Attribution-ShareAlike 4.0 International
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Considerations for licensors: Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. More considerations for licensors.
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Creative Commons Attribution-ShareAlike 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-ShareAlike 4.0 International Public License (“Public License”). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.
Section 1 – Definitions.
a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
b. Adapter’s License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License.
c. BY-SA Compatible License means a license listed at creativecommons.org/compatiblelicenses, approved by Creative Commons as essentially the equivalent of this Public License.
d. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
e. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
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g. License Elements means the license attributes listed in the name of a Creative Commons Public License. The License Elements of this Public License are Attribution and ShareAlike.
h. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
i. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
j. Licensor means the individual(s) or entity(ies) granting rights under this Public License.
k. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
l. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
m. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
a. License grant.
1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
A. reproduce and Share the Licensed Material, in whole or in part; and
B. produce, reproduce, and Share Adapted Material.
2. __Exceptions and Limitations.__ For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
3. __Term.__ The term of this Public License is specified in Section 6(a).
4. __Media and formats; technical modifications allowed.__ The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
5. __Downstream recipients.__
A. __Offer from the Licensor – Licensed Material.__ Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
B. __Additional offer from the Licensor – Adapted Material. Every recipient of Adapted Material from You automatically receives an offer from the Licensor to exercise the Licensed Rights in the Adapted Material under the conditions of the Adapter’s License You apply.
C. __No downstream restrictions.__ You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
6. __No endorsement.__ Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
b. Other rights.
1. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
2. Patent and trademark rights are not licensed under this Public License.
3. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
a. Attribution.
1. If You Share the Licensed Material (including in modified form), You must:
A. retain the following if it is supplied by the Licensor with the Licensed Material:
i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
ii. a copyright notice;
iii. a notice that refers to this Public License;
iv. a notice that refers to the disclaimer of warranties;
v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
2. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
3. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
b. ShareAlike.
In addition to the conditions in Section 3(a), if You Share Adapted Material You produce, the following conditions also apply.
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The Adapter’s License You apply must be a Creative Commons license with the same License Elements, this version or later, or a BY-SA Compatible License.
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You must include the text of, or the URI or hyperlink to, the Adapter’s License You apply. You may satisfy this condition in any reasonable manner based on the medium, means, and context in which You Share Adapted Material.
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You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, Adapted Material that restrict exercise of the rights granted under the Adapter’s License You apply.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database;
b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material, including for purposes of Section 3(b); and
c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
1. automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
2. upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.t stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
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Creative Commons may be contacted at creativecommons.org


---
layout: component-layout
title: Steps for creating Ticket Types
link-title: Ticket Types
category: Productivity
url: /uvdemo/productivity/ticket-type.html
tags: [Ticket Type, Ticket Types, Ticket, Create Ticket Types, Creating Ticket Type, Creating Ticket Types, New Ticket Type, New Ticket Types, Categorize Ticket]
---
UVdesk - Steps for creating Ticket Types
What are Ticket Types?
The ticket types help to distinguish the ticket according to different kinds of problems the support group deals with and for providing the true nature to the submitted requests.
Need for categorizing a ticket:
- It helps in the management of problems process.
- Improve ticket trend reporting.
- Helps in enabling proper escalation routing of tickets.
- Helps in the reduction of support cost.
Quicks steps for creating Ticket Types:
- Login to your UVdesk Support Panel.
- On the Dashboard, you will see "PRODUCTIVITY" menu and under it, you will find "Ticket Types".
- Click on the "Ticket Types" Icon and then you will be redirected to a new page.
- In the new page, you will see a list of existing Ticket Types and "New Type" button.
- Hit the "New Type" button and then you will see a page for creating a new Ticket Type.
- Enter Name, Description and Click on "Save Changes" to save information and create a Ticket Type.
- You can also sort the Ticket Types by Id, Name, and Status.


--- title: "Adding data to a platform" permalink: "/topic/addingdata/" layout: "topic" topic-tag: "addingdata" show-resources: true ---
A critical aspect of each open-source SDG reporting platform is the mechanism used to enter and maintain the statistics and metadata. This will be important to consider early in the project, in order to enable data providers to populate the platform effectively. Important considerations include the expected data format, the workflow for data updates, and the available options for secure user-management.


{% extends '500.html' %} {% block message %}
A database programming error was detected while processing this request. Common causes include the following:
Database migrations missing - When upgrading to a new NetBox release,
the upgrade script must be run to apply any new database migrations. You can run migrations manually by executing
python3 manage.py migrate
from the command line.
Unsupported PostgreSQL version - Ensure that PostgreSQL version 10
or later is in use. You can check this by connecting to the database using NetBox's credentials and issuing a query
for SELECT VERSION()
.


Jeff Bezos' led Amazon in an innovative bussiness model that relied growth through debt financing instead of traditional revenue-led growth models. This change represented a significant advantage for digital companies as they had far more accessibility compared to traditional brick and mortar based store which were traditionally limited to serving only people in close geographical proximity.
Chad Hurley created youtube, a video streaming platform which revolutionized digital content by allowing users to upload and edit digital videos from home, thereby changing everyday people from simply being consumers of content, transforming them into producers of content themselves. A process which completely flipped the traditional entertainment production model upside down. This inversion was made possible primarily by breakthroughs in end-to-end encryption which allowed users to safely purchase and store bank information online without fear of identity theft and helped usher in the new era of the digital economy.
Sean Fanningis the creator of Napster, the music file sharing service which became popular in the early 2000’s and resulted in high profile litigation between record companies and musicians and their fan bases who enjoyed having access to music catalogs on a digital platform for free. The core issue presented in the napster case was intellectual property rights and a musicians right to license out their material. As a platform, Napster disrupted this model by directly connecting music to listeners, completely removing the recording label from serving as the gatekeepers of musicians intellectual property.


Welcome to Crazy Ana
format_quoteShare about Crazy Programming and Crazy Idea.
Principles
Programming
format_quote Programming is an academic discipline. Here, I will share some of what I've learned about programming in an objective way. May be valid, or it may be ineffective. Programming is developing rapidly, that we need a clear understanding of it to make programming easy and more efficient.
Hexo & Done
format_quote The Crazy Ana is based on Hexo and Done theme. The Hexo is a fast, simple & powerful blog framework. The Done theme of Hexo is made by me, used Google Material Design Lite.
Terms
format_quote Crazy Ana is under the Attribution-NonCommercial-ShareAlike 4.0 International term. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. But you may not use the material for commercial purposes.
Who am I
format_quote Hello! I am Wang Min Xiang, a Programming Architect based in Shanghai, CN. I enjoy working on usable, practical and amazing programming project.
format_quoteHello! I am Wang Min Xiang, a Programming Architect based in Shanghai, CN. I enjoy working on usable, practical and amazing programming project.
Help Crazy Ana Grow
format_quoteI hope you have enjoyed using Crazy Ana and if you feel like it has helped you out and want to support the project you can help me by donating or start it on Github. Any amount would help support and continue development on this project and is greatly appreciated.
Join the Discussion
format_quote It has a Github Issue Room set up where you can talk directly with us. Come in and discuss new features, future goals, general problems or questions, or anything else you can think of.


