Blackboard is a learning management system employed by many tertiary education providers globally. The website provides end users with the ability to organise, store, share, and assess academic content As this system’s user interface can vary significantly per institute, it is essential that end users relevant to the institute in question are the main focus for its implementation. End-user experience and interaction are vital in determining the success of any product and Blackboard is no exception. By measuring the five Usability quality components, we can identify areas in need of improvement to increase its ease of use. The five areas identified are Learnability, Efficiency, Memorability, Errors, and Satisfaction (Nielsen, 2012)
As the study seeks to identify the issues experienced by the existing user base, Efficiency, Memorability, Errors, and Satisfaction were defined as the main categories of interest. Due to their previous exposure to Blackboard, Learnability was unable to be measured. The four identified categories are defined by Isa, Lokman, Wahid, & Sulaiman (2014) as follows.
To measure the impact of Auckland University of Technologies Blackboard design, 12 third-year students were selected to perform a series of tasks reflecting a typical interaction with the website. The 12 participants whose demographic details were assessed pre-study, were subjected to qualitative, quantitative, attitudinal, and behavioural assessment. These assessments took the form of a task-oriented Interview, Intercept Surveys, Usability Lab Studies, and Ethnographic Field Studies (Rohrer, 2014).
The assessment methods selected for this study enabled the results to reflect both a direct measure of the impact Blackboard’s design had on participants, in specific areas and record observations of areas previously not identified as relevant to the study.
The task-oriented interview, Intercept surveys, and elements of the Ethnographic field studies were selected to measure the attitudes expressed by participants pre, peri, and post-study in using both a qualitative and quantitative approach. The remaining assessment techniques, Usability Lab studies and elements of the Ethnographic Field study were selected to assess the behaviour of the participants using a qualitative approach.
The QuestionnaireThe pre-study questionnaire allowed us to obtain demographic data. This allowed us to compare variables such as experience, age, ethnicity, field of study, and year of study. These variables enabled us to explore ethnographic research areas during the study. The main focus of the pre-study questionnaire was to observe the correlation between demographic variables and those ethnographic and experimental data collected.
The post-study questionnaire alternated between ‘positive’ and ‘negative’ tone, where the users were able to select the response of ‘1’ where they strongly agreed with the statement proposed, or select ‘5’ where they strongly disagreed. The design of the questionnaire allowed us to both include the intensity of the response in the survey and eliminate personality bias where users could have potentially agree blindly to the questions provided.
Pre-study activities include all research processes that take place before the usability experiment. Before the usability session, all participants are asked questions oriented around their demographic, previous experience with the software, and how long they have been using Blackboard. These questions seek to measure how the participants feel about the software leading up to the study, their level of expertise with the software, and how their background may influence the results.
The experiment involves participants completing three tasks in a one-to-one usability session. All tasks begin from the homepage of a logged-in blackboard user. The three tasks are as follows:
A post-study questionnaire of 10 questions was provided covering the features, user interface, and performance of the website after the usability experiment. The survey required users to indicate their level of agreement with different statements on a scale of 1 - 5 (1 - Strongly Agree; 2 - Agree; 3 - Neutral; 4 - Disagree; 5 - Strongly Disagree). The questionnaire incorporates sections that enable participants to provide qualitative written data in the form of comments and recommendations.
Finding: We ordered the tasks from top to bottom where the first task consisted of something which all students do on a daily or weekly basis (opening the lecture notes), followed by viewing grades (done monthly or more) and lastly viewing exam timetables (done end of the semester). This gradual increase allowed us to judge memorability within the users as we noticed that each task took longer than the one before, where task one averaged at 21.62 seconds, task two averaged at 35.19 seconds and task three averaged at 39.89 seconds (after taking the outliers out). This in turn proves the memorability part of our research criteria where tasks that users have done more often are completed faster. Task one and task three required the same amount of clicks to open and load the file.
Method: Once the usability tasks were complete, participants completed a questionnaire. Participants indicated a level of agreement with ten statements, using a 1-5 scale, and had the option to write an additional comment per statement. For analysis, the results of the questionnaire are grouped into three usability categories; features, user interface and performance.
The first four questions of the usability study focussed on features of the website with a focus on interactions with navigation menus and links.
When looking at features of the website, participant highlighted navigation as a potnetial usability issues issue. Participants noted they need to click through too many menus, and that the pages contained information not relevant to what they are doing. They commented that navigation menus were not always clear where they would take the user. Finding exam timetables was highlighted as particularly difficult and potentially undoable without prior experience using the Blackboard system. At least two participants indicated that finding the exam time table would be impossible if they had not already how to found it previously. However, overall, our participant agreed with positives statements about the features of the website and disagreed with the negative one.
Questionnaire response shows most participants disagreed with the statement that the user did not like the colour palette. Comments on colour palette were generally favourable to neutral, with only one participant commenting that the colours could be more appealing. Most user’s also agreed that the website was visually appealing, comments included mentioning the colour palette being another critical feature as for why the site was attractive.
On average, there was a marginal agreement with the statement that the user interface was cluttered. This was the only negative statement in the post-questionnaire study that more people agreed with than disagreed. Comments indicated that potentially there are too many things going on per webpage, particularly the home page. It is interesting to note that the average score for users who found Blackboard cluttered (2.5) is very similar to the score of users who found Blackboard hard to navigate (2.75). There could be a possible correlation between the users who find Blackboard easy to see, also end up finding Blackboard easy to navigate. In a study done by Oxford’s Dr David R Danielson (2002) on ‘Web Navigation & Behavioural Effects’, it states that the lack of visual cues present in navigation can contribute to the website feeling “cluttered”, which ties back to our hypothesis and correlation found above.
Questionnaire results covering the performance of the website scored the two highest positive responses. Participants on average felt load time were appropriate and didn’t agree that the site was unresponsive. Some comments mentioned how fast the website loaded and said it felt responsive as well. One commenter commented that they thought some part of the page could load a bit faster, but otherwise the usability feedback indicate usability issues of performance are very limited, suggesting the website may be well optimised. A contributing factor to the positive performance of the Blackboard site may be the high broadband speed available at the Auckland University of Technology, where the usability studies took place. Testing the perceived performance of the site at differing speeds of broadband would be useful, but is beyond the scope of this project.
Based on the feedback gathered and reviews given by the users, this study recommends the following
changes for AUT to consider:
The study I participated in for Applied Human-Computer Interaction
(HCI) was a Usability Study
of the Blackboard learning management system used at Auckland University of Technology (AUT).
I was involved in the research and development section of the study as well as the analysis and
organisation of our findings. Once the initial research, experiment, and analysis had been
completed I began constructing a webpage to display our findings in an easy to read format.
I designed and built the template and assisted in inserting and formatting our results.
This study gave me valuable experience of usability testing and the importance of human-centered
design. Understanding the issues and difficulties our participants experienced and collecting
meaningful data from our experiment gave me insight into where the Blackboard system/website can
be improved. I found it interesting that prior experience with Blackboard had a significant impact
on usability and navigation with most of our participants mentioning at some point the initial
difficulty they faced when using the system for the first time.
Specifically, I learned a lot in regard to the Usability Testing Framework and qualitative
testing methods such as learnability, efficiency, memorability, errors and satisfaction, and
how users interact with a product via these methods. I have been able to apply these usability
testing methods to another project (a calendar management tool), specifically learnability for new
users. I have been working on the project with two others and for the most part have built the
project using unstructured feedback from our client. Following a testing framework for this
assignment has allowed me to better understand how we can quantify and measure this feedback and have had
great success in the steps we have made towards implementing this approach for that project.
Overall this project was highly beneficial to me as an aspiring User Experience/User Interface
designer and will be leaning heavily into all the lessons I learned as I move forward and on to
new projects. The big takeaway for me was how important Human Centered Design is in the software
or product development cycle - if we can get this part right from the start, the rest of the
development will make sense and have a higher chance of success when released to the end users.
I was responsible for performing some one-on-one usability interviews, researching differing usability methods, contributing questions to the questionnaire & pre-study interview and collecting and analysing the results of the study. As a team, we choose to let every member contribute to the project at all stages, meaning we all got experience with the processes of conducting an academic study.
Before this paper, I had absolutely no experience conducting or taking part in any usability studies. As a developer, I often divert my attention to the implementation problems and focus on the things I think would be fun to create, as opposed to thinking about how users will interact with my creation. My work on our year-long R & D project for AUT has highlighted this. Early in the project, we did not include user feedback thoroughly in the development process. Every 4-6 weeks we would meet our clients and tell them what we did and how to use our application. Although they would occasionally mention potential usability pitfalls, I feel because we coached and instructed them throughout the processed, we missed out on critical usability data. Once starting Human-Computer Interaction, my team and I started thinking about usability and user experience differently.
We didn’t do an official usability study till the last couple weeks of the project. However, the lessons learned were applied more generally far earlier. We began dedicating time at the beginning of each client meeting to having our client interact with the software unguided, and discover new features naturally. This uncovered a lot of usability issues which we had overlooked because of our proximity to the project. For instance, a core feature of our calendar application it the option to specify the office-status of a worker. This feature was activated by a right-click on a particular part of the page. Although we thought this would be self-evident, when showing the application to new users, almost no one was able to discover this feature naturally. Unfortunately, this discovery was made very late into development, so our devised solution lacked usability elegance and took the form of an info pop-up.
Conducting this usability study has given me valuable insight into structured methodologies which I believe will help me develop better software going forward.
My name is Saksham Anand, and I am in final semester of Bachelor of Computer & Information Sciences, majoring in Computational Intelligence. Our study was essentially based around the usability of AUT’s blackboard system, with different user demographics. The participants were bought into the lab and given a set of instructions to complete a task, this was combined with pre and post task questionnaire and allowed us to judge the users thought of chain. My role in this project was to work with the team in preparing the pre and post task questionnaires that the user will be using, and writing the research section of the report.
I discovered some interesting points about the human psychology during the user interviews of the blackboard. In specific, I found that people often like to categorize things in different ways, for example during our experiment some people expected grades to be under the ‘paper page’ on blackboard, however after not finding the grades under paper page, the user then decided to go look for a page which contains all the grades. This was interesting for me because the grades page is technically categorized as well, so I expected users to straight look there. One question this has me wondering is why do categorisations differ between users, for example some people sort out blocks by their shapes, however others may sort out blocks by their colour.
To sum it up, I believe the project accomplished its goal of obtaining insights into area of problems within AUT’s blackboard website. We discovered several points of interest where blackboard could be improved, such as having lecture powerpoints open within browser instead of forcing users to download them. With proper planning for pre and post activity questionnaire we were able to capture and analyze user data from the tasks, which allowed us to check if any correlation existed between users finding website easy to navigate and how fast they completed the task.
As a software development major within the Bachelor of
Computer and Information Science program, usability has been an idea which in spite of
its value and impact on the success of modern pieces of software has elluded my studies
so far. As such, the initial undertaking of this study proved to be both exciting and
rudderless as the team scrambled to find an impactful piece of software to assess.
The study saw many hurdles in its inception. From the inability to study the preferred R&D
project produced by members of the assessment team, to difficulty sourcing appropriate research
material to guide the structure of the study. These challenges however due to the strong team
dynamic were largely remedied through careful discussion or pivoting when required.
Through the initial crafting of our study, I learnt the how valuable the selecting your
participants to reflect the user base, construction of appropriately worded questions for
survey, and use of consistent interviewing techniques could be.
The selection of participants had a greater impact on the study than intended. We sought
to minimize the impact of previous experience with the website on the study by selecting
individuals with seemingly similar exposure to the website. This proved to still vary the
results more than expected. Participants often did not have experience with all functionality
with the website in spite of all having a minimum of three years exposure to Blackboard. The
inclusion of ethnographic assessment techniques did manage to allow for these unexpected elements
to be recorded and commented on.
However In future I would insist that the inclusion of participants unfamiliar to the website
be included as it would potentially enable all five usability quality areas to be more fully
assessed.
The construction of the questions used during the survey portion of study proved valuable,
however a free word association technique may have proved to be more beneficial in the
assessment of the website as this would have allowed participant responses to be more distinct
from individual to individual.
During the course of the study, the potential impact of straying from use of consistent
interviewing techniques became apparent. This was made apparent when a participant was
instructed to narrate their actions as they occurred. This enabled assessors to record the
participants path through the website, however appeared to impact the duration tasks took to
complete as the participant used time to articulate their actions in addition to second guessing
their approach. For any future usability studies conducted , ensuring each interview or assessment
contains the same components per participant will be a primary concern, even going as far as
using the same assessor of each interview as I observed the tone appeared to vary per assessor
used, which may or may not have impacted the results produced.
Overall the I found the study illuminating in its ability to place assessors within the minds
of end users. The results yielded and experience gained in conducting the study I found rewarding
and especially relevant as a software development major, and would stress that other papers
containing coding elements take cues as students and potential employers stand to gain greatly
though keeping designers and developers focused on user focused development.


