Introduction
Welcome to the Nymi SDK 4.1
This release contains significant improvements and functionality changes. NEA developers are advised to read and understand these changes before updating their projects to this release of the Nymi SDK. NEA code updates are required to consume these changes.
Note: If you choose not to upgrade to version 4.1, documentation for 4.0 is still available here.
New in SDK 4.1
In SDK 4.1, the Nymi API (NAPI) is provided as a dynamic library on Windows, and a framework on macOS.
This version of the SDK targets Nymi firmware version 1.9.7. and later.
Improvements
-
Provision management has been restructured so that NEAs are now responsible for managing the provision data. The
provisions.json
file has been deprecated. There is provision management functionality that allows the NEA to add and remove provisions from consideration without restarting. NEAs can provide the existing saved provision data to NAPI as part of the call tonapi::configure
.
For details, see Migrating to NAPI 4.1. -
Important: Provisions can be shared only across Windows machines. A provision created on a macOS/OSX machine cannot be shared to other Mac or Windows machines.
-
C++/C entry points have been renamed to simplify and standardize the interface.
For example:
OLD:nymi:jsonNapiGet
NEW:napi:get
For details, see Migrating to NAPI 4.1. -
The
config.json
file has been deprecated. Configuration options are now provided in the call tonapi::configure
. -
The
operation
field has been removed from JSON exchanges. -
NAPI for Windows is now available as a dynamic library. For details, see Migrating to NAPI 4.1.
-
NAPI for Mac is now available as a framework. For details, see Migrating to NAPI 4.1.
-
The SDK is now supported on macOS Sierra 10.12.2 or later. Note: Version 10.12.0 and 10.12.1 are NOT supported.
- Improved Bluetooth error handling
After a Bluetooth failure, NAPI now continues to attempt restarting indefinitely. Retries are 0.1s apart for the first 5 seconds, 1s apart for the first minute, and 5s apart after that. Two new error messages have been added:
- “An attempted restart of NAPI has failed, will try again” – issued on the general error channel after a failed restart
- “NAPI is unable to restart, NEA must exit” – if something happened that excluded restarting
-
NEAs can now choose to explicitly accept or reject a provision pattern through the action (accept, reject) argument to the
napi::ProvisionPattern
call. For details, see napi::ProvisionPattern in the NAPI JSON Reference. - The local signing function has been updated to require a setup call to NAPI before the signing function can be used. For details, see napi::SignSetup in the NAPI JSON Reference.
Fixes
-
A number of existing and/or potential memory leaks have been fixed.
-
NAPI internals now uses a secure JSON memory allocator.
-
napi::terminate
is considered safe to use, and will allow you to restart NAPI without restarting the entire NEA.
Known Issues
If there are operations in progress when terminate() is called, there may be a delay of up to 30 seconds before it completes.
Starting Points
-
For an overview of the new SDK and the sample app provided by Nymi, see the Developer Quick Start .
-
For a deeper look into the fundamentals of developing NEAs with SDK 4.0, see the API Concepts section.
-
For background information on the Nymi Band itself, see the The Nymi Band section of the Nymi Ecosystem.
-
Join the conversation with other NEA developers and the Nymi team in the Nymi Community.
Migrating to NAPI 4.1
If you have developed an NEA using the Nymi SDK 4.0, you must update your NEA before attempting to use SDK 4.1. This section describes the high-level steps required for migrating a project from NAPI 4.0 to 4.1.
- Rename entry points
- Link the dynamic library or framework
- Update configuration
- Update provision handling
Rename Entry Points
Open your project and do a Search and Replace on the following names:
| OLD | NEW |
|---|---|
| nymi:: | napi:: |
| JsonPutOutcome | PutOutcome |
| JsonGetOutcome | GetOutcome |
| JsonConfigOutcome | ConfigOutcome |
Link the Library
NAPI is now available as a dynamic library for Windows, and as a framework for macOS. Link your project against the new library or framework as required.
Windows:
- To implicitly link NAPI, use the provided
napi.h
header file, thenapi.lib
library, and thenapi.dll
file. - To explicitly link NAPI, make a function call to explicitly load
napi.dll
at run time.
Note: Both Debug and Release versions of NAPI are available in the SDK package.
macOS/OS X:
- An installer package is provided for the macOS/OS X frameworks. Run the
sdk4.1\API\framework_installer\NAPI_Installer.pkg
to install all the files required for developing with NAPI.
Update Configuration
C++
API ConfigOutcome configure( const char* neaName,
const char* logDirectory,
const char* provisions = "{}",
LogLevel logLevel = LogLevel::normal,
int port = -1,
const char* host = "" );
Python
def configure(
nea_name,
log_directory='.',
provisions=None,
log_level=0,
port=-1,
host=''
):
Update the function to configure and initialize NAPI (napi::configure
):
This function replaces the config.json
and the provisions.json
files. Instead of those two files, you must provide the NEA name and the list of any existing provisions to NAPI when it is initialized.
The default values are shown in the example code on the right.
Update Provision Handling
Add code to your NEA to manage the saving/storing of provisions. Your NEA must take on the task of managing provisions. It is up to you to decide how to implement this. Use the NAPI commands for putting and getting provisions to change the list of provisions that NAPI knows about.
Note: The provisions must be kept secret to the NEA.
Important: Provisions created on Windows PCs can only be shared across Windows devices. Provisions created on macOS/OS X are specific to the device they are created on and cannot be shared with any other devices, regardless of platform.
Although NAPI itself no long requires the provisions.json
file, you can still make use of it in your NEA. In general, the standard method for handling provisions is as follows:
- On first run, configure NAPI with an empty list of provisions.
- Provision Nymi Bands as you have previously done.
- Before quitting the NEA, save the provision information to the
provisions.json
file.
For details see NAPI JSON Examples.
For an example of this behaviour, see the apy
sample app.


"x" "1" "ISHIGURO,Y, LALONDE,RT, DENCE,CW AND SANTODONATO,J, MUTAGENICITY OFCHLORINE-SUBSTITUTED FURANONES AND THEIR INACTIVATION BY REACTION WITHNUCLEOPHILES,ENVIRON. TOXICOL. CHEM. 6(12):935-946, 1987"


--- layout: page title: "Locating Services for Calendaring Extensions to WebDAV (CalDAV) and vCard Extensions to WebDAV (CardDAV)" description: "This specification describes how DNS SRV records, DNS TXT records, and well-known URIs can be used together or separately to locate CalDAV (Calendaring Extensions to Web Distributed Authoring and Versioning (WebDAV)) or CardDAV (vCard Extensions to WebDAV) services." ---
| Document Name: | RFC 6764 |
|---|---|
| Document URI: |
urn:ietf:rfc:6764
|
| Online Version: |
https://datatracker.ietf.org/doc/html/rfc6764
|
| Organization: | Internet Engineering Task Force (IETF) |
| Series: | Request for Comments (RFC) |
| Abstract: | This specification describes how DNS SRV records, DNS TXT records, and well-known URIs can be used together or separately to locate CalDAV (Calendaring Extensions to Web Distributed Authoring and Versioning (WebDAV)) or CardDAV (vCard Extensions to WebDAV) services. |
caldav
,
carddav
Return to ( Series | Organization | all Specifications )


ikiwiki supports placing pages in a directory hierarchy. For example, this page, SubPage has some related pages placed under it, like LinkingRules. This is a useful way to add some order to your wiki rather than just having a great big directory full of pages.
To add a SubPage, just make a subdirectory and put pages in it. For example, this page is subpage.mdwn in this wiki's source, and there is also a subpage subdirectory, which contains subpage/linkingrules.mdwn. Subpages can be nested as deeply as you'd like.
Linking to and from a SubPage is explained in LinkingRules.


The XMPP protocol includes a base protocol and many optional extensions typically documented as "XEP's". Smack provides the org.jivesoftware.smack package for the core XMPP protocol, and the org.jivesoftware.smackx package for many of the protocol extensions.
This manual provides details about each of the "smackx" extensions, including what it is, how to use it, and some simple example code.
| Name | XEP # | Description |
| Private Data | XEP-0049 | Manages private data. |
| XHTML Messages | XEP-0071 | Allows send and receiving formatted messages using XHTML. |
| Message Events | JEP-0022 | Requests and responds to message events. |
| Data Forms | JEP-0004 | Allows to gather data using Forms. |
| Multi User Chat | JEP-0045 | Allows configuration of, participation in, and administration of individual text-based conference rooms. |
| Roster Item Exchange | XEP-0093 | Allows roster data to be shared between users. |
| Time Exchange | XEP-0090 | Allows local time information to be shared between users. |
| Group Chat Invitations | N/A | Send invitations to other users to join a group chat room. |
| Service Discovery | XEP-0030 | Allows to discover services in XMPP entities. |
| File Transfer | XEP-0096 | Transfer files between two users over XMPP. |
| PubSub | XEP-0060 | Generic publish and subscribe functionality. |


