuth redirect URI.
Declared In
SFAccountManager.h
setScopes:
Sets a new value for the OAuth scopes associated with the app.
+ (void)setScopes:(NSSet *)newScopes
Parameters
- newScopes
The new value for the OAuth scopes of the app.
Discussion
Sets a new value for the OAuth scopes associated with the app.
Declared In
SFAccountManager.h
sharedInstance
Returns the singleton instance of this class for the default account.
+ (SFAccountManager *)sharedInstance
Discussion
Returns the singleton instance of this class for the default account.
Declared In
SFAccountManager.h
sharedInstanceForAccount:
Returns the singleton instance of this class for the given account.
+ (SFAccountManager *)sharedInstanceForAccount:(NSString *)accountIdentifier
Parameters
- accountIdentifier
The account identifier of the class.
Discussion
Returns the singleton instance of this class for the given account.
Declared In
SFAccountManager.h
updateLoginHost
Synchronizes the app-level login host setting with the value in app settings.
+ (BOOL)updateLoginHost
Return Value
YES if the two values were initially different, NO otherwise.
Discussion
Synchronizes the app-level login host setting with the value in app settings.
Declared In
SFAccountManager.h
Instance Methods
clearAccountState:
Clears the account state of the given account (i.e. clears credentials, coordinator instances, etc.
- (void)clearAccountState:(BOOL)clearAccountData
Parameters
- clearAccountData
Whether to optionally revoke credentials and persisted data associated with the account.
Discussion
Clears the account state of the given account (i.e. clears credentials, coordinator instances, etc.
Declared In
SFAccountManager.h


The Definition of an Example
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the...
Read more


Before Change
"list of IDs or just an ID: %s" % type(vals))
if timeout is None:
timeout = 0
out = [self._get(x, timeout) for x in to_get]
if single:
out = out[0]
return out
After Change
else:
deadline = time.monotonic() + timeout
out = []
for obj_ref in to_get:
res = None
// Implement non-blocking get with a short-polling loop. This allows
// cancellation of gets via Ctrl-C, since we never block for long.
while True:
try:
if deadline:
op_timeout = min(
MAX_BLOCKING_OPERATION_TIME_S,
max(deadline - time.monotonic(), 0.001))
else:
op_timeout = MAX_BLOCKING_OPERATION_TIME_S
res = self._get(obj_ref, op_timeout)
break
except GetTimeoutError:
if deadline and time.monotonic() > deadline:
raise
logger.debug("Internal retry for get {}".format(obj_ref))
out.append(res)
if single:
out = out[0]
return out


Warehouse Chief Croop:
I'm Warehouse Chief Croop of the Iron Gate Guild. I deal with all the Scavengers who come into Oren, so naturally my skin's tougher than sheet metal these days!
Listen to information about second class transfer.
Sub Class.
Clan.
Alliance.
Quest.


You can customise the color of the signature as follows.
Clear Export Import Data to Image Import Data to Canvas


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/vec.2Econs.20in.20chapter.202.html ---
Hi, from chapter 2, vec.cons is defined as constant cons : Π {α : Type u} {n : ℕ}, α → vec α n → vec α (n + 1)
(I made the args optional). Trying #check cons 5
works as expected, but doing #check cons 5 (vec nat 10)
gives an error
error: type mismatch at application cons 5 (vec ℕ 10) term vec ℕ 10 has type Type : Type 1 but is expected to have type vec ?m_1 ?m_2 : Type ?
What is it that I am doing wrong?
the definition of cons
should be read as follows: given a value of type α
and a value of type vec α n
, you get a value of type vec α (n + 1)
. you are passing vec nat 10
, which is a type itself, not a value of type vec α n
(similar to how ℕ is a type while 5 is a value of type ℕ).
an example that would work is #check cons 5 empty
, for instance.
Thank you, I understand better now. So then to test with my own examples, I'd have to create vectors using these elementary functions
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


More Usage Examples¶
For checking all the optional arguments, please use –help.
$ GenEpi --help
You will obtain the following argument list:
usage: GenEpi [-h] -g G -p P [-s S] [-o O] [-m {c,r}] [-k K] [-t T]
[--updatedb] [-b {hg19,hg38}] [--compressld] [-d D] [-r R]
optional arguments:
-h, --help show this help message and exit
-g G filename of the input .gen file
-p P filename of the input phenotype
-s S self-defined genome regions
-o O output file path
-m {c,r} choose model type: c for classification; r for regression
-k K k of k-fold cross validation
-t T number of threads
update UCSC database:
--updatedb enable this function
-b {hg19,hg38} human genome build
compress data by LD block:
--compressld enable this function
-d D threshold for compression: D prime
-r R threshold for compression: R square
Applying on Your Data¶
$ GenEpi -g full_path_of_your_.GEN_file -p full_path_of_your_.CSV_file -o ./
For Quantitative Study¶
GenEpi can support both case/control and quantitative studies, for quantitative studies please modify the parameter -m. You could download the test data and excute the following command.
$ GenEpi -g sample.gen -p sample_q.csv -o ./ -m r
Changing the Genome Build¶
For changing the build of USCS genome browser, please modify the parameter -b.
$ GenEpi -g example -p example -o ./ --updatedb -b hg38
Threshold for Dimension Reduction¶
You could modify the threshold for Linkage Disequilibrium dimension reduction by following command.
$ GenEpi -g example -p example -o ./ --compressld -d 0.9 -r 0.9


Paragraph font variant set to normal.
Paragraph font variant set to small-caps.
i am green
b - bold tag


http://galogetlatorre.blogspot.com/2013/07/how-can-you-be-fooled-by-u202e-trick.html
Unicode U+202E knows as an annulment from right to left. Similar to a potential file upload bypass (where you can just rev(php.txt) or similar function to reverse the extensin.
Now we are going to follow the steps below to accomplish our goal:
Open the Windows Character Map (Start, Run, charmap)- Find and Copy the Unicode character U+202E. Notice that at the bottom left shows the ASCII value of the characters (see Figure 2).
- Paste (Ctrl + V) the character just before the extension point: "document[[U+202E]].exe"
- Enter the extension that you want but in reverse, for example, if we want "doc", we need to write "cod", or if we want "pdf", then we need to write "fdp".
- Finally, to perfect the infection vector, a good idea would be to change the icon of the malicious file and also use a name that can trick the user, considering that the "exe" or the original extension must remain.
Possible Countermeasures:
If we try to rename the file (F2), we will see something strange, this effect is produced by the special character, so this is a clue to realize that something is wrong.
If you are in a GNU/Linux based system, you can check the headers of the file with some commands.
Another trick that you can use to verify the name of any file and its extension could be using the command prompt, but how?


The gameplay starts with the setup phase. You get to remove an opponent's piece whenever you create a string of three pieces on one line. As this rule is in effect during setup as well as regular play, you will need to be strategic from the first moment of the game. You can't remove an opponent's piece that is part of a three-piece string (or mill) unless there are no other pieces of theirs to remove.
Players taking turns laying their pieces on vacant points. You each have nine pieces, so you choose from among the 24 points on the board to place your pieces. Each player alternates placing a piece. This is where you must use strategy, seeing where your opponent is placing a piece and where it will be advantageous for you to place yours. You want to block your opponent from creating a string of three pieces on one line, or they will be able to remove one of your pieces. You are also looking for your chance to create a string of three pieces on one line.


All Classes
Account
AccountManagementPanel
AccountSetPanel
BaseFrame
Board
Checker
Computer
Connect4Summative2
ControlPanel
FileManipFunctions
GamePanel
HelpPanel
ScorePanel
SendChangeSignal
SendChangeString
SortSearchFunctions
SplashScreenPanel


WITNESS LIST State Affairs Committee July 6, 2005 -11:00A SB 62 FOR: ., Sputnik (Self), Alvin, TX Gailes, Jimmy (Texas Landowners Council, Inc.), Dripping Springs, TX Gore, Wright (Western Seafood Co.), Freeport, TX Lee, Donald (Texas Conference of Urban Counties), Austin, TX Peacock, Bill (Texas Public Policy Foundation), Austin, TX Registering, but not testifying: For: Dixon, Patrick (Libertarian Party of Texas), Lago Vista, TX Phenix, Billy (Texas Land and Mineral Owners Association), Austin, TX SJR 9 FOR: ., Sputnik (Self), Alvin, TX Gailes, Jimmy (Texas Landowners Council, Inc.), Dripping Springs, TX Gore, Wright (Western Seafood Co.), Freeport, TX Lee, Donald (Texas Conference of Urban Counties), Austin, TX Peacock, Bill (Texas Public Policy Foundation), Austin, TX Registering, but not testifying: For: Dixon, Patrick (Libertarian Party of Texas), Lago Vista, TX Phenix, Billy (Texas Land and Mineral Owners Association), Austin, TX SJR 10 FOR: ., Sputnik (Self), Alvin, TX Gailes, Jimmy (Texas Landowners Council, Inc.), Dripping Springs, TX Gore, Wright (Western Seafood Co.), Freeport, TX Lee, Donald (Texas Conference of Urban Counties), Austin, TX Peacock, Bill (Texas Public Policy Foundation), Austin, TX Registering, but not testifying: For: Dixon, Patrick (Libertarian Party of Texas), Lago Vista, TX Phenix, Billy (Texas Land and Mineral Owners Association), Austin, TX


