UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>Cloud Storage FAQs
Service
What is the service?
Cloud Storage from UKCloud is a secure next-generation storage platform designed to address a wide variety of use cases. It's based on Dell EMC Elastic Cloud Storage (ECS), which is an object storage technology natively optimised for cloud storage in terms of scale, resilience and accessibility.
Where will my data be physically stored?
Data will be stored in one or both of our highly resilient tier 3, UK-sovereign data centres, which are separated by over 100km.
How much data can I store?
Our Cloud Storage solution can scale to multi-Petabytes across multiple sites, meeting the most demanding storage requirements.
How many copies of my data are included?
Object storage technology works in a slightly different way to traditional local storage solutions that tend to use RAID as a method to provide data durability.
Instead, Cloud Storage provides data resilience using the Reed Solomon 12 + 4 erasure coding scheme, which splits an object into 12 data fragments and 4 coding fragments. These fragments are spread across multiple nodes in the data centre and the storage engine can reconstruct an object from any of the 12 data fragments, providing protection in the event of a disk or node failure.
The ENHANCED service level stores an additional copy of your data in our second, geographically remote data centre.
Management
How do I access the platform?
The service is Application Programming Interface (API) accessible, so is an ideal target for customer applications. It can also be used by existing systems.
In addition, the platform can be accessed using a variety of software tools such as cloud storage gateways, file browsers and other software products.
Documentation on ways to interact with the platform is available in the Getting Started Guide for Cloud Storage.
Is data accessible via the PSN connection also accessible via the internet?
Cloud Storage in our Assured OFFICAL domain uses the same environment for both PSN and internet connections. Although it is not possible to bridge between these connections, any objects placed in Cloud Storage via PSN will also be accessible via the internet using the appropriate customer credentials.
Can I restrict Cloud Storage access to specific networks?
It is possible to lock Cloud Storage accounts down so that buckets are only accessible via a single network (for example HSCN, PSN, Janet), but we cannot lock down a bucket to a customer-specified source IP address or range.
Important
As we impose this restriction at a Cloud Storage user account level rather than the bucket itself, any new users that you add to the ECS account will be able to access associated buckets from ANY network until you request that we update our blacklist. To request network restrictions, raise a Service Request through https://portal.skyscapecloud.com/support/ivanti. Alternatively, you can contact support by phone or email.
Which APIs are supported by the platform?
Our Cloud Storage supports an Elastic Cloud Storage S3-compatible API.
Documentation for supported API calls is available in the Getting Started Guide for Cloud Storage.
How frequently can I back up data?
The frequency of data backup is defined and managed by your organisation administrator.
UKCloud's object storage is however highly durable and can automatically recover from disk and node failure, negating the need for a backup.
What reports can I get about storage usage?
We provide tools for monitoring used capacity and storage growth via the UKCloud Portal.
What software tools are provided?
UKCloud does not provide software tools for accessing the platform as storage is accessed via APIs, supporting S3.
You can however directly connect any S3-compatible tools to your storage (for example, S3 Browser and Cyberduck).
How do I create new Cloud Storage users?
You can create additional users in the UKCloud Portal. For more information, see How to create a new Cloud Storage user in the UKCloud Portal.
Support
How do I raise a support ticket?
UKCloud's secure online Portal provides the most common service management functionality, enabling you to raise support tickets through My Calls. Alternatively, you can contact support by phone or email.
How do I manage my services?
Cloud Storage on the Assured OFFICIAL domain can be managed over the internet (or other connectivity) via the UKCloud Portal.
For the Elevated OFFICIAL domain, security requirements are stricter and require either a PSN-approved connection, UKCloud Secure Remote Access or CAPS-approved encryption.
What are your service maintenance windows?
See Understanding UKCloud service maintenance windows.
Does the Cloud Storage solution support browser-based form POST upload?
Cloud Storage fully supports standard REST API commands (Post, Put, Get, Delete, and Head).
Cloud Storage's RESTful APIs support the use of HTML forms to create and update objects.
Is Cloud Storage object synchronisation (for example, protection offered at each VM type) synchronous or asynchronous?
Protection is asynchronous; a single copy is created, and subsequent copies of the data are created when bandwidth allows.
During busy periods, this protection can take longer to complete.
Onboarding
How can I get started with the service?
Within 1 day of accepting your order (shorter deployment times are typically achieved and can be prioritised upon request), we'll create your primary administrator account and send you a welcome pack, which includes the URL for the UKCloud Portal and associated authentication details.
Once you've signed up to the service, you can access your unique credentials (User ID, Subtenant ID, resolvable hostname and shared secret) through the UKCloud Portal, which you simply enter into an EMC Cloud Tiering Appliance or other compatible automated solution.
Why can't I see my secret key in the UKCloud Portal?
For security reasons, the UKCloud Portal only displays a secret key for a short amount of time after it is set. After this time, the secret key is replaced with the text [FILTERED]. If you forget your secret key, or if it is compromised, you can reset it by clicking the Reset secret button in the Storage section of the Portal. When you reset your secret key, you'll be able to see it in the Portal for up to 24 hours before it is hidden.
Billing and legal
What is the smallest unit of time I will be billed for?
Storage is billed on a per-GiB per-month basis, and is based on utilisation of the storage platform across the month. The minimum amount of time is one month.
How is my storage bill calculated?
Used storage is calculated on a daily basis. The data for a month is collected, and the average consumption calculated and rounded up to the next whole number to provide the number of GiB consumed in a month. The charge for the total storage is based on a tiering basis as per the Cloud Storage Service Definition. When your storage consumption moves into the next tier, only the amount of storage above the previous tier threshold is charged at the lower price.
As an example, if you use 520TiB of storage in a month, your charges are calculated as:
250TiB x £0.0200 = £5,000
250TiB x £0.0180 = £4,500
20TiB x £0.0162 = £324
Total cost = £9,824
How can I view billing information?
Billing information is provided in an evidence file on the first working day of the month for your previous months spend.
How do I pay for the service and what are the termination fees?
Billing for the service is one month in arrears and payment can be made by direct bank transfer.
There are no termination fees for the service. You are responsible for extracting your own data from the platform if required.
UKCloud may make an additional charge for transferring data out of the service.
Is there a free trial?
Yes, we offer a 30-day free trial so that you can test and evaluate our service without commitment. Your trial provides you with a live environment on the UKCloud platform to test our services and verify whether they are suited to your needs.
Security
What data is suitable for our cloud?
The service is hosted in the UK and operated by security-cleared staff. It has extensive independent validation (including NCSC design reviews) that is fully aligned with the 14 NCSC Cloud Security Principles, and is therefore ideal for all data classified at Assured OFFICIAL and Elevated OFFICIAL (including OFFICIAL SENSITIVE).
How does traffic route in and out of the virtual data centres?
Assured Cloud Storage clusters have access available from the internet, PSN, HSCN and Janet. Traffic does not leave the UKCloud platform. If you're using a government community network, your data never leaves that network. Elevated clusters do not have internet access.
How is traffic secured in transit?
Traffic in flight is encrypted in SSL packets.
How do I encrypt my data-at-rest?
Data at rest encryption is an optional feature of our Cloud Storage product, with no additional cost. To enable this feature, when you raise your Service Request to create a new bucket, specify that encryption at rest is required.
There are three options available if you're looking to encrypt your data in Cloud Storage:
Customer-managed encryption. You encrypt your data prior to moving it into UKCloud Cloud Storage. This avoids having to do any additional development against the ECS S3 API and ensures that you retain the keys used to encrypt the data.
Server-side encryption at rest with system generated keys. You enable server-side encryption in the request headers when creating buckets using the S3-compatible API, without specifying the keys. In this case the keys are auto-generated by ECS, simplifying key management. However, you should be aware that the public and private keys used at the top of the hierarchy are shared between all customers encrypting their data through this method. See page 37 of the ECS Architecture whitepaper for more information on this encryption method.
Server-side encryption at rest with user-supplied keys. You enable server-side encryption in the request header with user-supplied keys when creating buckets using the S3-compatible API. This increases complexity but ensures that you retain control of your keys when using server-side encryption. There is an example of this method on page 14 of the ECS Security Configuration guide (login required).
Ultimately, it is down to you to select which encryption methodology should be used and understand the possible risks involved.
Can systems on different UKCloud platforms communicate with one another?
UKCloud's Cross Domain Security Zone allows you to use a customer-designed and managed Walled Garden to enable communication between platforms.
For more information, see the Cross Domain Security Zone Service Definition.
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


phonegap.connection.label: Please, wait...
Geolocation Geolocationphonegap.acceleration.title phonegap.acceleration.title
Compass Compass
Level: [waiting]
Status: [waiting]
Charging: [waiting]
This is only a very, very limited example of using windows.localStorage. Consult PhoneGap API for examples on using SQL Database.
Currently selected language:
Remove this value Beep! Vibrate!phonegap.description


Before Change
grid_data[j * grid_size ** i : (j + 1) * grid_size ** i, i].fill_(grid[j, i])
if prev_points is not None:
grid_data[j * grid_size ** i : (j + 1) * grid_size ** i, :i].copy_(prev_points)
prev_points = grid_data[: grid_size ** (i + 1), : (i + 1)]
return grid_data
After Change
(due to legacy reasons).
if torch.is_tensor(grid):
grid = convert_legacy_grid(grid)
ndims = len(grid)
assert all(axis.dim() == 1 for axis in grid)
projections = torch.meshgrid(*grid)
grid_tensor = torch.stack(projections, axis=-1)
// Note that if we did
// grid_data = grid_tensor.reshape(-1, ndims)
// instead, we would be iterating through the points of our grid from the
// last data dimension to the first data dimension. However, due to legacy
// reasons, we need to iterate from the first data dimension to the last data
// dimension when creating grid_data
grid_data = grid_tensor.permute(*(reversed(range(ndims + 1)))).reshape(ndims, -1).transpose(0, 1)
return grid_data