 Chat supposed to be around 20k$ right now (re @N21_0: yup, i buy all my 100K at 9$ and it s my first wrong step)
[telegram] <N21_0> thank you too bro I share the suffering with you every day
Sometimes we get angry and sometimes laugh lol (re @Matterbridge_kmdbot: [discord] <ComputerGenie> > i buy all my 100K at 9$
And as a miner, I thank you for doing so.)
[telegram] <NoManSky> it was just a bad timing, we knew it's coming since december it's just the freakin media took over and the shills on twitter that no one even realised what is to come (re @N21_0: i hope and i pray for us everyday)
[telegram] <N21_0> no i do some trades some times and i buy more every dip (re @Sagarmatha: So you bought around 11.1k kmd? Chat supposed to be around 20k$ right now )
[telegram] <N21_0> True (re @NoManSky: it was just a bad timing, we knew it's coming since december it's just the freakin media took over and the shills on twitter that no one even realised what is to come)
[telegram] <Sagarmatha> anyway congratuations n 21. Somebody who invest 100k in experimental tech like cryptos are has to be very rich, at least 50M$
[telegram] <NoManSky> i know a lot of people that shorted the top of btc and they still short every bounce, no the whole thing is if you're still in it do not sell because this market has still alot of space to fall. we need to test 4-5k for btc first and only then we will know what the outcome will look like
[telegram] <N21_0> thank you bro i hope we can all win and reinvest more and more (re @Sagarmatha: anyway congratuations n 21. Somebody who invest 100k in experimental tech like cryptos are has to be very rich, at least 50M$)
[telegram] <N21_0> i hope one day Komodo will be not affected with Bitcoin movments (re @NoManSky: i know a lot of people that shorted the top of btc and they still short every bounce, no the whole thing is if you're still in it do not sell because this market has still alot of space to fall. we need to test 4-5k for btc first and only then we will know what the outcome will look like)
[telegram] <NoManSky> been around for a few good years, I've seen the same happening in 2014 and trust me, capitulation and fear is not here yet, only when we hit that level of depression is where the bottom is (re @N21_0: thank you bro i hope we can all win and reinvest more and more)
[telegram] <NoManSky> dunno about that, btc will always be the kind at least for the next 5-10 years to come, you just need to trade alts based on btc charts (re @N21_0: i hope one day Komodo will be not affected with Bitcoin movments)
[telegram] <Matt> So... we still need to crash to deeper levels and not recovering for many years to get a bottom here?
[telegram] <NoManSky> but kmd for me is the only project besides btc that I believe in
[telegram] <N21_0> same for me i meta believe in Komodo (re @NoManSky: but kmd for me is the only project besides btc that I believe in)
[telegram] <N21_0> i hope not (re @Matt: So... we still need to crash to deeper levels and not recovering for many years to get a bottom here?)
[telegram] <NoManSky> it might be a slow crash or a fast one, i hope it's a fast one since the faster we go down , the faster we get back up. but in my opinion, the next 4-6 months at least wont be beautiful (re @Matt: So... we still need to crash to deeper levels and not recovering for many years to get a bottom here?)
[telegram] <barbierir> I've been buying since it was Btcd. the equivalent of 2 cents, then all the way to last week. I will buy more when this market finds a bottom
[telegram] <barbierir> I don't think the bear will last 2 years like the previous time, much less
[telegram] <N21_0> thank you so much bro You look very wise (re @NoManSky: it might be a slow crash or a fast one, i hope it's a fast one since the faster we go down , the faster we get back up. but in my opinion, the next 4-6 months at least wont be beautiful)
[telegram] <barbierir> one of the few project I don't worry about, if crypto is not a fad thenKomodo is here to stay and find its place
[telegram] <Supppppppppp> I feel physically sick looking at my Blockfolio to the point I need to delete it and probably this telegram for awhile
[telegram] <Matt> I need to sell my mininghardware then... almost breakeven operation to elec costs
[telegram] <N21_0> how about rebranding of komodo ? (re @barbierir: I don't think the bear will last 2 years like the previous time, much less)
[telegram] <NoManSky> crypto right now is f@cked, there is no volume, too many people shilling shitcoins, there is no new money incoming, this is just an opportunity of getting rid of all the bad projects and have a fresh start
[telegram] <barbierir> this is for marketing team, I don't care for the name
[telegram] <N21_0> you want upgrade your mining rigs ? (re @Matt: I need to sell my mininghardware then... almost breakeven operation to elec costs)
[telegram] <Regnar> eality, but the Komodo team is actively building viable actual projects, and the marketing team is in fact ramping up behind the scenes. The community front (i'm not a team member, just active community member) we still need more help in, and would love more members to step up and do things like talk about Komodo on Social media, share and like pro KMD posts. tag their favorite influencers etc.
Hope this provides some more insight for you. (re @N21_0: yup it's common fact but i still angry from aminsoft that group or i dont know
they make market run to -53% till now )
[telegram] <N21_0> You tell the truth and i see you right (re @NoManSky: crypto right now is f@cked, there is no volume, too many people shilling shitcoins, there is no new money incoming, this is just an opportunity of getting rid of all the bad projects and have a fresh start)
[telegram] <barbierir> funny that one year ago we would have prayed for such prices :)
[telegram] <NoManSky> yeah, i knew what i'm getting myself into when i started and let's be honest crypto will be like any other markets, alot of people in but not too many making it alive (re @N21_0: You tell the truth and i see you right)
[telegram] <Rabatown> Hi Guys
[telegram] <Rabatown> If we keep Komodo on exchanges like Binance, can we still benefit from the annual 5% for instance?
[telegram] <NoManSky> nope (re @Rabatown: If we keep Komodo on exchanges like Binance, can we still benefit from the annual 5% for instance?)