Delete the row by pressing the delete button. Alternatively, play around by adding and deleting/removing the rows.


[SpringBoot/Vue.js Project] Blog
Develop an Web Application from start to finish! (with Oracle Cloud, Spring Boot, Vue.js)
1. Requirements
Project Objectives
- Develop an Web Application that works as a personal Blog.
Specification
- Article, Category CRUD.
- Parent/Child relationships in categories.
- Show projects by year.
- Backend API -> Frontend.
- Docker.
2. Structure
- Database: Oracle ATP Database, (Partial) Mongo DB
- Backend: SpringBoot
- Frontend: Node.js, Vue.js
3. Function
Backend
: There are three APIs that hand over data from ATP Database to frontend.
Menu
- One
Menu
is consisted of several(or one)Article
. Menu
has Parent/Child relationship. (ex. Computer Science Menu - iOS/Android Menu)
- One
Article
- One
Article
is a page of writing.
- One
Project
- Project is the has information(ex. started date, ended date, project title…) about previously conducted projects.
Project
API is mainly used on the homepage.
Frontend
: There are five main pages. (+ one sidebar)
- Main features and design were implemented using
Bootstrap-vue
. - Axios used to call Springboot APIs.
4. Implementation
Backend
Implemented
- Basic Settings
application.properties
file: See Code…
- Packages
- 5 packages for basic functions have been developed.
- Two packages are for setting:
ApiResponse
andConfig
. - The other packages are for API:
ArticleItem
,MenuItem
andProjectItem
.
- Two packages are for setting:
-
Except
Config
andApiResponse
package, one package consists ofItem
,Adapter
,Controller
,Repository
,Request
,Response
andService
.
- 5 packages for basic functions have been developed.
-
Config
package- This package is for the Database, Frontend and (+Security) connection settings.
- Class:
MongoConfig
,OracleConfig
,WebConfig
- Detailed implementation of the
Config
package: See Code…
ApiReseponse
package- This class is for the abstract
ApiResponse
. - API Response classes in other main three packages’ API Response classes are inherited from this Abstract.
ApiReseponse
Abstart: See Code..
- This class is for the abstract
ArticleItem
,MenuItem
,ProjectItem
packages- One package consists of 6 classes and 1 interface:
Item
,Repository
,Service
,Adapter
,Controller
,Request
,Response
. Item
Class: Model class.Adapter
Class:ItemRespoense
andItem
methods exist to build object.Repository
Interface: Send queries to Database.Service
Class: SendItem
to browser. (get
,getAll
,create
,update
,delete
methods)Controller
Class: URL resolution.Response
andRequest
Class- See Code…
- One package consists of 6 classes and 1 interface:
To Do / Ongoing
- Membership related parts:
Security
package,SecurityConfig
Class. - CI/CD with Docker!
Frontend
Implemented
- Main three Pages implemented. Screenshots of the pages are at the below.
Home.vue
: Projects and Articles.Posts.vue
: Entire articles with Menu(ex. Physics, Computer Science).Aricle.vue
: Descriptions.
To Do / Ongoing
Admin
page for the Blog. (This shoud be linked with the articleEdit, menuEdit pages.)
5. Result
v1.0.0
- Backend: Finish CRUD APIs for the Blog’s Article, Menu and Project.
- Frontend: Development of pages for visitor completed.(include edit article/menu which is not below screenshots.)
The above page is the acutually working, but Oracle Cloud does not support free domain services in my region, I can’t attach Blog URL.


|
SoundFontInfoLib
2.21.6
Library for parsing and rendering SF2 files
|
Container of SF2 entities. More...
#include <ChunkItems.hpp>
Inherits SF2::IO::ChunkItemsSupport.
Public Types | |
| using | ItemType = T |
| using | ItemCollection = std::vector< ItemType > |
| using | const_iterator = typename std::vector< ItemType >::const_iterator |
| using | ItemRefCollection = std::vector< std::reference_wrapper< ItemType const > > |
Public Member Functions | |
| ChunkItems () | |
| Constructor for an empty collection. More... | |
| ChunkItems (const ChunkList &source) | |
| Constructor that loads items from the file. More... | |
| size_t | size () const |
| Get the number of items in this collection. More... | |
| bool | empty () const |
| Determine if collection is empty. More... | |
| const ItemType & | operator[] (size_t index) const |
| Obtain a (read-only) reference to an entity in the collection. More... | |
| ItemRefCollection | slice (size_t first, size_t count) const |
| Obtain a read-only slice of the original collection. More... | |
| const_iterator | begin () const |
| Obtain iterator to the start of the collection. More... | |
| const_iterator | end () const |
| Obtain iterator at the end of the collection. More... | |
| void | dump (const std::string &indent) const |
| Utility to dump out the contents of the collection. More... | |
Static Public Attributes | |
| static constexpr size_t | itemSize = T::size |
| Definition of the size in bytes of each item in the collection. More... | |
Friends | |
| class | File |
Container of SF2 entities.
All SF2 containers are homogenous (all entities in the container have the same type). Compared to the ChunkType
type, this class holds actual values from an SF2 file while the former just knows where in the file to find the values.
Like most of the IO namespace, instances of this class are essentially immutable.
| using SF2::IO::ChunkItems< T >::const_iterator = typename std::vector<ItemType>::const_iterator |
| using SF2::IO::ChunkItems< T >::ItemCollection = std::vector<ItemType> |
| using SF2::IO::ChunkItems< T >::ItemRefCollection = std::vector<std::reference_wrapper<ItemType const> > |
| using SF2::IO::ChunkItems< T >::ItemType = T |
|
inline |
Constructor for an empty collection.
|
inlineexplicit |
Constructor that loads items from the file.
| source | defines where to load and how many items to load |
|
inline |
Obtain iterator to the start of the collection.
Referenced by SF2::IO::ChunkItems< T >::dump().
|
inline |
Utility to dump out the contents of the collection.
| indent | the prefix to use for all output |
References SF2::IO::ChunkItems< T >::begin(), SF2::IO::ChunkItemsSupport::beginDump(), SF2::IO::ChunkItems< T >::end(), and SF2::IO::ChunkItems< T >::size().
Referenced by SF2::IO::File::dump().
|
inline |
Determine if collection is empty.
|
inline |
Obtain iterator at the end of the collection.
Referenced by SF2::IO::ChunkItems< T >::dump().
|
inline |
Obtain a (read-only) reference to an entity in the collection.
| index | the entity to fetch |
|
inline |
Get the number of items in this collection.
Referenced by SF2::IO::ChunkItems< T >::dump(), and SF2::IO::ChunkItems< T >::slice().
|
inline |
Obtain a read-only slice of the original collection.
| first | the index of the first item to include in the collection |
| count | the number of items to have in the slice |
References SF2::IO::ChunkItems< T >::size().
|
friend |
|
staticconstexpr |
Definition of the size in bytes of each item in the collection.


START
CLEAR
A*
Dijkstra
Very Fast
Fast
Medium
Slow
Very Slow
Generate Weights
Clear Weights
Clear Walls
GitHub
Time to complete:
ms
Path length:


{% extends 'home/main.html' %} {% load static %} {% block css %} {{ block.super }} {% endblock css %} {% block content %}
Your password was changed.