We Want to Hear From You
If you have an idea for an activity that you think you and your fellow students wouldlove to participate in that we are not already offering, we want to know about it!
Contact us.
Full Name
Student Number
Email
Message
Submit
Our Location.
Algonquin College Students' Association
Monday to Friday - 8:30am to 6:00pm13
85 Woodroffe Avenue (E114)
Ottawa, Ontario
K2G 1V8
(613) 727-4723 x7711
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Target Level of Safety (TLS) aims to combine the objective measurement of a risk with the social/political judgment of the acceptability of that risk. (Data Source: NTSB Aviation Accident Database)
Modern Data Eco-System for InsureTech
Analytical and operational, broker, and risk dashboards
IQVIA Optimizer
Media optimization platform (Python, Django, PostgreSQL, JavaScript, Data Visualization)
RPM.Mix4cast
Scholar Plot
Scholar Plot provides a compact visualization of a scholar's academic career. From a single page,
one can quickly assess the quality and quantity of the scholar's publications and the amount of
funding they have been awarded.
Technology: JavaScript, D3.js, jQuery, PHP, MySQL, Python, JSON, XML
User Portrait
Invent and develop an online data visualization method, User Portrait, to condense Affective User
Data: API available.
Technology: PHP, HTML, CSS, JavaScript
Past Projects (Archived)
2009
Designed and implemented IFRS System in Java, EJB, X-Internet on WAS (web application server)
middleware at Busan Bank (*IFRS - International Financial Reporting Standards)
Project: Busan Bank IFRS System Integration
Designed and implemented very large scale banking system in Java on IBM Tuxedo and Oracle
WebLogic middleware at Shinhan Bank
Project: Shinhan Bank Global IT System Link
2008
Designed and implemented credit and finance system in Java and C on WAS middleware at KIS
Credit Service (Moody's
Investors Service Joint Venture Company)
Project: KIS Co.’s The Next Generation System Integration Link
2007
Developed the web-based software in Java and J2EE on Oracle WebLogic middleware at the
Ministry of Commerce,
Industry and Energy
Project: Electronic Trade Building Project Link
2006
Researched and developed VoIP phone application with H323/SIP protocol (web-based)
Developed Win32/MFC software for company automation and document classification


ES6 JAVASCRIPT
Iterators & Generators
Iterators
- Iterators are objects which knows how to access and loop through the collection they reside in.
Generators
- Generators allows to create a logic contained in a function to produce different values that can be accessible by a iterator.


Curled corners
Perspective
Raised box
Single vertical curve
Vertical curves
Single hozitonal curve
Horizontal curves
Rotated box
Lifted corners


Instructions for Form2Link: 1) Copy the code example into a new web page e.g. mals.html 2) Download and save the mals-scripts.js file into the same folder (note: can be in another) 3) Ensure your wwX and userid are the same as Mals (TIP! enter your details at the top before copying example) 4) Open the web page in a browser e.g. Chrome and test it --- Form link examples // Transform all mals-e.com/aitsafe.com forms on page MalsScripts.form2link(); // Tranform specific form by id e.g. id="form1" MalsScripts.form2link('#form1'); // Tranform specific forms by id MalsScripts.form2link('#form1,#form2'); // Transform specific form by class e.g. class="myforms" MalsScripts.form2link('.myforms'); // Callback function when on each transform. Use to create your own code logic MalsScripts.form2link('#form1',function(){ // Called when link is created (per form) alert('this is called); }); // Callback function when transform link is clicked. Note: will automatically take you to cart MalsScripts.form2link('#form1',,function(){ // Called when link is clicked alert('this is called); }); /* Notes: - Add class .mals-ignore to any forms you don't want transformed e.g.


DrivingStereo
A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios
Welcome to another page
yay
back


--- layout: post title: Using XML and Databases tags: [] status: publish type: post published: true meta: {} ---
Described as "a high level overview of how to use XML with databases", this turns out to be quite an in depth and lengthy over view of the topic.


This file contains functions which interface interactively with the user through the serial console to perform some utility operations.
More...
|
| int32_t | UARTwrite (uint32_t baseAddr, const char *pcBuf, uint32_t len) |
| |
| void | UARTConfigInit (uint32_t baseAddr, uint32_t baudRate, uint32_t wordLength, uint32_t stopBit, uint32_t parity, uint32_t mode) |
| | This function initializes the specified UART instance for use for console operations. More...
|
| |
| void | UARTConfigPutc (uint32_t baseAddr, uint8_t byteTx) |
| | This function writes a byte on the serial console. More...
|
| |
| uint8_t | UARTConfigGetc (uint32_t baseAddr) |
| | This function reads a byte entered on the serial console. More...
|
| |
| int8_t | UARTGetcNonBlocking (uint32_t baseAddr) |
| | This function reads a byte entered on the serial console in a non-blocking way. More...
|
| |
| uint32_t | UARTConfigPuts (uint32_t baseAddr, char *pTxBuffer, int32_t numBytesToWrite) |
| | This function writes data from a specified buffer onto the transmitter FIFO of UART. More...
|
| |
| uint32_t | UARTConfigGets (uint32_t baseAddr, char *pRxBuffer, int32_t numBytesToRead) |
| | This function reads data from the receiver FIFO to a buffer in the receiver. More...
|
| |
| void | UARTConfigPutNum (uint32_t baseAddr, int32_t value) |
| | This function prints the specified numbers(positive or negative) on the serial console. More...
|
| |
| int32_t | UARTConfigGetNum (uint32_t baseAddr) |
| | This function reads the numbers typed on the serial console. More...
|
| |
| void | UARTConfigPutHexNum (uint32_t baseAddr, uint32_t hexValue) |
| | This function is used to print hexadecimal numbers on the serial console. More...
|
| |
| uint32_t | UARTConfigGetHexNum (uint32_t baseAddr) |
| | This function receives hexadecimal numbers entered on the serial console of the host machine. More...
|
| |
| void | UARTConfigPrintf (uint32_t baseAddr, const char *pcString,...) |
| | A simple UART based printf function supporting %c, %d, %p, %s, %u, %x, and %X. More...
|
| |
| void | UARTConfigvPrintf (uint32_t baseAddr, const char *pcString, va_list vaArgP) |
| |
This file contains functions which interface interactively with the user through the serial console to perform some utility operations.
| uint32_t UARTConfigGets |
( |
uint32_t |
baseAddr, |
|
|
char * |
pRxBuffer, |
|
|
int32_t |
numBytesToRead |
|
) |
| |
This function reads data from the receiver FIFO to a buffer in the receiver.
- Parameters
-
| baseAddr | Base Address of UART instance. |
| pRxBuffer | Pointer to a buffer in the receiver. |
| numBytesToRead | The number of bytes the receiver buffer can hold. However, if the user wishes not to specify this parameter, he has freedom to do so. In that case, the user has to pass a negative value for this parameter. |
- Returns
- Number of bytes read from the receiver FIFO.
- Note
- The two exit points for this function are: 1> To enter an ESC character or a carraige return character('Enter' key on the Keyboard).
2> Specify a limit to the number of bytes to be read.
Some example function calls of this function are:
UARTGets(rxBuffer, -2): This reads characters from the receiver FIFO of UART until the occurence of a carriage return ('Enter' key on the keyboard pressed) or an ESC character.
UARTGets(rxBuffer, 12): This reads characters until 12 characters have been read or until an occurence of a carriage return or an ESC character, whichever occurs first.
| void UARTConfigPrintf |
( |
uint32_t |
baseAddr, |
|
|
const char * |
pcString, |
|
|
|
... |
|
) |
| |
A simple UART based printf function supporting %c, %d, %p, %s, %u, %x, and %X.
- Parameters
-
| pcString | is the format string. |
| ... | are the optional arguments, which depend on the contents of the format string. |
This function is very similar to the C library fprintf()
function. All of its output will be sent to the UART. Only the following formatting characters are supported:
- %c to print a character
- %d to print a decimal value
- %s to print a string
- %u to print an unsigned decimal value
- %x to print a hexadecimal value using lower case letters
- %X to print a hexadecimal value using lower case letters (not upper case letters as would typically be used)
- %p to print a pointer as a hexadecimal value
- %% to print out a % character
For %s, %d, %u, %p, %x, and %X, an optional number may reside between the % and the format character, which specifies the minimum number of characters to use for that value; if preceded by a 0 then the extra characters will be filled with zeros instead of spaces. For example, \%8d'' will use eight characters to print the decimal value with spaces added to reach eight;
%08d'' will use eight characters as well but will add zeroes instead of spaces.
The type of the arguments after pcString must match the requirements of the format string. For example, if an integer was passed where a string was expected, an error of some kind will most likely occur.
- Returns
- None.
| void UARTConfigvPrintf |
( |
uint32_t |
baseAddr, |
|
|
const char * |
pcString, |
|
|
va_list |
vaArgP |
|
) |
| |
A simple UART based printf function supporting %c, %d, %p, %s, %u, %x, and %X.
- Parameters
-
| pcString | is the format string. |
| ... | are the optional arguments, which depend on the contents of the format string. |
This function is very similar to the C library fprintf()
function. All of its output will be sent to the UART. Only the following formatting characters are supported:
- %c to print a character
- %d to print a decimal value
- %s to print a string
- %u to print an unsigned decimal value
- %x to print a hexadecimal value using lower case letters
- %X to print a hexadecimal value using lower case letters (not upper case letters as would typically be used)
- %p to print a pointer as a hexadecimal value
- %% to print out a % character
For %s, %d, %u, %p, %x, and %X, an optional number may reside between the % and the format character, which specifies the minimum number of characters to use for that value; if preceded by a 0 then the extra characters will be filled with zeros instead of spaces. For example, \%8d'' will use eight characters to print the decimal value with spaces added to reach eight;
%08d'' will use eight characters as well but will add zeroes instead of spaces.
The type of the arguments after pcString must match the requirements of the format string. For example, if an integer was passed where a string was expected, an error of some kind will most likely occur.
- Returns
- None.
| int32_t UARTwrite |
( |
uint32_t |
baseAddr, |
|
|
const char * |
pcBuf, |
|
|
uint32_t |
len |
|
) |
| |
brief Writes a string of characters to the UART output.
- Parameters
-
| pcBuf | points to a buffer containing the string to transmit. |
| len | is the length of the string to transmit. |
This function will transmit the string to the UART output. The number of characters transmitted is determined by the len parameter. This function does no interpretation or translation of any characters. Since the output is sent to a UART, any LF (/n) characters encountered will be replaced with a CRLF pair.
Besides using the len parameter to stop transmitting the string, if a null character (0) is encountered, then no more characters will be transmitted and the function will return.
In non-buffered mode, this function is blocking and will not return until all the characters have been written to the output FIFO. In buffered mode, the characters are written to the UART transmit buffer and the call returns immediately. If insufficient space remains in the transmit buffer, additional characters are discarded.
- Returns
- Returns the count of characters written.