See: Description
| Class | Description |
|---|---|
| AbstractWSAddressingObject |
AbstractWSAddressingObject.
|
| AbstractWSAddressingObjectBuilder<WSAddressingObjectType extends WSAddressingObject> |
AbstractWSAddressObjectBuilder.
|
| AbstractWSAddressingObjectMarshaller |
An abstract marshaller implementation for XMLObjects from
org.opensaml.soap.wsaddressing.impl . |
| AbstractWSAddressingObjectUnmarshaller |
An abstract unmarshaller implementation for XMLObjects from
org.opensaml.soap.wsaddressing.impl . |
| ActionBuilder |
ActionBuilder.
|
| ActionImpl |
ActionImpl is the concrete implementation of
Action . |
| ActionMarshaller |
Marshaller for the <wsa:Action> element.
|
| ActionUnmarshaller |
Unmarshaller for the <wsa:Action> element.
|
| AddressBuilder |
AddressBuilder.
|
| AddressImpl |
AddressImpl is the concrete implementation of
Address . |
| AddressMarshaller |
Marshaller for the <wsa:Address> element.
|
| AddressUnmarshaller |
Unmarshaller for the <wsa:Address> element.
|
| AttributedQNameImpl |
Implementation of
AttributedQName . |
| AttributedQNameMarshaller |
Marshaller for instances of
AttributedQName . |
| AttributedQNameUnmarshaller |
Unmarshaller for instances of
AttributedQName . |
| AttributedUnsignedLongImpl |
Implementation of
AttributedUnsignedLong . |
| AttributedUnsignedLongMarshaller |
Marshaller for instances of
AttributedUnsignedLong . |
| AttributedUnsignedLongUnmarshaller |
Unmarshaller for instances of
AttributedUnsignedLong . |
| AttributedURIImpl |
AbstractAttributedURIType is the abstract implementation of
AttributedURI . |
| AttributedURIMarshaller |
Abstract marshaller for the element of type
AttributedURI . |
| AttributedURIUnmarshaller |
Abstract unmarshaller for the element of type
AttributedURI . |
| EndpointReferenceBuilder |
EndpointReferenceBuilder.
|
| EndpointReferenceImpl |
Concrete implementation of element
EndpointReference . |
| EndpointReferenceMarshaller |
Marshaller for the <wsa:EndpointReference> element.
|
| EndpointReferenceTypeImpl |
Abstract implementation of the element of type
EndpointReferenceType . |
| EndpointReferenceTypeMarshaller |
Abstract marshaller for the element of type
EndpointReferenceType . |
| EndpointReferenceTypeUnmarshaller |
Abstract unmarshaller for the element of type
EndpointReferenceType . |
| EndpointReferenceUnmarshaller |
Unmarshaller for the <wsa:EndpointReference> element.
|
| FaultToBuilder |
Builder for the
FaultTo element. |
| FaultToImpl |
Concrete implementation of element
FaultTo . |
| FaultToMarshaller |
Marshaller for the <wsa:FaultTo> element.
|
| FaultToUnmarshaller |
Unmarshaller for the <wsa:FaultTo> element.
|
| FromBuilder |
Builder for the
From element. |
| FromImpl |
Concrete implementation of element
From . |
| FromMarshaller |
Marshaller for the <wsa:From> element.
|
| FromUnmarshaller |
Unmarshaller for the <wsa:From> element.
|
| MessageIDBuilder |
MessageIDBuilder.
|
| MessageIDImpl |
Concrete implementation for the
MessageID element. |
| MessageIDMarshaller |
Marshaller for the <wsa:MessageID> element.
|
| MessageIDUnmarshaller |
Unmarshaller for the <wsa:MessageID> element.
|
| MetadataBuilder |
MetadataBuilder.
|
| MetadataImpl |
MetadataImpl.
|
| MetadataMarshaller |
MetadataMarshaller.
|
| MetadataUnmarshaller |
MetadataUnmarshaller.
|
| ProblemActionBuilder |
ProblemActionBuilder.
|
| ProblemActionImpl |
Implementation of
ProblemAction . |
| ProblemActionMarshaller |
Marshaller for instances of
ProblemAction . |
| ProblemActionUnmarshaller |
Unmarshaller for instances of
ProblemActionUnmarshaller . |
| ProblemHeaderQNameBuilder |
ProblemHeaderQNameBuilder.
|
| ProblemHeaderQNameImpl |
Concrete implementation for the
ProblemHeaderQName element. |
| ProblemHeaderQNameMarshaller |
Marshaller for the <wsa:ProblemHeaderQName> element.
|
| ProblemHeaderQNameUnmarshaller |
Unmarshaller for the <wsa:ProblemHeaderQName> element.
|
| ProblemIRIBuilder |
ProblemIRIBuilder.
|
| ProblemIRIImpl |
ProblemIRIImpl is the concrete implementation of
ProblemIRI . |
| ProblemIRIMarshaller |
Marshaller for the <wsa:ProblemIRI> element.
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| ProblemIRIUnmarshaller |
Unmarshaller for the <wsa:ProblemIRI> element.
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| ReferenceParametersBuilder |
ReferenceParametersBuilder.
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| ReferenceParametersImpl |
ReferenceParametersImpl.
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| ReferenceParametersMarshaller |
ReferenceParametersMarshaller.
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| ReferenceParametersUnmarshaller |
ReferenceParametersUnmarshaller.
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| RelatesToBuilder |
RelatesToBuilder.
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| RelatesToImpl |
Implementation of
RelatesTo . |
| RelatesToMarshaller |
Marshaller for instances of
RelatesTo . |
| RelatesToUnmarshaller |
Unmarshaller for instances of
RelatesTo . |
| ReplyToBuilder |
Builder for the
ReplyTo element. |
| ReplyToImpl |
Concrete implementation of element
ReplyTo . |
| ReplyToMarshaller |
Marshaller for the <wsa:ReplyTo> element.
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| ReplyToUnmarshaller |
Unmarshaller for the <wsa:ReplyTo> element.
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| RetryAfterBuilder |
RetryAfterBuilder.
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| RetryAfterImpl |
Implementation of
RetryAfter . |
| RetryAfterMarshaller |
Marshaller for instances of <wsa:RetryAfter>.
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| RetryAfterUnmarshaller |
Unmarshaller for instances of <wsa:RetryAfter>.
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SoapActionBuilder.
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| SoapActionImpl |
Implementation of
SoapAction . |
| SoapActionMarshaller |
Marshaller for instances of <wsa:SoapAction>.
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| SoapActionUnmarshaller |
Unmarshaller for instances of <SoapAction>.
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| ToBuilder |
Builder for the
To element. |
| ToImpl |
Concrete implementation for the
To element. |
| ToMarshaller |
Marshaller for the <wsa:To> element.
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| ToUnmarshaller |
Unmarshaller for the <wsa:To> element.
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Prime Or Not
Check
List Of Odd and Even Numbers
Select
Odd
Even
Find
Sum Of N Numbers
Result
Reverse of a Number
Result
Palindrome or Not
Result
Sum Of Digits in a Number
Result
Fibonacci Series
Result
Outer-Inner Loop
Result
Patterns
Select
Row Pattern
Column Pattern
Reverse Pattern
Square Pattern
Inverted Pattern
Star Pattern
Mirror Pattern
Half Diamond Pattern
Full Diamond Pattern
Result
Number Convert
this Learning
Click Me


The town blog captures the life of the town, the mysteries behind the blocks, and allies that live through the times. We show you the most pleasant and interesting corners, the spaces that come alive in the night and breed the spirit of the townThrough the texture of Style, freedom, courage, thrill and adventure, the town lives through us all and carries us on its bold shoulders.
Each dealt with their own hand, we move forward with a wash of hope and excitement to make our mark and leave a our print on the town. Still but yet alive as could be, the town serves us with its share of life that breeds yet more life for the next day.
Holding the sun every day on its forehead and the moon every night on its bare-back, we always have a light as we walk through the tunnel. Yes the town we come and go, we bless and curse but its spirit still remains faithful to our journeys.Work for our hands, food for our bellies, and life for our spirits, the town nourishes us through it all.
Let's rise to the call of life, the call of our destiny and breath life into the town, fearless and still tender we tread forward anchoring to the deep roots of the town.
"The more of the town we open up to , the more beauty and goodness blossoms out of our lives"


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WebcamJS Test Page
Demonstrates File Upload capture & display


To succeed in an environment of growth and excellence and earn a job which provides me job satisfaction and self development and help me achieve personal
as well as organization goals.
Education
Sambalpur University Institute of Information Technology
Aug 2016 - Mar 2019
Master of Computer Applications
Naraina Group Of Institutions
Aug 2013 - Mar 2016
Bachelor in Computer Application
Strengths
Good Communication Skills
Team Player
Self Motivated
Focused and Confident With positive attitude
Workshops
Participated in machine learning workshop held in SUIIT.


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--- layout: post title: UpdateModel requires properties --- I was working on a feature at work this week on a ASP MVC project. I was attempting to use UpdateModel to update an object retrieved from the database with values from the POST. If you aren't familiar with it, ASP MVC has a "magic method" to map these POST values to an object. One simply needs to call
UpdateModel(myObjectFromDatabase);
and the new values will be changed on the object.
That is all fine and dandy unless it doesn't work. I had checked everything. I could verify that the values were in the Request header. I could verify that UpdateModel was called and did not fail. The values simply were not updated.
I finally found the key (thanks to my supervisor). The object I was updating had a mix of auto-properties and fields. Apparently, UpdateModel will only update properties. A quick change from a field to an auto-property and everything worked as it should. Thanks Aaron!