Total Summary
Calculating data summaries is a must-have feature of a data grid. The GridControl provides a set of data summary calculation options.
A total summary is an aggregate function value calculated against all rows within the specified column and displayed within the total summary panel at the bottom of the grid. Built-in aggregate functions include: Min, Max, Average, Sum, and Count.
In this demo, a total summary is created to calculate the sum of values of the Total column. You can select another aggregate function for this total summary, or create total summaries for other columns using the context menu invoked when you touch and hold the total summary panel.
Related APIs:


|
Hunter S. ThompsonAuthor, Fear and Loathing in Las Vegas and Hells Angels.There have been two Hollywood attempts to turn Hunter's writings into movies. The first, Where the Buffalo Roam starring Bill Murray, was an astounding failure. However, it did have one unintended effect: in hanging around with Thompson for a while, it turned Murray into an unrepetant jerk for a good amount of time after production wrapped, affecting his relationship/interaction with the Saturday Night Live production. The second attempt, Fear and Loathing in Las Vegas, was more successful, with the efforts of Terry Gilliam and Johnny Depp providing a near-exacting portrayal of the novel. George W Bush once attended a Superbowl party hosted by Thompson, back in his cocaine days. |


Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel (not for Windows), and 'Rcpp' has been used to speed up calculations. Other R packages that perform similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
| Version: | 0.2.2 |
| Imports: | Rcpp, R6, lbfgs |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat, knitr, rmarkdown, microbenchmark, numDeriv, MASS |
| Published: | 2017-09-11 |
| Author: | Collin Erickson |
| Maintainer: | Collin Erickson <collinberickson at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | GauPro results |
| Reference manual: | GauPro.pdf |
| Vignettes: |
Leave-one-out cross-validation and error correction A Guide to the GauPro R package Introduction to Gaussian Processes Derivatives for estimating Gaussian process parameters Spatial derivatives of Gaussian process models |
| Package source: | GauPro_0.2.2.tar.gz |
| Windows binaries: | r-devel: GauPro_0.2.2.zip, r-release: GauPro_0.2.2.zip, r-oldrel: GauPro_0.2.2.zip |
| OS X binaries: | r-release: GauPro_0.2.2.tgz, r-oldrel: GauPro_0.2.2.tgz |
| Old sources: | GauPro archive |
| Reverse depends: | ParBayesianOptimization |
| Reverse suggests: | IGP |
Please use the canonical form https://CRAN.R-project.org/package=GauPro to link to this page.


This is an interesting read - https://harizanov.com/2015/11/rfm69-to-mqtt-gateway-using-esp8266/
I seem to recall that Martin designed the original RFM2Pi module, and this development appears to open all sorts of opportunities.
Paul
Archived Forum |
|
RFM to MQTTSubmitted by Paul Reed on Sat, 07/11/2015 - 20:17This is an interesting read - https://harizanov.com/2015/11/rfm69-to-mqtt-gateway-using-esp8266/ Paul » |
Re: RFM to MQTT
After reading Martin's article, I posted a comment on his page asking him:
I’d like to make a suggestion based on some user feedback I’ve seen on the OpenEnergyMonitor forum. When an OEM user runs more than one RF link, sometimes they lose data because of packet collisions. The idea I had in mind was to have the RFM69 receiver listen before transmitting. Something similar to the way Ethernet collision detection works. From as simple as detecting a carrier, to as sophisticated as detecting data. Is this a function that’s already present? If not, is it possible to incorporate it into the RFM69 code?
To which Martin replied:
Interesting, can you point me to that discussion? Generally it shouldn’t be a problem if the remote node requires ACK, as it will resend in case of non receipt. The RFM69 is capable of monitoring RSSI, in theory it could wait for the air to clear before transmission, but that would include buffering the packets and more complex handling. Also, what are the chances that a 30ms transmission that happens every 5min for example collides with another one?If packet loss is a problem, a dual gateway could be considered, one for incoming and the other for outgoing transmissions.
Another user commented:
The LowPowerLab driver implements CSMA (Carrier Sense Multiple Access) [https://github.com/LowPowerLab/RFM69/blob/master/RFM69.cpp#L210]. It does not however implement Collision Avoidance (CSMA/CA) needed to prevent nodes B and C from colliding once they both detect node A has stopped transmitting. Even if you only have a few nodes in your OEM system your home security system will cause disturbance at 868MHz, as well as your neighbors and so on. CSMA/CA and ACKs are crucial for a reliable RF-link. (emphasis mine)


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Overview and Demos
<EMAIL_ADDRESS>@UrthecastDev
It's an Application Programming Interface.
A "user interface" for the world of servers and software
A way for two software systems to communicate
All around you. Probably used 20+ APIs already today
Enable programmatic access to data and services
Humans aren't the only user.
24 / 7 / 365 access
Global events don't always happen during business hours
Response times measured in milliseconds
Scalable - serve many customers simultaneously
WYSIWYG vs WYSIAYG
Foundational layer for apps - internal and external
This presentation is a web application
All examples are making live API requests
Orange links make API requests
This presentation is open source and available on GitHub
Before we begin, need to add an API key and secret
Alright, let's begin!
The world is a big place
UrtheCast captures a lot of data
Not all data is relevant to every customer
So, while much of the world is beautiful...
You need a way to filter the data
Define a region of the planet you want to monitor
See capture opportunities from our satellites and sensors
Search our imagery archive for data that intersects
Track events happening within your AOI
Clip GeoTIFF imagery to save costs and bandwidth
GeoJSON:
{"name":"San Francisco","geometry":{"type": "Polygon", "coordinates": [[[-122.49927520751953,37.667516276171426],[-122.49824523925781,37.68137494751297],[-122.5191879272461,37.78726741375342],[-122.48794555664061,37.79350762410675],[-122.48176574707031,37.81114015184754],[-122.46665954589844,37.81439492742207],[-122.46219635009764,37.81005519477107],[-122.40795135498047,37.815751041563544],[-122.3650360107422,37.7761422535397],[-122.36125946044922,37.737598269779824],[-122.33963012695312,37.72320698914131],[-122.36949920654295,37.69821925772116],[-122.38391876220702,37.70012079430599],[-122.38048553466797,37.68137494751297],[-122.36984252929688,37.65909111310584],[-122.49927520751953,37.667516276171426]]]}}
We'll use our AOI in future examples.
Data begins with the upstream.
Satellite Tracker APIs enables you to:
Learn what satellites and sensors are available
Query past, present, predicted position of satellites
See historical capture opportunities and 14 day predictions
Access raw TLE data
When is the next capture opportunity for my AOI?
Every satellite has a different orbit.
Each sensor has a different capture area.
The Sat Tracker API makes it trivial to know when we might capture your AOI.
When is the next capture opportunity for my AOI from Theia?
When is the next capture opportunity for my AOI from Deimos-1?
When is the next capture opportunity for my AOI from Deimos-2?
When is the next capture opportunity for my AOI from Landsat 8?
Capture opportunities are not a guarantee of capture
How do you know if new data was captured?
Event Streams let you:
See all events happening across the web platform
Track activity within your AOI
Coming soon: Subscriptions to 'push' events to your app
A Scene ID represents a capture from one of our sensors.
The search engine for all of our archived data
Searchable archive of all imagery on platform
Multi sensor - Theia, Deimos-1, Deimos-2, Landsat 8
Search by: satellite, sensor, cloud coverage, sun elevation angle, geometry, area of interest, season, and more
How many scenes intersect with our AOI?
The planet is big, so we created an AOI
Forecasting captures is hard, so we used Satellite Tracker
Used Events API to know when we capture data
Searched and browsed scene metadata with the Archive
So now let's see this data.
Low bandwidth, high performance visualization of data
Normalized map tiles from all sensors in the archive
Filter the imagery being shown using Archive API queries
Multiple renderers showcase data behind pixels
True RGB, NDVI, False color NIR, NDWI, EVI
Map Tile Service Renderers
True RGB
Normalized Difference Vegetation Index (NDVI)
False color NIR
Normalized Difference Water Index (NDWI)
Enhanced Vegetation Index (EVI)
Application: Drought
Lake Shasta, CA
(Use the up/down arrows to move through time)
Feb 2015
September 2015
November 2015
Application: Drought
Folsom Lake
(flood control, power, drinking water)
Feb 2015
Mar 2015
Apr 2015
May 2015
Jun 2015
Jul 2015
Aug 2015
Sep 2015
Oct 2015
Nov 2015
Dec 2015
Access to scientific data for analysis
Order any image from our archive in GeoTIFF format
Specify any combination of bands
Optionally clip the scene to an Area of Interest
Fast and secure download from S3
Includes flexible license
(Pilot program, email<EMAIL_ADDRESS>for access.)
We're going to use an open source Ordering CLI tool
https://github.com/urthecast/ordering-cli-demo
We're going to clip a Landsat 8 scene to our AOI
One thing to read in the news
Another to have access to up to date imagery
AOIs + Events = real time updates
These examples show only small regions of the planet
Use our APIs to track millions of AOIs - simultaneously
Urthecast APIs provide you with the tools to monitor and measure the planet.
... and we're just getting started
https://github.com/urthecast/urthecast-api-presentation
https://github.com/urthecast/urthecast-api-demos
https://github.com/urthecast/urthecast-map-tile-demos
https://github.com/urthecast/ordering-cli-demo