Staff Procedure For Incident Response
Be sure to have a good understanding of our [Code of Conduct]{filename}code-of-conduct.md).
Also have a good understanding of what is expected from an attendee that wants to report an incident: Attendee Procedure For Reporting Code of Conduct Incidents.
When taking an incident report:
- Ask the reporter if they want to go to a quiet space
- Be an active listener and don't use judgement words
- Ensure the reporter's safety
- Seek additional emergency resources as needed
- Take down the report and ask any clarifying questions
- Thank the reporter for reporting the incident
- Report the incident to the lead incident responders
While gathering information from the reporter:
- Do not invite them to withdraw the incident report
- Do not ask for their advice on how to immediately respond to the incident
- Do not offer them input into the long-term response to the incident
- Do not promise any particular response, since it may differ from the official response decided by the lead incident responders
Try to get as much of the incident in written form by the reporter. If you cannot, transcribe it yourself as it was told to you. The important information to gather include the following:
- Current date and time
- Date and time of the incident
- Location of incident
- Description of the incident
- Identifying information of the reported person: name, physical appearance, height, clothing, voice accent
- Additional circumstances surrounding the incident
- Reporter’s name and contact information. If the reporter wants to make an anonymous report, allow them to do so. If responding to the incident would reveal who reported the incident (e.g. a micro-aggression in one-on-one conversation) ask the reporter if they have safety concerns about this.
- Other people involved in or witnesses to the incident and their contact information or description
- Do not prompt the reporter for suggestions of how to handle the incident, but record their suggested responses if they offer them
Assess whether an immediate response is necessary. This initial response is very important and will set the tone for Boston Python. Depending on the severity/details of the incident, please follow these guidelines:
- If there is any general threat to attendees or the safety of anyone including event staff, consult the volunteer guide for emergency and crisis resources.
- If everyone is presently physically safe, involve law enforcement or security only at a reporter’s request.
- After taking the report, assess whether you need a lead incident responder to immediately respond to the incident. If so, ask the reporter to stay with you and get in touch with the lead incident responders.
Respond to reporter needs. You can:
- Thank the reporter for making the incident report
- Reassure them that the incident report will be reviewed by the lead incident responders
- Gather their contact information to send a follow-up after the incident is resolved
- Offer to get them water
- Ask, "Is there a friend or trusted person who you would like to be with you?" If so, arrange for another incident responder to get this person.
- Ask them, "How else can I help?"
- Provide them with specific non-emergency or crisis contacts from the volunteer guide if necessary.
Once something is reported, the lead incident responders should meet. The main objectives of this meeting is to:
- Review report documentation to determine what happened
- Consult documentation of past incidents for patterns of behavior
- Discuss appropriate response(s) to the incident
- Assign a person to make those response(s)
- Determine the follow up actions for any impacted people and/or the reporter
- Assign a person to follow up with the impacted people
After the staff meeting and discussion, a staff member (preferably the event coordinator if available) may choose to communicate with the reported person.
When following up with a reported person:
- Explain what happened
- Focus on the impact of their behavior
- Reiterate the Code of Conduct and that their behavior was not appropriate
- Give them concrete examples of how they can improve their behavior
- Remind them of the consequences of their behavior, or future consequences if the behavior is repeated
People who are reported often get upset, defensive, or deny the report. Allow them to give any additional details about the incident. However, remember:
- It does not matter if they did not intend to hurt anyone -- their behavior still impacted attendees negatively
- It is not your job to reassure or forgive them
- Do not allow the reported person to make an apology to the reporter or impacted person. Often an apology centers on the reported person’s feelings and not the person who was impacted. You may accept their apology and offer to pass it on (but you’re not required to if you think it would negatively impact the reporter).
What follows are examples of possible responses to an incident report. This list is is not inclusive, and Boston Python reserves the right to take any action it deems necessary. Possible responses to an incident include:
- Nothing, if the behavior was determined to not be a Code of Conduct violation
- A verbal or emailed warning
- Requiring that the reported person avoid any interaction with, and physical proximity to, another person for the remainder of the event
- Requiring the reported person not attend evening events
- Refusal of alcoholic beverage purchases at events
- Ending a talk that violates the Code of Conduct early
- Not publishing the video or slides of a talk that violated the Code of Conduct
- Not allowing a speaker who violated the Code of Conduct to give (further) talks at the event now or in the future
- Immediately ending any event volunteer responsibilities and privileges the person holds
- Requiring that a person not volunteer for future events (either indefinitely or for a certain time period)
- Requiring that a person immediately leave the event and not return
- Banning a person from future events (either indefinitely or for a certain time period)
- Removing a person from membership of relevant organizations
- Publishing an account of the incident and (optionally) calling for the resignation of a person from their responsibilities
If a reported person wants to appeal the decision, notify them that they may contact the Boston Python leaders<EMAIL_ADDRESS>Keep in mind that it is not a good idea to encourage an apology from the reported person.
It is very important how we deal with the incident publicly. Our policy is to make sure that everyone aware of the initial incident is also made aware that it is not according to policy and that official action has been taken, while still respecting the privacy of individual attendees. When speaking to individuals (those who are aware of the incident, but were not involved with the incident) about the incident it is a good idea to keep the details out.
Depending on the incident, the organizers or their designate may decide to make one or more public announcements. No one other than the organizer or someone delegated authority from the organizer should make any announcements.
If some attendees were angered by the incident, it is best to apologize to them that the incident occurred to begin with. If there are residual hard feelings, suggest to them to write an email to organizers (leaders@bostonpython.com). It will be dealt with accordingly.
This procedure has been adapted from the Ada Initiative’s guide titled "Conference anti-harassment/Responding to Reports" and has been updated by Otter Tech.


Customer Details
Name of the Customer: *
Date of Birth: *
Nationality: *
Address Details
HouseNo./Street/Locality:
City:
State/Province:
Postal/Zip Code:
*
I hereby declare that the information provided in this form is accurate and complete. I confirm that any information is found incorrect and/or incomplete that leads a violation of regulations may initiate legal actions, and I accept that I am the responsible party for any and all charges, penalties and violations.


Network Status DashboardsNetwork dashboards for proof-of-work and proof-of-authority networks.
Reporting the node status is a voluntary process and the dashboard does not reflect the entire network. Adding a node to the dashboard requires an ethstats-client and a secret. To obtain the secret, please ask on Discord, Gitter, or message<EMAIL_ADDRESS>briefly describing your node and the network. Chain-Split MonitoringA fork monitoring dashboard that observes different implementations and/or versions of a certain protocol and compares their chain head's parameters.
The python app is available on github/byz-f/fork-monitor. Public Test-Mining PoolsTest-mining pools allow for testing mining and pool software as well as hardware. The following pools do not conduct any payouts and are for testing purposes only.
The pool code is available on github/open-etc-pool. Public API endpointsThe following endpoints expose HTTP-RPC APIs.
Terms of ServiceThe services offered by Byzantine Fault GmbH are free as in free beer and provided as they are. There is no guarantee for uptime or accuracy of data. Everyone is allowed to fairly use the services at their own risk. Privacy PolicyNo user data is being collected. No cookies are provided. This website works without any analytics. Enjoy! Impressum gibt es auf byzantine.fault.dev. |


 When we continue we will die and we will be the cause of death. We are killing this planet by burning the forerst and when we do this we can remove trees and oxygen from the earth.
How to fix this issue


Bazel BUILD Encyclopedia of Functions
Contents
Concepts and terminology
Rules
${HEADER_TABLE}Common definitions
This section defines various terms and concepts that are common to many functions or build rules below.
Bourne shell tokenization
Certain string attributes of some rules are split into multiple words according to the tokenization rules of the Bourne shell: unquoted spaces delimit separate words, and single- and double-quotes characters and backslashes are used to prevent tokenization.
Those attributes that are subject to this tokenization are explicitly indicated as such in their definitions in this document.
Attributes subject to "Make" variable expansion and Bourne shell
tokenization are typically used for passing arbitrary options to
compilers and other tools. Examples of such attributes are
cc_library.copts
and java_library.javacopts
.
Together these substitutions allow a
single string variable to expand into a configuration-specific list
of option words.
Label expansion
Some string attributes of a very few rules are subject to label
expansion: if those strings contain a valid label as a
substring, such as //mypkg:target
, and that label is a
declared prerequisite of the current rule, it is expanded into the
pathname of the file represented by the target //mypkg:target
.
Example attributes include genrule.cmd
and
cc_binary.linkopts
. The details may vary significantly in
each case, over such issues as: whether relative labels are
expanded; how labels that expand to multiple files are
treated, etc. Consult the rule attribute documentation for
specifics.
Attributes common to all build rules
This section describes attributes that are common to all build rules.
Please note that it is an error to list the same label twice in a list of
labels attribute.
Attributes common to all test rules (*_test)
This section describes attributes that are common to all test rules.
${TEST_ATTRIBUTE_DEFINITION}Attributes common to all binary rules (*_binary)
This section describes attributes that are common to all binary rules.
${BINARY_ATTRIBUTE_DEFINITION}Configurable attributes
Most rule attributes can be "configured" so that their values can
depend on the command-line flags passed to Bazel. This can be used,
for example, to declare platform-dependent srcs
or custom
compiler flags depending on the
compilation
mode. This feature is very close in spirit to
abi_deps, except that it's not
limited to cc_*
rules and the deps
attribute.
Implicit output targets
When you define a build rule in a BUILD file, you are explicitly
declaring a new, named rule target in a package. Many build rule
functions also implicitly entail one or more output file
targets, whose contents and meaning are rule-specific.
For example, when you explicitly declare a
java_binary(name='foo', ...)
rule, you are also
implicitly declaring an output file
target foo_deploy.jar
as a member of the same package.
(This particular target is a self-contained Java archive suitable
for deployment.)
Implicit output targets are first-class members of the global
target graph. Just like other targets, they are built on demand,
either when specified in the top-level built command, or when they
are necessary prerequisites for other build targets. They can be
referenced as dependencies in BUILD files, and can be observed in
the output of analysis tools such as bazel query
.
For each kind of build rule, the rule's documentation contains a special section detailing the names and contents of any implicit outputs entailed by a declaration of that kind of rule.
An important but somewhat subtle distinction between the
two namespaces used by the build system:
labels identify targets,
which may be rules or files, and file targets may be divided into
either source (or input) file targets and derived (or output) file
targets. These are the things you can mention in BUILD files,
build from the command-line, or examine using bazel query
;
this is the target namespace. Each file target corresponds
to one actual file on disk (the "file system namespace"); each rule
target may correspond to zero, one or more actual files on disk.
There may be files on disk that have no corresponding target; for
example, .o
object files produced during C++ compilation
cannot be referenced from within BUILD files or from the command line.
In this way, the build tool may hide certain implementation details of
how it does its job. This is explained more fully in
the BUILD Concept Reference.