Resources:
- An Introduction to Probabilistic Graphical Models: Conditional Independence and Factorization (M Jordan)
- An Intro to PGMs: The Elimination Algorithm (M Jordan)
- An Intro to PGMs: Probability Propagation and Factor Graphs
- An Intro to PGMs: The EM algorithm
- An Intro to PGMs: Hidden Markov Models
- A Brief Introduction to Graphical Models and Bayesian Networks (Paper!)
- Deep Learning vs Probabilistic Graphical Models vs Logic (Blog!)
- Probabilistic Graphical Models CS-708 (CMU!)
- Graphical Models, Exponential Families, and Variational Inference (M. Jordan)
Graphical Models
-
Motivation:
Machine learning algorithms often involve probability distributions over a very large number of random variables. Often, these probability distributions involve direct interactions between relatively few variables. Using a single function to describe the entire joint probability distribution can be very inefficient (both computationally and statistically).A description of a probability distribution is exponential in the number of variables it models.
- Graphical Model:
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure (factorization of a probability distribution) between random variables.Generally, this is one of the most common statistical models
Properties:
- Factorization
- Independence
Graph Structure:
A PGM uses a graph \(\mathcal{G}\) in which each node in the graph corresponds to a random variable, and an edge connecting two r.vs means that the probability distribution is able to represent interactions between those two r.v.s.Types:
- Directed:
Directed models use graphs with directed edges, and they represent factorizations into conditional probability distributions.
They contain one factor for every random variable \(x_i\) in the distribution, and that factor consists of the conditional distribution over \(x_i\) given the parents of \(x_i\). - Undirected:
Undirected models use graphs with undirected edges, and they represent factorizations into a set of functions; unlike in the directed case, these functions are usually not probability distributions of any kind.
Core Idea of Graphical Models:
The probability distribution factorizes according to the cliques in the graph, with the potentials usually being of the exponential family (and a graph expresses the conditional dependence structure between random variables).
- Neural Networks and Graphical Models:
Deep NNs as PGMs:
You can view a deep neural network as a graphical model, but here, the CPDs are not probabilistic but are deterministic. Consider for example that the input to a neuron is \(\vec{x}\) and the output of the neuron is \(y .\) In the CPD for this neuron we have, \(p(\vec{x}, y)=1,\) and \(p(\vec{x}, \hat{y})=0\) for \(\hat{y} \neq y .\) Refer to the section 10.2 .3 of Deep Learning Book for more details.
Bayesian Network
- Bayesian Network:
A Bayesian network, Bayes network, belief network, or probabilistic directed acyclic graphical model is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).E.g. a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
Bayes Nets (big picture):
Bayes Nets: a technique for describing complex joint distributions (models) using simple, local distributions (conditional probabilities).In other words, they are a device for describing a complex distribution, over a large number of variables, that is built up of small pieces (local interactions); with the assumptions necessary to conclude that the product of those local interactions describe the whole domain.
Formally, a Bayes Net consists of:
- A directed acyclic graph of nodes, one per variable \(X\)
- A conditional distribution for each node \(P(X\vert A_1\ldots A_n)\), where \(A_i\) is the \(i\)th parent of \(X\), stored as a conditional probability table or CPT.
Each CPT has \(n+2\) columns: one for the values of each of the \(n\) parent variables \(A_1 \ldots A_n\), one for the values of \(X\), and one for the conditional probability of \(X\).
Each node in the graph represents a single random variable and each directed edge represents one of the conditional probability distributions we choose to store (i.e. an edge from node \(A\) to node \(B\) indicates that we store the probability table for \(P(B\vert A)\)).
Each node is conditionally independent of all its ancestor nodes in the graph, given all of its parents. Thus, if we have a node representing variable \(X\), we store \(P(X\vert A_1,A_2,...,A_N)\), where \(A_1,\ldots,A_N\) are the parents of \(X\).The local probability tables (of conditional distributions) and the DAG together encode enough information to compute any probability distribution that we could have otherwise computed given the entire joint distribution.
Motivation:
There are problems with using full join distribution tables as our probabilistic models:- Unless there are only a few variables, the joint is WAY too big to represent explicitly
- Hard to learn (estimate) anything empirically about more than a few variables at a time
Examples of Bayes Nets:
- Coin Flips:
img1 - Traffic:
img2 - Traffic II:
img3 - Alarm Network:
img4
Probabilities in BNs:
- Bayes Nets implicitly encode joint distributions:
Encoded as a product of local conditional distributions:$$p(x_1, x_2, \ldots, x_n) = \prod_{i=1}^{n} p(x_i \vert \text{parents}(X_i)) \tag{1.1}$$
- We are guaranteed that \(1.1\) results in a proper joint distribution:
- Chain Rule is valid for all distributions:
$$p(x_1, x_2, \ldots, x_n) = \prod_{i=1}^{n} p(x_i \vert x_1, \ldots, x_{i-1})$$
- Conditional Independences Assumption:
$$p(x_1, x_2, \ldots, x_{i-1}) = \prod_{i=1}^{n} p(x_i \vert \text{parents}(X_i))$$
\(\implies\)
$$p(x_1, x_2, \ldots, x_n) = \prod_{i=1}^{n} p(x_i \vert \text{parents}(X_i)) \tag{1.1}$$
- Chain Rule is valid for all distributions:
Thus (from above) Not every BN can represent every joint distribution.
The topology enforces certain conditional independencies that need to be met.e.g. Only distributions whose variables are absolutely independent can be represented by a Bayes Net with no arcs
Causality:
Although the structure of the BN might be in a way that encodes causality, it is not necessary to define the joint distribution graphically. The two definitions below are the same:
img5
To summarize:- When BNs reflect the true causal patterns:
- Often simpler (nodes have fewer parents)
- Often easier to think about
- Often easier to elicit from experts
- BNs need NOT be causal:
- Sometimes no causal net exists over the domain (especially if variables are missing)
- e.g. consider the variables \(\text{Traffic}\) and \(\text{Drips}\)
- Results in arrows that reflect correlation, not causation
- Sometimes no causal net exists over the domain (especially if variables are missing)
- The meaning of the arrows:
- The topology may happen to encode causal structure
- But, the topology really encodes conditional independence
$$p(x_1, x_2, \ldots, x_{i-1}) = \prod_{i=1}^{n} p(x_i \vert \text{parents}(X_i))$$
Questions we can ask
Since a BN encodes a joint distribution we can ask any questions a joint distribution can answer:- Inference: given a fixed BN, what is \(P(X \vert \text { e)? }\)
- Representation: given a BN graph, what kinds of distributions can it encode?
- Modeling: what BN is most appropriate for a given domain?
Size of a BN:
- Size of a full joint distribution over \(N\) (boolean) variables: \(2^N\)
- Size of an \(N\)-node net with nodes having up to \(k\) parents: \(\mathcal{O}(N \times 2^{k+1}\)
Advantages of BN over full joint distribution:
- Both will model (calculate) \(p(X_1, X_2, \ldots, X_N)\)
- BNs give you huge space savings
- It is easier to elicit local CPTs
- Is faster to answer queries
Notes:
- The acyclicity gives an order to the (order-less) chain-rule of conditional probabilities
- Think of the conditional distribution for each node as a description of a noisy “causal” process
- The graph of the BN represents certain independencies directly, but also contains extra independence assumptions that can be “inferred” from the shape of the graph
- There could be extra independence relationships in the distribution that are not represented by the BN graph structure but can be read in the CPT. This happens when the structure of the BN is not “optimal” for the assumptions between the variables.
- Independence / D-Separation:
Goal is to find a graph algorithm that can show independencies between variables in BNs. Steps:- Study independence properties for triples
- Analyze complex cases (configurations) in terms of member triples
- D-separation: a condition / algorithm for answering such queries
Causal Chains:
A Causal Chain is a configuration of three nodes that expresses the following representation of the joint distribution over \(X\), \(Y\) and \(Z\):$$P(x, y, z)=P(z \vert y) P(y \vert x) P(x)$$
Let’s try to see if we can guarantee independence between \(X\) and \(Z\):
- No Observations:
\(X\) and \(Z\) are not guaranteed independent.- Proof:
By Counterexample:$$P(y \vert x)=\left\{\begin{array}{ll}{1} & {\text { if } x=y} \\ {0} & {\text { else }}\end{array} \quad P(z \vert y)=\left\{\begin{array}{ll}{1} & {\text { if } z=y} \\ {0} & {\text { else }}\end{array}\right.\right.$$
\(\text { In this case, } P(z \vert x)=1 \text { if } x=z \text { and } 0 \text { otherwise, so } X \text { and } Z \text { are not independent.}\)
- Proof:
- \(Y\) Observed:
\(X\) and \(Z\) are independent given \(Y\). i.e. \(P(X \vert Z, Y)=P(X \vert Y)\)- Proof:
$$\begin{aligned} P(X \vert Z, y) &=\frac{P(X, Z, y)}{P(Z, y)}=\frac{P(Z \vert y) P(y \vert X) P(X)}{\sum_{x} P(X, y, Z)}=\frac{P(Z \vert y) P(y \vert X) P(X)}{P(Z \vert y) \sum_{x} P(y \vert x) P(x)} \\ &=\frac{P(y \vert X) P(X)}{\sum_{x} P(y \vert x) P(x)}=\frac{P(y \vert X) P(X)}{P(y)}=P(X \vert y) \end{aligned}$$
An analogous proof can be used to show the same thing for the case where X has multiple parents.