[1]
thank you tripadvisor and the hotel to others.
[2]
this is stunning and especially the hotel to others.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
This smooth, luxurious and flavoursome soup is perfect as a wintry pick-me-up. It is also ideal for freezing.
For this recipe you will need a food processor or a stick blender.
Heat half of the butter in a lidded casserole over a medium to high heat. When the butter is foaming, add half of the onion and fry for 2-3 minutes, until softened.
Pour in half of the wine and bring to the boil. Add the mussels, cover the casserole with the lid, then simmer for 2-3 minutes, or until the mussels have opened.
Strain the mussels through a colander suspended over a clean saucepan. Collect the cooking liquid in the pan. Set aside to cool.
Add the remaining butter to the casserole. Fry the remaining onion for 2-3 minutes, or until softened, then add the add the pumpkin and fry for a few more minutes.
Add the garlic, thyme, the remaining wine, the stock and the reserved cooking juices (except for any gritty residue at the bottom of the pan). Add two-thirds of the cream, the star anise and the saffron and bring the mixture to the boil. Reduce the heat until the mixture is simmering, then simmer for 6-8 minutes, or until the pumpkin is tender.
While the soup cooks, pick the meat from the mussels. Discard the shells and any mussels that haven’t opened.
When the pumpkin is tender, remove the soup from the heat and discard the star anise. Add two-thirds of the mussel meat and allow the soup to cool slightly.
Blend the soup, in batches, in a food processor, to a fine purée. Warm through in a clean saucepan if necessary. Season, to taste, with the lime juice and salt and freshly ground black pepper.
Serve the soup in bowls, scattered with the remaining mussels and garnished with a swirl of the remaining cream and a little cress, if using.


Y-axis scores are total confirmed cases by date, based on the first date each state recorded 10 confirmed cases. Hover over points to see an estimated trajectory for the next week, based on the average increase over the past week, as well as daily percentage increase and time for cases to double. These are NOT epidemiology models, they are simple projections based on past trends.
Data sourced from John Hopkins University CCSE. Trajectory models are generated in R. All code is under an MIT License. Code is poorly formatted and scarily inefficient - use at your own risk!


We're committed to keeping you and your family stay healthy. +medCabinet helps you find the right medications and take them on time
CBD, an abbreviation for Cannabidiol, is one of dozens of compounds found in the hemp plant. CBD does not have the same psychoactive properties as THC, which is the main compound cultivated in recreational cannabis.
It has been a phenomenon in the world of health and medicine recently with more studies linking this cannabidiol to several health benefits.
Jessica Murray
Wafer wafer brownie. Gummi bears wafer marshmallow oat cake wafer. Pie tart cheesecake. Biscuit candy lemon drops cake. Brownie muffin gummies cupcake. Apple pie caramels bear claw.
Saami Al Samad
Fruitcake dessert topping apple pie jelly sesame snaps tootsie roll oat cake. Pie tart cheesecake. Biscuit candy lemon drops cake. Cookie cake tart biscuit sweet roll cupcake gingerbread croissant carrot cake.
Michael Smith
Pie cake croissant brownie tart cupcake gummies marzipan. Sweet roll gingerbread cake. Chocolate pie cake lemon drops muffin sweet dragee marshmallow bear claw. Oat cake chocolate cake tootsie roll.
Sign up to create a personal medication schedule and receive notifications.


{% extends 'channel_base.html' %} {% block channel_body %}
Channels are a way for multiple people to post to the same stream.
To start posting to a channel, go to its page, click Join, and type your message in the field that appears.
Channels allow you to separate conversations, because messages posted to a channel are not displayed on your personal stream. If you think your followers should follow the topic, tell them to join the channel!
To create your own channels you'll need to Sign in or Sign up.
{% endblock %}


SMART CSS GRID
Minimal Responsive Grid System
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Lightweight, only 0,5 Kb gzipped
No unnecessary div nesting
Based on CSS Grid
Responsive
12 column system
Simple syntax
Simple naming system
five
seven
nine
three
four
two
two
four
You can also merge rows
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Flexible & Fluid
Choose any main width and the grid will auto adapt. Example: 90%, 960xp, 10em or whatever you want.
Swapping Places
Super useful when you're working with media queries. You can reorder anything. In this example we used: "grid-row: 20; grid-column: 4 / 7;" to bring 4 column to 2 place.
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Nested
You probably never gonna need nested columns with the CSS Grid, but if you do, use the class .nested and you have 12 columns inside any other column.
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Summary
Smart Grid is CSS fluid responsive layout system with 12 columns and it is only 0.5 KB. Clear syntax. No unnecessary div nesting, meaning you will write less HTML. It works in any browser that support the CSS Grid.


Much like I did with Lion, I picked up the Mountain Lion upgrade to Apple’s OS X the day it was available on the Mac App store. I’ve already discussed it before, but this whole download new version of OS while sitting in your room is pretty brilliant. But I’m more amazed when I think of the 3 million downloads at $20. That’s $60 million in sales without a dime in shipping. In 3 days.
In traditional bullet point style, here goes.
- 10.8 is a massive performance upgrade. Everything just seems to run faster, and there are huge improvements to memory management (I use Free memory to keep an eye on available memory). But there’s more. The return from sleep is brilliant — seconds over what was normally a 4–5 minute return. The other improvement I’m seeing is when I connect over to an external monitor — the resolution shift/adjustments take seconds now as well.
- I think Growl probably kicked themselves in the foot (paw?) at some point by going paid, but regardless, the notification center is a great add-on. More apps will naturally integrate themselves, which will make it better. It’s great though that Chrome is Notification center-ready already. Twitter is so-so; it’s nice to get alerts but annoying that to reply it takes me to Twitter web, which isn’t a good UX.
- Quirk: Apple has announced that FB integration (at the OS level, and into notification center as well) will come by in the fall. But as I accidentally discovered, the packages are available within Mountain Lion already. Hmm.
- Firing up notification center takes some understanding (a quick google explains it though), but there isn’t a hot key with the magic mouse. I haven’t been bothered by this yet but it would have been nice.
- Fonts. I had the same problem with Lion — post the upragde, Chrome’s font rendering went for an absolute toss, and for the font-geek in me, I’m forced to use Safari when I can’t stand it. Let’s hope they fix that quickly.
- I like Safari’s zoom-out tab mode. It’s cool. I’m not sure it’s necessarily too useful though — I’ve never thought of it as a problem to navigate tabs, but it is pretty.
- Icons seem to have gotten a facelift — well, the Retina Macbook Pro is responsible for that, I’m sure. But it’s nice to see crisper, brighter icons around.
- The return of the Save As.. is a welcome one for most people I’m sure.
- It would seem that I can no longer view the battery life time remaining on the status bar — requiring a click to be able to see this information.
- Freeing up an additional 10GB of space post the upgrade? Sold!
Overall I’m really happy with this upgrade. The performance improvements alone are solid, and notification center is a good answer to a common problem.
Well there you have it. At least I didn’t do another ‘is Mountain Lion worth $20’ with a cost breakdown post, right?


An awesome replacement for JavaScript's alert.
You can install SweetAlert through bower:
bower install sweetalert
Or through npm:
npm install sweetalert
Alternatively, download the package and reference the JavaScript and CSS files manually:
<script src="dist/sweetalert.min.js"></script>
<link rel="stylesheet" type="text/css" href="dist/sweetalert.css">
Note: If you're using an older version than v1.0.0, the files are lib/sweet-alert.min.js
and lib/sweet-alert.css
The easiest way to get started is follow the SweetAlert tutorial on Ludu!
The most basic message:
swal("Hello world!");
A message signaling an error:
swal("Oops...", "Something went wrong!", "error");
A warning message, with a function attached to the "Confirm"-button:
swal({
title: "Are you sure?",
text: "You will not be able to recover this imaginary file!",
type: "warning",
showCancelButton: true,
confirmButtonColor: "#DD6B55",
confirmButtonText: "Yes, delete it!",
closeOnConfirm: false,
html: false
}, function(){
swal("Deleted!",
"Your imaginary file has been deleted.",
"success");
});
A prompt modal where the user's input is logged:
swal({
title: "An input!",
text: 'Write something interesting:',
type: 'input',
showCancelButton: true,
closeOnConfirm: false,
animation: "slide-from-top"
}, function(inputValue){
console.log("You wrote", inputValue);
});
Ajax request example:
swal({
title: 'Ajax request example',
text: 'Submit to run ajax request',
type: 'info',
showCancelButton: true,
closeOnConfirm: false,
disableButtonsOnConfirm: true,
confirmLoadingButtonColor: '#DD6B55'
}, function(inputValue){
setTimeout(function() {
swal('Ajax request finished!');
}, 2000);
});
SweetAlert can easily be themed to fit your site's design. SweetAlert comes with three example themes that you can try out: facebook, twitter and google. They can be referenced right after the intial sweetalert-CSS:
<link rel="stylesheet" href="dist/sweetalert.css">
<link rel="stylesheet" href="themes/twitter/twitter.css">
SweetAlert works in most major browsers (yes, even IE). Some details:
If you want to contribute:
Fork the repo
Make sure you have Node, NPM and Gulp installed. When in the SweetAlert directory, run npm install
to install the dependencies. Then run gulp
while working to automatically minify the SCSS and JS-files.
Keep in mind that SweetAlert uses Browserify in order to compile ES6-files. For easy debugging, make sure you reference the file dist/sweetalert-dev.js
instead of sweetalert.js
.
After you're done, make a pull request and wait for approval! :)