Academic/Research Publications
IEEE Control Systems Letters, 7, 655-660, 2023.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), National Harbor, USA, 23rd-27th Jan 2023.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), National Harbor, USA, 23rd-27th Jan 2023.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), National Harbor, USA, 23rd-27th Jan 2023.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), National Harbor, USA, 23rd-27th Jan 2023.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), National Harbor, USA, 23rd-27th Jan 2023.
in press, Journal of Guidance, Control, and Dynamics, 2022.
AIAA Journal of Guidance, Control, and Dynamics, 45 (11), 2182-2192, 2022.
22nd IFAC International Symposium on Automatic Control in Aerospace (ACA), Mumbai, India, 21st-25th Nov 2022.
Aerospace Science and Technology, 127, pp. 107686, 2022 [preprint available at arXiv:2106.01895].
Journal of Intelligent & Robotics Systems, 104 (28), 1-27, 2022 [preprint available at arXiv:2106.01895].
Aerospace Science and Technology, 120, pp. 107262, 2022.
IFAC PapersOnLine, 55 (1), 26–31, 2022.
IFAC PapersOnLine, 55 (1), 592–597, 2022.
7th Indian Control Conference (ICC), Mumbai, India, 20th-22nd Dec 2021.
7th Indian Control Conference (ICC), Mumbai, India, 20th-22nd Dec 2021.
American Control Conference (ACC), New Orleans, USA, 25th-28th May 2021.
Aerospace Science and Technology, 115, pp. 106824, 2021.
Aerospace Science and Technology, 115, pp. 106776, 2021.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), Virtual Event, USA, 11th-15th and 19th-21st Jan 2021.
IEEE Control Systems Letters, 5 (5), 1573-1578, 2021.
AIAA Journal of Guidance, Control, and Dynamics, 44 (2), 328-342, 2021.
AIAA Journal of Guidance, Control, and Dynamics, 44 (2), 343-359, 2021.
IEEE TechRxiv, 2020.
IEEE Transactions on Aerospace and Electronic Systems 56 (5), 3556-3565, 2020.
International Journal of Control 93 (4), 858-871, 2020.
IEEE Transactions on Cybernetics 50 (7), 3254-3263, 2020.
American Control Conference (ACC), Denver, USA, 1st-3rd Jul 2020.
American Control Conference (ACC), Denver, USA, 1st-3rd Jul 2020.
IFAC PapersOnLine, 53 (1), 93–98, 2020, Awarded Best Paper.
AIAA Guidance, Navigation, and Control Conference (AIAA SciTech Forum), Orlando, USA, 6th-10th Jan 2020.
IEEE Transactions on Cybernetics 49 (12), 4450-4459, 2019.
European Journal of Control 45, 30-44, 2019.
International Journal of Communication Systems 33 (10), e4206, 2019.
6th Indian Control Conference (ICC), Hyderabad, India, 18th-20th Dec 2019.
Chemical Engineering Science 187, 52-59, 2018.
AEU– International Journal of Electronics and Communication, 90, 155-162, 2018.
IFAC PapersOnLine, 51 (1), 401–406, 2018.
IEEE 3rd International Conference for Convergence in Technology, Pune, India, 7th-8th Apr 2018.
accepted, IEEE Symposium Series on Computational Intelligence, Bengaluru, India, 18th-21st Nov 2018.
arXiv:1711.03819, ArXiv e-prints, 2017.
Foundations and Frontiers in Computer, Communication and Electrical Engineering, Taylor and Francis, 2016.
Foundations and Frontiers in Computer, Communication and Electrical Engineering, Taylor and Francis, 2016.
International Journal of Electronics and Communication Technology, 61 (1), 32-37, 2015.
IEEE Power, Communication and Information Technology Conference (PCITC), Bhubaneswar, India, 15th-17th Oct, 2015.
IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 19th-20th Mar 2015.
IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 19th-20th Mar 2015.
IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 19th-20th Mar 2015.
IEEE International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 26th-27th Feb 2015, Awarded Best Paper.
IEEE International Conference on Computational Intelligence and Networks (CINE), Bhubaneswar, India, 12th-13th Jan 2015.
7th IEEE International Conference on Information and Automation for Sustainability (ICIAfS), Colombo, Sri Lanka, 22nd-24th Dec 2014.
IEEE International Conference on Control, Automation, Robotics and Embedded Systems (CARE), Jabalpur, India, 16th-18th Dec 2013.


climate weather change indices different future data impact downscaling {border-style WGs series using Climate generated model px;} rice #ffffff;} analysis reference baseline stochastic generation generators precipitation projections uncertainty generator sites used yield GCM × RCP crop models Fig. alternative based index results temperature GCMs changes daily indicate set studies study (Fig. GCM Supplementary factors projections. px; scenarios simulation values variability {font-family (). CLIMAK Figure LARS-WG Site air combinations density downscaled font-size impacts local site uncertainties ./s--- Stochastic WeaGETS double; effect estimates information multiple probability projected resolution yields ANOVA Environmental Meteorology Research Sciences Semenov affected border-color border-width calculated climatic concentration distribution distributions estimated extreme obtained scale scenarios, simulations solid; value {font-style {font-weight ./j.agrformet... Agricultural Change Climatology Commons Creative Development Forest GCMs, Meteorological Model RCP. Society WARM WG, WG-based WG. WGs, WeaGETS) Zhang assessed atmospheric bold coarse complete conditions considered coupled courier; em; ensemble extended fit horizontal included indicates integrated larger license, line lines location main metrics. monthly output perfect process provide pt;} significant statistical tests use variables vertical


In this new training
we’ll cover:
- Why the right OFFER generates more results than any other part of your message
- The 6 steps to easily craft an irresistible OFFER, everytime
- The 3 pillars of every irresistible offer (miss one and the offer is boring)
- The simple formula: HV + LI = Irresistible Offer
- PROOF: 5 brilliant IRRESISTIBLE OFFERS (that generated millions!)
- MORE PROOF: 3 wildly fun winning SPLIT-TESTS of irresistible OFFERS


Step 6 of test for Controllers, uses a ListController.
Select the Shipping Address to use.
New Address
New copy of selected.
Enter the Shipping Address
Address Name:
Full Name:
Address Line1:
Address Line2:
City:
State:
Zipcode:
Verify the shipping address for:
%{this.value},


About Me
Hi I am Nishah, pursuing as a User Experience designer and researcher.
I am also a cat owner, this means I have patience, compassion and dedication that may be a great fit to your company.
Always been interested in psychology and art, I went through a diploma in Interaction Design where the curriculum taught were on a spectrum of subjects on psychology in media.
Being diligent and having a can-do attitude are what I find the best traits about myself. I believe that experience is important however, having the mindset to strive to improve, learn new things along with having a good interpersonal skill are beneficial.
Testimonial
“She also drove herself hard and was diligent in meeting deadlines. Her can-do attitude was most obvious when she worked on tasks that were completely new to her.”
“We would highly recommend Nurnishah to anyone who is looking for a talent with positive attitude towards learning and continuous improvement.”


WELCOME!!! WELCOME!! WELCOME! to our website where users enter their GitHub username/anyone github username into a form, submit it, and see names and descriptions of that person's public repositories. A person can also look for repositories


Deprecated: This plugin has been removed from the Jenkins Update Centre
The plugin is deprecated like the Jenkow plugin
Plugin Information |
|---|
No information for the plugin 'jenkow-activiti-designer' is available. It may have been removed from distribution. |
Adds the Activiti Designer (Eclipse-based BPMN workflow editor) to Jenkow.
The Activiti Designer is an Eclipse-based workflow editor. It is part of the Activiti BPM Platform project. (The Activiti Modeler is another, web-based workflow editor.)
The Jenkow Activiti Designer plugin encapsulates the Activiti Designer together with the Designer extensions specific for Jenkow and offers them as a single Eclipse update site in Jenkins (using the Eclipse Update Site Plugin).
The update site is used as a source repository for installing the Activiti Designer in Eclipse.


Class NonWorkingPeriod represents named non-working, non-recurring periods. For example holidays and scheduled outages such as for preventive maintenance.
More...
Class NonWorkingPeriod represents named non-working, non-recurring periods. For example holidays and scheduled outages such as for preventive maintenance.
◆ NonWorkingPeriod()
| Point85.ShiftSharp.Schedule.NonWorkingPeriod.NonWorkingPeriod |
( |
| ) |
|
|
inline |
◆ CompareTo()
| int Point85.ShiftSharp.Schedule.NonWorkingPeriod.CompareTo |
( |
NonWorkingPeriod |
other | ) |
|
|
inline |
Compare this non-working period to another such period by start date and time of day
- Parameters
-
- Returns
- negative if less than, 0 if equal and positive if greater than
◆ GetEndDateTime()
| LocalDateTime Point85.ShiftSharp.Schedule.NonWorkingPeriod.GetEndDateTime |
( |
| ) |
|
|
inline |
Get period end date and time
- Returns
- Period end
◆ IsInPeriod()
| bool Point85.ShiftSharp.Schedule.NonWorkingPeriod.IsInPeriod |
( |
LocalDate |
day | ) |
|
|
inline |
Check to see if this day is contained in the non-working period
- Parameters
-
- Returns
- True if in the non-working period
◆ ToString()
| override string Point85.ShiftSharp.Schedule.NonWorkingPeriod.ToString |
( |
| ) |
|
|
inline |
Build a string representation of this non-working period
- Returns
◆ Duration
| Duration Point85.ShiftSharp.Schedule.NonWorkingPeriod.Duration |
◆ StartDateTime
| LocalDateTime Point85.ShiftSharp.Schedule.NonWorkingPeriod.StartDateTime |
|
getset |
starting date and time of day
◆ WorkSchedule
| WorkSchedule Point85.ShiftSharp.Schedule.NonWorkingPeriod.WorkSchedule |
|
getset |
The documentation for this class was generated from the following file:


"Waiting for the Weather to Change."
John R. Maloy is a peculiar man.  Mister Maloy is the well-known local rock climber who has spent the last 35 years of his life (since he was 8 years old) climbing rocks around the World.
At the age of eleven, he made a National Record for being the youngest man on record the 3,000 height feet Yosemite's El Capitán (that record has been already broken since, though). Five years later he would break another record, but this time, an international one, for been the first 16 years old man (in fact the youngest man) to complete the full Seven Summits Challenge (this challenge consisting of climbing the summit of all seven highest peeks on the Seven Continents).
To be continued...