- Proof:
To summarize, in the causal chain chain configuration, \(X \perp Z \vert Y\) .
Evidence along the chain “blocks” the influence.
Common Cause:
Common Cause is another configuration for a triple. It expresses the following representation:$$P(x, y, z)=P(x \vert y) P(z \vert y) P(y)$$
Let’s try to see if we can guarantee independence between \(X\) and \(Z\):
- No Observations:
\(X\) and \(Z\) are not guaranteed independent.- Proof:
By Counterexample:$$P(x \vert y)=\left\{\begin{array}{ll}{1} & {\text { if } x=y} \\ {0} & {\text { else }}\end{array} \quad P(z \vert y)=\left\{\begin{array}{ll}{1} & {\text { if } z=y} \\ {0} & {\text { else }}\end{array}\right.\right.$$
\text { Then } P(x \vert z)=1 \text { if } x=z \text { and } 0 \text { otherwise, so } X \text { and } Z \text { are not independent. }
- Proof:
- \(Y\) Observed:
\(X\) and \(Z\) are independent given \(Y\). i.e. \(P(X \vert Z, Y)=P(X \vert Y)\)- Proof:
$$P(X \vert Z, y)=\frac{P(X, Z, y)}{P(Z, y)}=\frac{P(X \vert y) P(Z \vert y) P(y)}{P(Z \vert y) P(y)}=P(X \vert y)$$
- Proof:
Observing the cause blocks the influence.
Common Effect:
Common Effect is the last configuration for a triplet. Expressing the representation:$$P(x, y, z)=P(y \vert x, z) P(x) P(z)$$
Let’s try to see if we can guarantee independence between \(X\) and \(Z\):
-
No Observations:
\(X\) and \(Z\) are, readily, guaranteed to be independent: \(X \perp Z\). -
\(Y\) Observed:
\(X\) and \(Z\) are not necessarily independent given \(Y\). i.e. \(P(X \vert Z, Y)\neq P(X \vert Y)\)- Proof:
By Counterexample:
\(\text { Suppose all three are binary variables. } X \text { and } Z \text { are true and false with equal probability: }\)$$\begin{array}{l}{P(X=\text {true})=P(X=\text { false })=0.5} \\ {P(Z=\text {true})=P(Z=\text { false })=0.5}\end{array}$$
\(\text { and } Y \text { is determined by whether } X \text { and } Z \text { have the same value: }\)
$$P(Y \vert X, Z)=\left\{\begin{array}{ll}{1} & {\text { if } X=Z \text { and } Y=\text { true }} \\ {1} & {\text { if } X \neq Z \text { and } Y=\text { false }} \\ {0} & {\text { else }}\end{array}\right.$$
\(\text { Then } X \text { and } Z \text { are independent if } Y \text { is unobserved. But if } Y \text { is observed, then knowing } X \text { will }\\ \text { tell us the value } {\text { of } Z, \text { and vice-versa. } \text{So } X \text { and } Z \text { are } \text {not} \text { conditionally independent given } Y \text {. }}\)
- Proof:
Common Effect can be viewed as “opposite” to Causal Chains and Common Cause-\(X\) and \(Z\) are guaranteed to be independent if \(Y\) is not conditioned on. But when conditioned on \(Y, X\) and \(Z\) may be dependent depending on the specific probability values for \(P(Y \vert X, Z)\)).
This same logic applies when conditioning on descendants of \(Y\) in the graph. If one of \(Y\) ‘s descendent nodes is observed, as in Figure \(7, X\) and \(Z\) are not guaranteed to be independent.
Observing an effect activates influence between possible causes.
General Case, and D-Separation:
We can use the previous three cases as building blocks to help us answer conditional independence questions on an arbitrary Bayes’ Net with more than three nodes and two edges. We formulate the problem as follows:
Given a Bayes Net \(G,\) two nodes \(X\) and \(Y,\) and a (possibly empty) set of nodes \(\left\{Z_{1}, \ldots Z_{k}\right\}\) that represent observed variables, must the following statement be true: \(X \perp Y |\left\{Z_{1}, \ldots Z_{k}\right\} ?\)D-Separation: (directed separation) is a property of the structure of the Bayes Net graph that implies this conditional independence relationship, and generalizes the cases we’ve seen above. If a set of variables \(Z_{1}, \cdots Z_{k} d-\) -separates \(X\) and \(Y,\) then \(X \perp Y \vert\left\{Z_{1}, \cdots Z_{k}\right\}\) in all possibutions that can be encoded by the Bayes net.
D-Separation Algorithm:
- Shade all observed nodes \(\left\{Z_{1}, \ldots, Z_{k}\right\}\) in the graph.
- Enumerate all undirected paths from \(X\) to \(Y\) .
- For each path:
- Decompose the path into triples (segments of 3 nodes).
- If all triples are active, this path is active and \(d\) -connects \(X\) to \(Y\).
- If no path d-connects \(X\) and \(Y\) and \(Y\) are d-separated, so they are conditionally independent given \(\left\{Z_{1}, \ldots, Z_{k}\right\}\)
Any path in a graph from \(X\) to \(Y\) can be decomposed into a set of 3 consecutive nodes and 2 edges - each of which is called a triple. A triple is active or inactive depending on whether or not the middle node is observed. If all triples in a path are active, then the path is active and \(d\) -connects \(X\) to \(Y,\) meaning \(X\) is not guaranteed to be conditionally independent of \(Y\) given the observed nodes. If all paths from \(X\) to \(Y\) are inactive, then \(X\) and \(Y\) are conditionally independent given the observed nodes.
Active Triples: We can enumerate all possibilities of active and inactive triples using the three canonical graphs we presented above in Figure 8 and 9.
Examples:
Structure Implications:
Given a Bayes net structure, can run d-separation algorithm to build a complete list of conditional independences that are necessarily true of the form.$$X_{i} \perp X_{j} |\left\{X_{k_{1}}, \ldots, X_{k_{n}}\right\}$$
This list determines the set of probability distributions that can be represented.
Topology Limits Distributions:
- Given some graph topology \(G\), only certain joint distributions can be encoded
- The graph structure guarantees certain (conditional) independences
- (There might be more independence)
- Adding arcs increases the set of distributions, but has several costs
- Full conditioning can encode any distribution
- The more assumptions you make the fewer the number of distributions you can represent
Bayes Nets Representation Summary:
- Bayes nets compactly encode joint distributions
- Guaranteed independencies of distributions can be deduced from BN graph structure
- D-separation gives precise conditional independence guarantees from graph alone
- A Bayes nets joint distribution may have further (conditional) independence that is not detectable until you inspect its specific distribution
-
Inference:
Lecture (188)Inference is the process of calculating the joint PDF for some set of query variables based on some set of observed variables.
For example:- Posterior Probability inference:
$$P\left(Q \vert E_{1}=e_{1}, \ldots E_{k}=e_{k}\right)$$
- Most Likely Explanation inference:
$$\operatorname{argmax}_{q} P\left(Q=q \vert E_{1}=e_{1} \ldots\right)$$
Notation - General Case:
$$ \left.\begin{array}{ll}{\textbf { Evidence variables: }} & {E_{1} \ldots E_{k}=e_{1} \ldots e_{k}} \\ {\textbf { Query}^{* } \textbf { variable: }} & {Q} \\ {\textbf { Hidden variables: }} & {H_{1} \ldots H_{r}}\end{array}\right\} \begin{array}{l}{X_{1}, X_{2}, \ldots X_{n}} \\ {\text { All variables }}\end{array} $$
Inference by Enumeration:
We can solve this problem naively by forming the joint PDF and using inference by enumeration as described above. This requires the creation of and iteration over an exponentially large table.
Algorithm:- Select the entries consistent with the evidence
- Sum out \(\mathrm{H}\) to get join of Query and evidence
- Normalize: \(\times \dfrac{1}{Z} = \dfrac{1}{\text{sum of entries}}\)
- Inference by Enumeration (188)
Variable Elimination:
Alternatively, we can use Variable Elimination: eliminate variables one by one.
To eliminate a variable \(X\), we:- Join (multiply together) all factors involving \(X\).
- Sum out \(X\).
A factor is an unnormalized probability; represented as a multidimensional array:
- Joint Distributions: \(P(X,Y) \in \mathbb{R}^2\)
- Entries \(P(x, y)\) for all \(x, y\)
- Sums to \(1\)
- Selected Joint: \(P(x,Y) \in \mathbb{R}\)
- A slice of the joint distribution
- Entries \({P}({x}, {y})\) for fixed \({x},\) all \({y}\)
- Sums to \(P(x)\)
- Single Conditional: \(P(Y \vert x)\)
- Entries \({P}({y} \vert {x})\) for fixed \({x},\) all \({y}\)
- Sums to \(1\)
- Family of Conditionals: \(P(Y \vert X)\)
- Multiple Conditionals
- Entries \({P}({y} \vert {x})\) for all \({x}, {y}\)
- Sums to \(\vert X\vert\)
- Specified family: \(P(y \vert X)\)
- Entries \(P(y \vert x)\) for fixed \(y\) but for all \(x\)
- Sums to random number; not a distribution
For Joint Distributions, the # capital variables dictates the “dimensionality” of the array.
At all points during variable elimination, each factor will be proportional to the probability it corresponds to but the underlying distribution for each factor won’t necessarily sum to \(1\) as a probability distribution should.
Inference by Enumeration vs. Variable Elimination:
Inference by Enumeration is very slow: You must join up the whole joint distribution before you sum out the hidden variables.
Variable Elimination: Interleave joining and marginalization.
Still NP-hard, but usually much faster.
Notice that \(\sum_r P(r) P(t \vert r) = P(t)\), thus, in VE, you end up with \(\sum_{t} P(L \vert t) P(t)\).General Variable Elimination - Algorithm:
VE - Computational and Space Complexity:
- The computational and space complexity of variable elimination is determined by the largest factor.
- The elimination ordering can greatly affect the size of the largest factor.
- There does NOT always exist an ordering that only results in small factors.
- VE is NP-Hard:
- Proof:
We can reduce 3-Sat to a BN-Inference problem.
We can encode a Constrained Satisfiability Problem (CSP) in a BN and use it to give a solution to the CSP; if the CSP consists of 3 clauses, then finding a solution for the CSP via BN-Inference is equivalent to solving 3-Sat.
- Proof:
- Thus, inference in Bayes’ nets is NP-hard.
No known efficient probabilistic inference in general.