It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at this layout
It is a long established fact that a reader will be distracted by the readable content of a page when looking at this layout
It is a long established fact that a reader will be distracted by the readable content of a page when looking at this layout
It is a long established fact that a reader will be distracted by the readable content of a page when looking at this layout
It is a long established fact that a reader will be distracted by the readable content of a page when looking at this layout
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
+985 123 7856
<EMAIL_ADDRESS>202, Grasselli Street, Conway


Available Courses
| Course ID | Tutor Name | Course Taught | Location | Rate (/hr) | Date | Duration | Enroll |
|---|
Available Seminars
| Seminar ID | Held By | Seminar Topic | Location | Price | Date and Time | Duration | Enroll |
|---|
Available Sessions
| Session ID | Taught By | Topic Taught | Location | Price | Date and Time | Duration | Enroll |
|---|


Please use the below map to find me.
Please check to see when I'm free.
Please use the below form to get in touch with me.


Mesh Autoencoders
Generate faces, clothing and more using mesh convolutional autoencoders
Learning to Generate Faces
(Ranjan, Bolkart, Sanyal, & Black, 2018)
Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions.
To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error.
Dressing Humans in 3D
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed humans and thus do not capture the complexity of dressed humans in common images and videos. To address this, we learn a generative 3D mesh model of clothing from 3D scans of people with varying pose. Going beyond previous work, our generative model is conditioned on different clothing types, giving the ability to dress different body shapes in a variety of clothing. To do so, we train a conditional Mesh-VAE-GAN on clothing displacements from a 3D SMPL body model.
This generative clothing model enables us to sample various types of clothing, in novel poses, on top of SMPL. With a focus on clothing geometry, the model captures both global shape and local structure, effectively extending the SMPL model to add clothing. To our knowledge, this is the first conditional VAE-GAN that works on 3D meshes. For clothing specifically, it is the first such model that directly dresses 3D human body meshes and generalizes to different poses.
-
Learning to Dress 3D People in Generative Clothing In Computer Vision and Pattern Recognition (CVPR) 2020 [arXiv] [Code] [URL]
CAPE is a Graph-CNN based generative model for dressing 3D meshes of human body. It is compatible with the popular body model, SMPL, and can generalize to diverse body shapes and body poses. It is designed to be "plug-and-play" for many applications that already use SMPL. The CAPE Dataset provides SMPL mesh registration of 4D scans of people in clothing, along with registered scans of the ground truth body shapes under clothing.
-
Generating 3D faces using convolutional mesh autoencoders In European Conference on Computer Vision (ECCV) 2018 [arXiv] [Code] [URL]
A non-linear model for generating 3D faces using a Convolutional Autoencoder that operates directly on meshes. Our model is state of the art in generating diverse range of 3D facial meshes.


A hard drive is separated in blocks of 512 bytes, grouped into 4 areas:
+------------+
| Boot | (4096 blocks)
+------------+
| Superblock | (2 blocks)
+------------+
| Bitmap | (n / (8 * 512) blocks)
+------------+
| Data | (n blocks)
+------------+
The first area contains the bootloader and the kernel, the second is a superblock with a magic string to identify the file system, the third is a bitmap mapping the allocated data blocks of the last area.
A location on the tree of dirs and files is named a path:
/
/usr
/usr/admin
$ qemu-img create disk.img 128M
Formatting 'disk.img', fmt=raw size=134217728
During boot MOROS will detect any hard drives present on the ATA buses, then
look for a filesystem on those hard drives. If no filesystem is found, MOROS
will open a console in diskless mode to allow the user to create one with
the disk format
command:
> disk format /dev/ata/0/0
This command will format the first disk on the first ATA bus by writing a magic string in a superblock, mounting the filesystem, and allocating the root directory.
The next step during setup is to create the directory structure:
> write /bin/ # Binaries
> write /dev/ # Devices
> write /ini/ # Initialisation files
> write /lib/ # Libraries
> write /net/ # Network
> write /src/ # Sources
> write /tmp/ # Temporary files
> write /usr/ # User directories
> write /var/ # Variable files
Then the following should be added to the boot script with the
command edit /ini/boot.sh
to allow MOROS to finish booting:
user login
shell
Finally a user can be created with the following command:
> user create
All of this can be made more easily by running the install
command instead.
This installer will also add additional files contained in the dsk
repository of the source code, like a nice login banner :)
Bitmap of allocated blocks in the data area.
A block is small area of 512 bytes on a hard drive, and it is also part of linked list representing a file or a directory.
The first 4 bytes of a block is the address of the next block on the list and the rest of block is the data stored in the block.
Structure:
0
0 1 2 3 4 5 6 n
+-+-+-+-+-+-+-+ // +-+
| addr | data |
+-+-+-+-+-+-+-+ // +-+
n = 512
0 1 2
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 n
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ // +-+
| signature |v|b| count | alloc | reserved |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ // +-+
signature = "MOROS FS"
v = version number of the FS
b = size of a block in 2 ^ (9 + b) bytes
count = number of blocks
alloc = number of allocated blocks
The first block of a contains the address of the next block where its contents is stored and the beginning of its contents in the rest of the block.
If all contents can fit into one block the address of the next block will be empty.
Structure:
0
0 1 2 3 4 5 6 7 8 n
+-+-+-+-+-+-+-+-+-+ // +-+
| addr | contents |
+-+-+-+-+-+-+-+-+-+ // +-+
n = 512
The first block of a directory contains the address of the next block where its directory entries are stored and the first entries in the rest of the block.
If all entries can fit into one block the address of the next block will be empty.
Structure:
0 1
0 1 2 3 4 5 6 7 8 9 0 n
+-+-+-+-+-+-+-+-+-+-+-+ // +-+-+-+-+-+-+-+-+ // +-+
| addr | dir entry 1 | dir entry 2 |
+-+-+-+-+-+-+-+-+-+-+-+ // +-+-+-+-+-+-+-+-+ // +-+
n = 512
A directory entry represents a file or a directory contained inside a directory. Each entry use a variable number of bytes that must fit inside the data of one block. Those bytes represent the kind of entry (file or dir), the address of the first block, the filesize (max 4GB), the last modified time in seconds since Unix Epoch, the length of the filename, and the filename (max 255 chars) of the entry.
Structure:
0 1 2
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 m
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ // +-+
|k| addr | size | time |n| name buffer |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ // +-+
k = kind of entry
n = length of name buffer
m = 17 + n
The info
syscall on a file or directory and the read
syscall on a directory
return a subset of a directory entry for userspace programs.
Structure:
0 1 2
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 m
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ // +-+
|k| size | time |n| name buffer |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ // +-+
k = kind of entry
n = length of name buffer
m = 13 + n


(Appears in: NamedAuthInfo)
AuthInfo contains information that describes identity information. This is use to tell the kubernetes cluster who you are.
| Field | Description |
|---|---|
client-certificate
string
|
ClientCertificate is the path to a client cert file for TLS. |
client-certificate-data
[]byte
|
ClientCertificateData contains PEM-encoded data from a client cert file for TLS. Overrides ClientCertificate |
client-key
string
|
ClientKey is the path to a client key file for TLS. |
client-key-data
[]byte
|
ClientKeyData contains PEM-encoded data from a client key file for TLS. Overrides ClientKey |
token
string
|
Token is the bearer token for authentication to the kubernetes cluster. |
tokenFile
string
|
TokenFile is a pointer to a file that contains a bearer token (as described above). If both Token and TokenFile are present, Token takes precedence. |
as
string
|
Impersonate is the username to impersonate. The name matches the flag. |
as-uid
string
|
ImpersonateUID is the uid to impersonate. |
as-groups
[]string
|
ImpersonateGroups is the groups to impersonate. |
as-user-extra
map[string][]string
|
ImpersonateUserExtra contains additional information for impersonated user. |
username
string
|
Username is the username for basic authentication to the kubernetes cluster. |
password
string
|
Password is the password for basic authentication to the kubernetes cluster. |
auth-provider
AuthProviderConfig
|
AuthProvider specifies a custom authentication plugin for the kubernetes cluster. |
exec
ExecConfig
|
Exec specifies a custom exec-based authentication plugin for the kubernetes cluster. |
extensions
[]NamedExtension
|
Extensions holds additional information. This is useful for extenders so that reads and writes don't clobber unknown fields |
(Appears in: AuthInfo)
AuthProviderConfig holds the configuration for a specified auth provider.
| Field | Description |
|---|---|
name
*
string
|
|
config
*
map[string]string
|
(Appears in: NamedCluster)
Cluster contains information about how to communicate with a kubernetes cluster
| Field | Description |
|---|---|
server
*
string
|
Server is the address of the kubernetes cluster (https://hostname:port). |
tls-server-name
string
|
TLSServerName is used to check server certificate. If TLSServerName is empty, the hostname used to contact the server is used. |
insecure-skip-tls-verify
bool
|
InsecureSkipTLSVerify skips the validity check for the server's certificate. This will make your HTTPS connections insecure. |
certificate-authority
string
|
CertificateAuthority is the path to a cert file for the certificate authority. |
certificate-authority-data
[]byte
|
CertificateAuthorityData contains PEM-encoded certificate authority certificates. Overrides CertificateAuthority |
proxy-url
string
|
ProxyURL is the URL to the proxy to be used for all requests made by this client. URLs with "http", "https", and "socks5" schemes are supported. If this configuration is not provided or the empty string, the client attempts to construct a proxy configuration from http_proxy and https_proxy environment variables. If these environment variables are not set, the client does not attempt to proxy requests. socks5 proxying does not currently support spdy streaming endpoints (exec, attach, port forward). |
disable-compression
bool
|
DisableCompression allows client to opt-out of response compression for all requests to the server. This is useful to speed up requests (specifically lists) when client-server network bandwidth is ample, by saving time on compression (server-side) and decompression (client-side): https://github.com/kubernetes/kubernetes/issues/112296. |
extensions
[]NamedExtension
|
Extensions holds additional information. This is useful for extenders so that reads and writes don't clobber unknown fields |
(Appears in: NamedContext)
Context is a tuple of references to a cluster (how do I communicate with a kubernetes cluster), a user (how do I identify myself), and a namespace (what subset of resources do I want to work with)
| Field | Description |
|---|---|
cluster
*
string
|
Cluster is the name of the cluster for this context |
user
*
string
|
AuthInfo is the name of the authInfo for this context |
namespace
string
|
Namespace is the default namespace to use on unspecified requests |
extensions
[]NamedExtension
|
Extensions holds additional information. This is useful for extenders so that reads and writes don't clobber unknown fields |
(Appears in: AuthInfo)
ExecConfig specifies a command to provide client credentials. The command is exec'd and outputs structured stdout holding credentials.
See the client.authentication.k8s.io API group for specifications of the exact input and output format
| Field | Description |
|---|---|
command
*
string
|
Command to execute. |
args
[]string
|
Arguments to pass to the command when executing it. |
env
[]ExecEnvVar
|
Env defines additional environment variables to expose to the process. These are unioned with the host's environment, as well as variables client-go uses to pass argument to the plugin. |
apiVersion
*
string
|
Preferred input version of the ExecInfo. The returned ExecCredentials MUST use the same encoding version as the input. |
installHint
*
string
|
This text is shown to the user when the executable doesn't seem to be
present. For example, |
provideClusterInfo
*
bool
|
ProvideClusterInfo determines whether or not to provide cluster information, which could potentially contain very large CA data, to this exec plugin as a part of the KUBERNETES_EXEC_INFO environment variable. By default, it is set to false. Package k8s.io/client-go/tools/auth/exec provides helper methods for reading this environment variable. |
interactiveMode
ExecInteractiveMode
|
InteractiveMode determines this plugin's relationship with standard input. Valid values are "Never" (this exec plugin never uses standard input), "IfAvailable" (this exec plugin wants to use standard input if it is available), or "Always" (this exec plugin requires standard input to function). See ExecInteractiveMode values for more details. If APIVersion is client.authentication.k8s.io/v1alpha1 or client.authentication.k8s.io/v1beta1, then this field is optional and defaults to "IfAvailable" when unset. Otherwise, this field is required. |
(Appears in: ExecConfig)
ExecEnvVar is used for setting environment variables when executing an exec-based credential plugin.
| Field | Description |
|---|---|
name
*
string
|
|
value
*
string
|
string
alias)(Appears in: ExecConfig)
ExecInteractiveMode is a string that describes an exec plugin's relationship with standard input.
(Appears in: Config)
NamedAuthInfo relates nicknames to auth information
| Field | Description |
|---|---|
name
*
string
|
Name is the nickname for this AuthInfo |
user
*
AuthInfo
|
AuthInfo holds the auth information |
(Appears in: Config)
NamedCluster relates nicknames to cluster information
| Field | Description |
|---|---|
name
*
string
|
Name is the nickname for this Cluster |
cluster
*
Cluster
|
Cluster holds the cluster information |
(Appears in: Config)
NamedContext relates nicknames to context information
| Field | Description |
|---|---|
name
*
string
|
Name is the nickname for this Context |
context
*
Context
|
Context holds the context information |
(Appears in: Config, AuthInfo, Cluster, Context, Preferences)
NamedExtension relates nicknames to extension information
| Field | Description |
|---|---|
name
*
string
|
Name is the nickname for this Extension |
extension
*
k8s.io/apimachinery/pkg/runtime.RawExtension
|
Extension holds the extension information |
(Appears in: Config)
| Field | Description |
|---|---|
colors
bool
|
|
extensions
[]NamedExtension
|
Extensions holds additional information. This is useful for extenders so that reads and writes don't clobber unknown fields |
Generated with genref
on git commit 4247aac7