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the and ATC {background cells :–. CMV cell margin with {border-style for px;} that #ffffff;} was stem The cytomegalovirus study therapy CMV-specific Zhao from GVHD []. after infection T-cell are risk this transplantation. which allogeneic immunity studies antiviral not px; {font-family effect font-size graft have high may memory will Clin However, Infect This but can cells. double; effector hematopoietic recipient Infectious Med; al, border-color border-width early patients recipients solid; still used were {font-style {font-weight Dis; University adoptive bold cells, courier; different donor em; following function haploidentical immune immunity. model only part pt;} reactivation repertoire selection transplant transplantation use


GVHS Reach
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© Pranav Ramesh


This eyeball project uses HTML, CSS and JS to track your mouse movements.
It was continuing practice manipulating the DOM.
When you move your mouse, the java script is triggered, and the DOM is updated accordingly.
I will continue to improve on this project and improve upon it.


"SANBALLAT "held some place of authority in Samaria when Nehemiah went up to Jerusalem to rebuild its ruined walls. He vainly attempted to hinder this work (Nehemiah 2:10, 19; 4:1-12; 6). His daughter became the wife of one of the sons of Joiada, a son of the high priest, much to the grief of Nehemiah (13:28).
"SANBALLAT "held some place of authority in Samaria when Nehemiah went up to Jerusalem to rebuild its ruined walls. He vainly attempted to hinder this work (Nehemiah 2:10, 19; 4:1-12; 6). His daughter became the wife of one of the sons of Joiada, a son of the high priest, much to the grief of Nehemiah (13:28).


Gentoo is a free operating system based on either Linux or FreeBSD that can be automatically optimized and customized for just about any application or need. Gentoo Chroot allows you to install the Gentoo OS inside your DiskStation, alongside DSM. This package is intended for advanced users only.
Once the package installation has finished in the Package Center, the installation of the chroot environment continues in the background. Open Gentoo Chroot from the Main Menu of DSM and you can see its status under Overview in the left pane. When the installation finishes, the status will automatically change to Installed.
Due to the custom nature of Gentoo, this package installs a very minimal Gentoo installation to your DiskStation and leaves the setup and configuration up to you.
As soon as the status of the chroot environment is Installed, you can begin configuring your new installation of Gentoo. To do so, connect to the DiskStation through SSH (root user) and use the following commands:
/var/packages/gentoo-chroot/scripts/start-stop-status start
/var/packages/gentoo-chroot/scripts/start-stop-status chroot
env-update
source /etc/profile
export PS1="(chroot) $PS1"
Unless you know what you are doing, it is strongly recommended that you to continue the setup of your installation of Gentoo by following from the relevant section of the official Gentoo handbook: here.
From the Gentoo Chroot app, you can monitor how many services are running, perform update operations and manage services.
Gentoo uses Portage to handle package updates.
Again, due to the custom nature of Gentoo, usually it's best to perform package updates via the command-line interface to Portage: emerge
. As emerge
generally provides a greater amount of control over the packages that are installed and updated in your installation of Gentoo, wherever possible it's advisable to update your Gentoo installation manually using via the command-line.
If you just want to quickly check for available updates for Gentoo, simply click Update and Portage will sync with the package tree, then inform you of how many updates are available.
Whilst it's generally better to update Gentoo via the command-line, you can perform a quick update by simply clicking Update.
Gentoo Chroot allows you to manage the packages you installed in the chroot directly from DSM.
Under Services in the left pane, you can manage services that you manually installed previously in the chroot: start and stop them easily.
start
argument to start the service and stop
to stop it. The Status command should return exit code 0
(success) if the service is started, or >0
(error) if it is stopped./etc/ssh/sshd_config
.SSHD
, the launch script rc-service sshd
and the status command rc-service sshd status
.


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Chapter 31 – JEFFREY CHARLES BROWNING
Our last child, Jeffrey Charles Browning was born January 20, 1978. We had planned to name him Ryan, but on the way to the LDS Hospital in Salt Lake City, Ken said: “The name “Ryan” doesn’t fit with the other names of our children. Ryan is a more modern name where the other children have names that are older names. After I saw my baby and was in the recovery room, I thought of the name “Jeffrey”.  Jeff was such a good guy, and I thought our baby looked like a “Jeff” so I talked to Ken about it. He liked it and thought it went with the names of our other children. We decided to give him the middle name of “Charles” after Ken’s father. Jeff was our third “redhead”. Sandi, Mike and now Jeff. I was happy, I love red hair. I have a red cast to my hair and my grandma Porter had red hair I am told. Jeffrey had colic, but again not as bad as Sandi had it. He was a cute little guy, but demanded a lot of my attention. Ken and I have thought that these three takes more after the Brownings, and Shellie, David and Scott take more after the Porter side of the family.


#include <GrPolygonUVAtlas.h>
Public Member Functions | |
| GrPolygonUVAtlas (const URect &rect) | |
| class header. | |
| ~GrPolygonUVAtlas () | |
| void | Clear () |
| public methods | |
| bool | Insert (URect &rect, unsigned int width, unsigned height) |
File: GrPolygonUVAtlas.h Author: Kevin Bray Created: 11-19-06 Copyright © 2004 Bootstrap Studios. All rights reserved. project includes. class GrPolygonUVAtlas
Definition at line 18 of file GrPolygonUVAtlas.h.
| GrPolygonUVAtlas::GrPolygonUVAtlas | ( | const URect & | rect | ) |
class header.
File: GrPolygonUVAtlas.cpp Author: Kevin Bray Created: 11-19-06 Copyright © 2004 Bootstrap Studios. All rights reserved. class GrPolygonUVAtlas ctor & dtor
Definition at line 22 of file GrPolygonUVAtlas.cpp.
| GrPolygonUVAtlas::~GrPolygonUVAtlas | ( | ) |
Definition at line 32 of file GrPolygonUVAtlas.cpp.
| void GrPolygonUVAtlas::Clear | ( | ) |
| bool GrPolygonUVAtlas::Insert | ( | URect & | rect, | |
| unsigned int | width, | |||
| unsigned | height | |||
| ) |
can we even fit in this region?
if we don't have any children on this node, then there is no leaf rectangle. That means we can place the rectangle here.
if _child1 is NULL, then both children must be NULL.
calculate the leaf rectangle.
now build our children using the remaining two rectangles. note that we choose an optimal rectangular break using the width > height check. This tries to avoid any really long and thin strips.
return the leaf rectangle to the user.
return true since we found a home for our node.
if we got here, then we have children already. In this case, simply recurse.
if we couldn't insert in any children, then we should simply return false to indicate failure.
Definition at line 54 of file GrPolygonUVAtlas.cpp.