For polytrees we can always find an ordering that is efficient.- Cut-set conditioning for Bayes’ net inference: Choose set of variables such that if removed only a polytree remains.
- Posterior Probability inference:
-
Sampling:
Sampling is the process of generating observations/samples from a distribution.
- Sampling is like doing repeated (probabilistic) simulation.
- Sampling could be used for learning (e.g. RL). But in the context of BNs, it is used for Inference.
- Sampling provides a way to do efficient inference, by presenting us with a tradeoff between accuracy and computation (time).
- The Goal is to prove that as the number of samples you generate \(N\) goes to \(\infty\), the approximation converges to the true probability you are trying to compute.
- Using sampling in a BN from the entire network is necessary, because listing all the outcomes is too expensive even if we can create them given infinite time.Idea/Algorithm for Inference:
- Draw \(N\) samples from a sampling distribution \(S\)
- Compute an approximate posterior probability
- Show this converges to the true probability \(P\)
Sampling from a given distribution:
- Get sample \(u\) from uniform distribution over \([0,1)\)
- Convert this sample \(u\) into an outcome for the given distribution by having each target outcome associated with a sub-interval of \([0,1)\) with sub-interval size equal to probability of the outcome
Sampling Algorithms in BNs:
- Prior Sampling
- Rejection Sampling
- Likelihood Weighting
- Gibbs Sampling
Prior Sampling:
Algorithm:
- For \(i=1,2, \ldots, n\)
- Sample \(x_{i}\) from \(P\left(X_{i} \vert \text { Parents }\left(X_{i}\right)\right)\)
- Return \(\left(x_{1}, x_{2}, \ldots, x_{n}\right)\)
Notes:
- This process generates samples with probability:
$$ \begin{aligned} S_{P S}\left(x_{1} \ldots x_{n}\right)=\prod_{i=1}^{n} P\left(x_{i} \vert \text { Parents }\left(X_{i}\right)\right)=P\left(x_{1} \ldots x_{n}\right) \\ \text { ...i.e. the BN's joint probability } \end{aligned} $$
- Let the number of samples of an event be \(N_{P S}\left(x_{1} \cdots x_{n}\right)\)
- Thus,
$$\begin{aligned} \lim _{N \rightarrow \infty} \widehat{P}\left(x_{1}, \ldots, x_{n}\right) &=\lim _{N \rightarrow \infty} N_{P S}\left(x_{1}, \ldots, x_{n}\right) / N \\ &=S_{P S}\left(x_{1}, \ldots, x_{n}\right) \\ &=P\left(x_{1} \ldots x_{n}\right) \end{aligned}$$
- I.e., the sampling procedure is consistent
Rejection Sampling:
Rejection SamplingIt is also consistent.
Idea:
Same as Prior Sampling, but no point in keeping all of the samples. We just tally the outcomes that match our evidence and reject the rest.
Algorithm:
- Input: evidence instantiation
- For \(i=1,2, \ldots, n\)
- Sample \(x_{i}\) from \(P\left(X_{i} \vert \text { Parents }\left(X_{i}\right)\right)\)
- If $x_{i}$ not consistent with evidence
- Reject: return - no sample is generated in this cycle
- Return \(\left(x_{1}, x_{2}, \ldots, x_{n}\right)\)
Likelihood Weighting:
Likelihood WeightingKey Ideas:
Fixes a problem with Rejection Sampling:
- If evidence is unlikely, rejects lots of samples
- Evidence not exploited as you sample
Idea: fix evidence variables and sample the rest. - Problem: sample distribution not consistent!
- Solution: weight by probability of evidence given parents.
Algorithm:
- Input: evidence instantiation
- \[w=1.0\]
- for \(i=1,2, \dots, n\)
- if \(\mathrm{x}_ {\mathrm{i}}\) is an evidence variable
- \(\mathrm{n} \mathrm{x} _ {\mathrm{i}}=\) observation \(\mathrm{x}_ {\mathrm{i}}\) for \(\mathrm{x}_ {\mathrm{i}}\)
- \[\operatorname{set} \mathrm{w}=\mathrm{w} * \mathrm{P}\left(\mathrm{x}_ {\mathrm{i}} \vert \text { Parents(X.) }\right.\]
- else
- Sample \(x_ i\) from \(P\left(X _ {i} \vert \text { Parents }\left(X _ {i}\right)\right)\)
- if \(\mathrm{x}_ {\mathrm{i}}\) is an evidence variable
- Return \(\left(\mathrm{x}_ {1}, \mathrm{x}_ {2}, \ldots, \mathrm{x}_ {\mathrm{n}}\right), \mathrm{w}\)
Notes:
- Sampling distribution if \(z\) sampled and \(e\) fixed evidence
$$S_{W S}(\mathbf{z}, \mathbf{e})=\prod_{i=1}^{l} P\left(z_{i} \vert \text { Parents }\left(Z_{i}\right)\right)$$
- Now, samples have weights
$$w(\mathbf{z}, \mathbf{e})=\prod_{i=1}^{m} P\left(e_{i} \vert \text { Parents }\left(E_{i}\right)\right)$$
- Together, weighted sampling distribution is consistent
$$\begin{aligned} S_{\mathrm{WS}}(z, e) \cdot w(z, e) &=\prod_{i=1}^{l} P\left(z_{i} \vert \text { Parents }\left(z_{i}\right)\right) \prod_{i=1}^{m} P\left(e_{i} \vert \text { Parents }\left(e_{i}\right)\right) \\ &=P(\mathrm{z}, \mathrm{e}) \end{aligned}$$
- Likelihood weighting is good
- We have taken evidence into account as we generate the sample
- E.g. here, \(W\)’s value will get picked based on the evidence values of \(S\), \(R\)
- More of our samples will reflect the state of the world suggested by the evidence
- Likelihood weighting doesn’t solve all our problems
- Evidence influences the choice of downstream variables, but not upstream ones (C isn’t more likely to get a value matching the evidence)
- We would like to consider evidence when we sample every variable (leads to Gibbs sampling)
Gibbs Sampling:
Gibbs Sampling- Procedure: keep track of a full instantiation \(x_1, x_2, \ldots, x_n\). Start with an arbitrary instantiation consistent with the evidence. Sample one variable at a time, conditioned on all the rest, but keep evidence fixed. Keep repeating this for a long time.
- Property: in the limit of repeating this infinitely many times the resulting samples come from the correct distribution (i.e. conditioned on evidence).
- Rationale: both upstream and downstream variables condition on evidence.
-
In contrast: likelihood weighting only conditions on upstream evidence, and hence weights obtained in likelihood weighting can sometimes be very small. Sum of weights over all samples is indicative of how many “effective” samples were obtained, so we want high weight.
- Gibbs sampling produces sample from the query distribution \(P(Q \vert \text { e })\) in limit of re-sampling infinitely often
- Gibbs sampling is a special case of more general methods called Markov chain Monte Carlo (MCMC) methods
- Metropolis-Hastings is one of the more famous MCMC methods (in fact, Gibbs sampling is a special case of Metropolis-Hastings)
- Monte Carlo Methods are just sampling
Algorithm by Example:
Efficient Resampling of One Variable:
- Sample from \(\mathrm{P}(\mathrm{S} \vert+\mathrm{c},+\mathrm{r},-\mathrm{w})\):
$$\begin{aligned} P(S \vert+c,+r,-w) &=\frac{P(S,+c,+r,-w)}{P(+c,+r,-w)} \\ &=\frac{P(S,+c,+r,-w)}{\sum_{s} P(s,+c,+r,-w)} \\ &=\frac{P(+c) P(S \vert+c) P(+r \vert+c) P(-w \vert S,+r)}{\sum_{s} P(+c) P(s \vert+c) P(+r \vert+c) P(-w \vert s,+r)} \\ &=\frac{P(+c) P(S \vert+c) P(+r \vert+c) P(-w \vert S,+r)}{P(+c) P(+r \vert+c) \sum_{s} P(s \vert+c)} \\ &=\frac{P(S \vert+c) P(-w \vert S,+r)}{\sum_{s} P(s \vert+c) P(-w \vert s,+r)} \end{aligned}$$
- Many things cancel out – only CPTs with \(S\) remain!
- More generally: only CPTs that have resampled variable need to be considered, and joined together
Bayes’ Net Sampling Summary:
- Decision Networks / VPI (Value of Perfect Information):
Decision Networks / VPI (188)
Random Field Techniques
- An Introduction to Conditional Random Fields & graphical models (Thesis!)
- Classical Probabilistic Models and Conditional Random Fields (Technical Report!)
- HMM, MEMM, and CRF: A Comparative Analysis of Statistical Modeling Methods (Blog)
- Intro to Conditional Random Field (blog!)
-
Random Field:
A Random Field is a random function over an arbitrary domain (usually a multi-dimensional space such as \(\mathbb{R}^{n}\) ). That is, it is a function \(f(x)\) that takes on a random value at each point \(x \in \mathbb{R}^{n}\) (or some other domain). It is also sometimes thought of as a synonym for a stochastic process with some restriction on its index set. That is, by modern definitions, a random field is a generalization of a stochastic process where the underlying parameter need no longer be real or integer valued “but can instead take values that are multidimensional vectors on some manifold.Formally
Given a probability space \((\Omega, \mathcal{F}, P),\) an \(X\) -valued random field is a collection of \(X\) -valued random variables indexed by elements in a topological space \(T\). That is, a random field \(F\) is a collection$$\left\{F_{t} : t \in T\right\}$$
where each \(F_{t}\) is an \(X\)-valued random variable.
Notes:
- Random Field (wiki)
- Generative vs Discriminative Models for Sequence Labeling Tasks:
Generative model makes more restrictive assumption about the distribution of \(x\).
“Unlike traditional generative random fields, CRFs only model the conditional distribution \(p(t | x)\) and do not explicitly model the marginal \(p(x)\). Note that the labels \(t i\) are globally conditioned on the whole observation \(x\) in CRF. Thus, we do not assume that the observed data \(x\) are conditionally independent as in a generative random field.” - Minka