Purchase A Membership To Earn Points Every Flight And Pay For Future Trips!
Check in 36 hours before your flight to get a better boarding position, ranging from $10-$20 per passenger.


| RFC 1912839 | Fantom WASM Virtual Machine | November 2019 |
| Foundation | Informational | [Page] |
This document is not an Internet Standards Track specification; it is published for informational purposes.¶
This document is a product of the Internet Engineering Task Force (IETF). It represents the consensus of the IETF community. It has received public review and has been approved for publication by the Internet Engineering Steering Group (IESG). Not all documents approved by the IESG are candidates for any level of Internet Standard; see Section 2 of RFC 7841.¶
Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at https://www.rfc-editor.org/info/rfc1912839.¶
Copyright (c) 2019 IETF Trust and the persons identified as the document authors. All rights reserved.¶
This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.¶
Fantom application are compiled to a Fantom flavoured WASM and are executed on the FWASM virtual machine.¶
TODO¶
TODO¶


Decoder for Avro binary format. More...
Public Member Functions | |
| BinaryDecoder (Stream stream) | |
| void | ReadNull () |
| null is written as zero bytes | |
| bool | ReadBoolean () |
| a boolean is written as a single byte whose value is either 0 (false) or 1 (true). | |
| int | ReadInt () |
| int and long values are written using variable-length, zig-zag coding. | |
| long | ReadLong () |
| int and long values are written using variable-length, zig-zag coding. | |
| float | ReadFloat () |
| A float is written as 4 bytes. The float is converted into a 32-bit integer using a method equivalent to Java's floatToIntBits and then encoded in little-endian format. | |
| double | ReadDouble () |
| A double is written as 8 bytes. The double is converted into a 64-bit integer using a method equivalent to Java's doubleToLongBits and then encoded in little-endian format. | |
| byte[] | ReadBytes () |
| Bytes are encoded as a long followed by that many bytes of data. | |
| string | ReadString () |
| Reads a string Avro type. | |
| int | ReadEnum () |
| Reads an enum AvroType. | |
| long | ReadArrayStart () |
| Starts reading the array Avro type. This, together with ReadArrayNext() is used to read the items from Avro array. This returns the number of entries in the initial chunk. After consuming the chunk, the client should call ReadArrayNext() to get the number of entries in the next chunk. The client should repeat the procedure until there are no more entries in the array. | |
| long | ReadArrayNext () |
| See ReadArrayStart(). | |
| long | ReadMapStart () |
| Starts reading the map Avro type. This, together with ReadMapNext() is used to read the entries from Avro map. This returns the number of entries in the initial chunk. After consuming the chunk, the client should call ReadMapNext() to get the number of entriess in the next chunk. The client should repeat the procedure until there are no more entries in the array. for (int n = decoder.ReadMapStart(); n > 0; n = decoder.ReadMapNext()) { Read one map entry. }. | |
| long | ReadMapNext () |
| See ReadMapStart(). | |
| int | ReadUnionIndex () |
| Reads the index, which determines the type in an union Avro type. | |
| void | ReadFixed (byte[] buffer) |
| A convenience method for ReadFixed(buffer, 0, buffer.Length);. | |
| void | ReadFixed (byte[] buffer, int start, int length) |
| Read a Fixed Avro type of length. | |
| void | SkipNull () |
| Skips a null Avro type on the stream. | |
| void | SkipBoolean () |
| Skips a boolean Avro type on the stream. | |
| void | SkipInt () |
| Skips a int Avro type on the stream. | |
| void | SkipLong () |
| Skips a long Avro type on the stream. | |
| void | SkipFloat () |
| Skips a float Avro type on the stream. | |
| void | SkipDouble () |
| Skips a double Avro type on the stream. | |
| void | SkipBytes () |
| Skips a bytes Avro type on the stream. | |
| void | SkipString () |
| Skips a string Avro type on the stream. | |
| void | SkipEnum () |
| void | SkipUnionIndex () |
| void | SkipFixed (int len) |
| long Avro.IO.BinaryDecoder.ReadArrayNext | ( | ) | [inline] |
See ReadArrayStart().
Implements Avro.IO.Decoder.
| long Avro.IO.BinaryDecoder.ReadArrayStart | ( | ) | [inline] |
Starts reading the array Avro type. This, together with ReadArrayNext() is used to read the items from Avro array. This returns the number of entries in the initial chunk. After consuming the chunk, the client should call ReadArrayNext() to get the number of entries in the next chunk. The client should repeat the procedure until there are no more entries in the array.
for (int n = decoder.ReadArrayStart(); n > 0; n = decoder.ReadArrayNext()) { Read one array entry. }
Implements Avro.IO.Decoder.
| bool Avro.IO.BinaryDecoder.ReadBoolean | ( | ) | [inline] |
a boolean is written as a single byte whose value is either 0 (false) or 1 (true).
Implements Avro.IO.Decoder.
| byte [] Avro.IO.BinaryDecoder.ReadBytes | ( | ) | [inline] |
Bytes are encoded as a long followed by that many bytes of data.
Implements Avro.IO.Decoder.
| double Avro.IO.BinaryDecoder.ReadDouble | ( | ) | [inline] |
A double is written as 8 bytes. The double is converted into a 64-bit integer using a method equivalent to Java's doubleToLongBits and then encoded in little-endian format.
| ? |
Implements Avro.IO.Decoder.
| int Avro.IO.BinaryDecoder.ReadEnum | ( | ) | [inline] |
| void Avro.IO.BinaryDecoder.ReadFixed | ( | byte[] | buffer | ) | [inline] |
A convenience method for ReadFixed(buffer, 0, buffer.Length);.
| buffer | The buffer to read into. |
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.ReadFixed | ( | byte[] | buffer, |
| int | start, | ||
| int | length | ||
| ) | [inline] |
Read a Fixed Avro type of length.
| buffer | Buffer to read into |
| start | Starting position of buffer to read into |
| length | Number of bytes to read |
Implements Avro.IO.Decoder.
| float Avro.IO.BinaryDecoder.ReadFloat | ( | ) | [inline] |
A float is written as 4 bytes. The float is converted into a 32-bit integer using a method equivalent to Java's floatToIntBits and then encoded in little-endian format.
Implements Avro.IO.Decoder.
| int Avro.IO.BinaryDecoder.ReadInt | ( | ) | [inline] |
int and long values are written using variable-length, zig-zag coding.
| ? |
Implements Avro.IO.Decoder.
| long Avro.IO.BinaryDecoder.ReadLong | ( | ) | [inline] |
int and long values are written using variable-length, zig-zag coding.
| ? |
Implements Avro.IO.Decoder.
| long Avro.IO.BinaryDecoder.ReadMapNext | ( | ) | [inline] |
See ReadMapStart().
Implements Avro.IO.Decoder.
| long Avro.IO.BinaryDecoder.ReadMapStart | ( | ) | [inline] |
Starts reading the map Avro type. This, together with ReadMapNext() is used to read the entries from Avro map. This returns the number of entries in the initial chunk. After consuming the chunk, the client should call ReadMapNext() to get the number of entriess in the next chunk. The client should repeat the procedure until there are no more entries in the array. for (int n = decoder.ReadMapStart(); n > 0; n = decoder.ReadMapNext()) { Read one map entry. }.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.ReadNull | ( | ) | [inline] |
null is written as zero bytes
Implements Avro.IO.Decoder.
| string Avro.IO.BinaryDecoder.ReadString | ( | ) | [inline] |
| int Avro.IO.BinaryDecoder.ReadUnionIndex | ( | ) | [inline] |
Reads the index, which determines the type in an union Avro type.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipBoolean | ( | ) | [inline] |
Skips a boolean Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipBytes | ( | ) | [inline] |
Skips a bytes Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipDouble | ( | ) | [inline] |
Skips a double Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipFloat | ( | ) | [inline] |
Skips a float Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipInt | ( | ) | [inline] |
Skips a int Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipLong | ( | ) | [inline] |
Skips a long Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipNull | ( | ) | [inline] |
Skips a null Avro type on the stream.
Implements Avro.IO.Decoder.
| void Avro.IO.BinaryDecoder.SkipString | ( | ) | [inline] |
Skips a string Avro type on the stream.
Implements Avro.IO.Decoder.


|
Another Game of Life
Cross-platform visual Game of Life
|
Automated cells container. More...
#include <Board.hpp>
Data Structures | |
| struct | Size |
| Size of a board. More... | |
Public Types | |
| enum | State { State::Dead, State::Alive, State::OutOfBound } |
| States of the cells. More... | |
Public Member Functions | |
| Board () | |
| Default constructor. More... | |
| Board (unsigned int width, unsigned int height) | |
| Parametric constructor. More... | |
| struct Size | size () |
| Get the size of the board. More... | |
| State | at (int x, int y) |
| Safely query one cell on the board. More... | |
| void | dumpCurrent () |
| Display the content of the board into stdout. | |
| void | next () |
| Generate future generation. More... | |
Automated cells container.
Register cells on its intenal container and provoke their mutation (generation growth).
|
strong |
| agof::Board::Board | ( | ) |
Default constructor.
Create the board using the value of the Parameters singleton.
| agof::Board::Board | ( | unsigned int | width, |
| unsigned int | height | ||
| ) |
Parametric constructor.
Create the board using the given dimensions.
| agof::Board::State agof::Board::at | ( | int | x, |
| int | y | ||
| ) |
Safely query one cell on the board.
Query the state of the cell on a given location on the board. Invalid location, such as negative indexes, returns safely an OutOfBound value.
| void agof::Board::next | ( | ) |
Generate future generation.
Replace the current generation of cells by the next one using the established rule of the Game of Life.
| struct agof::Board::Size agof::Board::size | ( | ) |
Get the size of the board.


Libidn2 Reference Manual
for Libidn2 2.3.3 The latest version of this documentation can be found on-line at https://www.gnu.org/software/libidn/#libidn2.