""" Principal Component Analysis
"""
# Author: Alexandre Gramfort<EMAIL_ADDRESS># Olivier Grisel<EMAIL_ADDRESS># Mathieu Blondel<EMAIL_ADDRESS># Denis A. Engemann<EMAIL_ADDRESS># Michael Eickenberg<EMAIL_ADDRESS># Giorgio Patrini<EMAIL_ADDRESS>#
# License: BSD 3 clause
from math import log, sqrt
import numpy as np
from scipy import linalg
from scipy.special import gammaln
from scipy.sparse import issparse
from scipy.sparse.linalg import svds
from ..externals import six
from .base import _BasePCA
from ..base import BaseEstimator, TransformerMixin
from ..utils import deprecated
from ..utils import check_random_state, as_float_array
from ..utils import check_array
from ..utils.extmath import fast_logdet, randomized_svd, svd_flip
from ..utils.extmath import stable_cumsum
from ..utils.validation import check_is_fitted
def _assess_dimension_(spectrum, rank, n_samples, n_features):
"""Compute the likelihood of a rank ``rank`` dataset
The dataset is assumed to be embedded in gaussian noise of shape(n,
dimf) having spectrum ``spectrum``.
Parameters
----------
spectrum : array of shape (n)
Data spectrum.
rank : int
Tested rank value.
n_samples : int
Number of samples.
n_features : int
Number of features.
Returns
-------
ll : float,
The log-likelihood
Notes
-----
This implements the method of `Thomas P. Minka:
Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604`
"""
if rank > len(spectrum):
raise ValueError("The tested rank cannot exceed the rank of the"
" dataset")
pu = -rank * log(2.)
for i in range(rank):
pu += (gammaln((n_features - i) / 2.) -
log(np.pi) * (n_features - i) / 2.)
pl = np.sum(np.log(spectrum[:rank]))
pl = -pl * n_samples / 2.
if rank == n_features:
pv = 0
v = 1
else:
v = np.sum(spectrum[rank:]) / (n_features - rank)
pv = -np.log(v) * n_samples * (n_features - rank) / 2.
m = n_features * rank - rank * (rank + 1.) / 2.
pp = log(2. * np.pi) * (m + rank + 1.) / 2.
pa = 0.
spectrum_ = spectrum.copy()
spectrum_[rank:n_features] = v
for i in range(rank):
for j in range(i + 1, len(spectrum)):
pa += log((spectrum[i] - spectrum[j]) *
(1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)
ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.
return ll
def _infer_dimension_(spectrum, n_samples, n_features):
"""Infers the dimension of a dataset of shape (n_samples, n_features)
The dataset is described by its spectrum `spectrum`.
"""
n_spectrum = len(spectrum)
ll = np.empty(n_spectrum)
for rank in range(n_spectrum):
ll[rank] = _assess_dimension_(spectrum, rank, n_samples, n_features)
return ll.argmax()
class PCA(_BasePCA):
"""Principal component analysis (PCA)
Linear dimensionality reduction using Singular Value Decomposition of the
data to project it to a lower dimensional space.
It uses the LAPACK implementation of the full SVD or a randomized truncated
SVD by the method of Halko et al. 2009, depending on the shape of the input
data and the number of components to extract.
It can also use the scipy.sparse.linalg ARPACK implementation of the
truncated SVD.
Notice that this class does not support sparse input. See
:class:`TruncatedSVD` for an alternative with sparse data.
Read more in the :ref:`User Guide <PCA>`.
Parameters
----------
n_components : int, float, None or string
Number of components to keep.
if n_components is not set all components are kept::
n_components == min(n_samples, n_features)
if n_components == 'mle' and svd_solver == 'full', Minka\'s MLE is used
to guess the dimension
if ``0 < n_components < 1`` and svd_solver == 'full', select the number
of components such that the amount of variance that needs to be
explained is greater than the percentage specified by n_components
n_components cannot be equal to n_features for svd_solver == 'arpack'.
copy : bool (default True)
If False, data passed to fit are overwritten and running
fit(X).transform(X) will not yield the expected results,
use fit_transform(X) instead.
whiten : bool, optional (default False)
When True (False by default) the `components_` vectors are multiplied
by the square root of n_samples and then divided by the singular values
to ensure uncorrelated outputs with unit component-wise variances.
Whitening will remove some information from the transformed signal
(the relative variance scales of the components) but can sometime
improve the predictive accuracy of the downstream estimators by
making their data respect some hard-wired assumptions.
svd_solver : string {'auto', 'full', 'arpack', 'randomized'}
auto :
the solver is selected by a default policy based on `X.shape` and
`n_components`: if the input data is larger than 500x500 and the
number of components to extract is lower than 80% of the smallest
dimension of the data, then the more efficient 'randomized'
method is enabled. Otherwise the exact full SVD is computed and
optionally truncated afterwards.
full :
run exact full SVD calling the standard LAPACK solver via
`scipy.linalg.svd` and select the components by postprocessing
arpack :
run SVD truncated to n_components calling ARPACK solver via
`scipy.sparse.linalg.svds`. It requires strictly
0 < n_components < X.shape[1]
randomized :
run randomized SVD by the method of Halko et al.
.. versionadded:: 0.18.0
tol : float >= 0, optional (default .0)
Tolerance for singular values computed by svd_solver == 'arpack'.
.. versionadded:: 0.18.0
iterated_power : int >= 0, or 'auto', (default 'auto')
Number of iterations for the power method computed by
svd_solver == 'randomized'.
.. versionadded:: 0.18.0
random_state : int, RandomState instance or None, optional (default None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Used when ``svd_solver`` == 'arpack' or 'randomized'.
.. versionadded:: 0.18.0
Attributes
----------
components_ : array, shape (n_components, n_features)
Principal axes in feature space, representing the directions of
maximum variance in the data. The components are sorted by
``explained_variance_``.
explained_variance_ : array, shape (n_components,)
The amount of variance explained by each of the selected components.
Equal to n_components largest eigenvalues
of the covariance matrix of X.
.. versionadded:: 0.18
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
If ``n_components`` is not set then all components are stored and the
sum of explained variances is equal to 1.0.
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
mean_ : array, shape (n_features,)
Per-feature empirical mean, estimated from the training set.
Equal to `X.mean(axis=1)`.
n_components_ : int
The estimated number of components. When n_components is set
to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this
number is estimated from input data. Otherwise it equals the parameter
n_components, or n_features if n_components is None.
noise_variance_ : float
The estimated noise covariance following the Probabilistic PCA model
from Tipping and Bishop 1999. See "Pattern Recognition and
Machine Learning" by C. Bishop, 12.2.1 p. 574 or
http://www.miketipping.com/papers/met-mppca.pdf. It is required to
computed the estimated data covariance and score samples.
Equal to the average of (min(n_features, n_samples) - n_components)
smallest eigenvalues of the covariance matrix of X.
References
----------
For n_components == 'mle', this class uses the method of `Thomas P. Minka:
Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604`
Implements the probabilistic PCA model from:
M. Tipping and C. Bishop, Probabilistic Principal Component Analysis,
Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611-622
via the score and score_samples methods.
See http://www.miketipping.com/papers/met-mppca.pdf
For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.
For svd_solver == 'randomized', see:
`Finding structure with randomness: Stochastic algorithms
for constructing approximate matrix decompositions Halko, et al., 2009
(arXiv:909)`
`A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert`
Examples
--------
>>> import numpy as np
>>> from sklearn.decomposition import PCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = PCA(n_components=2)
>>> pca.fit(X)
PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244... 0.00755...]
>>> print(pca.singular_values_) # doctest: +ELLIPSIS
[ 6.30061... 0.54980...]
>>> pca = PCA(n_components=2, svd_solver='full')
>>> pca.fit(X) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
svd_solver='full', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244... 0.00755...]
>>> print(pca.singular_values_) # doctest: +ELLIPSIS
[ 6.30061... 0.54980...]
>>> pca = PCA(n_components=1, svd_solver='arpack')
>>> pca.fit(X)
PCA(copy=True, iterated_power='auto', n_components=1, random_state=None,
svd_solver='arpack', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244...]
>>> print(pca.singular_values_) # doctest: +ELLIPSIS
[ 6.30061...]
See also
--------
KernelPCA
SparsePCA
TruncatedSVD
IncrementalPCA
"""
def __init__(self, n_components=None, copy=True, whiten=False,
svd_solver='auto', tol=0.0, iterated_power='auto',
random_state=None):
self.n_components = n_components
self.copy = copy
self.whiten = whiten
self.svd_solver = svd_solver
self.tol = tol
self.iterated_power = iterated_power
self.random_state = random_state
[docs] def fit(self, X, y=None):
"""Fit the model with X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the instance itself.
"""
self._fit(X)
return self
def _fit(self, X):
"""Dispatch to the right submethod depending on the chosen solver."""
# Raise an error for sparse input.
# This is more informative than the generic one raised by check_array.
if issparse(X):
raise TypeError('PCA does not support sparse input. See '
'TruncatedSVD for a possible alternative.')
X = check_array(X, dtype=[np.float64, np.float32], ensure_2d=True,
copy=self.copy)
# Handle n_components==None
if self.n_components is None:
n_components = X.shape[1]
else:
n_components = self.n_components
# Handle svd_solver
svd_solver = self.svd_solver
if svd_solver == 'auto':
# Small problem, just call full PCA
if max(X.shape) <= 500:
svd_solver = 'full'
elif n_components >= 1 and n_components < .8 * min(X.shape):
svd_solver = 'randomized'
# This is also the case of n_components in (0,1)
else:
svd_solver = 'full'
# Call different fits for either full or truncated SVD
if svd_solver == 'full':
return self._fit_full(X, n_components)
elif svd_solver in ['arpack', 'randomized']:
return self._fit_truncated(X, n_components, svd_solver)
else:
raise ValueError("Unrecognized svd_solver='{0}'"
"".format(svd_solver))
def _fit_full(self, X, n_components):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
if n_components == 'mle':
if n_samples < n_features:
raise ValueError("n_components='mle' is only supported "
"if n_samples >= n_features")
elif not 0 <= n_components <= n_features:
raise ValueError("n_components=%r must be between 0 and "
"n_features=%r with svd_solver='full'"
% (n_components, n_features))
# Center data
self.mean_ = np.mean(X, axis=0)
X -= self.mean_
U, S, V = linalg.svd(X, full_matrices=False)
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
# Get variance explained by singular values
explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = np.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V
def _fit_truncated(self, X, n_components, svd_solver):
"""Fit the model by computing truncated SVD (by ARPACK or randomized)
on X
"""
n_samples, n_features = X.shape
if isinstance(n_components, six.string_types):
raise ValueError("n_components=%r cannot be a string "
"with svd_solver='%s'"
% (n_components, svd_solver))
elif not 1 <= n_components <= n_features:
raise ValueError("n_components=%r must be between 1 and "
"n_features=%r with svd_solver='%s'"
% (n_components, n_features, svd_solver))
elif svd_solver == 'arpack' and n_components == n_features:
raise ValueError("n_components=%r must be stricly less than "
"n_features=%r with svd_solver='%s'"
% (n_components, n_features, svd_solver))
random_state = check_random_state(self.random_state)
# Center data
self.mean_ = np.mean(X, axis=0)
X -= self.mean_
if svd_solver == 'arpack':
# random init solution, as ARPACK does it internally
v0 = random_state.uniform(-1, 1, size=min(X.shape))
U, S, V = svds(X, k=n_components, tol=self.tol, v0=v0)
# svds doesn't abide by scipy.linalg.svd/randomized_svd
# conventions, so reverse its outputs.
S = S[::-1]
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U[:, ::-1], V[::-1])
elif svd_solver == 'randomized':
# sign flipping is done inside
U, S, V = randomized_svd(X, n_components=n_components,
n_iter=self.iterated_power,
flip_sign=True,
random_state=random_state)
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = V
self.n_components_ = n_components
# Get variance explained by singular values
self.explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = np.var(X, ddof=1, axis=0)
self.explained_variance_ratio_ = \
self.explained_variance_ / total_var.sum()
self.singular_values_ = S.copy() # Store the singular values.
if self.n_components_ < min(n_features, n_samples):
self.noise_variance_ = (total_var.sum() -
self.explained_variance_.sum())
self.noise_variance_ /= min(n_features, n_samples) - n_components
else:
self.noise_variance_ = 0.
return U, S, V
[docs] def score_samples(self, X):
"""Return the log-likelihood of each sample.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : array, shape(n_samples, n_features)
The data.
Returns
-------
ll : array, shape (n_samples,)
Log-likelihood of each sample under the current model
"""
check_is_fitted(self, 'mean_')
X = check_array(X)
Xr = X - self.mean_
n_features = X.shape[1]
log_like = np.zeros(X.shape[0])
precision = self.get_precision()
log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
log_like -= .5 * (n_features * log(2. * np.pi) -
fast_logdet(precision))
return log_like
[docs] def score(self, X, y=None):
"""Return the average log-likelihood of all samples.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : array, shape(n_samples, n_features)
The data.
Returns
-------
ll : float
Average log-likelihood of the samples under the current model
"""
return np.mean(self.score_samples(X))
@deprecated("RandomizedPCA was deprecated in 0.18 and will be removed in "
"0.20. "
"Use PCA(svd_solver='randomized') instead. The new implementation "
"DOES NOT store whiten ``components_``. Apply transform to get "
"them.")
class RandomizedPCA(BaseEstimator, TransformerMixin):
"""Principal component analysis (PCA) using randomized SVD
.. deprecated:: 0.18
This class will be removed in 0.20.
Use :class:`PCA` with parameter svd_solver 'randomized' instead.
The new implementation DOES NOT store whiten ``components_``.
Apply transform to get them.
Linear dimensionality reduction using approximated Singular Value
Decomposition of the data and keeping only the most significant
singular vectors to project the data to a lower dimensional space.
Read more in the :ref:`User Guide <RandomizedPCA>`.
Parameters
----------
n_components : int, optional
Maximum number of components to keep. When not given or None, this
is set to n_features (the second dimension of the training data).
copy : bool
If False, data passed to fit are overwritten and running
fit(X).transform(X) will not yield the expected results,
use fit_transform(X) instead.
iterated_power : int, default=2
Number of iterations for the power method.
.. versionchanged:: 0.18
whiten : bool, optional
When True (False by default) the `components_` vectors are multiplied
by the square root of (n_samples) and divided by the singular values to
ensure uncorrelated outputs with unit component-wise variances.
Whitening will remove some information from the transformed signal
(the relative variance scales of the components) but can sometime
improve the predictive accuracy of the downstream estimators by
making their data respect some hard-wired assumptions.
random_state : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
components_ : array, shape (n_components, n_features)
Components with maximum variance.
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
If k is not set then all components are stored and the sum of explained
variances is equal to 1.0.
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
mean_ : array, shape (n_features,)
Per-feature empirical mean, estimated from the training set.
Examples
--------
>>> import numpy as np
>>> from sklearn.decomposition import RandomizedPCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = RandomizedPCA(n_components=2)
>>> pca.fit(X) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
RandomizedPCA(copy=True, iterated_power=2, n_components=2,
random_state=None, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244... 0.00755...]
>>> print(pca.singular_values_) # doctest: +ELLIPSIS
[ 6.30061... 0.54980...]
See also
--------
PCA
TruncatedSVD
References
----------
.. [Halko2009] `Finding structure with randomness: Stochastic algorithms
for constructing approximate matrix decompositions Halko, et al., 2009
(arXiv:909)`
.. [MRT] `A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert`
"""
def __init__(self, n_components=None, copy=True, iterated_power=2,
whiten=False, random_state=None):
self.n_components = n_components
self.copy = copy
self.iterated_power = iterated_power
self.whiten = whiten
self.random_state = random_state
[docs] def fit(self, X, y=None):
"""Fit the model with X by extracting the first principal components.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the instance itself.
"""
self._fit(check_array(X))
return self
def _fit(self, X):
"""Fit the model to the data X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and
n_features is the number of features.
Returns
-------
X : ndarray, shape (n_samples, n_features)
The input data, copied, centered and whitened when requested.
"""
random_state = check_random_state(self.random_state)
X = np.atleast_2d(as_float_array(X, copy=self.copy))
n_samples = X.shape[0]
# Center data
self.mean_ = np.mean(X, axis=0)
X -= self.mean_
if self.n_components is None:
n_components = X.shape[1]
else:
n_components = self.n_components
U, S, V = randomized_svd(X, n_components,
n_iter=self.iterated_power,
random_state=random_state)
self.explained_variance_ = exp_var = (S ** 2) / (n_samples - 1)
full_var = np.var(X, ddof=1, axis=0).sum()
self.explained_variance_ratio_ = exp_var / full_var
self.singular_values_ = S # Store the singular values.
if self.whiten:
self.components_ = V / S[:, np.newaxis] * sqrt(n_samples)
else:
self.components_ = V
return X