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Summary of my past teching activities:
- 2020-2021 Teaching the course of ‘‘Computer Architecture’’ at the Department of Mathematics and Computer Science. University of Catania, Catania
- 2019 Laboratory teaching assistant for the course of ‘‘Basics of Multimedia Systems’’ at the Department of Electrical and Computer Engineering. Ryerson University, Toronto
- 2018 Co-supervisor M.Sc. thesis on autonomous UAV navigation using vision and deep reinforcement learning. VisLab, ISR, IST, Lisbon
- 2017-2018 Co-supervisor M.Sc. thesis on UAV autonomous landing on a mobile base using vision. VisLab, ISR, IST, Lisbon
- 2017-2018 Help in supervising Ph.D. thesis on learning from demonstration how to clean a table using deep neural networks. VisLab, ISR, IST, Lisbon
- 2013 Laboratory assistant in the M.Sc. course of ‘‘Robotic Perception’’ at the School of Computer Science. University of Pisa, Italy
For an updated list of my publications please refer to google scholar account.


[ all classes ]
[ emily.command.administrative ]
Coverage Summary for Package: emily.command.administrative
| Package | Class, % | Method, % | Line, % |
|---|---|---|---|
| emily.command.administrative | 0% (0/ 23) | 0% (0/ 173) | 0% (0/ 1126) |
| Class | Class, % | Method, % | Line, % |
|---|---|---|---|
| SystemCommand | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 26) |
| CommandAdminCommand | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 111) |
| ConfigCommand | 0% (0/ 1) | 0% (0/ 12) | 0% (0/ 116) |
| TempBanCommand | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 8) |
| MuteCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 25) |
| GuildStatsCommand | 0% (0/ 1) | 0% (0/ 11) | 0% (0/ 119) |
| KickCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 8) |
| WarnCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 7) |
| ModCaseCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 8) |
| AutoReplyCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 100) |
| Subscribe | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 63) |
| ReactionRoleCommand | 0% (0/ 1) | 0% (0/ 11) | 0% (0/ 86) |
| PMCommand | 0% (0/ 1) | 0% (0/ 6) | 0% (0/ 18) |
| CustomCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 29) |
| RoleAdminCommand | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 87) |
| BanCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 8) |
| TemplateCommand | 0% (0/ 1) | 0% (0/ 6) | 0% (0/ 93) |
| ReactionsCommand | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 18) |
| LeaveGuildCommand | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 28) |
| CaseCommand | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 31) |
| PurgeComand | 0% (0/ 3) | 0% (0/ 12) | 0% (0/ 137) |