In last years there were introduced many dataset for Face Recognition, named several:
- CASIA-WebFace: 0.5M of images
- FaceScrub: 0.1M of images
- MsCeleb: 10M of images
- UMD: 0.38M of images
- VGG: 2.6M of images
The main difference between them are the number of images and identities. But they're also different in sth like signal-to-noise ratio
, where we mean that ratio between correctly labeled images to all collected images. We do not know such value for any provided database, but some researcher provided the clean-list which provided images which was verified to be correct (by human or face-recognition algorithm). For example CASIA-WebFace have 90% of correct images, in the other side VGG have only 38% (rest of images have noisy labels). But what does it mean relative to accuracy of Face Recognition model? Does noise really hurt the performance in verification/identification protocol?
Noisy Database
First of all, let's make some literature review.
One of the publication which we know about noise in data for Deep Learning is The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition. The idea was simple: instead of marking pictures manually use information gathered in Web (metadata and search-engine). This enable to collect many more images than existed in current training dataset of several benchmarks. The results indicate that when training on original images and from web, the final accuracy is always higher (despite the fact, that the data in noisy).
The first public face-recognition database with significant amount of noise was VGG. We will describe their method in more details. They collected 5M images from both Google and Bing Image Search. Then using Machine Learning algorithm with Fisher Vector Faces descriptor, half of images was removed. The last stage was manually filtering, which take 10 days (really hard work), ending with ~1M of images. Additional the images before manually filtering were provided, what end up with 2.6M. In experiments, Oxford team was using both dataset (named full and curated). And the bigger one leads to better results at all of their experiments, although it contain more noisy images than right one. Before jumping to any conclusion, let's take a closer look into manually filtering process by reading the paper:
The aim of this final stage is to increase the purity (precision) of the data using human annotations. However, in order to make the annotation task less burdensome and hence avoid high annotation costs, annotators are aided by using automatic ranking once more. This time, however, a multi-way CNN is trained to discriminate between the 2,622 face identities using the AlexNet architecture; then the softmax scores are used to rank images within each identity set by decreasing likelihood of being an inlier. In order to accelerate the work of the annotators, the ranked images of each identity are displayed in blocks of 200 and annotators are asked to validate blocks as a whole. In particular, a block is declared good if approximate purity is greater than 95%.
So filtering was done using block of images, which were accepted or refused by looking into purity of it. It means that if there is even some noise in block (more than 5%), then block is removed. To sum up, in 1.6M of noisy images there could be even ~1.4 of good labels. The similar situation is at other dataset like CASIA or MsCeleb: we are not sure that noise is really a noise, because most of time manually annotators does not investigate each of image independently. But from both of them researchers provided list of clean images by removing the noise manually (CASIA) or in automatic way (MsCeleb).
Experiments on CASIA-WebFace
The first test will be based on UMD database (as very clean database) and different version of CASIA-WebFace database:
- full (0.5M of images)
- clean-v1 (0.47M, taken from Happy-Near page)
- clean-v2 (0.445M, taken from project Face Recognition via Active Annotation and Learning)
Idea behind using this database is testing algorithm on very clean database with smaller size (UMD) versus bigger and noisier database with some efforts of cleaning it.
For training the network, we will be using the same setting like for baseline. Here are results.
There is no clean conclusion from this experiments. First of all, we can see that delivered clean-list for CASIA-WebFace really have lower noise, because network trained using them have lower loss and higher validation accuracy. But this is the main difference, all network achieve similar score at LFW and BLUFR benchmarks. The highest score achieve the cleanest one, clean-v2, but the difference is only ~1%. Maybe if dataset would contain more images, the bigger change would be noticed. UMD database despite of being very clean, achieve the lowest score.
Experiments on VGG-Database
As the first experiments does not make any clean conclusion, let's use VGG database. As the level of noisy labels in VGG can be much bigger, here will be conducted experiments using different ratio of noise (exactly images labeled as noise to all images in training set). We hope that it will show how different amount of noise impact the final accuracy (remembering that the noise in case of VGG maybe not a real noise). For coherence of results, in each experiment same validation dataset is used.
Unfortunately, the results are again not conclusive. In contrast to earlier results, here more noise make result better. We think that it is because of noisy labels, which have a lot of true labels.
Experiments using Real Noise
In such situation, we decided to carry out last experiment. Using the cleanest version of CASIA-WebFace we will be injecting real noise to data. The noise will be formed from images taken from VGG database with random labels. As we would like to have a small amount of noise in base training data, we will use clean-v2 list.
Finally, this experiments provide us clear conclusion. The real noise, even in small amout, hurt the final accuracy. Although the model with 10% of noise achieve better score at LFW, the BLUFR protocol show the real difference. So we can conclude that is nice to have less than 10% of noise. Also strange situation happened with noise ratio of 50%: the network diverge, so we should lower the learning-rate.
In the other hard, it is really intriguing that having the 80% of noisy labels still network can learn anything useful. The similar result was achieved by researcher in paper Deep Learning is Robust to Massive Label Noise, even that the experiments have 99% of noise. The researcher have very novel conclusions:
- Deep neural networks are able to learn from data that has been diluted by an arbitrary amount of noise.
- A sufficiently large training set is more important than a lower level of noise.
- Choosing good hyperparameters can allow conventional neural networks to operate in the regime of very high label noise.
For sure Deep Learning is amazing in handling the noisy data, but we cannot agree with second conclusion, already owning ~0.5M of data example. Having 0.4M of images with clean labels is better than any database with larger amount of images but with noise. Other pros of having clean and small database is faster convergence (as there is less training examples). In the other hand, when we are collecting our own dataset it is inevitable to have noisy labels. Our experiments show that data should not have more than 10% of real noise.
Conclusion
Look like noisy labels impact the final accuracy of model, so it is not a good idea to artificially make dataset bigger by injecting of noise (like a data augmentation method). Also, the cleaner dataset with enough training examples the better. It is contradict to conclusion from paper The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition. It is because of different initial assumptions as their dataset have sizes: 6k, 10k, 20k, 50k. So each of them is much smaller that we used. In other hand, if our noisy dataset have sth like >90% of good labels, it is recommended to use it at training as well.
In such situation we need to agree with conclusion from Deep Learning is Robust to Massive Label Noise, with slight modification:
A sufficiently large training set is more important than a lower level of noise only if your original dataset have not enough data (which number depend on task, ex. Face-Recognition is ~0.4M)
Final conclusion:
- Cleaning the dataset can lead to better accuracy, but gain will be minimal if there were less than 10% of noise.
- If we have database which have only 5%-10% of noisy labels, it is advised to use it for training.
- Adding images with real noise is not beneficial.
Ok, so test another aspect of preparing the dataset for Face-Recognition. The next question which will we ask about pipeline is:
How the input image should be aligned before feeding it to network?
Reference
- Learning Face Representation from Scratch”
- A data-driven approach to cleaning large face datasets.
- MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
- UMDFaces: An Annotated Face Dataset for Training Deep Networks
- VGG-Deep Face Recognition
- The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
- Deep Learning is Robust to Massive Label Noise
- Face Recognition via Active Annotation and Learning
- Happy-Near page


--- layout: page title: A Day in the Big Apple subtitle: Daily Crime in New York City use-site-title: true ---
Info
Felonies:
Misdemeanors:
Violations:
Evening:


Total Tasks: ${{vm.total_time()}}
| Prioritize | Task | Completion Time (mins) | Number of people requested | Special Instructions | |
|---|---|---|---|---|---|
| . | {{todo.task}} | ${{todo.people_requested}} | {{todo.completion_time}} |
| Prioritize | Task | Completion Time (mins) | Number of people requested | Special Instructions | |
|---|---|---|---|---|---|
| . | {{todo.task}} | ${{todo.people_requested}} | {{todo.completion_time}} |


| Package | Description |
|---|---|
| com.github.sviengine | |
| com.github.sviengine.animation | |
| com.github.sviengine.slide |
| Class and Description |
|---|
| SVIAnimationSet
SVIAnimation Set Class
This class is Animation Set.
|
| SVIBasicAnimation
SVIBasicAnimation Class
SVIBasicAnimation extends
SVIPropertyAnimation |
| SVIKeyFrameAnimation
SVIKeyFrameAnimation Class
This Class is make animation use Key frame. you can use key frame time ( 0.0f ~ 1.0f) to control animation
Key frame must bigger than past key frame value.
|
| SVIParticleAnimation
SVIParticleAnimation Class
Particle Animation Class make Particle Animation.
|
| SVISpriteAnimation
SVISpriteAnimation Class
Sprite Animation use One image resource and make animation via divided same rectangle from resource.
|
| SVISpriteAnimation.PlayType
PlayType
PLAY_ALL or PLAY_PARTIAL
|
| SVITransitionAnimation
SVITransitionAnimation Class
Transition Animation Class make Transition Animation.
|
| Class and Description |
|---|
| SVIAnimation
SVIAnimation Class
include control animation property and type.
|
| SVIAnimation.SVIAnimationListener
SVIAnimationListener Interface
|
| SVIAnimationSet
SVIAnimation Set Class
This class is Animation Set.
|
| SVIBasicAnimation
SVIBasicAnimation Class
SVIBasicAnimation extends
SVIPropertyAnimation |
| SVIKeyFrameAnimation
SVIKeyFrameAnimation Class
This Class is make animation use Key frame. you can use key frame time ( 0.0f ~ 1.0f) to control animation
Key frame must bigger than past key frame value.
|
| SVIParticleAnimation
SVIParticleAnimation Class
Particle Animation Class make Particle Animation.
|
| SVIPropertyAnimation |
| SVISpriteAnimation
SVISpriteAnimation Class
Sprite Animation use One image resource and make animation via divided same rectangle from resource.
|
| SVISpriteAnimation.PlayType
PlayType
PLAY_ALL or PLAY_PARTIAL
|
| SVITransitionAnimation
SVITransitionAnimation Class
Transition Animation Class make Transition Animation.
|
| Class and Description |
|---|
| SVIAnimation
SVIAnimation Class
include control animation property and type.
|