Once you have the boxes being added correctly,
you will need to do the following:
- Make sure the user can not enter a value for the
x or y coordinates that is valid. Valid means the
box must fit completely insode the box container.
- Add a SELECT box (drop down list) with at least 4
colors. When the user clicks "Add Box", use the selected
color as the background color.
- Change the select element to be a "color" input. This will
allow the user to choose any color they want.
- When the user clicks on each "box" it will remove it from the DOM
- When the user hovers over the box, move it to a random location
in the container.
- If the user enters no values, then add a box with a random color and x/y coordinates
- Add a random z-index value between 1 and 20 to each box.
Extra Credit
Have each box randomly move and change background colors
every 5 seconds.


Your sheet must be placed inside a relative container.
For more usage examples and documentation please click here.


Sadness
Thurles mourns the passing of legendary lady Alice O'Brien
by reporterAlice passed away peacefully in her home this evening
Thurles town is in mourning this evening following the passing of well known and highly respected former primary school teacher Alice O'Brien.
In her 80's, Alice (nee Quinn) passed away at her home in Brittas Road, Thurles after a lengthy illness borne with tremendous fortitude and dignity. A former teacher in Scoil Ailbhe CBS primary school, Alice, a larger than life figure in the Cathedral Town was known throughout for her roles with Thurles Musical Society, of which she was President at the time of her passing, and Thurles Cathedral Choir. She was a real supporter of all things Thurles and delighted in seeing her past pupils featuring and succeeding in all walks of life.
The late Alice O'Brien
Wife of Antoin, retired principal of Gairm Scoil Mhuire in Thurles, who was constantly by her side and who cared for her every need so lovingly and with such devotion especially during her recent years of confinement, Alice is survived by Antoin, her son Dr Jim, daughter Edel, grandchildren, sister Mary (Guilfoyle) nieces, nephews, relatives and a wide wide circle of friends and neighbours. She was predeceased by her loving son Michéal, who was killed tragically in an accident overseas 35 years ago - Alice, a lady who was close to her Maker always, had tremendous faith and fully believed that she would be reunited with her son and family members again.
Funeral arrangements will be announced in due course.
May Alice rest peacefully in the Heaven she strived for throughout her life.


{% extends 'base.html' %} {% block pageDescription %} This is a user account page created by Team PASS: an undergraduate Gemstone research team from UMD studying cybersecurity. {% endblock %} {% block title %}User Account{% endblock %} {% block cardTitle %}
Earn credit for each day you complete all daily login tasks following your complete registration day!
Total Credit Earned by Logging in: ${{ "%0.2f" % get_credit(current_user.login_bitfield) }}
Remaining Daily Tasks:
{% if not current_user.has_password() %}


NY college student's killer sentenced to 30 years in Nicaragua prison
by Robert GeartyA New York college student’s convicted killer has been sentenced to a maximum of 30 years in prison in Nicaragua where he fled after the murder.
Orlando Tercero, 23, was found guilty of last year's murder of Binghamton University student 22-year-old Haley Anderson in a Nicaragua courtroom Nov. 1. The sentencing was Friday.
Nicaragua prohibits the extradition of its own citizens, leading to the rare legal proceeding in which New York prosecutors participated in the case via a video hookup by lining up witnesses. Tercero is a dual citizen of the U.S. and Nicaragua.
COLLEGE STUDENT'S MURDER IN NEW YORK LEADS TO A GUILTY VERDICT IN NICARAGUA
Investigators believe Anderson was asleep when Tercero strangled her to death, Broome County District Attorney Steve Cornwell said Friday, according to Fox 40 Binghamton.
"It doesn't get any more sick and depraved than that," he said after the sentencing, according to the station. "Not only is he using his physical abilities, but praying on somebody who is helpless."
Anderson’s parents joined Cornwell to watch the sentencing in his office, the station reported.
Friends of Anderson, who was from Long Island, said Tercero was obsessed with her. He met her at Binghamton where he was also a student.
Her body was found in his off-campus apartment.
A police investigator testified that Tercero left a note at his residence saying he was sorry
Cornwell praised Nicaraguan authorities, saying they stepped up and fought for Anderson as if she was their own victim.
CLICK HERE TO GET THE FOX NEWS APP
“We are all very thankful,” he said.
The Associated Press contributed to this report.


Homology of modular abelian varieties#
Sage can compute with homology groups associated to modular abelian varieties with coefficients in any commutative ring. Supported operations include computing matrices and characteristic polynomials of Hecke operators, rank, and rational decomposition as a direct sum of factors (obtained by cutting out kernels of Hecke operators).
AUTHORS:
William Stein (2007-03)
EXAMPLES:
sage: J = J0(43)
sage: H = J.integral_homology()
sage: H
Integral Homology of Abelian variety J0(43) of dimension 3
sage: H.hecke_matrix(19)
[ 0 0 -2 0 2 0]
[ 2 -4 -2 0 2 0]
[ 0 0 -2 -2 0 0]
[ 2 0 -2 -4 2 -2]
[ 0 2 0 -2 -2 0]
[ 0 2 0 -2 0 0]
sage: H.base_ring()
Integer Ring
sage: d = H.decomposition(); d
[
Submodule of rank 2 of Integral Homology of Abelian variety J0(43) of dimension 3,
Submodule of rank 4 of Integral Homology of Abelian variety J0(43) of dimension 3
]
sage: a = d[0]
sage: a.hecke_matrix(5)
[-4 0]
[ 0 -4]
sage: a.T(7)
Hecke operator T_7 on Submodule of rank 2 of Integral Homology of Abelian variety J0(43) of dimension 3
- class sage.modular.abvar.homology.Homology(base_ring, level, weight, category=None)#
Bases:
HeckeModule_free_module
A homology group of an abelian variety, equipped with a Hecke action.
- hecke_polynomial(n, var='x')#
Return the n-th Hecke polynomial in the given variable.
INPUT:
n
- positive integervar
- string (default: ‘x’) the variable name
OUTPUT: a polynomial over ZZ in the given variable
EXAMPLES:
sage: H = J0(43).integral_homology(); H Integral Homology of Abelian variety J0(43) of dimension 3 sage: f = H.hecke_polynomial(3); f x^6 + 4*x^5 - 16*x^3 - 12*x^2 + 16*x + 16 sage: parent(f) Univariate Polynomial Ring in x over Integer Ring sage: H.hecke_polynomial(3,'w') w^6 + 4*w^5 - 16*w^3 - 12*w^2 + 16*w + 16
- class sage.modular.abvar.homology.Homology_abvar(abvar, base)#
Bases:
Homology
The homology of a modular abelian variety.
- abelian_variety()#
Return the abelian variety that this is the homology of.
EXAMPLES:
sage: H = J0(48).homology() sage: H.abelian_variety() Abelian variety J0(48) of dimension 3
- ambient_hecke_module()#
Return the ambient Hecke module that this homology is contained in.
EXAMPLES:
sage: H = J0(48).homology(); H Integral Homology of Abelian variety J0(48) of dimension 3 sage: H.ambient_hecke_module() Integral Homology of Abelian variety J0(48) of dimension 3
- free_module()#
Return the underlying free module of this homology group.
EXAMPLES:
sage: H = J0(48).homology() sage: H.free_module() Ambient free module of rank 6 over the principal ideal domain Integer Ring
- gen(n)#
Return \(n^{th}\) generator of self.
This is not yet implemented!
EXAMPLES:
sage: H = J0(37).homology() sage: H.gen(0) # this will change Traceback (most recent call last): ... NotImplementedError: homology classes not yet implemented
- gens()#
Return generators of self.
This is not yet implemented!
EXAMPLES:
sage: H = J0(37).homology() sage: H.gens() # this will change Traceback (most recent call last): ... NotImplementedError: homology classes not yet implemented
- hecke_bound()#
Return bound on the number of Hecke operators needed to generate the Hecke algebra as a \(\ZZ\)-module acting on this space.
EXAMPLES:
sage: J0(48).homology().hecke_bound() 16 sage: J1(15).homology().hecke_bound() 32
- hecke_matrix(n)#
Return the matrix of the n-th Hecke operator acting on this homology group.
INPUT:
n
- a positive integer
OUTPUT: a matrix over the coefficient ring of this homology group
EXAMPLES:
sage: H = J0(23).integral_homology() sage: H.hecke_matrix(3) [-1 -2 2 0] [ 0 -3 2 -2] [ 2 -4 3 -2] [ 2 -2 0 1]
The matrix is over the coefficient ring:
sage: J = J0(23) sage: J.homology(QQ[I]).hecke_matrix(3).parent() Full MatrixSpace of 4 by 4 dense matrices over Number Field in I with defining polynomial x^2 + 1 with I = 1*I
- rank()#
Return the rank as a module or vector space of this homology group.
EXAMPLES:
sage: H = J0(5077).homology(); H Integral Homology of Abelian variety J0(5077) of dimension 422 sage: H.rank() 844
- submodule(U, check=True)#
Return the submodule of this homology group given by \(U\), which should be a submodule of the free module associated to this homology group.
INPUT:
U
- submodule of ambient free module (or something that defines one)check
- currently ignored.
Note
We do not check that U is invariant under all Hecke operators.
EXAMPLES:
sage: H = J0(23).homology(); H Integral Homology of Abelian variety J0(23) of dimension 2 sage: F = H.free_module() sage: U = F.span([[1,2,3,4]]) sage: M = H.submodule(U); M Submodule of rank 1 of Integral Homology of Abelian variety J0(23) of dimension 2
Note that the submodule command doesn’t actually check that the object defined is a homology group or is invariant under the Hecke operators. For example, the fairly random \(M\) that we just defined is not invariant under the Hecke operators, so it is not a Hecke submodule - it is only a \(\ZZ\)-submodule.
sage: M.hecke_matrix(3) Traceback (most recent call last): ... ArithmeticError: subspace is not invariant under matrix
- class sage.modular.abvar.homology.Homology_over_base(abvar, base_ring)#
Bases:
Homology_abvar
The homology over a modular abelian variety over an arbitrary base commutative ring (not \(\ZZ\) or \(\QQ\)).
- hecke_matrix(n)#
Return the matrix of the n-th Hecke operator acting on this homology group.
EXAMPLES:
sage: t = J1(13).homology(GF(3)).hecke_matrix(3); t [1 2 2 1] [1 0 2 0] [0 0 0 1] [0 0 2 1] sage: t.base_ring() Finite Field of size 3
- class sage.modular.abvar.homology.Homology_submodule(ambient, submodule)#
Bases:
Homology
A submodule of the homology of a modular abelian variety.
- ambient_hecke_module()#
Return the ambient Hecke module that this homology is contained in.
EXAMPLES:
sage: H = J0(48).homology(); H Integral Homology of Abelian variety J0(48) of dimension 3 sage: d = H.decomposition(); d [ Submodule of rank 2 of Integral Homology of Abelian variety J0(48) of dimension 3, Submodule of rank 4 of Integral Homology of Abelian variety J0(48) of dimension 3 ] sage: d[0].ambient_hecke_module() Integral Homology of Abelian variety J0(48) of dimension 3
- free_module()#
Return the underlying free module of the homology group.
EXAMPLES:
sage: H = J0(48).homology() sage: K = H.decomposition()[1]; K Submodule of rank 4 of Integral Homology of Abelian variety J0(48) of dimension 3 sage: K.free_module() Free module of degree 6 and rank 4 over Integer Ring Echelon basis matrix: [ 1 0 0 0 0 0] [ 0 1 0 0 1 -1] [ 0 0 1 0 -1 1] [ 0 0 0 1 0 -1]
- hecke_bound()#
Return a bound on the number of Hecke operators needed to generate the Hecke algebra acting on this homology group.
EXAMPLES:
sage: d = J0(43).homology().decomposition(2); d [ Submodule of rank 2 of Integral Homology of Abelian variety J0(43) of dimension 3, Submodule of rank 4 of Integral Homology of Abelian variety J0(43) of dimension 3 ]
Because the first factor has dimension 2 it corresponds to an elliptic curve, so we have a Hecke bound of 1.
sage: d[0].hecke_bound() 1 sage: d[1].hecke_bound() 8
- hecke_matrix(n)#
Return the matrix of the n-th Hecke operator acting on this homology group.
EXAMPLES:
sage: d = J0(125).homology(GF(17)).decomposition(2); d [ Submodule of rank 4 of Homology with coefficients in Finite Field of size 17 of Abelian variety J0(125) of dimension 8, Submodule of rank 4 of Homology with coefficients in Finite Field of size 17 of Abelian variety J0(125) of dimension 8, Submodule of rank 8 of Homology with coefficients in Finite Field of size 17 of Abelian variety J0(125) of dimension 8 ] sage: t = d[0].hecke_matrix(17); t [16 15 15 0] [ 0 5 0 2] [ 2 0 5 15] [ 0 15 0 16] sage: t.base_ring() Finite Field of size 17 sage: t.fcp() (x^2 + 13*x + 16)^2
- rank()#
Return the rank of this homology group.
EXAMPLES:
sage: d = J0(43).homology().decomposition(2) sage: [H.rank() for H in d] [2, 4]
- class sage.modular.abvar.homology.IntegralHomology(abvar)#
Bases:
Homology_abvar
The integral homology \(H_1(A,\ZZ)\) of a modular abelian variety.
- hecke_matrix(n)#
Return the matrix of the n-th Hecke operator acting on this homology group.
EXAMPLES:
sage: J0(48).integral_homology().hecke_bound() 16 sage: t = J1(13).integral_homology().hecke_matrix(3); t [-2 2 2 -2] [-2 0 2 0] [ 0 0 0 -2] [ 0 0 2 -2] sage: t.base_ring() Integer Ring
- hecke_polynomial(n, var='x')#
Return the n-th Hecke polynomial on this integral homology group.
EXAMPLES:
sage: f = J0(43).integral_homology().hecke_polynomial(2) sage: f.base_ring() Integer Ring sage: factor(f) (x + 2)^2 * (x^2 - 2)^2
- class sage.modular.abvar.homology.RationalHomology(abvar)#
Bases:
Homology_abvar
The rational homology \(H_1(A,\QQ)\) of a modular abelian variety.
- hecke_matrix(n)#
Return the matrix of the n-th Hecke operator acting on this homology group.
EXAMPLES:
sage: t = J1(13).homology(QQ).hecke_matrix(3); t [-2 2 2 -2] [-2 0 2 0] [ 0 0 0 -2] [ 0 0 2 -2] sage: t.base_ring() Rational Field sage: t = J1(13).homology(GF(3)).hecke_matrix(3); t [1 2 2 1] [1 0 2 0] [0 0 0 1] [0 0 2 1] sage: t.base_ring() Finite Field of size 3
- hecke_polynomial(n, var='x')#
Return the n-th Hecke polynomial on this rational homology group.
EXAMPLES:
sage: f = J0(43).rational_homology().hecke_polynomial(2) sage: f.base_ring() Rational Field sage: factor(f) (x + 2) * (x^2 - 2)