Arsenal scout 19-year-old Hungary midfielder to replace Granit Xhaka – report
Arsenal are looking at RB Salzburg midfielder Dominik Szoboszlai as a replacement for Granit Xhaka, according to a report
by Transfer AgentArsenal are set to scout Red Bull Salzburg midfielder Dominik Szoboszlai as a potential replacement for Granit Xhaka, according to a report in England.
Website Football Insider is reporting that Arsenal view Szoboszlai as a “genuine contender” to fill Xhaka’s void in midfield amid uncertainty over the Switzerland international’s future at the north London side.
The same article states that Arsenal boss Unai Emery is eager to secure reinforcements in the middle of the park in the January transfer window as Xhaka looks set to leave The Emirates.
According to the same story, the Gunners dispatched scouts to watch the 19-year-old represent Hungary in their clash against Wales in Euro 2020 qualifying on Tuesday night.
Football Insider go on to add that the north London side are long-term admirers of Szoboszlai after Arsenal expressed an interest in signing the Hungarian playmaker in the summer.
The report lists Serie A giants Juventus and Inter Milan as potential rivals for the Hungary international signature.
Szoboszlai has scored two goals and has made two assists in 10 games in the Austrian Premier League so far this season.
The teenager moved to Red Bull Salzburg two years ago after the Austrian club signed the midfielder from FC Liefering.
Xhaka hasn’t featured for Arsenal since his fall-out with Gunners fans in a 2-2 draw with Crystal Palace at The Emirates last month.


Posted by Danielle Gambino January 21, 2014 3:57 pm
Posted by Team LovePanky
Posted by Our Bodies Ourselves on January 9, 2011
Complied by Aileen Corr


The rights of citizens have been codified in the Law, and include the following
- In a case of cognizable offence, arrests will normally be made without warrant of arrest.
- When an arrest is made, the police officer apprehending the person will bear clear identification by way of name
tags designation etc. Police may use force, if warranted, while making the arrest, however, will protect
the dignity of the arrested person. For instance, there will be no public display or parading of the arrested
persons(s).
- The Police officer, arresting the person will prepare a memo of the arrest, which will be got attested by a witness
available at the site. Importantly, the arrested person will be granted access to contact his relatives and
lawyers.
- When children and juveniles are required to be arrested, Police will bestow special care to avoid any force whatever.
Further, one or more respectable citizens of the locality will be associated on such occasions to ensure
that minimal coercion is employed and that they are not frightened.
- Women & children below 15 years of age are entitled to refuse to go to the Police Station from their place
of residence, if they are summoned from a Police Station.
- The arrested persons will be produced in the jurisdictional courts within 24 hours of the arrest. Further, interrogations
of the accused will be in consonance with the recognized rights to life, dignity and protection against torture
and degrading treatments.
- An arrested person remanded to the Police custody by the Court has to be examined by a government doctor and
a Medical Certificate obtained. Articles, which are seized from the arrested person, will be produced before
the court and the person could reclaim only under the orders of the court.


It’s me Manoj Shrestha from Banepa, Kavre, Nepal. My birth date is 24th May 1988. I’m student of KCGI, I’m studying Masters in IT. My college is located in Kyoto Ekimai and Hyakumaben.
My major concentration of study is Networking and Administration. I’m fond of Information and technology. I used to work in the IT company as HR Manager and Business Development Manager in Nepal. So, to upgrade my knowledge, I joined to KCGI.
My ambition is to be a successful business person related in IT field.
My hobby is to travel and explore new places. I often sing a songs and dance as well.
I love to listen music.


Array of the item objects.
Array, default = []
The .items property defines all toolbar items (buttons, drop down menus, etc.) You
can define them during object creation or at run-time.
The structure of the item is defined in .item_template()
If you change this property during run-time, do not forget to call the .refresh()
method.
To add/remove items during run-time, you should better call
.add(),
.insert(),
.remove() methods.