--- layout: post title: SourceGear.Rust.NET preview 0.1.0 date: 2020-04-20 12:00:00 keywords: rust dotnet front llama teaser: Don't even think about trying to use this for real work. ---
A few weeks ago I posted a blog entry about stuff I've been building to compile Rust code for .NET.
You wanna try it? :-)
First let me try to scare away the sane people.
This project is not production ready.
Think of this as just a demo or a proof of concept.
Don't even think about trying to use this for real work.
Lots of things are broken or incomplete.
This has never been used to write anything but Hello World and test cases.
This is not open source.
I am making no promises about the future of this project.
If more releases happen in the future, things may change with no regard whatsoever for backward compatibility.
This preview release is simply for interested folks who want to fiddle around with Rust and .NET.
Making a release of this project has forced me to give it a name, so I'm calling it SourceGear.Rust.NET. The preview release consists of two NuGet packages:
SourceGear.Rust.NET.Templates -- for use with `dotnet new`
SourceGear.Rust.NET.Sdk -- an MSBuild Sdk
You'll need .NET Core 3.1 on one of the following platforms:
Windows (x64)
MacOS
Ubuntu 18.04 (x64)
(The Sdk includes a native code library (libLLVM), and for now, I am only bundling it for the 3 platforms above.)
And you'll need Rust nightly, plus its `rust-src` component.
rustup toolchain install nightly rustup component add rust-src --toolchain nightly
First, install the templates:
dotnet new -i SourceGear.Rust.NET.Templates
Now create a project:
mkdir MyRustProject1 cd MyRustProject1 dotnet new console -lang Rust
The project directory should now contain an .rsproj file. This is very similar to a .csproj or .fsproj, except of course that it's for Rust.
The source code for the console app is in `src/main.rs`, and it's pretty ugly at the moment, because there is a bunch of ceremony at the top that I need to find a way to move out.
Now build:
dotnet build
This will take quite a while and you'll see a bunch of output scrolling by. Subsequent builds of the project will be quicker because the Rust core library and the .NET Core bindings are already compiled.
If the build succeeds, try running the app:
dotnet run
And you should see it say Hello World.
The Sdk's compiler can only target .NET Core 3.1. No netstandard, no Xamarin, no Mono, no .NET Framework.
The Rust std library is absent. Only core and alloc.
The Sdk only works with Rust nightly, not stable.
There is no support yet for Rust code referencing other assemblies (besides .NET Core itself).
The bindings for .NET Core are missing quite a few methods.
If you want to talk to me about this project, feel free to drop me an email or a tweet.


Welcome
Testing Table of contents
This site demonstrates some of the features for generating sitemaps, module maps and in-module table of content. The site as such is rather messy, the idea is to deonstrate possibilities.
Script
script
Templates
auto-template
colelct-template


4:30PM
Cocktail
5:00PM
Welcome Remarks
Debutant's Grand Entrance
Intermission Number
(Fire Dance)
Games
Debutant's Grand Entrance
Intermission Number
(Fire Dance)
Games
7:00PM
Games
Intermission Number
(Singing Performance)
Intermission Number
(Singing Performance)
8:00PM
Parent's Speech
Toast of Honor
Solo Performance
Toast of Honor
Solo Performance
9:00PM
Dancing
An hour will be given for dancing and other stuff until the party ends at 10:00PM
An hour will be given for dancing and other stuff until the party ends at 10:00PM


Community Buffer
Buffing your character and your Pet!
Target:
%topbtn%
Buffs in Scheme
%addedBuffs%
Buffs You can Add:
Buffs Left: %bcount% Dances / Songs Left: %dscount%
%availableBuffs%


# Examples
## Supported file formats
### TOML
```cfg [default] DEBUG = true SERVER = “flaskdynaconf.com” PORT = 6666 MESSAGE = “Dynaconf works like a charm with Flask and TOML” TEST_RULE = ‘/flask_with_toml’
[development] DEBUG = true SERVER = “dev.flaskdynaconf.com”
[production] DEBUG = false SERVER = “prod.flaskdynaconf.com” ```
### YAML
DEBUG: true SERVER: flaskdynaconf.com PORT: 6666 MESSAGE: Dynaconf works like a charm with Flask and YAML TEST_RULE: /flask_with_yaml
- development:
DEBUG: true SERVER: dev.flaskdynaconf.com
- production:
DEBUG: false SERVER: prod.flaskdynaconf.com
### INI
```ini [default] DEBUG = true SERVER = “flaskdynaconf.com” PORT = 6666 MESSAGE = “Dynaconf works like a charm with Flask and INI” TEST_RULE = ‘/flask_with_ini’
[development] DEBUG = true SERVER = “dev.flaskdynaconf.com”
[production] DEBUG = false SERVER = “prod.flaskdynaconf.com” ```
### JSON
- “default”: {
“DEBUG”: true, “SERVER”: “flaskdynaconf.com”, “PORT”: 6666, “MESSAGE”: “Dynaconf works like a charm with Flask and JSON”, “TEST_RULE”: “/flask_with_json”
}, “development”: {
“DEBUG”: true, “SERVER”: “dev.flaskdynaconf.com”
}, “production”: {
“DEBUG”: false, “SERVER”: “prod.flaskdynaconf.com”
}
}¶
### PY
In python fils the environment is set by prefixing the file names
settings.py
`python
DEBUG = True
SERVER = "flaskdynaconf.com"
PORT = 6666
MESSAGE = "Dynaconf works like a charm with Flask and .py"
TEST_RULE = '/flask_with_ini'
`
development_settings.py
`python
DEBUG = True
SERVER = "dev.flaskdynaconf.com"
`
production_settings.py
`python
DEBUG = False
SERVER = "prod.flaskdynaconf.com"
`
### .env
.env allows only the global environment (overrides everything)
`bash
DEBUG=true
SERVER="flaskdynaconf.com"
PORT=6666
MESSAGE="Dynaconf works like a charm with Flask and .env"
TEST_RULE='/flask_with_ini'
`
## Using a default main config file plus variable settings file
On the .env
`bash
export SETTINGS_FILE_FOR_DYNACONF="default.toml"
`
The default file
`ini
[default]
variable1 = 'value1'
`
Now having specific settings per environment
Use cases:
plugin based apps
user specific settings
dev specific settings
On the user1 environment
`bash
export INCLUDES_FOR_DYNACONF='/path/to/user1_specific_settings.toml'
`
On the user2 environment
`bash
export INCLUDES_FOR_DYNACONF='/path/to/user2_specific_settings.toml'
`
It can be a glob
`bash
export INCLUDES_FOR_DYNACONF='/path/to/config/*.toml'
`
And also supports having a ; or , separated list of paths or globs.
## More examples
Take a look at [example/](https://github.com/rochacbruno/dynaconf/tree/master/example) for more examples.


You use
Old Header
gallons of water per week!
blank
blank
blank
Track your daily water usage!
Facts about water!
How you can save!


--- layout: post title: "New Method to Convert Unusable Mars Water into Beneficial Fuel" subtitle: "Scientists can turn the unpotable water on Mars into fuel and oxygen." date: 2020-12-02 21:00:13 +0530 background: '/img/posts/06.jpg' published: true categories: [ Technology, Science ] tags: [ mars, water, fuel, mars water, science, oxygen, electric, martian, space, extra-planetary ] ---
For years human wondered if there was water on Mars, we now know that there is, in fact, water on Mars, but it’s either frozen or salty beyond human use. Then we wondered if humans could ever live on Mars, and if yes, how will we survive on the barren planet?
Scientists at Washington University in St. Louise have come up with a solution: turn the unpotable water on Mars into fuel and oxygen.
They have devised an electric system to break apart the salty water to its constituent molecules; oxygen and hydrogen. They are fairly certain that their experiment on Earth can survive the Martian environment with temperatures as low as -33 degrees Fahrenheit.


Transport Container Rental System, Design Study
This WebApp is intended as a demonstration for BPW Logistics Centre in Wiehl.
The only goal is to demonstrate the capabilities of Web Applications as replacement for Excel Spreadsheets or Access Databases
which are used frequently by individuals, smaller or larger groups in order to simply the daily work.