Interface IClient
Interface for connections.
Namespace: Fibula.Client.Contracts.Abstractions
Assembly: Fibula.Client.Contracts.dll
Syntax
public interface IClient
Fields
| Improve this Doc View SourceKnownCreatureLimit
The limit of creatures that a client can keep track of.
Declaration
public const int KnownCreatureLimit = 150
Field Value
| Type | Description |
|---|---|
| System.Int32 |
Properties
| Improve this Doc View SourceConnection
Gets the connection enstablished by this client.
Declaration
IConnection Connection { get; }
Property Value
| Type | Description |
|---|---|
| IConnection |
Information
Gets the information about the client on the other side of this connection.
Declaration
ClientInformation Information { get; }
Property Value
| Type | Description |
|---|---|
| ClientInformation |
IsIdle
Gets a value indicating whether this client is idle.
Declaration
bool IsIdle { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
PlayerId
Gets or sets the id of the player that this client is tied to.
Declaration
uint PlayerId { get; set; }
Property Value
| Type | Description |
|---|---|
| System.UInt32 |
Methods
| Improve this Doc View SourceAddKnownCreature(UInt32)
Adds the given creature to this client's known creatures collection.
Declaration
void AddKnownCreature(uint creatureId)
Parameters
| Type | Name | Description |
|---|---|---|
| System.UInt32 | creatureId | The id of the creature to add to the known creatures collection. |
ChooseCreatureToRemoveFromKnownSet(Int32)
Chooses a creature to remove from this player's known creatures collection, if it has reached the collection size limit.
Declaration
uint ChooseCreatureToRemoveFromKnownSet(int skip = 0)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | skip | Optional. A number of creatures to skip during selection. Used for multiple creature picking. |
Returns
| Type | Description |
|---|---|
| System.UInt32 | The id of the chosen creature, if any, or System.UInt32.MinValue if no creature was chosen. |
KnowsCreatureWithId(UInt32)
Checks if this client knows the given creature.
Declaration
bool KnowsCreatureWithId(uint creatureId)
Parameters
| Type | Name | Description |
|---|---|---|
| System.UInt32 | creatureId | The id of the creature to check. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the client knows the creature, false otherwise. |
RemoveKnownCreature(UInt32)
Removes the given creature from this player's known collection.
Declaration
void RemoveKnownCreature(uint creatureId)
Parameters
| Type | Name | Description |
|---|---|---|
| System.UInt32 | creatureId | The id of the creature to remove from the known creatures collection. |
Send(IEnumerable<IOutboundPacket>)
Sends the packets supplied over the Connection.
Declaration
void Send(IEnumerable<IOutboundPacket> packetsToSend)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Collections.Generic.IEnumerable<IOutboundPacket> | packetsToSend | The packets to send. |


In order to facilitate a smooth deleveraging of your favorite coffee roaster, we've decided to use a "smart contract" on the Ethereum Blockchain to raise funds on a per-week basis. Caffeine LaManna will now offer Angel Tokens purchased at our storefront. Each token will come minted with a sale price, an associated amount of roasted & shipped whole coffee beans, and a share of the next round's profits once it executes.
For our seed round, we're minting
and selling them for
At the very least, you get the same fresh bean deal you'd get otherwise and you could stock up for the year if you wanted! But if all goes well, Caffeine LaManna mints another round of tokens. You'll get an email to let you in on the details of the new round in case you want in again, or want to see your possible share should the new round execute.
When the new round is executed, the wallet associated with the token receives its designated share and an email is sent to the Angel holding the tokens to let them know the round is complete. And that's it!
After just a few rounds, we will have covered startup costs for your favorite coffee company and if I like to roast coffee, I'll just keep minting rounds. If I get tired of roasting, then we'll have to find someone else to roast the coffee, and we'll find something else to mint an angel token for. And I'll find something better to do. Soon to come .... Farmer LaManna!
If you are interested and want to know more about what the blockchain could do for you, contact me and I'll aim you in a good direction!
Thanks to all my Angels. You know who you are. Jolly LaMa J@caffeinelamanna.com


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Training
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Zoom through the Infinite World!
The world is generated with a complex algorithm in each session!
But you dont have to. There are only four buttons.
We're not social. Actually, you can find our Githubs down below.


ADD VENUE
Venue
Address
Cross Street
City
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Category
Tap the map to adjust the location of the venue
Adding a non-US venue? Enter everything except for the address, state, and zip and Foursquare will fill in the rest using your current location.


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{block name="main"}{/block}
{include file="public:foot" /} {block name="foot"}{/block}


If what you are looing is a practical example of how you can mock sensors in your RaspberryPi, please continue with this tutorial. DilbertPi can help you generate some data that will make your project look beatiful.


{% block head %} {% endblock %}
Welcome to Thing Finder!
Thing Finder is the hugely necessary new app by Craig Dickson which allows you to remember where you put your things.
All you have to do is visit this website every time you put anything anywhere, and add (or update) the details of the thing that you will later wish to find.
Truly, the optimal solution!