Discretization - PUMI
Description
Dependencies
Parameters
Example
Albany Examples
External Links
An efficient distributed mesh data structure is needed to support parallel adaptive analysis since
it strongly influences the overall performance of adaptive mesh-based simulations. In addition
to the general mesh-based operations, such as mesh entity creation/deletion, adjacency and
geometric classication, iterators, arbitrary attachable data to mesh entities, etc., the distributed
mesh data structure must support (i) efficient communication between entities duplicated over
multiple processors, (ii) migration of mesh entities between processors, and (iii) dynamic load
balancing.
Issues associated with supporting parallel adaptive analysis on a given unstructured mesh include
dynamic mesh load balancing techniques, and data structure and algorithms for parallel
mesh adaptation.
The Parallel Unstructured Mesh Infrastructure (PUMI) is a distributed mesh data management system
that is capable of a parallel mesh infrastructure capable of handling general non-manifold
models and effectively supporting automated adaptive analysis.
SCOREC
- 1D Elements
- Data Type
<int>
- Default Value
0
- Description
"Number of Elements in X discretization"
- 1D Scale
- Data Type
<double>
- Default Value
1.0
- Description
"Width of X discretization"
- 2D Elements
- Data Type
<int>
- Default Value
0
- Description
"Number of Elements in Y discretization"
- 2D Scale
- Data Type
<double>
- Default Value
1.0
- Description
"Depth of Y discretization"
- 2nd Order Mesh
- Data Type
<bool>
- Default Value
false
- Description
"Flag to indicate 2nd order Lagrange shape functions"
- 3D Elements
- Data Type
<int>
- Default Value
0
- Description
"Number of Elements in Z discretization"
- 3D Scale
- Data Type
<double>
- Default Value
1.0
- Description
"Height of Z discretization"
- Hexahedral
- Data Type
<bool>
- Default Value
true
- Description
"Build hexahedral elements"
- Max Number of Mesh Adapt Iterations
- Data Type
<int>
- Default Value
1
- Description
"Upper bound on iterations taken by Mesh Adapt"
- Mesh Model Input File Name
- Data Type
<std::string>
- Default Value
""
- Description
"meshmodel geometry file"
- PUMI Input File Name
- Data Type
<std::string>
- Default Value
""
- Description
"File Name For PUMI Mesh Input"
- PUMI Output File Name
- Data Type
<std::string>
- Default Value
""
- Description
"File Name For PUMI Mesh Output"
- PUMI Write Interval
- Data Type
<int>
- Default Value
3
- Description
"Step interval to write solution data to output file"
- Resize Input Mesh Element Size
- Data Type
<double>
- Default Value
1.0
- Description
"Resize mesh element to this size at input"
- Use Serial Mesh
- Data Type
<bool>
- Default Value
false
- Description
"Read in a single mesh on PE 0 and rebalance"
- Acis Model Input File Name
- Data Type
<std::string>
- Default Value
""
- Description
"File Name For ACIS Model Input"
- Cubature Degree
- Data Type
<int>
- Default Value
3
- Description
"Integration order sent to Intrepid"
- Edge Node Set Associations
- Data Type
<Teuchos::TwoDArray<std::string> >
- Default Value
defaultData
- Description
"Association between geometric edge ID and node set string"
- Element Block Associations
- Data Type
<Teuchos::TwoDArray<std::string> >
- Default Value
defaultData
- Description
"Association between region ID and element block string"
- Interleaved Ordering
- Data Type
<bool>
- Default Value
true
- Description
"Flag for interleaved or blocked unknown ordering"
- Method
- Data Type
<std::string>
- Default Value
""
- Description
"The discretization method, parsed in the Discretization Factory"
- Model Associations File Name
- Data Type
<std::string>
- Default Value
""
- Description
"File with element block/sideset/nodeset associations"
- Node Set Associations
- Data Type
<Teuchos::TwoDArray<std::string> >
- Default Value
defaultData
- Description
"Association between geometric face ID and node set string"
- Parasolid Model Input File Name
- Data Type
<std::string>
- Default Value
""
- Description
"File Name For PARASOLID Model Input"
- QP Temperature from Nodes
- Data Type
<bool>
- Default Value
false
- Description
"Hack to initialize QP Temperature from Solution"
- Separate Evaluators by Element Block
- Data Type
<bool>
- Default Value
false
- Description
"Flag for different evaluation trees for each Element Block"
- Side Set Associations
- Data Type
<Teuchos::TwoDArray<std::string> >
- Default Value
defaultData
- Description
"Association between face ID and side set string"
- Solution Vector Components
- Data Type
<Teuchos::Array<std::string> >
- Default Value
defaultFields
- Description
"Names and layouts of solution vector components"
- Use Nullspace Translation Only
- Data Type
<bool>
- Default Value
false
- Description
"Temporary hack to get MueLu (possibly) working for us"
- Vertex Node Set Associations
- Data Type
<Teuchos::TwoDArray<std::string> >
- Default Value
defaultData
- Description
"Association between geometric edge ID and node set string"
- Workset Size
- Data Type
<int>
- Default Value
50
- Description
"Upper bound on workset (bucket) size"
<ParameterList name="Discretization">
<Parameter name="Method" type="string" value="PUMI"/>
<Parameter name="Workset Size" type="int" value="50"/>
<Parameter name="Mesh Model Input File Name" type="string" value="../meshes/bar/bar.dmg"/>
<Parameter name="PUMI Input File Name" type="string" value="../meshes/bar/bar.smb"/>
<Parameter name="PUMI Output File Name" type="string" value="out_elastic.vtk"/>
<Parameter name="Element Block Associations" type="TwoDArray(string)" value="2x1:{115,bar}"/>
<Parameter name="Node Set Associations" type="TwoDArray(string)" value="2x4:{97,101,51,95,ns1,ns2,ns3,ns4}"/>
<Parameter name="2nd Order Mesh" type="bool" value="false"/>
<Parameter name="Cubature Degree" type="int" value="1"/>
<Parameter name="Separate Evaluators by Element Block" type="bool" value="false"/>
</ParameterList>
- Albany/examples/Heat3DPUMI/input.xml
- Albany/examples/Heat3DPUMI/inputSerial.xml
- Albany/examples/LCM/SCOREC/BimetallicStrip/inputElasticT.xml
- Albany/examples/LCM/SCOREC/BimetallicStrip/inputT.xml
- Albany/examples/LCM/SCOREC/ElastAdapt/inputNeckingSerialT.xml
- Albany/examples/LCM/SCOREC/ElastAdapt/inputNeckingT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputEdgeDBCT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputNeckingSingleMeshT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputNeckingT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputRenameT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputSingleMeshT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputSquoze.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputSquoze2.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputSquozeSerial.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputSquozeSerial2.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputT.xml
- Albany/examples/LCM/SCOREC/Elasticity/inputVtxDBCT.xml
- Albany/examples/LCM/SCOREC/Elasticity2D/inputT.xml
- Albany/examples/LCM/SCOREC/GOAL/inputElasticT.xml
- Albany/examples/LCM/SCOREC/GOAL/inputJ2T.xml
- Albany/examples/LCM/SCOREC/MechWithTemp/inputT.xml
- Albany/examples/LCM/SCOREC/MechWithTemp/inputUnifT.xml
- Albany/examples/LCM/SCOREC/RCU/inputSquareStabilized.xml
- Albany/examples/LCM/SCOREC/ThermoMechanicalCan/MechanicsT.xml
- Albany/examples/LCM/SCOREC/ThermoMechanicalCan/MechanicsTimeDepTemperatureT.xml
- Albany/examples/LCM/SCOREC/ThermoMechanicalCan/MechanicsWithTemperatureT.xml
- Albany/examples/LCM/SCOREC/TransientHeat/input.xml
- Albany/examples/LCM/ThermoElasticity3D/inputT_pumi.xml
- Albany/examples/PerformanceTests/AdaptiveCan/MechanicsWithTemperatureT.xml
- Albany/examples/PerformanceTests/Necking3D/inputElastSPRAdaptMmodel.xml
- Albany/examples/PerformanceTests/Necking3D/inputElastSPRAdaptMmodelMueLuT64.xml
- Albany/examples/PerformanceTests/Necking3D/inputElastSPRStressAdaptMmodel1M.xml
- Albany/examples/PerformanceTests/Necking3D/inputElastSPRStressAdaptParasolid.xml
- Albany/examples/PerformanceTests/Necking3D/inputLD.xml
- Albany/examples/PerformanceTests/Necking3D/inputPlasLD.xml
- Albany/examples/PerformanceTests/Necking3D/inputPlasLDMueLuT64.xml
- Albany/examples/PerformanceTests/NotchedTensionTet10/NotchedTensileTet10FMDB.xml
- Albany/examples/SteadyHeat3D/inputT_pumi.xml
PUMI GitHub page
PUMI Documents


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Click one of the buttons to read about the contrats between parent and children about games.
Click one of the buttons to read about the contrats between parent and children about games.
Any form of games have been a kids escape from everything, where they can focus on something they love and enjoy life to the fullest.
For me I started off with sports but with the increase in school work, I had to quit that rush of fun was lacking in life till I found out about VideoGame.
I have played many game from hardcore COD to Stardew Vally. These games allow me to have fun with my friends along with give me some leisure time.
These games allowed me to communicate with people that had a very diverse background than the people I would be around in real life, I made friends that knew me.
Around the time I quit sports and found other ways to have fun that did not need me to commit as much time I was rideculed with comments from my parents about video games.
They told me that it would rot my brain and that it was a wast of time, my time would be better spent working on school work(I was exhausted from).
To some part I guess they were right, it was a distraction from my school work and me working toward from my dream job, but i dont think they saw the benifits in games as well.