This project was generated with Angular CLI version 6.2.3.
Below you will find some information on how to perform common tasks.
To install the Dependencies App you need the following tools instaled in your machine:
nodejse
LTS Version.angular-cli
LTS Version.http-server
LTS Version.To run the App you need:
npm install
commandFor this we used the following tools:
GENOME
as CSS StyleAngular
as a JS framework version 6NPM
as a task runner and package managerwebcomponent/
dist/
element-demo
doc/
e2e/
output
card1
card2
card3
card4
src/
app/
bz-card/
bz-card.component.css
bz-card.component.html
bz-card.component.spec.ts
bz-card.component.ts
app.component.css
app.component.html
app.component.spec.ts
app.component.ts
app.module.ts
assets/
css/
bizagi-genome-genome.min.css
fonts/
opensans/
opensans-italic.woff2
opensans-regular.woff2
opensans-semibold.woff2
open-sans-italic.ttf
open-sans-light.ttf
open-sans-semibold.ttf
environments
environment.prod.ts
environment.ts
packege.json
runApp.bat
build.bat
angular.json
This is the package json configuration 3/15/2018
{
"name": "elements-demo",
"version": "0.0.0",
"scripts": {
"ng": "ng",
"start": "ng serve",
"build": "ng build",
"test": "ng test",
"lint": "ng lint",
"e2e": "ng e2e"
},
"private": true,
"dependencies": {
"@angular/animations": "^6.1.0",
"@angular/common": "^6.1.0",
"@angular/compiler": "^6.1.0",
"@angular/core": "^6.1.0",
"@angular/elements": "^6.1.9",
"@angular/forms": "^6.1.0",
"@angular/platform-browser": "^6.1.0",
"@angular/platform-browser-dynamic": "^6.1.0",
"@angular/router": "^6.1.0",
"core-js": "^2.5.4",
"rxjs": "~6.2.0",
"zone.js": "~0.8.26",
"document-register-element": "^1.7.2"
},
"devDependencies": {
"@angular-devkit/build-angular": "~0.8.0",
"@angular/cli": "~6.2.3",
"@angular/compiler-cli": "^6.1.0",
"@angular/language-service": "^6.1.0",
"@types/jasmine": "~2.8.8",
"@types/jasminewd2": "~2.0.3",
"@types/node": "~8.9.4",
"codelyzer": "~4.3.0",
"concat": "^1.0.3",
"fs-extra": "^7.0.0",
"jasmine-core": "~2.99.1",
"jasmine-spec-reporter": "~4.2.1",
"karma": "~3.0.0",
"karma-chrome-launcher": "~2.2.0",
"karma-coverage-istanbul-reporter": "~2.0.1",
"karma-jasmine": "~1.1.2",
"karma-jasmine-html-reporter": "^0.2.2",
"protractor": "~5.4.0",
"ts-node": "~7.0.0",
"tslint": "~5.11.0",
"typescript": "~2.9.2"
}
}
Run
ng serve
for a dev server. Navigate to
http://localhost:4200/
. The app will automatically reload if you change any of the source files.
Run
ng generate component component-name
to generate a new component. You can also use
ng generate directive|pipe|service|class|guard|interface|enum|module
.
Run
ng build
to build the project. The build artifacts will be stored in the
dist/
directory. Use the
--prod
flag for a production build.
Run
ng test
to execute the unit tests via
Karma.
Run
ng e2e
to execute the end-to-end tests via
Protractor.
To get more help on the Angular CLI use
ng help
or go check out the
Angular CLI README.
The type 1 is a simple card which contains only two icons (left and right) and the title, as follow:
Implementation in HTML:
<bz-card
card-type="card1"
header-title="Title card 1"
header-icon="icon-map-bundle">
</bz-card>
Implementation in Javascript:
var card = document.querySelector('bz-card');
card.setAttribute('card-type', 'card1');
card.setAttribute('header-title', 'Title card 1');
card.setAttribute('header-icon', 'icon-map-bundle');
The card of type 2 is used to show information in status success (green) with the respective generation date. The card contains the respective icons, the title, the subtitle (status description) and the date, as follow:
Implementation in HTML:
<bz-card
card-type="card2"
header-title="Title card 2"
header-subtitle="Accepted in"
header-date="12/12/17"
header-icon="icon-gear">
</bz-card>
Implementation in Javascript:
var card = document.querySelector('bz-card');
card.setAttribute('card-type', 'card2');
card.setAttribute('header-title', 'Title card 2');
card.setAttribute('header-subtitle', 'Accepted in');
card.setAttribute('header-date', '12/12/17');
card.setAttribute('header-icon', 'icon-gear');
The card of type 3 is used to show information in status warning (orange). The card containd the respective icons, the title and the subtitle (status description), as follow:
Implementation in HTML:
<bz-card
card-type="card3"
header-title="Title card 3"
header-subtitle="Pending"
header-icon="icon-gear">
</bz-card>
Implementation in Javascript:
var card = document.querySelector('bz-card');
card.setAttribute('card-type', 'card3');
card.setAttribute('header-title', 'Title card 3');
card.setAttribute('header-subtitle', 'Pending');
card.setAttribute('header-icon', 'icon-gear');
The card of type 4 is more complete, this is used to show a content with his respective image. This card also contains two (2) buttons (cancel and accept) to execute some action.
Implementation in HTML:
<bz-card
card-type="card4"
header-title="Title card 4"
content-title="Bizagi"
content-subtitle="Bizagi LATAM is a company in Bogotá D.C."
header-icon="icon-gear"
first-button-text="Cancel"
second-button-text="Accept"
url-content-image="http://imparcialoaxaca.mx/wp-content/uploads/2018/02/masterhakcs_google_recibe_multa_union_europea.jpg">
</bz-card>
Implementation in Javascript:
var card = document.querySelector('bz-card');
card.setAttribute('card-type', 'card4');
card.setAttribute('header-title', 'Title card 4');
card.setAttribute('content-title', 'Bizagi');
card.setAttribute('content-subtitle', 'Bizagi LATAM is a company in Bogotá D.C.');
card.setAttribute('first-button-text', 'Cancel');
card.setAttribute('second-button-text', 'Accept');
card.setAttribute('url-content-image', 'http://imparcialoaxaca.mx/wp-content/uploads/2018/02/masterhakcs_google_recibe_multa_union_europea.jpg');
card.setAttribute('header-icon', 'icon-gear');


Xilinx Processor IP Library
Software Drivers
Main Page
|
Data Structures
|
File List
|
Data Fields
|
Globals
Xilinx Driver axidma v3_00_a Data Structures
Here are the data structures with brief descriptions:
XAxiDma
XAxiDma_BdRing
XAxiDma_Config
Copyright © 1995-2010 Xilinx, Inc. All rights reserved.


PICKING UP THE PIECES (director: Alfonso Arau; screenwriter: Bill Wilson; cinematographer: Vittorio Storaro; editor: Michael R. Miller; cast: Woody Allen (Tex), Alfonso Arau (Doctor Amado), David Schwimmer (Father Leo Jerome), Kiefer Sutherland (Sheriff Bobo), Sharon Stone (Candy), Cheech Marin (Mayor), Maria Grazia Cucinotta (Desi), Elliott Gould (Father LaCage), Fran Drescher (Sister Frida), Joseph Gordon-Levitt (Flaco), Lupe Ontiveros (Blind Woman), Eddie Griffin (singer), Lou Diamond Phillips (Officer Alfonso), Andy Dick (Father Bunuel); Runtime: 95; Artisan; 2000)
Reviewed by Dennis Schwartz
A good spoof on the church, but it is ruined by its vulgarity, excesses and absurdity. The ensemble cast is made up of many stars in cameo roles, but that does not guarantee that the acting is good. This black comedy premiered on Cinemax cable TV after the film's controversial religious themes prevented it from getting a distribution deal with a U.S. studio. Even if there was no controversy, this film is too weird and too poorly put together to deserve a theater release.
Tex (Woody Allen) is married to an ex-lap dancer, the beautiful Candy (Stone), but they have to split NYC because the mob's after her. They move to Texas, where he becomes a kosher butcher. He's so much in love with her that he would kill for love. When he finds that his wife is unfaithful, he puts a chain saw to her and dismembers her.  The New Mexico town of El Nino learns of this miracle and attributes the hand to being the Virgin's.  The town made of misfits and a corrupt mayor (Cheech) and a thriving prostitution trade, exploits this miracle and it overnight becomes known as the place with the hottest shrine in the world. The tourists flock here in droves and everyone in this once poor town is thriving.
The local padre, Father Leo Jerome (Schwimmer), is having a torrid affair with the hottest prostitute in town, Desi (Cucinotta). He has lost belief in his religion and doesn't know how to stop this miracle farce from continuing, especially when the church sends its miracle investigators (Gould, Drescher, and Dick) there and they confirm that it must be the hand of a saint. It is their belief, that they should not ruin a good thing that the church has going for it. Besides the priest, the mayor also knows the hand is that of a murdered woman; but, he doesn't want to spoil the town's financial boon.
It all comes to a head, when Sheriff Bobo (Kiefer Sutherland) from Texas comes to arrest Tex for killing his wife. He's upset that the butcher killed the best lay that he ever had. The film is a series of sight gags, nasty one-liners, pronouncements against the church, and awkward comic scenes. Most of it is inane, but one of its good moments is when it is said: a good reason for keeping the hand as a miracle, is that it gives people a religious belief in the hereafter and because of that they would be nicer to each other.
It is directed with reckless abandon by Alfonso Arau (Like Water for Chocolate/A Walk in the Clouds), with a disarming heavy-handedness and lack of subtlety (it ends by this in-your-face statement: God is sending a message, if you can't take a joke, go f*ck yourself). It's a strictly for the pits film, but it did get in a few good body punches on the church. The one I liked best is: If Jesus wasn't killed, you wouldn't have a religion. His death is, supposedly, a good argument to keep capital punishment.
REVIEWED ON 5/16/2001 GRADE: C-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Usually, a
FlashingNoteClass
object (and its children) should be contained within their own resource. The resource should remain unattached to the application tree. The routine
PutUpDialogViaUIThread()
will take care of copying and attaching the group of objects.
To set up your template, perform the following steps:
notDetachable
.
FlashingNoteClass
object.
duration
instance field to the number of ticks (1/60ths of a second) you wish to have the dialog box on-screen. By default, this is 180 ticks (3 seconds).
ATTR_FLASHING_NOTE_MINIMUM_DURATION
and set it to the number of ticks (1/60ths of a second) to remain on-screen. By default, this is 60 ticks (one second).
HINT_FLASHING_NOTE_DESTROY_SELF_AFTER_DISMISS
if you don't want to manually destroy it.
HINT_WINDOW_NOT_TITLE_BAR
and
HINT_DRAW_IN_BOX
.Code Display 2-10 FlashingNote Template
@start FlashingNoteTemplateResource, notDetachable;
@object FlashingNoteClass FlashingNoteTemplate = { GI_states = @default & ~GS_USABLE; GI_comp = @DialogBoxTemplate; FNI_duration = 600; /* 10 seconds */
HINT_DRAW_IN_BOX; HINT_FLASHING_NOTE_DESTROY_SELF_AFTER_DISMISS; HINT_WINDOW_NO_TITLE_BAR; ATTR_FLASHING_NOTE_MINIMUM_DURATION = 300; /* 5 seconds */ }
/* * We want a separator, so we will create a sub-group interaction as a complex * moniker. */
@chunk TCHAR DialogTemplateText[] = "Flashing Note";
@object ComplexMonikerClass DialogBoxTemplate = { ComplexMoniker = GenInteractionClass; CMI_topText = @DialogTemplateText; CMI_fontSize = FOAM_NORMAL_FONT_SIZE; GI_comp = @DialogText;
HINT_PLACE_MONIKER_ABOVE; HINT_COMPLEX_MONIKER_DRAW_SEPARATOR; HINT_FIXED_SIZE = { SST_PIXELS | FOAM_DEFAULT_BUBBLE_WIDTH, 0 }; }
@chunk TCHAR DialogTextText[] = "This particular flashing note will stay up a minimum \r of 5 seconds, and will dismiss itself if you press any key after that.\r\r\ It will dismiss itself automatically after 10 seconds.\r";
@object GenTextClass DialogText = { GTXI_text = @DialogTextText; GI_attrs = @default | GA_READ_ONLY; }
@end FlashingNoteTemplateResource;