Sep 16, 2013 milly cyrus, molly, cyrus, koz, koz pink, greaney, tj greaney, girls hunting, fatherless, fathers and daughters Download Miley Cyrus free MIDI songs including We cant stop, Lilac wine, See You Again, Party in the usa and many more MIDI files
Sep 3, 2013. Miley Cyrus says critics were "over-thinking" her raunchy performance of "We Can't Stop" at last month's MTV Video Music Awards In a clip for
A documentary about wounded veterans that Roger championed gets a screening at the Rehabilitation Institute of Chicago and screenings in New York and Los Angeles…
The latest from Miley Cyrus Zone (@MileyCyrus_Zone) All Miley all the time!! Planet Earth 1 day ago. Pop singer Miley Cyrus dressed as Lil. Kim in the iconic purple outfit with Tech Zone. Pop star Miley Cyrus is all about pushing the envelope
3 days. ago Miley Cyrus did it on television, but administrators at Aliso Niguel High School. want students to know there's no twerking allowed on campus halloween-miley-cyrus Posted By Vicki McClure Davidson on October 31, 2013 halloween-miley-cyrus · Related Posts Plugin for WordPress, Blogger.
Aug 27, 2013. Queens of the Stone Age Ding Miley Cyrus in Barclays Performance the Brooklyn venue into a "(relatively) twerk-free-zone" during their Dec
Jun 12, 2013 The scumbags over at TMZ are reporting that's Biebs' R8 Audi and it was leaving Miley's pad That'd be the face of power couples for the new Aug 26, 2013 After a weird intro from Shailene Woodley, Blurred Lines has been. a duo of symphony and sexiness, with Robin Thicke and stripped down


Exception that indicates a passed argument was null
.
Exception that indicates a validation failed.
Exception that indicates a validation failed.
This exception is used to indicate and describe a failed validation by the validating
methods
of TrySugar
and FutureSugar
.
a string describing the validation failure
Exception that indicates a passed argument was
null
.Prior to ScalaTest and Scalactic 3.0.0, a
null
argument (where non-null
was required) would result in aNullPointerException
. Although throwingNullPointerException
is the tradition in Java APIs, Scala.js does not support aNullPointerException
. To make the behavior the same on both the JVM and Scala.js, the change to throwingNullArgumentException
was made throughout the ScalaTest and Scalactic, including therequireNonNull
method of traitRequirements
.


I’m a product designer at Cleartrip, Bangalore. I graduated from IIT Roorkee in 2017.
View ResumeAt IIT Roorkee, I have been involved in various campus groups as a designer and have worked on diverse projects ranging from product design, website design, illustration design and frontend development.
Designing a personalized year in review for every user of HackerEarth was no easy challenge. It demanded everything from user research to graphic designing.
View detailsBuy Sell is the intranet E-commerce portal of IIT Roorkee. Redesigning this portal was my first project as a web designer.
View detailscodeYudhh lets coders compete one-on-one thus enthusing the learning process. Put it simple: 2 coders, 1 problem, limited time. Solve first and win.
View detailsIMG (Information Management Group) is a students' group in IIT Roorkee. This website had been designed to publicize the recruitment of IMG.
View detailsAnd there are more to see. You can view them on my Dribbble profile.
I love to talk about design and productivity or anything that interests you.
Have something to say?


'Besotted' Rosanna Davison shares photos of baby daughter Sophia hours after birth
She is absolutely tiny, how beautiful!
by Blathin de PaorRosanna Davison has admitted she is completely 'Besotted' as she continued to share sweet photos of baby daughter Sophia hours after her birth on Thursday.
Rosanna Davison and husband Wes Quirke welcomed their first child together, Sophia Rose this week.
Announcing news of the birth on Instagram, Rosanna said: "And the dreams that you dare to dream really do come true. Today, November 21st 2019 at 11.57am, our beautiful and healthy daughter Sophia Rose Quirke arrived safely into the world weighing 6lbs8oz... she’s absolutely perfect.
"We’re completely overwhelmed with love and gratitude for our incredible gestational surrogate, who has given us the greatest gift of all and made our family dream a reality."
Swipe across below to see the full gallery of pictures of baby Sophia
And sharing more pictures as she spent her first night up with her daughter, Rosanna shared an Instagram video taken at 4.41 am as she sat up cradling her baby girl.
 Thank you for all the kind messages".
Rosanna and Wes revealed they were having their first child via gestational surrogate earlier this year.
Rosanna told her 153k social media followers back in July: "Half you and half me @wesquirke & I are overjoyed to share the news that we’re expecting a baby girl in November (by gestational surrogate) As our families & close friends know, this has been a long & very tough journey for us, sadly with multiple miscarriages along the way."
"We’ve dreamed of being parents for many years and I would have loved to carry my own baby, but for reasons unknown (most likely embryo rejection due to an overactive immune system) & although tests show excellent fertility, I’ve been unable to sustain any of my pregnancies , despite the best efforts of reproductive immunology experts & a lot of immune-suppressant medication, injections, intravenous infusions & surgery.
"Following medical advice, gestational surrogacy abroad became our only viable option for a biological child & we’re so thankful for the wonders of modern medicine & reproductive science."
The Wicklow native recently opened up to RSVP Live about her and Wes's decision to go down the surrogacy route after the model was diagnosed with an immune system issue.
Saying it was a "long journey" and not an easy process, Rosanna told us: "By February when we are finished the legal side, it will have been a two year journey for us.
"We began surrogacy in February 2018 so it does take a while.
"We have had a challenging few years trying to get to the bottom of my immune system issues, and that still isn’t figured out."


Landrick - Saas & Software Template
Introduction
Landrick is a Powerful Saas & Software Bootstrap Template. It is an excellent HTML template for startup, Social media marketing, Digital markering/agency, Email Templates, Online Learning Course, coworking space, cloud hosting, car ride, classic saas, classic application, event, business, application, educational course, personal portfolio, services, enterprise, minimal portfolio single product, saas, marketing, agency, Careers, Customer Supports, Onepage Landing etc. Landrick is fully updated with latest bootstrap v4.5.3. It is 100% responsive and looks stunning on all types of screens and devices. Users will love your site because it gives them a unique user experience (UX), clean, modern & beautiful design. They are also easy to customize and to combine with other components.
We have added a Dark version with RTL supported in both Light and Dark version.
We have added a new demos and important inner pages and fixed responsive issues.
Please feel free to get back to me in case if you are having any question or feedback.
Structure
I have tried to follow the standards and modular structure while developing the theme. Following sections are explaining the theme File & Folder, structure, html file struture and plugins.
 Bootstrap --> <link href="css/bootstrap.min.css" rel="stylesheet" type="text/css" />  <link href="css/style.css" rel="stylesheet" type="text/css" /> <link href="css/colors/default.css" rel="stylesheet" id="color-opt" />
 -------- --------

 -------- -------- -------- --------

 -------- -------- -------- --------

 -------- -------- -------- --------
     -------- --------      -------- -------- --------
CSS
Files are explained below:
| File | Description |
|---|---|
bootstrap.min.css
|
Landrick uses the latest bootstrap v4.5.3. The core bootstrap file is being used in all the pages. |
style.css
|
This file is containing all common styles for all the pages. |
style-rtl.css
|
In order to have RTL mode enabled, replace the reference of style.css to style-rtl.css
|
style-dark.css
|
In order to have Dark mode enabled, replace the reference of style.css to style-dark.css
|
style-dark-rtl.css
|
In order to have Dark RTL mode enabled, replace the reference of style.css to style-dark-rtl.css
|
Javascript
Files are explained below:
| File | Description |
|---|---|
jquery-3.5.1.min, bootstrap.bundle.min.js , etc.
|
These files are used at core of the theme. |
app.js
|
This is a main js file. It contains the custom JS code needed for features including layout, sidebar, etc. |
Menu
Some changes like that:
1) Menu Center to Right
To move menu from center to right side then just add class navigation-menu
along with nav-right
.
Navbar Center
Navbar Right
2) Menu Center to Left
To move menu from center to Left side then just add class navigation-menu
along with nav-left
.
Navbar Left
3) Menu Light
If you want menu center and light navigation just add class navigation-menu
along with nav-light
.
Navbar Light with Center
Navbar Light with Right
If you want menu right side and light navigation just add class navigation-menu nav-right
along with nav-light
.
Navbar Light with Left
If you want menu left side and light navigation just add class navigation-menu nav-left
along with nav-light
.
Loader
To add loader
you need to add the below code after the
tag.
<-- loader start -->

<-- loader end -->
Credits & Official Documentation
We've used the following resources as listed. These are some awesome creation and we are thankful to the community.
- Bootstrap 4.5
- Jquery
- Feather Icons
- Material Design Icons
- Flaticon Icons
- Iconscout Icons
- Owl Carousel
- Swiper Slider
- Flex Slider
- Slick Slider
- Parallax
- Countdown
- Magnific Popup
- Typed Text
- Lukaszadam Illustrations Images
- Stories Illustrations Images
- Manypixels Illustrations Images
- Isometric Illustrations Images
- Flatpickr - Datepicker
- Freepic Images
- Unsplash Images
- Animation Plugin
- AOS Animation Plugin
Supports
Once again thank you for your purchase. I'll be happy to answer the the questions you have related to the theme. In case if you have any suggestion or feature, request please feel free to contact me, I'll try to implement it and will release as part of future updates.
If you have any questions that are beyond the scope of this documentation, please feel free to email or contact us via my page.
Stay awesome
- Shreethemes
Changelog
Version v2.6.1 - 26th November 2020
- Fixed : Fixed some issues in shop pages
- Fixed : Fixed some issues in shop pages (Menu)
Version v2.6.0 - 07th November 2020
- Update : Update Latest Bootstrap v4.5.3
- Update : Update Latest Material Design Icons v5.8.55
- Update : Update Latest Flex SLider
- Update : Update Latest Isotope Plugin
- New : Developer Landing
- New : SEO Agency Landing
- New : Construction Landing
- New : Real Estate Landing
- New : Hospital Landing
- New : Integration Landing
- New : Landing Four
- Added : Product Listing page in Shop pages
- Added : Company Listing page in Career pages
- Added : Candidate Listing page in Career pages
- Added : Thank you page in Spacial pages
- Fixed : Fixed some issues in Auth page(like: Login, Signup, Forgot Password)
- Fixed : Fixed some issues in Responsive
- Fixed : Fixed some issues in Shop pages
Version v2.5.1 - 1st August 2020
- Fixed : Fixed some menu and responsive issues (Changes in css)
Version v2.5 - 31st July, 2020
- New : IT Solution Landing
- New : Corporate Business
- New : Email Inbox landing
- New : Task Management Landing
- New : Travel Landing
- New : Blog Landing
- New : Landing One
- New : Landing Two
- New : Landing Three
- New : Forums
- Forum Topic
- Forum Comments
- New : Case Study Pages
- All Cases
- Case Details
- Added : New Blog Details Page
- Added : New About Us Page
- Added : New Company History Page
- Added : User Account Pages
- Profile Members
- Profile Works
- Profile Messages
- Profile Payments
- Fixed : Fixed issue in Comingsoon page
- Fixed : Fixed issue in Comingsoon two page
- Fixed : Fixed issue in Maintenance page
- Added : 8 Elegent Color schemes
Version v2.2 - 26th May 2020
- Update Latest Bootstrap v4.5
- Update Latest Jquery v3.5.1
- Update Latest Material Design Icons v5.3.45
- New Shop landing landing
- New Added Shop Inner Pages
- Shop Products
- Product Details
- Shop Cart
- Checkouts
- My Profile / Account
- New Insurance Landing Live Preview
- New E-Book Landing Live Preview
- New Added Help Center Pages
- Help center(Overview)
- FAQs
- Guides
- Request Form
- Fixed some menu issues
- Menu easily move center to right (css based) and add light menu (css based)
- Fixed some responsive issues
Version v2.1 - 1st April, 2020
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Changes in SCSS files v2.6
Some add and changes CSS in SCSS files are here,
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> Home Education Experience Interest
My main school is expo but i mostly do my work on edgenuity.I am doing courses in the Waterloo career center for programming.I'm still figuring out what i want to do for a job, but right now i'm leaning towards programming.