vignettes/a02_understanding_suit.Rmd
a02_understanding_suit.Rmd
The suit
function is used for computing the suitability score of the land units for a given target crop. The function has the following usage:
suit(
crop,
terrain = NULL,
water = NULL,
temp = NULL,
mf = "triangular",
sow_month = NULL,
min = NULL,
max = "average",
interval = NULL,
sigma = NULL
)
Check the documentation for details on the argument. This article will focus on how to use this. To evaluate the suitability score of the Marinduque land units for terrain, soil, water and temperature characteristics is done as follows:
## Loading required package: Rcpp
banana_suit <- suit("banana", terrain=MarinduqueLT)
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
names(banana_suit)
## [1] "terrain" "soil"
The warning above simply tells the user that one of the factor, CECc, in the target crop requirement, has parameter intervals for all suitability classes equal to 16, and the package used this value to assign to max constant used for computing the suitability score. For more, please refer to the article, Methodology used in ALUES.
The output of the suit
function is a list of output of target characteristics, in this case terrain
and soil
. To access the output for terrain
and soil
simply use:
banana_suit[["terrain"]]
banana_suit[["soil"]]
The output of the codes above are lists, with the following names:
names(banana_suit[["soil"]])
## [1] "Factors Evaluated" "Suitability Score"
## [3] "Suitability Class" "Factors' Minimum Values"
## [5] "Factors' Maximum Values" "Factors' Weights"
## [7] "Crop Evaluated" "Warning"
Therefore, one can access any of those names. For example, to access the factors evaluated, simply run the following
banana_suit[["soil"]][["Factors Evaluated"]]
## [1] "CFragm" "CECc" "pHH2O" "SoilTe"
There are 56 crops available in ALUES, and what we’ve illustrated above are for banana only. The other crops are listed below:
d <- utils::data(package = "ALUES")
alues_data <- d$results[, "Item"]
crop_data <- regmatches(alues_data, gregexpr(paste0("^[A-Z]{2,}", collapse = "|"), alues_data))
crop_data <- unique(unlist(lapply(crop_data, function(x) substr(x, 1, nchar(x)-1))))
crop_data
## [1] "ALFALFA" "AVOCADO" "BAMBOO" "BANANA" "BARLEY"
## [6] "BEANCA" "BEANS" "CABBAGE" "CARROTS" "CASHEW"
## [11] "CASSAVA" "CHICKPEA" "CINNAMON" "CITRUS" "COCOA"
## [16] "COCONUT" "COFFEEAR" "COFFEERO" "COTTON" "COWPEA"
## [21] "CUCUMBER" "GROUNDNUTS" "GUAVA" "LONGAN" "MAIZE"
## [26] "MANGO" "MILLETS" "OILPALM" "OLIVES" "ONION"
## [31] "PAPAYA" "PEACH" "PEAR" "PEA" "PEPPERGR"
## [36] "PERSIMMON" "PINEAPPLE" "PLUM" "POTATOSW" "POTATO"
## [41] "RICEBR" "RICEIW" "RICENF" "RICEUR" "RUBBER"
## [46] "SAFFLOWER" "SESAME" "SORGHUM" "SOYA" "SUGARCANE"
## [51] "SUNFLOWER" "TEA" "TOBACCO" "TOMATO" "WATERMELON"
## [56] "WHEAT"
These are the names for the input string for the suit
function. For example, to target sweet potato the input string is not "sweet potato"
but rather potatosw
. That is,
potato_suit1 <- suit("sweet potato", terrain=MarinduqueLT)
## Error in suit("sweet potato", terrain = MarinduqueLT): Input crop='sweet potato' is not available in the database, see docs for list of ALUES data.
potato_suit2 <- suit("potatosw", terrain=MarinduqueLT)
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
The idea of evaluating a land unit is to match quality of the land against the standard value of the target factor. Therefore, if the crop does not include the factor you are targeting, then there won’t be any matching to be done. For example, the land units evaluated above are those in Marindque, which has the following soil and terrain characteristics:
head(MarinduqueLT)
## Lat Lon CECc pHH2O CFragm SoilTe
## 1 121.8792 13.52084 12 53 11 12
## 2 121.8875 13.52084 12 52 9 12
## 3 121.8958 13.52084 12 53 10 12
## 4 121.9375 13.52084 12 52 10 12
## 5 121.9458 13.52084 12 54 12 12
## 6 121.9542 13.52084 13 54 11 12
The crop that we are trying to target is banana. The suit
function simply require the user to input a string name for the target crop, and the function will look for the corresponding crop datasets. For example, for banana these are the crop requirements datasets for the four characteristics:
BANANATerrain
## code s3_a s2_a s1_a s1_b s2_b s3_b wts
## 1 Slope1 6.0 4 2 <NA> <NA> <NA> <NA>
## 2 Slope2 16.0 8 4 <NA> <NA> <NA> <NA>
## 3 Slope3 30.0 16 8 <NA> <NA> <NA> <NA>
## 4 Flood 2.5 2 1 <NA> <NA> <NA> 1
## 5 Drainage 4.0 3 2 <NA> <NA> <NA> 2
## 6 SlopeD 3.0 2 1 <NA> <NA> <NA> 1
BANANASoil
## code s3_a s2_a s1_a s1_b s2_b s3_b wts
## 1 CFragm 55.0 35.0 15.0 <NA> <NA> <NA> 3
## 2 SoilDpt 50.0 75.0 100.0 <NA> <NA> <NA> 2
## 3 CaCO3 15.0 10.0 0.0 <NA> <NA> <NA> 3
## 4 Gyps 10.0 4.0 0.0 <NA> <NA> <NA> 3
## 5 CECc 16.0 16.0 16.0 <NA> <NA> <NA> 3
## 6 BS 19.9 20.0 35.0 <NA> <NA> <NA> 3
## 7 SumBCs 2.8 2.8 4.0 <NA> <NA> <NA> 3
## 8 pHH2O 4.5 5.2 5.6 7.5 8 8.2 3
## 9 OC 0.7 0.8 1.5 <NA> <NA> <NA> 2
## 10 ECedS 6.0 4.0 2.0 <NA> <NA> <NA> 3
## 11 ESP 12.0 8.0 4.0 <NA> <NA> <NA> 3
## 12 SoilTe 2.0 4.0 9.0 <NA> <NA> <NA> 2
BANANAWater
## code s3_a s2_a s1_a s1_b s2_b s3_b wts
## 1 WyAv 1000 1250 1500 <NA> <NA> <NA> <NA>
## 2 WmDryLen 6 4 3 <NA> <NA> <NA> <NA>
BANANATemp
## code s3_a s2_a s1_a s1_b s2_b s3_b wts
## 1 TyMaxAv 14 16 18 <NA> <NA> <NA> 2
## 2 TmMinXm 2 8 15 <NA> <NA> <NA> 2
## 3 TmMinXmAb -2 0 8 <NA> <NA> <NA> <NA>
These datasets are used by the suit
function depending on the specified characteristics of the input land units. So for banana_suit
object above, the targetted crop datasets are BANANATerrain
and BANANASoil
since the input land unit specified is terrain=MarinduqueLT
. However, input land unit only targetted the soil factors and not the terrain factors, since none of the factors in MarinduqueLT
matched with the factors in BANANATerrain
. That is why, accessing the output for the terrain characteristics for the banana_suit
object will return the following:
banana_suit[["terrain"]]
## [1] "Error: No factor(s) to be evaluated, since none matches with the crop requirements. If water or temp characteristics was specified then maybe you forgot to specify the sow_month argument, read doc for suit."
The example above only targetted the terrain and soil characteristics, but the suit
function allows user to also target water and temp simultaneously. This is done as follows:
banana_multi <- suit("banana", terrain=MarinduqueLT, water=MarinduqueWater, temp=MarinduqueTemp, sow_month=2)
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
names(banana_multi)
## [1] "terrain" "soil" "water" "temp"
It is necessary to specify the sowing month when specifying the water and temperature characteristics of the input land units. In this case, we are saying that the first sowing month in the water and temperature characteristics correspond to February. No factors were targetted by input land unit for banana for terrain, water and temperature characteristics.
banana_suit[["terrain"]]
## [1] "Error: No factor(s) to be evaluated, since none matches with the crop requirements. If water or temp characteristics was specified then maybe you forgot to specify the sow_month argument, read doc for suit."
banana_suit[["water"]]
## NULL
banana_suit[["temp"]]
## NULL
## $`Factors Evaluated`
## [1] "CFragm" "CECc" "pHH2O" "SoilTe"
##
## $`Suitability Score`
## CFragm CECc pHH2O SoilTe
## 1 0.8533333 0.7500 0 0.96
## 2 0.8800000 0.7500 0 0.96
## 3 0.8666667 0.7500 0 0.96
## 4 0.8666667 0.7500 0 0.96
## 5 0.8400000 0.7500 0 0.96
## 6 0.8533333 0.8125 0 0.96
##
## $`Suitability Class`
## CFragm CECc pHH2O SoilTe
## 1 S1 S1 N S1
## 2 S1 S1 N S1
## 3 S1 S1 N S1
## 4 S1 S1 N S1
## 5 S1 S1 N S1
## 6 S1 S1 N S1
##
## $`Factors' Minimum Values`
## CFragm CECc pHH2O SoilTe
## 0 0 0 0
##
## $`Factors' Maximum Values`
## CFragm CECc pHH2O SoilTe
## 75.00 16.00 8.94 12.50
##
## $`Factors' Weights`
## [1] 3 3 3 2
##
## $`Crop Evaluated`
## [1] "BANANASoil"
##
## $Warning
## [1] "max is set to 16 for factor CECc since all parameter intervals are equal."
Only the head (first six) of the output of the items in the soil characteristics are shown.
There are three membership function (MF) available in suit
function, namely triangular, trapezoidal and Gaussian. For example, the following computes the suitability scores and classes using Guassian MF.
banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal")
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
head(banana_suit[["soil"]][["Suitability Score"]])
## CFragm CECc pHH2O SoilTe
## 1 1 0.7500 0 1
## 2 1 0.7500 0 1
## 3 1 0.7500 0 1
## 4 1 0.7500 0 1
## 5 1 0.7500 0 1
## 6 1 0.8125 0 1
head(banana_suit[["soil"]][["Suitability Class"]])
## CFragm CECc pHH2O SoilTe
## 1 S1 S1 N S1
## 2 S1 S1 N S1
## 3 S1 S1 N S1
## 4 S1 S1 N S1
## 5 S1 S1 N S1
## 6 S1 S1 N S1
Another option available in the suit
function is the interval
. By default, ALUES uses an equally spaced suitability class intervals for deriving the suitability class. That is, for N [0, 0.25), S3 [0.25, 0.50), S2 [0.50, 0.75), and S1 [0.75, 1].
Users can modify the default equally spaced intervals, for example:
banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval=c(0, 0.3, 0.6, 0.9, 1))
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
head(banana_suit[["soil"]][["Suitability Score"]])
## CFragm CECc pHH2O SoilTe
## 1 1 0.7500 0 1
## 2 1 0.7500 0 1
## 3 1 0.7500 0 1
## 4 1 0.7500 0 1
## 5 1 0.7500 0 1
## 6 1 0.8125 0 1
head(banana_suit[["soil"]][["Suitability Class"]])
## CFragm CECc pHH2O SoilTe
## 1 S1 S2 N S1
## 2 S1 S2 N S1
## 3 S1 S2 N S1
## 4 S1 S2 N S1
## 5 S1 S2 N S1
## 6 S1 S2 N S1
The above code sets the new suitability class intervals into: N [0, 0.3), S3 [0.3, 0.6), S2 [0.6, 0.9), and S1 [0.9, 1].
The problem with the fixed interval is that the said intervals does not take into account the shape of the membership function and the spacing of the parameter interval limits (See Article 2 for parameter intervals). Custom intervals might be able to capture this if the user computed the interval limits manually, but ALUES provides an option just for this, by setting interval="unbias"
. That is,
banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval="unbias")
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
head(banana_suit[["soil"]][["Suitability Score"]])
## CFragm CECc pHH2O SoilTe
## 1 1 0.7500 0 1
## 2 1 0.7500 0 1
## 3 1 0.7500 0 1
## 4 1 0.7500 0 1
## 5 1 0.7500 0 1
## 6 1 0.8125 0 1
head(banana_suit[["soil"]][["Suitability Class"]])
## CFragm CECc pHH2O SoilTe
## 1 S1 N N S1
## 2 S1 N N S1
## 3 S1 N N S1
## 4 S1 N N S1
## 5 S1 N N S1
## 6 S1 N N S1
By setting the interval="unbias"
, will generate a different likely unequally spaced suitability class intervals, but the interval limits are mathematically correct mapping of the parameter interval limits to suitability class limits via the membership function.
Another parameter that can be set for suit
are the min
and max
. These are the constants used by the membership function for computing the suitability score.
banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval="unbias")
## Warning in suitability(terrain, crop_soil, mf = mf, sow_month = NULL, min =
## min, : max is set to 16 for factor CECc since all parameter intervals are equal.
banana_suit[["soil"]][["Factors Evaluated"]]
## [1] "CFragm" "CECc" "pHH2O" "SoilTe"
From the above result, there are four factors targetted by the input land unit, these are CFragm, CECc, pHH2O and SoilTe. Suppose we know the maximum value that these factors can take, say 60 for CFragm, 20 for CECc, 9 for pHH2O, and 10 for SoilTe. We can specify these as follows:
banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval="unbias", max=c(60, 20, 9, 10))
banana_suit
## $terrain
## [1] "Error: No factor(s) to be evaluated, since none matches with the crop requirements. If water or temp characteristics was specified then maybe you forgot to specify the sow_month argument, read doc for suit."
##
## $soil
## [1] "Error: max length should be equal to the number of factors in the input land units."
The result gave us an error. We understand the error for terrain characteristics, but for soil it says the argument max must be equal in length with the target factors specified in the input land units dataset. We know that there should be 4 factors, but upon checking we see that the MarinduqueLT
also have Lon and Lat columns, which ALUES assumes to be a target factor as well. Indeed, we need to remove other columns that are not the target factors when specifing min
or max
constants.