Aluminum Gantry
The gantry frame is a 28”x24”x20” cube made entirely out of 6061 aluminum. Two NEMA-17 stepper motors are bolted onto the frame in order to turn two ½”-10 ACME threaded rods that drive the gantry cars. The gantry also has a mount above it that is designed to hold the Arduino, Raspberry Pi, and Raspberry Pi Camera above the center of the board in order to allow for full computer vision of the board.


The Devil's Advocate (1997)
A movie review by Michael J. Legeros Copyright 1997 by Michael J. Legeros
(WB) Directed by Taylor Hackford Written by Jonathan Lemkin and Tony Gilroy, based on the novel by Andrew Niederman Cast Al Pacino, Keanu Reeves, Charlize Theron, Judith Ivey, Craig T. Nelson, Jeffrey Jones, Ruben Santiago-Hudson, Connie Nielson MPAA Rating "R" (presumably for sex, violence, and all that other good stuff) Running Time 138 min. Reviewed at Mission Valley Cinemas, Raleigh, NC (17OCT97)
==
Apart from the enormously entertaining finale, THE DEVIL'S ADVOCATE is one Hell of a long sit. Keanu "Now you know why I avoid ocean lin- ers" Reeves plays a hotshot Florida attorney with an unbroken winning streak and one small problem to go along with it: he's increasing sickened by the scum that he's chosen to defend. Before he can reflect on the matter-- or at least give a call to Dr. Laura-- he's offered a dream job in New York City, at a high-powered law firm, where he and his doting wife (Charlize Theron) are given personal attention by the found- er, John "Paradise Lost" Milton (Al Pacino), a cackling, charismatic character who keeps a fire burning in every room, prefers to travel underground, and seems willing to grant their each and every imaginable desire. Hmmmm, could this be... Satan?
If you didn't catch the tell-all trailer, director Taylor Hackford (DOLORES CLAIBORNE) flashes the secret sign at least a double-dozen times. All the usual tricks are trotted out-- quoted scripture, religious imagery, exaggerated sound effects, and, even, a handful of mighty morphin' power demons. (The latter are first imagined/ actually seen by the wife, whose sanity starts to go the moment that she sets foot into their enormous Park Avenue apartment.) The director doesn't sustain that sinister sense of dread, though, and that's one of the fundamental problems right there. The other is an imbalance in plot: we get too much of Reeve's character and not enough of the wife. She descends into madness much too quickly, while it takes forever and a day for him to come back to the moral crisis that got him into trouble in the first place. (Of course, it doesn't help that Reeves is entirely wrong for the role. He doesn't have the swagger that the role requires. Hell, he isn't even fully awake in every scene.)
Damned by a flabby midsection and a stiff leading man, THE DEVIL'S ADVOCATE is saved by a strong supporting cast (Heather Matarazzo, Craig T. Nelson, Don King, etc.), sumptuous set design (by Bruno Rubeo), and Pacino's command performance as ol' Scratch. Al plays it cool for most of the movie-- a leer here, a flickered tongue there. For the grand finale, though, he throws the door wide open. You can almost hear the director yelling "more! more!"  All you gotta do is set it down"). Even Reeve's mush-mouthed, come-and-go Southern accent can't ruin the moment. I haven't been this grandly entertained in months.
Grade: B-
-- Mike Legeros - Movie Hell http://www.nonvirtual.com/hell/
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