MCMC Sampling#
Overview#
Stan’s MCMC sampler implements the Hamiltonian Monte Carlo (HMC) algorithm and its adaptive variant the no-U-turn sampler (NUTS). It creates a set of draws from the posterior distribution of the model conditioned on the data, allowing for exact Bayesian inference of the model parameters. Each draw consists of the values for all parameter, transformed parameter, and generated quantities variables, reported on the constrained scale.
The CmdStanModel sample method wraps the CmdStan sample method. Underlyingly, the CmdStan outputs are a set of per-chain Stan CSV files. In addition to the resulting sample, reported as one row per draw, the Stan CSV files encode information about the inference engine configuration and the sampler state. The NUTS-HMC adaptive sampler algorithm also outputs the
per-chain HMC tuning parameters step_size
and metric
.
The sample
method returns a CmdStanMCMC object, which provides access to the disparate information from the Stan CSV files. Accessor functions allow the user to access the sample in whatever data format is needed for further analysis, either as tabular data (i.e., in terms of the per-chain CSV file rows and columns), or as structured objects which correspond to the variables in the Stan model and the individual diagnostics produced by
the inference method.
The stan_variable and stan_variables methods return a Python numpy.ndarray containing all draws from the sample where the structure of each draw corresponds to the structure of the Stan variable.
The draws method returns the sample as either a 2-D or 3-D numpy.ndarray.
The draws_pd method returns the entire sample or selected variables as a pandas.DataFrame.
The draws_xr method returns a structured Xarray dataset over the Stan model variables.
The method_variables returns a Python dict over all sampler method variables.
In addition, the CmdStanMCMC
object has accessor methods for
The per-chain HMC tuning parameters
step_size
andmetric
The CmdStan run configuration and console outputs
The mapping between the Stan model variables and the corresponding CSV file columns.
Notebook prerequisites#
CmdStanPy displays progress bars during sampling via use of package tqdm. In order for these to display properly in a Jupyter notebook, you must have the ipywidgets package installed, and depending on your version of Jupyter or JupyterLab, you must enable it via command:
[1]:
!jupyter nbextension enable --py widgetsnbextension
Enabling notebook extension jupyter-js-widgets/extension...
- Validating: OK
For more information, see the the installation instructions, also this tqdm GitHub issue.
Fitting the model and data#
In this example we use the CmdStan example model bernoulli.stan and data file bernoulli.data.json.
We instantiate a CmdStanModel
from the Stan program file
[2]:
import os
from cmdstanpy import CmdStanModel
# instantiate, compile bernoulli model
model = CmdStanModel(stan_file='bernoulli.stan')
By default, the model is compiled during instantiation. The compiled executable is created in the same directory as the program file. If the directory already contains an executable file with a newer timestamp, the model is not recompiled.
We run the sampler on the data using all default settings: 4 chains, each of which runs 1000 warmup and sampling iterations.
[3]:
# run CmdStan's sample method, returns object `CmdStanMCMC`
fit = model.sample(data='bernoulli.data.json')
14:29:56 - cmdstanpy - INFO - CmdStan start processing
14:29:56 - cmdstanpy - INFO - CmdStan done processing.
The CmdStanMCMC
object records the command, the return code, and the paths to the sampler output csv and console files. The sample is lazily instantiated on first access of either the draws or the HMC tuning parameters, i.e., the step size and metric.
The string representation of this object displays the CmdStan commands and the location of the output files. Output filenames are composed of the model name, a timestamp in the form YYYYMMDDhhmmss and the chain id, plus the corresponding filetype suffix, either ‘.csv’ for the CmdStan output or ‘.txt’ for the console messages, e.g. bernoulli-20220617170100_1.csv.
[4]:
fit
[4]:
CmdStanMCMC: model=bernoulli chains=4['method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
csv_files:
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_1.csv
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_2.csv
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_3.csv
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_4.csv
output_files:
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_0-stdout.txt
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_1-stdout.txt
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_2-stdout.txt
/tmp/tmp576tsk6g/bernoullikolo7cku/bernoulli-20220822142956_3-stdout.txt
[5]:
print(f'draws as array: {fit.draws().shape}')
print(f'draws as structured object:\n\t{fit.stan_variables().keys()}')
print(f'sampler diagnostics:\n\t{fit.method_variables().keys()}')
draws as array: (1000, 4, 8)
draws as structured object:
dict_keys(['theta'])
sampler diagnostics:
dict_keys(['lp__', 'accept_stat__', 'stepsize__', 'treedepth__', 'n_leapfrog__', 'divergent__', 'energy__'])
Sampler Progress#
Your model make take a long time to fit. The sample
method provides two arguments:
visual progress bar:
show_progress=True
stream CmdStan output to the console -
show_console=True
By default, CmdStanPy displays a progress bar during sampling, as seen above. Since the progress bars are only visible while the sampler is running and the bernoulli example model takes no time at all to fit, we run this model for 200K iterations, in order to see the progress bars in action.
[6]:
fit = model.sample(data='bernoulli.data.json', iter_warmup=100000, iter_sampling=100000, show_progress=True)
14:29:56 - cmdstanpy - INFO - CmdStan start processing
14:29:58 - cmdstanpy - INFO - CmdStan done processing.
To see the CmdStan console outputs instead of progress bars, specify show_console=True
This will stream all CmdStan messages to the terminal while the sampler is running. This option will allow you to debug a Stan program using the Stan language print
statement.
[7]:
fit = model.sample(data='bernoulli.data.json', chains=2, parallel_chains=1, show_console=True)
14:30:00 - cmdstanpy - INFO - Chain [1] start processing
14:30:00 - cmdstanpy - INFO - Chain [1] done processing
14:30:00 - cmdstanpy - INFO - Chain [2] start processing
14:30:00 - cmdstanpy - INFO - Chain [2] done processing
Chain [1] method = sample (Default)
Chain [1] sample
Chain [1] num_samples = 1000 (Default)
Chain [1] num_warmup = 1000 (Default)
Chain [1] save_warmup = 0 (Default)
Chain [1] thin = 1 (Default)
Chain [1] adapt
Chain [1] engaged = 1 (Default)
Chain [1] gamma = 0.050000000000000003 (Default)
Chain [1] delta = 0.80000000000000004 (Default)
Chain [1] kappa = 0.75 (Default)
Chain [1] t0 = 10 (Default)
Chain [1] init_buffer = 75 (Default)
Chain [1] term_buffer = 50 (Default)
Chain [1] window = 25 (Default)
Chain [1] algorithm = hmc (Default)
Chain [1] hmc
Chain [1] engine = nuts (Default)
Chain [1] nuts
Chain [1] max_depth = 10 (Default)
Chain [1] metric = diag_e (Default)
Chain [1] metric_file = (Default)
Chain [1] stepsize = 1 (Default)
Chain [1] stepsize_jitter = 0 (Default)
Chain [1] id = 1
Chain [1] data
Chain [1] file = bernoulli.data.json
Chain [1] init = 2 (Default)
Chain [1] random
Chain [1] seed = 70257
Chain [1] output
Chain [1] file = /tmp/tmp576tsk6g/bernoullitkllicz1/bernoulli-20220822143000_1.csv
Chain [1] diagnostic_file = (Default)
Chain [1] refresh = 100 (Default)
Chain [1] sig_figs = -1 (Default)
Chain [1] profile_file = profile.csv (Default)
Chain [1] num_threads = 1
Chain [1]
Chain [1]
Chain [1] Gradient evaluation took 2e-06 seconds
Chain [1] 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
Chain [1] Adjust your expectations accordingly!
Chain [1]
Chain [1]
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Chain [1]
Chain [1] Elapsed Time: 0.005 seconds (Warm-up)
Chain [1] 0.009 seconds (Sampling)
Chain [1] 0.014 seconds (Total)
Chain [1]
Chain [2] method = sample (Default)
Chain [2] sample
Chain [2] num_samples = 1000 (Default)
Chain [2] num_warmup = 1000 (Default)
Chain [2] save_warmup = 0 (Default)
Chain [2] thin = 1 (Default)
Chain [2] adapt
Chain [2] engaged = 1 (Default)
Chain [2] gamma = 0.050000000000000003 (Default)
Chain [2] delta = 0.80000000000000004 (Default)
Chain [2] kappa = 0.75 (Default)
Chain [2] t0 = 10 (Default)
Chain [2] init_buffer = 75 (Default)
Chain [2] term_buffer = 50 (Default)
Chain [2] window = 25 (Default)
Chain [2] algorithm = hmc (Default)
Chain [2] hmc
Chain [2] engine = nuts (Default)
Chain [2] nuts
Chain [2] max_depth = 10 (Default)
Chain [2] metric = diag_e (Default)
Chain [2] metric_file = (Default)
Chain [2] stepsize = 1 (Default)
Chain [2] stepsize_jitter = 0 (Default)
Chain [2] id = 2
Chain [2] data
Chain [2] file = bernoulli.data.json
Chain [2] init = 2 (Default)
Chain [2] random
Chain [2] seed = 70257
Chain [2] output
Chain [2] file = /tmp/tmp576tsk6g/bernoullitkllicz1/bernoulli-20220822143000_2.csv
Chain [2] diagnostic_file = (Default)
Chain [2] refresh = 100 (Default)
Chain [2] sig_figs = -1 (Default)
Chain [2] profile_file = profile.csv (Default)
Chain [2] num_threads = 1
Chain [2]
Chain [2]
Chain [2] Gradient evaluation took 4e-06 seconds
Chain [2] 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
Chain [2] Adjust your expectations accordingly!
Chain [2]
Chain [2]
Chain [2] Iteration: 1 / 2000 [ 0%] (Warmup)
Chain [2] Iteration: 100 / 2000 [ 5%] (Warmup)
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Chain [2]
Chain [2] Elapsed Time: 0.008 seconds (Warm-up)
Chain [2] 0.014 seconds (Sampling)
Chain [2] 0.022 seconds (Total)
Chain [2]
Chain [2]
Checking the fit#
The first question to ask of the CmdStanMCMC
object is: is this a valid sample from the posterior?
It is important to check whether or not the sampler was able to fit the model given the data. Often, this is not possible, for any number of reasons. To appreciate the sampler diagnostics, we use a hierarchical model which, given a small amount of data, encounters difficulty: the centered parameterization of the “8-schools” model (Rubin, 1981). The “8-schools” model is a simple hierarchical model, first developed on a dataset taken from an experiment was conducted in 8 schools, with only treatment effects and their standard errors reported.
The Stan model and the original dataset are in files eight_schools.stan
and eight_schools.data.json
.
eight_schools.stan
[8]:
with open('eight_schools.stan', 'r') as fd:
print(fd.read())
data {
int<lower=0> J; // number of schools
array[J] real y; // estimated treatment effect (school j)
array[J] real<lower=0> sigma; // std err of effect estimate (school j)
}
parameters {
real mu;
array[J] real theta;
real<lower=0> tau;
}
model {
theta ~ normal(mu, tau);
y ~ normal(theta, sigma);
}
eight_schools.data.json
[9]:
with open('eight_schools.data.json', 'r') as fd:
print(fd.read())
{
"J" : 8,
"y" : [28,8,-3,7,-1,1,18,12],
"sigma" : [15,10,16,11,9,11,10,18],
"tau" : 25
}
Because there is not much data, the geometry of posterior distribution is highly curved, thus the sampler may encounter difficulty in fitting the model. By specifying the initial seed for the pseudo-random number generator, we insure that the sampler will have difficulty in fitting this model. In particular, some post-warmup iterations diverge, resulting in a biased sample. In addition, some post-warmup iterations hit the maximum allowed treedepth before the trajectory hits the “U-turn” condition of the NUTS algorithm, in which case the sampler may fail to properly explore the entire posterior.
These diagnostics are checked for automatically at the end of each run; if problems are detected, a WARNING message is logged.
[10]:
eight_schools_model = CmdStanModel(stan_file='eight_schools.stan')
eight_schools_fit = eight_schools_model.sample(data='eight_schools.data.json', seed=55157)
14:30:00 - cmdstanpy - INFO - CmdStan start processing
14:30:00 - cmdstanpy - INFO - CmdStan done processing.