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CLASSIFICATION IN MACHINE LEARNING
What is Classification?
Classification is a process of categorizing a given set of data into classes. It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories.
The classification predictive modeling is the task of approximating the mapping function from input variables to discrete output variables. The main goal is to identify which class/category the new data will fall into.
Classification Terminologies In Machine Learning
- Classifier – It is an algorithm that is used to map the input data to a specific category.
- Classification Model – The model predicts or draws a conclusion to the input data given for training, it will predict the class or category for the data.
- Feature – A feature is an individual measurable property of the phenomenon being observed.
- Binary Classification – It is a type of classification with two outcomes, for eg – either true or false.
- Multi-Class Classification – The classification with more than two classes, in multi-class classification each sample is assigned to one and only one label or target.
- Multi-label Classification – This is a type of classification where each sample is assigned to a set of labels or targets.
- Initialize – It is to assign the classifier to be used for the
- Train the Classifier – Each classifier in sci-kit learn uses the fit(X, y) method to fit the model for training the train X and train label y.
- Predict the Target – For an unlabeled observation X, the predict(X) method returns predicted label y.
- Evaluate – This basically means the evaluation of the model i.e classification report, accuracy score, etc.
Classification Algorithms
Logistic Regression
It is a classification algorithm in machine learning that uses one or more independent variables to determine an outcome. The outcome is measured with a dichotomous variable meaning it will have only two possible outcomes. The goal of logistic regression is to find a best-fitting relationship between the dependent variable and a set of independent variables. It is better than other binary classification algorithms like nearest neighbor since it quantitatively explains the factors leading to classification.
Use Cases
- Identifying risk factors for diseases
- Word classification
- Weather Prediction
- Voting Applications
Naive Bayes Classifier
It is a classification algorithm based on Bayes’s theorem which gives an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Even if the features depend on each other, all of these properties contribute to the probability independently. Naive Bayes model is easy to make and is particularly useful for comparatively large data sets. Even with a simplistic approach, Naive Bayes is known to outperform most of the classification methods in machine learning. Following is the Bayes theorem to implement the Naive Bayes Theorem.
Use Cases
- Disease Predictions
- Document Classification
- Spam Filters
- Sentiment Analysis
K-Nearest Neighbor
It is a lazy learning algorithm that stores all instances corresponding to training data in n-dimensional space. It is a lazy learning algorithm as it does not focus on constructing a general internal model, instead, it works on storing instances of training data.
Use Cases
- Industrial applications to look for similar tasks in comparison to others
- Handwriting detection applications
- Image recognition
- Video recognition
- Stock analysis
Decision Tree
The decision tree algorithm builds the classification model in the form of a tree structure. It utilizes the if-then rules which are equally exhaustive and mutually exclusive in classification. The process goes on with breaking down the data into smaller structures and eventually associating it with an incremental decision tree. The final structure looks like a tree with nodes and leaves. The rules are learned sequentially using the training data one at a time. Each time a rule is learned, the tuples covering the rules are removed. The process continues on the training set until the termination point is met.
Use Cases
- Data exploration
- Pattern Recognition
- Option pricing in finances
- Identifying disease and risk threats
Random Forest
Random decision trees or random forest are an ensemble learning method for classification, regression, etc. It operates by constructing a multitude of decision trees at training time and outputs the class that is the mode of the classes or classification or mean prediction(regression) of the individual trees.
Use Cases
- Industrial applications such as finding if a loan applicant is high-risk or low-risk
- For Predicting the failure of mechanical parts in automobile engines
- Predicting social media share scores
- Performance scores
Support Vector Machines
An SVM model is basically a representation of different classes in a hyperplane in multidimensional space. The hyperplane will be generated in an iterative manner by SVM so that the error can be minimized. The goal of SVM is to divide the datasets into classes to find a maximum marginal hyperplane (MMH).
Use Cases
- Face detection
- Text and hypertext categorization
- Classification of images
- Bioinformatics
- Handwriting recognition
Classifier Evaluation
Classification Report
A classification report will give the following results, it is a sample classification report of an SVM classifier using a cancer_data dataset.
- Accuracy
- Accuracy is a ratio of correctly predicted observation to the total observations
- True Positive: The number of correct predictions that the occurrence is positive.
- True Negative: Number of correct predictions that the occurrence is negative.
- F1- Score
- It is the weighted average of precision and recall
- Precision And Recall
- Precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of relevant instances that have been retrieved over the total number of instances. They are basically used as the measure of relevance.
You can learn more about this topic fromhere.


{% extends "base.html" %} {% block content %}
Welcome to the new smart home interface for TurboMansion


SCC
It calculates the strongly connected components of directed graphs.
Constructor
scc_graph graph(int n)
It creates a directed graph with $n$ vertices and $0$ edges.
Constraints
Complexity
add_edge
void graph.add_edge(int from, int to)
It adds a directed edge from the vertex from
to the vertex to
.
Constraints
- $0 \leq \mathrm{from} \lt n$
- $0 \leq \mathrm{to} \lt n$
Complexity
scc
vector<vector<int>> graph.scc()
It returns the list of the "list of the vertices" that satisfies the following.
- Each vertex is in exactly one "list of the vertices".
- Each "list of the vertices" corresponds to the vertex set of a strongly connected component. The order of the vertices in the list is undefined.
- The list of "list of the vertices" are sorted in topological order, i.e., for two vertices $u, v$ in different strongly connected components, if there is a directed path from $u$ to $v$, the list contains $u$ appears earlier than the list contains $v$.
Complexity
- $O(n + m)$, where $m$ is the number of added edges.
Examples
#include <atcoder/scc>
#include <cstdio>
using namespace std;
using namespace atcoder;
int main() {
int n, m;
scanf("%d %d", &n, &m);
scc_graph g(n);
for (int i = 0; i < m; i++) {
int u, v;
scanf("%d %d", &u, &v);
g.add_edge(u, v);
}
auto scc = g.scc();
printf("%d\n", int(scc.size()));
for (auto v : scc) {
printf("%d", int(v.size()));
for (int x : v) {
printf(" %d", x);
}
printf("\n");
}
return 0;
}


This is the (unsecured) ‘www’ section of the Koa Sample App github.com/chrisveness/koa-sample-web-app-api-mysql, showing the github readme.
{{{content}}}
This is the (unsecured) ‘www’ section of the Koa Sample App github.com/chrisveness/koa-sample-web-app-api-mysql, showing the github readme.


City hosting job fair
by Colin DacreAs job losses pile up in the local modular home manufacturing industry, the City of Penticton and Work BC are hosting a job fair.
The public is invited to the Work BC offices at 174-1848 Main St. on Nov. 26, 10 a.m. to 1 p.m. Several local employers will be in attendance looking to fill “outstanding” job openings.
Metric Modular laid off 20 employees earlier this month, with workers telling Castanet the future of the plant is in question. Moduline also laid off about half its workforce in March. Both companies blamed a drop in new orders.
“The regional unemployment rate remains quite low at 4.5 per cent and local employers I speak with continue to indicate the need for new employees. This event is an opportunity to provide those who are recently out of work employment opportunities and perhaps a new career choice,” said city economic development specialist Andrew Kemp
“Providing opportunity and proactively engaging with our local employers is a focus of our local Economic Development Department”, said Mayor Vassilaki. “This is a great event for those looking to get back to work while continuing to live in our beautiful City.”
Metric Modular president Stephen Branch thanked the city for hosting the job fair, “we want to support our employees in finding new opportunities in the community."


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--- layout: post title: Notes from last evenings NServiceBus presentation date: 2010-05-12 09:47:00.000000000 +02:00 categories: - NServiceBus - Speaking tags: [] status: publish type: post published: true meta: blogger_blog: andreasohlund.blogspot.com blogger_permalink: "/2010/05/notes-from-last-evenings-nservicebus.html" _edit_last: '1' author: login: andreas.ohlund email<EMAIL_ADDRESS>display_name: Andreas Öhlund first_name: Andreas last_name: "Öhlund" ---
I spent last evening giving a presentation/workshop on NServiceBus for the alt.net guys in Stockholm. I had a great time and was really impressed with the skill level of the audience. There was a lot of great questions and discussions that made the evening even more valuable for me. Hopefully everyone left with a better understanding of what NServiceBus is and what problems it's meant to solve.
Code
I did some live coding using a to small and very unstable table so my ReSharper-fu wasn't to impressive. Anyways here is the resulting code:
http://github.com/andreasohlund/Blog/tree/master/AltNetSample/
Slides
http://www.slideshare.net/andreasohlund/nservicebus-altnet-20100511
NServiceBus Links
http://www.nservicebus.com
http://nservicebus.grouply.com/
Continuous Integration
http://teamcity.codebetter.com
The "to be resurrected" contrib project
http://code.google.com/p/nservicebus-contrib/
The new and shiny but, empty contrib project
http://github.com/NServiceBus/NServiceBus-Contrib
I promised to get back with some links to all the bloggers that I name dropped yesterday :)
Mark Nijhof - The guy with a nice CQRS "ala Greg Young" implementation
http://blog.fohjin.com/
Jonathan Oliver
http://jonathan-oliver.blogspot.com/
Bill Poole - Event Driven SOA
http://bill-poole.blogspot.com/
It's a great read and I suggest that you start with the first post and work your way up!
Thanks to everyone that showed up and especially to Emil Cardell and Valtec for arranging the event!


Ohio State Announces Senior Captain Is Redshirting, Will Return In 2020
by Andrew McCartyJust before Ohio State and Penn State took the field for their showdown on Saturday afternoon, the Buckeyes announced a major personnel decision.
Senior captain Jonathon Cooper will redshirt this season and be back for the Buckeyes in 2020.
Cooper has been battling a high ankle sprain for the better part of the season. He’s managed to see the field in three games so far this season. He’s eligible to play in one more game in 2019, which could be in today’s game against Penn State.
Here’s the news from Ralph Russo.
Ohio State welcomes Chase Young back into the fold for today’s game, which gives the Buckeyes a huge boost on their defensive line. Young missed the last two games while serving a suspension, but he’s hungry and motivated to go this afternoon.
As for Cooper, he’ll likely either play this afternoon or next weekend against Michigan. It’s possible Ohio State holds him out for a more meaningful game later in the season as well. With the Buckeyes in position for a College Football Playoff berth, Ryan Day could decide to hold Cooper out for a while.
Ohio State and Penn State kick off at 12:00 p.m. ET. The game will be broadcast on FOX.