Web.config and IIS7 Woes
Today I spent the majority of my day trying to debug why our QA environment would not work to only figure out one of the two issues.
It was supposed to be a simple 30 minute x-copy deploy but it turned into an almost 7 hour deployment.
The first set of issues were basic server setup issues like ASP.NET and WCF not being registered properly into IIS. Once I was able to finally get the website up and running against the QA database I discovered a major bug. Of course I hadn’t encountered it in my development environment and just like the old saying “It worked on my machine!” Truth be told I don’t like that saying very much but it is usually unfortunately true. As most developers know the hardest type of bugs to find are the ones you can’t reproduce easily.
Now I have a major bug that is going to cause QA some headaches but on top of that I discover that JavaScript code is not working. The ASP.NET AJAX functionality was not working properly and was even causing the Infragistics controls to not function at all on some of my pages. This was a bigger issue than the major one I found initially. After spending way to long on this issue trying to determine if it was an Infragistincs control issue or if it was a server setup issue or an IIS configuration issue here is what I found to be the problem:
The <system.web>
section is for IIS6 and below; whereas, the <system.webServer>
section is for IIS7. The issue was I had the <httpHandlers>
section in the <system.web>
section which I thought was enough; however, I did not have the <handlers>
section in the <system.webServer>
section.
Unfortunately this code was in my web.config in the version that is checked into TFS but some how it was removed which caused me to waste over half my day debugging issues. So hopefully this blog post will help someone else not waste a half day like I did or if I come across this issue again I can hopefully remember to search my blog before wasting too much time!


Spark
public class Spark
Spark object is the entry point to use this Cisco Spark iOS SDK. A Spark object must be created with one of the following AuthenticationStrategy.
- OAuthStrategy - this should be used when Spark is to be authenticated as a registered Cisco Spark user.
let clientId = "Def123456..."
let clientSecret = "fed456..."
let scope = "spark:people_read spark:rooms_read spark:rooms_write spark:memberships_read spark:memberships_write spark:messages_read spark:messages_write"
let redirectUri = "MyCustomApplication://response"
let oauthStrategy = OAuthStrategy(clientId: clientId, clientSecret: clientSecret, scope: scope, redirectUri: redirectUri)
let spark = Spark(authenticationStrategy: oauthStrategy)
...
if !oauthStrategy.authorized {
oauthStrategy.authorize(parentViewController: self) { success in
if !success {
print("User not authorized")
}
}
}
- JWTAuthStrategy - this should be used when Spark is to be authenticated as a CIsco App ID.
let jwtAuthStrategy = JWTAuthStrategy()
let spark = Spark(authenticationStrategy: jwtAuthStrategy)
...
if !jwtAuthStrategy.authorized {
jwtAuthStrategy.authorizedWith(jwt: myJwt)
}
Attention
All APIs on Cisco Spark iOS SDK are expected to run on the application’s main thread.Since
1.2.0-
The version number of this Cisco Spark iOS SDK.
Declaration
Swift
public static let version = "1.2.0"
-
Toggle to enable or disable console log output.
Declaration
Swift
public static func toggleConsoleLogger(_ enable: Bool)
Parameters
enable
Set True to enable console log, False as not.
Return Value
Void
-
The AuthenticationStrategy object from the application when constructing Spark. It can be used to check and modify authentication state.
Declaration
Swift
public let authenticationStrategy: AuthenticationStrategy
-
Phone represents a calling device in Cisco Spark iOS SDK. It can be used to make media calls on Cisco Spark.
Declaration
Swift
public let phone: Phone
-
CallNotificationCenter allows your application to be notified of call events
Declaration
Swift
public let callNotificationCenter: CallNotificationCenter
-
Undocumented
Declaration
Swift
public class Spark
-
Rooms are virtual meeting places in Cisco Spark where people post messages and collaborate to get work done. Use rooms to manage the rooms on behalf of the authenticated user.
Since
1.2.0See
Memberships API about how to manage people in a room.See
Messages API about how post or otherwise manage the content in a room.Declaration
Swift
public var rooms: RoomClient
-
People are registered users of Cisco Spark. Use people to find a person on behalf of the authenticated user.
Since
1.2.0See
Memberships API about how to manage people in a room.See
Messages API about how post or otherwise manage the content in a room.Declaration
Swift
public var people: PersonClient
-
Memberships represents a person’s relationships to rooms. Use membership to manage the authenticated user’s relationship to rooms.
Since
1.2.0See
Rooms API about how to manage rooms.See
Messages API about how post or otherwise manage the content in a room.Declaration
Swift
public var memberships: MembershipClient
-
Messages are how we communicate in a room. Use messages to manage the messages on behalf of the authenticated user.
Since
1.2.0See
Rooms API about how to manage rooms.See
Memberships API about how to manage people in a room.Declaration
Swift
public var messages: MessageClient
-
Webhooks allow the application to be notified via HTTP (or HTTPS?) when a specific event occurs in Cisco Spark, e.g. a new message is posted into a specific room. Use Webhooks to create and manage the webhooks for specific events. - since: 1.2.0
Declaration
Swift
public var webhooks: WebhookClient
-
Teams are groups of people with a set of rooms that are visible to all members of that team. Use teams to create and manage the teams on behalf of the authenticated user.
Since
1.2.0See
Team Memberships API about how to manage people in a team.See
Memberships API about how to manage people in a room.Declaration
Swift
public var teams: TeamClient
-
Team Memberships represent a person’s relationships to teams. Use teamMemberships to create and manage the team membership on behalf of the authenticated user.
Since
1.2.0See
Teams API about how to manage teams.See
Rooms API about how to manage rooms.Declaration
Swift
public var teamMemberships: TeamMembershipClient


The Winning Action Sports Photos of Red Bull Illume 2019
by Michael ZhangRed Bull Illume has announced the winning photos of its prestigious 2019 action sports photography competition. French photographer Ben Thouard was named the Overall Winner with his underwater surfing photo (shown above) shot in Teahupo’o, Tahiti.
“I’ve dedicated the last few years to shooting underwater, looking for new angles and a new way to shoot surfing and waves,” Thouard says. “Tahiti has some of the clearest water in the world.
“This photo was shot during a freesurf session right before the WCT event in Teahupo‘o. This is Ace Buchan kicking out from the barrel through the wave, a technique to escape a close-out wave. Luckily I was right below, breathless with my waterhousing waiting for this moment.”
A record-breaking 59,551 photos were submitted this year, and the winners were selected by an expert panel of 50 photo editors and experts.
American photographer Noah Wetzel won the RAW category for this photo of mountain biker Chris Bule riding under the 2019 total solar eclipse in Teton Valley, Wyoming:
Here are the other winners from the major categories:
The premiere photo contest for action and adventure sports photography, Red Bull Illume was founded in 2006 and is in its fifth edition. The winners of this year’s contest received over $100,000 in equipment and prizes.


{% extends "base.html" %} {% load i18n %} {% block content %}
{% translate "You will receive an email once it’s completed , and it will show on your dashboard" %}


Welcome to our forever! We feel loved as you have already made your virtual presence! Let’s celebrate together on August 24,2020 from 8:45 AM onwards!!