14:30:00 - cmdstanpy - WARNING - Some chains may have failed to converge.
Chain 1 had 29 divergent transitions (2.9%)
Chain 2 had 208 divergent transitions (20.8%)
Chain 3 had 17 divergent transitions (1.7%)
Chain 4 had 31 divergent transitions (3.1%)
Use function "diagnose()" to see further information.
More information on how to address convergence problems can be found at https://mc-stan.org/misc/warnings
The number of post-warmup divergences and iterations which hit the maximum treedepth can be inspected directly via properties divergences
and max_treedepths
.
[11]:
print(f'divergences:\n{eight_schools_fit.divergences}\niterations at max_treedepth:\n{eight_schools_fit.max_treedepths}')
divergences:
[ 29 208 17 31]
iterations at max_treedepth:
[0 0 0 0]
Summarizing the sample#
The summary
method reports the R-hat statistic, a measure of how well the sampler chains have converged.
[12]:
eight_schools_fit.summary()
[12]:
| Mean | MCSE | StdDev | 5% | 50% | 95% | N_Eff | N_Eff/s | R_hat | |
|---|---|---|---|---|---|---|---|---|---|
| lp__ | -17.80420 | 1.297600 | 5.69607 | -26.306500 | -18.54890 | -8.18723 | 19.26950 | 86.41040 | 1.18588 |
| mu | 7.98088 | 0.196141 | 5.17030 | -0.847043 | 8.46135 | 16.43540 | 694.85500 | 3115.94000 | 1.00830 |
| theta[1] | 11.69530 | 0.357324 | 8.65978 | -0.384251 | 10.32760 | 28.16590 | 587.33900 | 2633.81000 | 1.00867 |
| theta[2] | 7.76656 | 0.201329 | 6.38375 | -2.518540 | 7.25419 | 18.44390 | 1005.40000 | 4508.53000 | 1.00249 |
| theta[3] | 5.96852 | 0.239867 | 8.27095 | -8.756270 | 7.21487 | 18.39070 | 1188.96000 | 5331.68000 | 1.01065 |
| theta[4] | 7.71660 | 0.201524 | 6.85139 | -3.644710 | 8.26866 | 18.80780 | 1155.86000 | 5183.21000 | 1.00593 |
| theta[5] | 4.97621 | 0.447597 | 6.65889 | -6.701880 | 5.63811 | 14.88990 | 221.32500 | 992.48700 | 1.03708 |
| theta[6] | 5.88040 | 0.212933 | 6.89437 | -6.316140 | 6.83414 | 16.41370 | 1048.34000 | 4701.09000 | 1.01177 |
| theta[7] | 10.95250 | 0.243737 | 6.90153 | 0.384918 | 9.93285 | 23.58810 | 801.76400 | 3595.35000 | 1.00408 |
| theta[8] | 8.47301 | 0.218012 | 8.06921 | -4.222510 | 8.14475 | 22.19720 | 1369.94000 | 6143.23000 | 1.00222 |
| tau | 7.01515 | 0.772870 | 5.53896 | 1.022930 | 5.78582 | 17.32530 | 51.36157 | 230.32095 | 1.07508 |
Sampler Diagnostics#
The diagnose()
method provides more information about the sample.
[13]:
print(eight_schools_fit.diagnose())
Processing csv files: /tmp/tmp576tsk6g/eight_schoolsk2xullo4/eight_schools-20220822143000_1.csv, /tmp/tmp576tsk6g/eight_schoolsk2xullo4/eight_schools-20220822143000_2.csv, /tmp/tmp576tsk6g/eight_schoolsk2xullo4/eight_schools-20220822143000_3.csv, /tmp/tmp576tsk6g/eight_schoolsk2xullo4/eight_schools-20220822143000_4.csv
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
285 of 4000 (7.12%) transitions ended with a divergence.
These divergent transitions indicate that HMC is not fully able to explore the posterior distribution.
Try increasing adapt delta closer to 1.
If this doesn't remove all divergences, try to reparameterize the model.
Checking E-BFMI - sampler transitions HMC potential energy.
The E-BFMI, 0.28, is below the nominal threshold of 0.30 which suggests that HMC may have trouble exploring the target distribution.
If possible, try to reparameterize the model.
Effective sample size satisfactory.
The following parameters had split R-hat greater than 1.05:
tau
Such high values indicate incomplete mixing and biased estimation.
You should consider regularizating your model with additional prior information or a more effective parameterization.
Processing complete.
Accessing the sampler outputs#
[14]:
fit = model.sample(data='bernoulli.data.json')
14:30:01 - cmdstanpy - INFO - CmdStan start processing
14:30:01 - cmdstanpy - INFO - CmdStan done processing.
Extracting the draws as structured Stan program variables#
Per-variable draws can be accessed as either a numpy.ndarray object via method stan_variable
or as an xarray.Dataset object via draws_xr
.
[15]:
print(fit.stan_variable('theta'))
[0.319739 0.384567 0.298657 ... 0.15686 0.251358 0.306462]
The stan_variables
method returns a Python dict
over all Stan variables in the output.
[16]:
for k, v in fit.stan_variables().items():
print(f'name: {k}, shape: {v.shape}')
name: theta, shape: (4000,)
[17]:
print(fit.draws_xr('theta'))
<xarray.Dataset>
Dimensions: (chain: 4, draw: 1000)
Coordinates:
* chain (chain) int64 1 2 3 4
* draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999
Data variables:
theta (chain, draw) float64 0.3197 0.3846 0.2987 ... 0.1569 0.2514 0.3065
Attributes:
stan_version: 2.27.0
model: bernoulli_model
num_draws_sampling: 1000
Extracting the draws in tabular format#
The sample can be accessed either as a numpy
array or a pandas DataFrame
:
[18]:
print(f'sample as ndarray: {fit.draws().shape}\nfirst 2 draws, chain 1:\n{fit.draws()[:2, 0, :]}')
sample as ndarray: (1000, 4, 8)
first 2 draws, chain 1:
[[-6.88826 0.971817 0.998143 1. 1. 0. 6.89162
0.319739]
[-7.23577 0.893005 0.998143 1. 1. 0. 7.24696
0.384567]]
[19]:
fit.draws_pd().head()
[19]:
| lp__ | accept_stat__ | stepsize__ | treedepth__ | n_leapfrog__ | divergent__ | energy__ | theta | |
|---|---|---|---|---|---|---|---|---|
| 0 | -6.88826 | 0.971817 | 0.998143 | 1.0 | 1.0 | 0.0 | 6.89162 | 0.319739 |
| 1 | -7.23577 | 0.893005 | 0.998143 | 1.0 | 1.0 | 0.0 | 7.24696 | 0.384567 |
| 2 | -6.81820 | 1.000000 | 0.998143 | 2.0 | 3.0 | 0.0 | 7.10348 | 0.298657 |
| 3 | -6.79992 | 1.000000 | 0.998143 | 1.0 | 1.0 | 0.0 | 6.82455 | 0.291636 |
| 4 | -6.80004 | 0.999965 | 0.998143 | 1.0 | 1.0 | 0.0 | 6.81503 | 0.291685 |
Extracting sampler method diagnostics#
[20]:
for k, v in fit.method_variables().items():
print(f'name: {k}, shape: {v.shape}')
name: lp__, shape: (1000, 4)
name: accept_stat__, shape: (1000, 4)
name: stepsize__, shape: (1000, 4)
name: treedepth__, shape: (1000, 4)
name: n_leapfrog__, shape: (1000, 4)
name: divergent__, shape: (1000, 4)
name: energy__, shape: (1000, 4)
Extracting the per-chain HMC tuning parameters#
[21]:
print(f'adapted step_size per chain\n{fit.step_size}\nmetric_type: {fit.metric_type}\nmetric:\n{fit.metric}')
adapted step_size per chain
[0.998143 0.991067 0.894233 0.896342]
metric_type: diag_e
metric:
[[0.482997]
[0.485668]
[0.444746]
[0.535956]]
Extracting the sample meta-data#
[22]:
print('sample method variables:\n{}\n'.format(fit.metadata.method_vars_cols.keys()))
print('stan model variables:\n{}'.format(fit.metadata.stan_vars_cols.keys()))
sample method variables:
dict_keys(['lp__', 'accept_stat__', 'stepsize__', 'treedepth__', 'n_leapfrog__', 'divergent__', 'energy__'])
stan model variables:
dict_keys(['theta'])
Saving the sampler output files#
The sampler output files are written to a temporary directory which is deleted upon session exit unless the output_dir
argument is specified. The save_csvfiles
function moves the CmdStan CSV output files to a specified directory without having to re-run the sampler. The console output files are not saved. These files are treated as ephemeral; if the sample is valid, all relevant information is recorded in the CSV files.
Parallelization via multi-threaded processing#
Stan’s multi-threaded processing is based on the Intel Threading Building Blocks (TBB) library, which must be linked to by the C++ compiler. To take advantage of this option, you must compile (or recompile) the program with the the C++ compiler option STAN_THREADS
. The CmdStanModel object constructor and its compile
method both have argument cpp_options
which takes as its value a dictionary of compiler flags.
We compile the example model bernoulli.stan
, this time with arguments cpp_options
and compile
, and use the function exe_info()
to check that the model has been compiled for multi-threading.
[23]:
model = CmdStanModel(stan_file='bernoulli.stan',
cpp_options={'STAN_THREADS': 'TRUE'},
compile='force')
model.exe_info()
14:30:01 - cmdstanpy - INFO - compiling stan file /home/brian/Dev/py/cmdstanpy/docsrc/users-guide/examples/bernoulli.stan to exe file /home/brian/Dev/py/cmdstanpy/docsrc/users-guide/examples/bernoulli
14:30:18 - cmdstanpy - INFO - compiled model executable: /home/brian/Dev/py/cmdstanpy/docsrc/users-guide/examples/bernoulli
[23]:
{'stan_version_major': '2',
'stan_version_minor': '29',
'stan_version_patch': '0',
'STAN_THREADS': 'true',
'STAN_MPI': 'false',
'STAN_OPENCL': 'false',
'STAN_NO_RANGE_CHECKS': 'false',
'STAN_CPP_OPTIMS': 'false'}
Cross-chain multi-threading#
As of version CmdStan 2.28, it is possible to run the NUTS-HMC sampler on multiple chains from within a single executable using threads. This has the potential to speed up sampling. It also reduces the overall memory footprint required for sampling as all chains share the same copy of data.the input data. When using within-chain parallelization all chains started within a single executable can share all the available threads and once a chain finishes the threads will be reused.
The sample program argument parallel_chains
takes an integer value which specifies how many chains to run in parallel. For models which have been compiled with option STAN_THREADS
set, all chains are run from within a single process and the value of the parallel_chains
argument specifies the total number of threads.
[24]:
fit = model.sample(data='bernoulli.data.json', parallel_chains=4)
14:30:18 - cmdstanpy - INFO - CmdStan start processing
14:30:18 - cmdstanpy - INFO - CmdStan done processing.
Within-chain multi-threading#
The Stan language reduce_sum function provides within-chain parallelization. For models which require computing the sum of a number of independent function evaluations, e.g., when evaluating a number of conditionally independent terms in a log-likelihood, the reduce_sum
function is used to parallelize this computation.
To see how this works, we run the “reflag” model, used in the reduce_sum minimal example case study. The Stan model and the original dataset are in files “redcard_reduce_sum.stan” and “redcard.json”.
[25]:
with open('redcard_reduce_sum.stan', 'r') as fd:
print(fd.read())
functions {
real partial_sum(array[] int slice_n_redcards, int start, int end,
array[] int n_games, vector rating, vector beta) {
return binomial_logit_lpmf(slice_n_redcards | n_games[start : end], beta[1]
+ beta[2]
* rating[start : end]);
}
}
data {
int<lower=0> N;
array[N] int<lower=0> n_redcards;
array[N] int<lower=0> n_games;
vector[N] rating;
int<lower=1> grainsize;
}
parameters {
vector[2] beta;
}
model {
beta[1] ~ normal(0, 10);
beta[2] ~ normal(0, 1);
target += reduce_sum(partial_sum, n_redcards, grainsize, n_games, rating,
beta);
}
As before, we compile the model specifying argument cpp_options
.
[26]:
redcard_model = CmdStanModel(stan_file='redcard_reduce_sum.stan',
cpp_options={'STAN_THREADS': 'TRUE'},
compile='force')
redcard_model.exe_info()
14:30:18 - cmdstanpy - INFO - compiling stan file /home/brian/Dev/py/cmdstanpy/docsrc/users-guide/examples/redcard_reduce_sum.stan to exe file /home/brian/Dev/py/cmdstanpy/docsrc/users-guide/examples/redcard_reduce_sum
14:30:40 - cmdstanpy - INFO - compiled model executable: /home/brian/Dev/py/cmdstanpy/docsrc/users-guide/examples/redcard_reduce_sum
[26]:
{'stan_version_major': '2',
'stan_version_minor': '29',
'stan_version_patch': '0',
'STAN_THREADS': 'true',
'STAN_MPI': 'false',
'STAN_OPENCL': 'false',
'STAN_NO_RANGE_CHECKS': 'false',
'STAN_CPP_OPTIMS': 'false'}
The sample
method argument threads_per_chain
specifies the number of threads allotted to each chain; this corresponds to CmdStan’s num_threads
argument.
[27]:
redcard_fit = redcard_model.sample(data='redcard.json', threads_per_chain=4)
14:30:40 - cmdstanpy - INFO - CmdStan start processing
14:31:48 - cmdstanpy - INFO - CmdStan done processing.
The number of threads to use is passed to the model exe file by means of the shell environment variable STAN_NUM_THREADS
.
On my machine, which has 4 cores, all 4 chains are run in parallel from within a single process. Therefore, the total number of threads used by this process will be threads_per_chain
* chains
. To check this, we examine the shell environment variable STAN_NUM_THREADS
.
[28]:
os.environ['STAN_NUM_THREADS']
[28]:
'16'