--- name: first-commit layout: post title: First Commit time: 2011-12-02 16:41:00.001000 +05:30 tags: random ---
I am not much of a blogger but i started this weblog so that i keep writing and also to improve coding skills. This site is based on Jekyll, a ruby based static site generator.
Since this is my first post i would write a little about myself. I am not a hardcore hacker. A little geeky guy with a good sense of humour. I love working in linux. Currently i am using debian. I have an experince of Ubuntu 10.10, Ubuntu 11.04. In a hurry sometimes i use windows but linux brings a big smile on my face sometimes, when i discover some new functionality. I enjoy sleeping. I enjoy reading fiction. At times i spend long time googling random things and reading new things.
I hope this blog proves useful and fullfills the purpose it was meant to.


Klopp’s influence & Trent under the microscope – Monday’s Liverpool FC News
by This Is AnfieldWe’re creeping closer to a return to domestic action, but for now international games take centre stage as well as the influence of Jurgen Klopp.
Klopp culture the key for Reds success
Jurgen Klopp has been a massive factor in Liverpool’s upward turn in fortunes; of that there can be no doubt.
Even so, the level of impact he has had at the club is enormously impressive, with his brand of positivity and determination to win having seeped into every player and supporter.
And his approach to management has been hailed by two absolutely critical members of the first-team squad: Virgil van Dijk and Roberto Firmino.
Praise doesn’t come much more relevant than from those two – and the latter also speaks about his own positional changes.
International wrap
We are still in the international break, with several players still to feature.
In case you missed anything over the weekend, Alex Oxlade-Chamberlain continued his massive upturn in fortunes, while Van Dijk departed from the Netherlands squad early.
On Monday, there’s not much from a Liverpool perspective to worry about, but our Brazilian contingent are in action on Tuesday.
Youth progress a point of discussion
We’re always looking to the future at Liverpool, whether that’s with refining Academy processes, investing in personnel or making sure the pathway from youth to first-team level is as open as possible.
- New coach Vitor Matos explains his role and why Trent is an example of the perfect project
- But there’s also an element of concern over how much football Alexander-Arnold is playing at present
- U23s midfielder Elijah Dixon-Bonner has signed a new contract with the Reds
Quickfire LFC news
- Liverpool FC Women suffered derby defeat, but playing at Anfield was still a big step forward
- Gini Wijnaldum says the squad want to make history by winning the FIFA Club World Cup
- Chelsea will battle Liverpool and Napoli for Genk midfielder Sander Berge
- Liverpool-linked Dennis Zakaria will definitely not be concluding a transfer in January, says his agent
Around the Prem
- Man United have seen an increase in net debt of around 55% this season (Independent)
- Barcelona want Chelsea‘s Willian on a free transfer (Mail Online)
- Leicester duo Johnny Evans and Caglar Soyuncu are on Man City‘s January wishlist (Sky Sports)
Stupid decisions of the day
Former Everton director of football Steve Walsh says he had Andy Robertson and Harry Maguire lined up for the Blues for £20 million – but the club said no.
Tweet of the day
What we’re reading
The Telegraph‘s Jason Burt asks if Harry Kane should be considering a Tottenham departure.
Worth watching tonight
Argentina vs. Uruguay is a friendly which should be anything but, kicking off at 7:15pm. On the qualifier front, Spain vs. Romania or Republic of Ireland vs. Denmark are the pick of the Euro 2020 action. Both start at 7:45pm.


"Desired product app _UI"
"How many times have you found yourself lost in the long aisles of big bazaar trying to find that one shampoo? Well, this app saves you that time! It will guide you from where you're standing to the place where you can find your desired product. "
"https://www.figma.com/file/3qCp54rOadvWHWcnMPTFHd/Trova?node-id=0%3A1"


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Welcome to Uki
Hai Uki!
This Coding Accelerator program is an intensive training on full-stack development with server-side Node.js and modern, client-side, front-end libraries and frameworks.Throughout this intensive course the student will study professional software development techniques and practices as he/she advances his/her skills on JavaScript.


Angela’s Reel January 2015 from Angela Lee on Vimeo.
Hi there! I’m Angela (Anais). Welcome to my page! You can find my reel to the left, and additional work by navigating through the page using the links above. Below are some of my best and favorite works. Feel free to explore and contact me for more information! I'd love to talk.
HEAD <<<<<<< HEAD


|
reVision
5.0.0
Video game engine project.
|
A post-processing technique for generating bloom from a lighting buffer. More...
#include <Bloom.h>
Public Member Functions | |
| ~Bloom () | |
| Destroy this effect. More... | |
| Bloom (Engine &engine) | |
| Construct a bloom effect. More... | |
| void | clearCache (const float &deltaTime) noexcept final |
| Prepare this technique for the next frame, swapping any of its buffers. More... | |
| void | renderTechnique (const float &deltaTime, Viewport &viewport, const std::vector< std::pair< int, int >> &perspectives) final |
| Apply this lighting technique. More... | |
| Public Member Functions inherited from Graphics_Technique | |
| virtual | ~Graphics_Technique ()=default |
| Virtual Destructor. More... | |
| Graphics_Technique () noexcept=default | |
| Default constructor. More... | |
| Graphics_Technique (const Technique_Category &category) noexcept | |
| Constructor. More... | |
| Graphics_Technique (Graphics_Technique &&) noexcept=default | |
| Move constructor. More... | |
| Graphics_Technique (const Graphics_Technique &) noexcept=default | |
| Copy constructor. More... | |
| Technique_Category | getCategory () const noexcept |
| Retrieve the category this technique belongs to. More... | |
| void | setEnabled (const bool &state) noexcept |
| Turn this technique on or off. More... | |
| Graphics_Technique & | operator= (Graphics_Technique &&) noexcept=default |
| Move assignment. More... | |
| Graphics_Technique & | operator= (const Graphics_Technique &) noexcept=default |
| Copy assignment. More... | |
| virtual void | updateCache (const float &deltaTime, ecsWorld &world) |
| Update any data needed before rendering this frame. More... | |
| virtual void | updatePass (const float &deltaTime) |
| Perform any pre-requisite rendering passes. More... | |
Additional Inherited Members | |
| Public Types inherited from Graphics_Technique | |
| enum | Technique_Category : unsigned int { GEOMETRY = 0b0000'0001, PRIMARY_LIGHTING = 0b0000'0010, SECONDARY_LIGHTING = 0b0000'0100, POST_PROCESSING = 0b0000'1000, ALL = 0b1111'1111 } |
| The category of the technique. More... | |
| Protected Attributes inherited from Graphics_Technique | |
| bool | m_enabled = true |
| Technique_Category | m_category |
A post-processing technique for generating bloom from a lighting buffer.
| Bloom::~Bloom | ( | ) |
Destroy this effect.
|
explicit |
Construct a bloom effect.
| engine | reference to the engine to use. |
|
finalvirtualnoexcept |
Prepare this technique for the next frame, swapping any of its buffers.
| deltaTime | the amount of time passed since last frame. |
Reimplemented from Graphics_Technique.
|
finalvirtual |
Apply this lighting technique.
| deltaTime | the amount of time passed since last frame. |
| viewport | the viewport to render from. |
| perspectives | the viewing perspectives to render from. |
Reimplemented from Graphics_Technique.


ReMOTEable stands for
We want to implement an agent-system on the telos motes. On each mote, there will run an agent-interpreter (maybe a virtual machine). The whole system should work with (high) dynamic "mote networks". In order to achieve this, we need and want to implement the following "features":
The agents are using high level commands with the purpose to acquire data and provide them to the "master" (any computer connected to a mote).
Possible Applications:


poses
Table of Contents
Purpose
SwiftUI pose image catalog app engine for Poses Professional 3.0.
Requirements
- Xcode 11.3 or later
- iOS 13.0 or later
Tools:
- Bundler for Ruby dependency management
- Codebeat for automated code review
- Codecov for test coverage statistics
- Fastlane for release management
- Github Actions for CI testing
- Gitter for chat
- Jazzy for generating documentation
- Mint for tool dependency management
- SwiftLint for opinionated coding style enforcement
- SwiftMockGeneratorForXcode for generating mocks
- SwiftyBeaver for logging
Libraries:
- R.swift for typed resource identifiers
Usage
- Install Ruby + Bundler + Mint as system tools, Homebrew is a good start
bundle install
to install the versioned tools inGemfile
mint bootstrap
to install the versioned tools inMintfile
- Build the app with the ‘Poses’ target.
Documentation
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License
The MIT License. See the LICENSE.md file for details.
Copyright ©2008-2020 Trollwerks Inc.


This is a module that supports the Microsoft Simple List Extensions.
This is a feed-level module. However, for the purposes of sorting and goupding
it provides the SleEntry module, which is dynamically generated with values for
sorting and grouping.
Sample Usage:
SimpleListExtension sle = (SimpleListExtension) feed.getModule( SimpleListExtension.URI );
System.out.println( sle );
Group[] groups = sle.getGroupFields();
System.out.println( groups[0].getLabel() );
//You can use the SleUtility class to do sorting and grouping:
List sortedEntries = SleUtility.sort( feed.getEntries(), sle.getSortFields()[1], true );
SyndEntry entry = (SyndEntry) sortedEntries.get( 0 );
//You can also Group or Sort and Group
List sortedAndGroupedEntries = SleUtility.sortAndGroup( feed.getEntries, sle.getGroupFields(), sle.getSortFields()[0], false );
// If you change, for instance, module values on a feed and want to reinitialize it for
// grouping and sorting...
SleUtility.initializeForSorting( feed );
// Be aware, this is a VERY heavy operation and should not be used frequently.