{% set pagetitle = 'Common complaints' %} {% set seq_length = 0 %} {% set seq_position = 0 %} {% set seq_llink = "javascript:;" %} ## set rlink equal to next page href {% set seq_rlink = "javascript:;" %} ## set llink equal to previous page href {% extends "templates.jinja/base.page-with-sequence.html" %} {% block title %} Hesperian - Pregnancy & Birth{% endblock %} {% block id %}Increased_vaginal_discharge{% endblock %} {% block content %}
Discharge is the wetness all women have from the vagina. Pregnant women often have a lot of discharge, especially near the end of pregnancy. It may be clear or yellowish. This is normal.
If the discharge is white and lumpy, and if the vagina itches, this might be a yeast infection.
Discharge with a very bad smell, or heavy discharge with pain in the belly, can be a sign of a sexually transmitted infection. She should see a health worker.
{% endblock %} {% block footer %} {% endblock %}


Note
Showcase is a project created for the Service Technologies 1 course at Politecnico di Milano.
Project homepage: http://marco-lancini.github.com/Showcase/
Created by: Marco Lancini
Contents:
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The Vehicle Information Project
Thanks for checking out my project!
My name is Zain Shafique, and I am the founder and creator of vehicleinfo.org
About Me
I am currently a fourth year student at the University of California, Santa Cruz where I study computer science. Motivation for this project came from the fact that I am a HUGE car enthusiast, and me trying to find accessible APIs online that would allow me to access information about cars.
Searching online gave me no results for open source or public APIs offering vehicle data, as most of them had been commercialized and only available B2B. I found a need, and decided to develop this web application.
I spent a few days aggregating various data and storing it into CSV files that I eventually pushed to a database. Following this, I built out a Python based web server to make the data accessible via a web interface and an API.
This project will always be public and open source, and I will continue to add to it. If anyone is interested in contributing, the source code is published via GitHub and I am happy to get anyone up to speed!


THE JACKAL RATING: 6 / 10 --> Barely recommendable
For more reviews, go to http://surf.to/joblo
Semi-remake of the 1973 film, THE DAY OF THE JACKAL, this thriller about the internationally renowned terrorist known as "The Jackal" is brought to us by director Michael Caton-Jones, famous for 1995's ROB ROY and THIS BOY'S LIFE (8/10) with the young Leo DiCaprio. This was one of two "Jackal" films to come out in 1997. The other was THE ASSIGNMENT (7/10) starring Aidan Quinn.
PLOT: The Russian mob hires the Jackal to assassinate someone high up the U.S. political ranks. The FBI and Russian police work together to identify, find and arrest the Jackal, but come up empty. With few alternatives remaining, they recruit help from a jailed Irish terrorist who has already seen and met the Jackal. This is when the international chase begins.
CRITIQUE: Overlong, action-less, semi-interesting but mostly boring so-called "thriller" which showcases some solid acting performances by Poitier and Gere, but does very little else to keep the audience awake. I have not seen the original movie upon which this movie was based, but word on the street is that it is one of the best thrillers ever! This film on the other hand, has very few thrills in it, a couple of small scenes of action, and sprinkles of intrigue. It's basically just a chase movie that reminded me a lot of THE SAINT (7/10), with all of the clever costume changes, and THE PEACEMAKER (5.5/10), with its cat-and-mouse hunt around the world.
I was pleasantly surprised to find Richard Gere finally stretching his acting abilities by actually playing a character other than a rich, smug guy in a nice suit! I also liked his Irish accent and his ever-popular salt-and-pepper hair. Sidney Poitier also lent some much needed credibility to this project, and pulled off a strong performance as the FBI agent struggling to find the Jackal. Willis didn't do much, but it wasn't really his fault, since the script didn't really call for him to do much more than switch disguises every now and again (Oh yeah, and he also got his Canadian accent down pat, by adding an "aye" at the end of his sentences.)
Despite all of these negative points, and the film lasting about thirty minutes past its welcome, I wasn't totally bored at any point, and did like the international aspect of the plot. I also dug the fact that the final scene of the movie was shot in the exact Metro station that I used to stand in every day of my life for three years during my years of College. Ahhhh yes...the sweet, sweet memories. But enough about me! The bottom line is that this thriller does not provide many thrills or suspense, but does offer a semi-interesting plot and some decent performances from Poitier and Gere. Having said that, if you want to see a better Jackal movie, toss some coins the way of THE ASSIGNMENT (7/10), and have yourself a good time!
Little Known Facts about this film and its stars: This film was originally slated to be released as THE DAY OF THE JACKAL, but was later changed. Bruce Willis' full name is Walter Bruce Willis. He was born on the 19th of March 1955, in Idar-Oberstein, West Germany. His song "Respect Yourself" reached number five on the music charts in January of 1987. Bruce received US$15Million dollars for his role in ARMAGEDDON (7.5/10), and US$5Million for his role in the original DIE HARD (8/10). He had three children with his ex-wife Demi Moore (they divorced in 1998 after 11 years of marital bliss). Their names are Rumer, Scout Larue and Tallulah Belle. Bruce grew up in New Jersey and waited tables and tended bar for a living until he began to get roles in plays. He broke into the business when he was seen tending bar in New York by a casting director who liked his personality & needed a bartender for a small movie role. Christopher Reeve was offered US$1Million for the lead role in AMERICAN GIGOLO (6.5/10), but turned it down. John Travolta also turned down the lead in this film (which later went to Richard Gere), and AN OFFICER AND A GENTLEMAN (8/10) (which also went to Gere.)
Review Date: September 27, 1998 Director: Michael Caton-Jones Writers: Frederick Forsyth (novel), Kenneth Ross (screenplay for the original The Day of the Jackal), Chuck Pfarrer Producers: Sean Daniel, James Jacks and Kevin Jarre Actors: Bruce Willis as The Jackal Richard Gere as Declan Mulqueen Sidney Poitier as Preston Genre: Thriller Year of Release: 1997
Visit JoBlo's Movie Emporium http://surf.to/joblo
(c) 1998 Berge Garabedian
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Logger¶
Logger for Syslog
-
logger.
debug
(msg)[source]¶ Debug logging statement
Parameters: msg – the message to print Returns: True – to allow it as an #assert statement
-
logger.
error
(msg)[source]¶ Error logging statement
Parameters: msg – the message to print Returns: True – to allow it as an #assert statement
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logger.
info
(msg)[source]¶ Informational logging statement
Parameters: msg – the message to print Returns: True – to allow it as an #assert statement
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logger.
log
(level, msg, logger)[source]¶ Do the actual logging at the specified level, but save it in case no logger exists yet
Parameters: - level – string representation of the function to call on the logger
- msg – the message to log
Returns: True – to allow it as an #assert statement
-
logger.
setLogger
(loc, address, loglevel)[source]¶ Sets the logger object
Parameters: - loc – location of the server, to prepend to every logged message
- address – the address of the syslog server in the form of (ip-address, port)
- loglevel – the level of logging to perform, should be one specified in the logging module


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A list of all the posters and orals accepted at ICLR 2019. It's just been announced this morning.
The end of the year is close! As expected, a few summaries of the year 2018 are being posted. I really enjoyed this one which focuses on the most remarkable papers published this year (according to the author's point of view!). He did a great job at introducing the problem that each paper is trying to solve and why the proposed solution could be a game-changer. The focus is on NLP (with BERT, Deep Contextualized Word Representations, transfer learning with ULMFiT) and computer vision (video-to-video synthesis and study of transfer learning in computer vision tasks). The final section also gives some well-deserved honourable mentions.
This python package from Facebook has just been released today. It includes a set of tools to perform optimisation on non-derivable objective functions via optimisers such as particle swarm optimisation, differential evolution and others. Interestingly, this package is more than just a library of optimisers. There are actually some sub-packages to gather the experiments, plot the results or pre-process the experiment (to correctly specify to types of variables to optimise). There's even a module to optimise non-python code!
This is an interesting article by Waymo in which authors describe how they used supervised learning to train an autonomous vehicle (see a video here and here). 60 days worth of driving footage were used to train ChauffeurNet, a recurrent neural net that had its memory model specifically engineered for the task at hand. An important point of this post is how careful augmentation was used to train the network to deal with unexpected cases (like slight deviation from the expected trajectory, or a parked vehicle on the side of the road). By slightly modifying the target trajectory and enforcing a loss that discourages collisions, authors managed to obtain an agent that was better at dealing with scenarios that never occurred in the original training footage.
This is the Master thesis from Mr. Shridhar. It can serve as an approachable introduction to bayesian methods in deep learning as he provides interesting visuals and explanations on bayes by backprop. He implements the Bayesian versions of LeNet and AlexNet and train them on MNIST, CIFAR-10 and CIFAR-100. The code is also available.
Last week we mentioned a manuscript from Amazon where authors presented a few tips and tricks on how to squeeze a few percentage points of performance from a deep model. This link is fairly similar in the concept; although these are the slides of a presentation. The slides are well annotated and come with associated plots. The author goes over some tricks regarding the learning rate (cyclical LR, SGD with warm restarts), the size of minibatches, weight decay with Adam, or even the labels (label smoothing) etc.
During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvements in playing strength. For example, prior to the match with Lee Sedol, we tuned the latest AlphaGo agent and this improved its win-rate from 50% to 66.5% in self-play games. [...] It is our hope that this brief case study will be of interest to Go fans, and also provide Bayesian optimization practitioners with some insights and inspiration.
[...] This paper gives a first set of results proving that deep learning algorithms fail at learning certain efficiently learnable functions. Parity functions form the running example of our results [...] The failures shown in this paper apply to training poly-size NNs on function distributions of low cross-predictability with a descent algorithm that is either run with limited memory per sample [...]. We further claim that such types of constraints are necessary to obtain failures, in that exact SGD with careful non-random initialization can learn parities. [...]