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New cell therapy improves memory and stops seizures following TBI
Transplanting new inhibitory neurons may repair damaged brain circuits
| Source: | University of California - Irvine |
| Summary: | Researchers have developed a breakthrough cell therapy to improve memory and prevent seizures in mice following traumatic brain injury. |
Researchers from the University of California, Irvine developed a breakthrough cell therapy to improve memory and prevent seizures in mice following traumatic brain injury. The study, titled "Transplanted interneurons improve memory precision after traumatic brain injury," was published today in Nature Communications.
Traumatic brain injuries affect 2 million Americans each year and cause cell death and inflammation in the brain. People who experience a head injury often suffer from lifelong memory loss and can develop epilepsy.
In the study, the UCI team transplanted embryonic progenitor cells capable of generating inhibitory interneurons, a specific type of nerve cell that controls the activity of brain circuits, into the brains of mice with traumatic brain injury. They targeted the hippocampus, a brain region responsible for learning and memory.
The researchers discovered that the transplanted neurons migrated into the injury where they formed new connections with the injured brain cells and thrived long term. Within a month after treatment, the mice showed signs of memory improvement, such as being able to tell the difference between a box where they had an unpleasant experience from one where they did not.  The cell transplants also prevented the mice from developing epilepsy, which affected more than half of the mice who were not treated with new interneurons.
"Inhibitory neurons are critically involved in many aspects of memory, and they are extremely vulnerable to dying after a brain injury," said Robert Hunt, PhD, assistant professor of anatomy and neurobiology at UCI School of Medicine who led the study. "While we cannot stop interneurons from dying, it was exciting to find that we can replace them and rebuild their circuits."
This is not the first time Hunt and his team has used interneuron transplantation therapy to restore memory in mice. In 2018, the UCI team used a similar approach, delivered the same way but to newborn mice, to improve memory of mice with a genetic disorder.
Still, this was an exciting advance for the researchers. "The idea to regrow neurons that die off after a brain injury is something that neuroscientists have been trying to do for a long time," Hunt said. "But often, the transplanted cells don't survive, or they aren't able to migrate or develop into functional neurons."
To further test their observations, Hunt and his team silenced the transplanted neurons with a drug, which caused the memory problems to return.
"It was exciting to see the animals' memory problems come back after we silenced the transplanted cells, because it showed that the new neurons really were the reason for the memory improvement," said Bingyao Zhu, a junior specialist and first author of the study.
Currently, there are no treatments for people who experience a head injury. If the results in mice can be replicated in humans, it could have a tremendous impact for patients. The next step is to create interneurons from human stem cells.
"So far, nobody has been able to convincingly create the same types of interneurons from human pluripotent stem cells," Hunt said. "But I think we're close to being able to do this."
Jisu Eom, an undergraduate researcher, also contributed to this study. Funding was provided by the National Institutes of Health.
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Story Source:
Materials provided by University of California - Irvine.
Note: Content may be edited for style and length.
Journal Reference:
- Bingyao Zhu, Jisu Eom, Robert F. Hunt. Transplanted interneurons improve memory precision after traumatic brain injury. Nature Communications, 2019; 10 (1) DOI: 10.1038/s41467-019-13170-w
Cite This Page:
| MLA | University of California - Irvine. "New cell therapy improves memory and stops seizures following TBI: Transplanting new inhibitory neurons may repair damaged brain circuits." ScienceDaily. ScienceDaily, 15 November 2019. <www.sciencedaily.com/releases/2019/11/191115101102.htm>. |
| APA | University of California - Irvine. (2019, November 15). New cell therapy improves memory and stops seizures following TBI: Transplanting new inhibitory neurons may repair damaged brain circuits. ScienceDaily. Retrieved November 17, 2019 from www.sciencedaily.com/releases/2019/11/191115101102.htm |
| Chicago | University of California - Irvine. "New cell therapy improves memory and stops seizures following TBI: Transplanting new inhibitory neurons may repair damaged brain circuits." ScienceDaily. www.sciencedaily.com/releases/2019/11/191115101102.htm (accessed November 17, 2019). |