Implements a method based on the fast Fourier transform to estimate delays of expression initiation between trajectories to integrate and analyse time course omics data.
| Version: | 1.2 |
| Depends: | R (≥ 3.0.0), ggplot2 |
| Imports: | methods, parallel, gplots |
| Suggests: | knitr, lmms, nlme, testthat, snow |
| Published: | 2018-06-28 |
| Author: | Jasmin Straube [aut, cre], Kim-Anh Le Cao [aut], Emma Huang [aut], Anne Bernard [ctb] |
| Maintainer: | Jasmin Straube <jasmin.straube at qimrberghofer.edu.au> |
| License: | GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE] |
| NeedsCompilation: | no |
| Citation: | dynOmics citation info |
| Materials: | README |
| CRAN checks: | dynOmics results |
| Reference manual: | dynOmics.pdf |
| Vignettes: |
DynOmics user manual |
| Package source: | dynOmics_1.2.tar.gz |
| Windows binaries: | r-devel: dynOmics_1.2.zip, r-release: dynOmics_1.2.zip, r-oldrel: dynOmics_1.2.zip |
| OS X binaries: | r-release: dynOmics_1.2.tgz, r-oldrel: dynOmics_1.2.tgz |
| Old sources: | dynOmics archive |
Please use the canonical form https://CRAN.R-project.org/package=dynOmics to link to this page.


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Space Time
's blog
Bayes Rule explained with evidence and facts examples
25 Apr 2016
Terms Used in Bayesian
18 Apr 2016
Distributions with examples
16 Apr 2016
Funny Quotes of Modelling
15 Apr 2016
Important workshops and publications
14 Apr 2016
Whats I know about Probabilistic Programming
13 Apr 2016
Probability explained !
16 Dec 2015
Statistics vs Frequentist vs Bayesian
05 Dec 2015
Problem Statement
04 Nov 2015
Example content
09 Aug 2015
Whats Jekyll?
08 Aug 2015


Part of: Get help paying for childcare
Get working tax credits for childcare
If you are eligible for tax credits you can get an extra amount for help with childcare costs.
Overview
Working Tax Credit
- Check if your are using eligible childcare
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You must be using "approved childcare" to get help. Check if a childcare provider is approved or search for one in:
You will need to know how much you spend on childcare, your income and any benefits you or your child get.
If you receive other benefits.
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During this year’s MongoSV, 10gen presented awards to peer-nominated community members for their contributions to the MongoDB project.
There were 3 different categories:
- Innovative Application Award - recognizes a company or individual who has built an innovative application using MongoDB.
- Core Contributor Award - recognizes a community member for significant contribution to the codebase of the MongoDB core server, language drivers, or tools.
- Community Champion Award - recognizes an individual for their efforts evangelizing and growing the MongoDB community.
This is the first year that 10gen has organized these awards. I think they’re a great way to recognize the contributions made by a few members of the community. I hope a significant portion of people in the community took the time to nominate or vote for another individual. I suspect more community members will participate and 10gen will do a more to advertise the program in years to come.


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Diabetes, PTSD
Keep track of all your patients' progress.
Anger Issues, Social Anxiety
Sadistic, Personality Disorder
Diabetes, PTSD


Custom Group Providers¶
Kanboard is able to load groups from an external system. This feature is mainly used for project permissions.
Project managers can allow access to a project for a group. The end-user will use an auto-complete box and search for a group.
Each time a group query is executed, all registered group providers are executed.
Group Provider Workflow¶
- The end-user start to type the group name in the auto-complete field
- The
GroupManager
class will execute the query across all registered group providers - Results are merged and returned to the user interface
- After selecting a group, the information of the group are synced to the local database if necessary
Group Provider Interface¶
Interface to implement: Kanboard\Core\Group\GroupProviderInterface
.
Classes that implements this interface abstract the group information, there are only 3 methods:
getInternalId()
: Get internal database id, return 0 otherwisegetExternalId()
: Get external unique idgetName()
: Get group name
Kanboard will use the external id to sync with the local database.
Group Backend Provider Interface¶
Interface to implement:
Kanboard\Core\Group\GroupBackendProviderInterface
.
This interface requires only one method: find($input)
. The argument
$input
is the text entered from the user interface.
This method must return a list of GroupProviderInterface
, this is
the result of the search.
Backend Registration from Plugins¶
In the method initialize()
of your plugin register your custom
backend like that:
$groupManager->register(new MyCustomLdapBackendGroupProvider($this->container));


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| Library: | Base |
| Introduced: | 2.0 Beta 1 |
The menu tool permits the user to pull up a pop-up menu for components that already exist. By default, right-clicking or control-clicking a component will bring up this pop-up menu; however, the Mouse tab of the project options allows a user to configure the mouse buttons to work differently.
The pop-up menu for most components has two items.
For some components, however, the menu has additional items. Subcircuits (that is, instances of using one circuit as a "black box" within another) are one example of this: In addition to the above two items, the pop-up menu includes another item.
Other components may extend the pop-up menu also. In the built-in libraries of the current version of Logisim, the only such components are RAM and ROM.
None.


This figure, from the section Architecture of Java Access Bridge, contains two frames; these two frames overlap each other:
Java Runtime
Java Access Bridge for Windows
The following components are in the Java Runtime frame:
Java Application, which contains the component UI Toolkit, which contains the component JAAPI (Java Accessibility API)
Java Accessibility Utilities
The following component is in the Java Access Bridge for Windows frame:
Windows DLL
The following components are in both the Java Runtime frame and the Java Access Bridge for Windows frames (they are located where both frames overlap each other):
Java Access Bridge JAR
Runtime DLLs
The component Native Assistive Technology is not in either frame.
Double arrows connect the following components:
Native Assistive Technology and Windows DLL
Windows DLL and Runtime DLLs
Runtime DLLs and Java Access Bridge JAR
Java Access Bridge JAR and Java Application
Java Access Bridge JAR and Java Accessibility Utilities
Java Access Bridge JAR and Java Accessibility Utilities


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/transfinite.20compositions.html ---
Suppose P is a complete lattice. I can define an increasing sequence of finite length in P either as a subtype of a function type or as an inductive type, as shown below.
import order.complete_lattice open lattice variables {P : Type} [complete_lattice P] def seq1 (n : ℕ) : Type := {f : fin (n+1) → P // ∀ i j, i ≤ j → f i ≤ f j} inductive seq2 (a : P) : P → ℕ → Type | nil : seq2 a 0 | cons : Π b b' n, seq2 b n → b ≤ b' → seq2 b' (n+1)
The first one generalizes easily to any ordinal (or any well-ordered set). Is there a way to generalize the second?
I mention that P is a complete lattice because I'm happy to assume (if it helps) that at each limit stage, the value of the sequence is equal to the sup of all the previous values.
The only way I can think to handle limit ordinal stages is to ask for an increasing sequence of every smaller ordinal length, such that for any α ≤ β, the sequence of length α is a prefix of the sequence of length β
But this seems a bit awkward.
(What I'm really trying to do is model https://ncatlab.org/nlab/show/transfinite+composition)
I'm not sure if this is what you're looking for. The following is used in Isabelle to model transfinite recursion:
inductive_set iterates :: "('a ⇒ 'a) ⇒ 'a set" for f :: "'a ⇒ 'a" where | step: "x ∈ iterates f ⟹ f x ∈ iterates f" | Sup: "chain (≤) M ⟹ ∀x∈M. x ∈ iterates f ⟹ Sup M ∈ iterates f"
Maybe you can use a similar approach?
Hmm, interesting
Indeed we basically always construct such transfinite compositions by iterating some construction at successor stages, and taking a sup/colimit at limit stages
What kind of thing is M there? a subset of 'a?
and chain (≤)
means it's totally ordered?
Ah, I found the source.
Yes, M
is a subset of 'a
and chain (≤) M
says that M
is totally ordered
the interesting part is that iterates
is a chain
in itself, so it contains its supremum, which is the fixed point of f
. In this theory 'a
is a chain complete partial order (i.e. all non-empty chains have a supremum)
One thing that occurs to me is that my finite sequences are themselves partially ordered by "extends", and the finite prefixes of a countable sequence form a chain
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


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setpriority
Syntax
setpriority ( p : nat )
Description
The setpriority procedure is used to set the priority of a process in a concurrent program. This priority cannot be counted on to guarantee critical access to shared variables. A smaller value of p means increased speed. The argument to setpriority may be limited to the range 0 to 2**15 - 1.
See also
getpriority, fork and monitor.
See also predefined unit Concurrency.


#include <tinyxml.h>
Inheritance diagram for TiXmlText:
Public Member Functions | |
| TiXmlText (const char *initValue) | |
| Constructor for text element. | |
| TiXmlText (const std::string &initValue) | |
| Constructor. | |
| virtual void | Print (FILE *cfile, int depth) const |
| All TinyXml classes can print themselves to a filestream or the string class (TiXmlString in non-STL mode, std::string in STL mode. | |
| bool | CDATA () const |
| Queries whether this represents text using a CDATA section. | |
| void | SetCDATA (bool _cdata) |
| Turns on or off a CDATA representation of text. | |
| virtual const TiXmlText * | ToText () const |
| Cast to a more defined type. Will return null not of the requested type. | |
| virtual TiXmlText * | ToText () |
| Cast to a more defined type. Will return null not of the requested type. | |
| virtual bool | Accept (TiXmlVisitor *content) const |
| Walk the XML tree visiting this node and all of its children. | |
Protected Member Functions | |
| virtual TiXmlNode * | Clone () const |
| [internal use] Creates a new Element and returns it. | |
Friends | |
| class | TiXmlElement |
A text node can have 2 ways to output the next. "normal" output and CDATA. It will default to the mode it was parsed from the XML file and you generally want to leave it alone, but you can change the output mode with SetCDATA() and query it with CDATA().
| TiXmlText::TiXmlText | ( | const char * | initValue | ) | [inline] |
Constructor for text element.
By default, it is treated as normal, encoded text. If you want it be output as a CDATA text element, set the parameter _cdata to 'true'
| virtual void TiXmlText::Print | ( | FILE * | cfile, | |
| int | depth | |||
| ) | const [virtual] |
All TinyXml classes can print themselves to a filestream or the string class (TiXmlString in non-STL mode, std::string in STL mode.
) Either or both cfile and str can be null.
This is a formatted print, and will insert tabs and newlines.
(For an unformatted stream, use the << operator.)
Implements TiXmlBase.