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Edit this page and add whatever you feel is necessary to solve the tasks. The AngularJS library is already included so you don't have to worry about that. Otherwise, you're on your own.
If you'd like to step it up a notch you can do the Extra Credits as well.
Populate the list below from an angular controller. It can contain whatever you want.
Make the buttons below clickable. When each button is pressed, the corresponding paragraph should become visible (and all others should be hidden).
This is the first paragraph.
This is the second paragraph.
This is the third paragraph.
Make the content of the area below change depending on which link is selected (i.e. it should depend on the current url). You can make it change to whatever you like, but the different URLs should have different content.
This content should change when the links above are selected.


[REPORT_SUMMARY]
Phase:
[VERSION_PHASE]
|
Distribution:
[VERSION_DISTRO]
Vulnerability Status Filter: [VULNERABILITY_STATUS_FILTER]
VULNERABLE COMPONENTS SUMMARY
[REPORT_COMPONENT_SUMMARY]
VULNERABLE COMPONENTS DETAILS
[REPORT_DETAILS]
NOTE: This report lists only the vulnerable components for this Hub project. To view a complete list
of Open Source components identified, go to [PROJECT_NAME_VERSION].


{{< layout}} {{$title}}Delivery options{{/title}} {{$header}}
This will be sent by secure delivery. The cost is included in the passport fee.
Choose how your old passport and any extra documents are returned. They won't be in the same envelope as your new passport.
{{^values.veteran}} {{#radio-group}}secure-return{{/radio-group}}We’ll send everything to {{/values.veteran}} {{values.address1}}, {{#values.address2}} {{values.address2}}, {{/values.address2}} {{values.town}}, {{values.postcode}}.
{{#input-submit}}{{/input-submit}} {{/form}} {{/ hmpo-partials-form}} {{/content}} {{/ layout}}


{% extends "pretixcontrol/base.html" %} {% load i18n %} {% load bootstrap3 %} {% block title %}{% trans "Organizer" %}{% endblock %} {% block content %}
{% trans "You currently do not have access to any events." %}
{% blocktrans trimmed %} You can use the following list to control who can create new events in the name of this organizer and who can add more people to this list. This does not control who has access to a particular event. You can control the access to an event in the "Permissions" section of the event's settings. A user does not need to be on the list here to get access to an event. {% endblocktrans %}


Usually, exceptions that occur during debugging sessions are displayed
only in trace mode and for invocation boxes for
predicates with spypoints on them, and not during skips.
However, it is sometimes useful to make exceptions trap to the debugger
at the earliest opportunity instead. The hook predicate
user:error_exception/1
provides such a possibility:
error_exception(
+Exception)
hookuser:error_exception(
+Exception)
Note that this hook takes effect when the debugger arrives at an Exception port. For this to happen, procedure boxes have to be built, e.g. by running (the relevant parts of) the program in debug mode.


Before I begin the log, here's a little intrduction. I saw my family doctor for insomnia, and I had to try a bunch of homeopathis remedies (i.e. valerian root), but they didnt help. He gave me a bottle of Sonata and a business card for a local Psychiatrist who happens to be my brother's-friend'-dad, so I am worried about trying to get pills I dont 'NEED' from him because that could get back to my family. He basically provided me a few options, most were benzo's.
The two that appealed to me the most were Valium and XanaX because I had heard of and done them before, I opted for the Xanax and he gave me a month supply of Xanax Extended Release. I tried it once and it made me late for school because it lasts for 12 hours. I went back to the doctor and got a 1 month supply of regular 1mg Generic Xanax, with a refill. I took it before bed and it worked awesome.
Anyways, I have excess because I dont use them on weekends and school holidays. In this particular situaion, I had the last 4 of my bottle, and decided to try snorting them.
------------------------------------------------------------------
BEGIN LOG:
12:30 AM
- Crushed one (1) 1mg pill (not a bar) of generic alprazolam with an razor, on top of a mirror. Formed two thin lines with a library card, and snorted each with a rolled up dollar bill. It left a funny burn in my nose, and nothing's happened yet.
12:45 AM
- Repeated process. After snorting this set of rails, the onset of a normal 1mg dosage occurs. Conclusion thus far: Snorting 2 mg in 15 minutes produces the same effect as waiting 30 minutes for an orally consumed 1mg pill. (keep in mind the tolerance factor though)
12:55
-Repeated initial process of snorting 1 mg pill. Now we're talkin! At this point I have snorted 3mg, but I feel like I've taken about 5mg an hour or so earlier.
1:05
- Snorted the last pill (now totalling 4mg, the equalivent of 2 'bars') and I sort of dissapointed. I have the weird burn feeling you get from snorting, and moderate Xanax effects, I am considering absorbing some sonota through my tounge to make this worthiwle.
1:20
- Xanax in full effect, about to find and open up a 5mg capsule of sonota.
1:30
- Opened up o 5mg capsule of sonota, formed one long line, and snorted it all at once. It makes a much better rail than the Xanax because it's already a fine powder, and it tastes like flour, so it doesnt burn. MY guess is that the sonota will simply add to the Xanax high because it is a sleep aid. If it doesn't, however, I'll pop open another cap.
1:45
- Opened another Sonata and snorted it. None of the 10mg of sonota has seemed to affect me yet but It most likely is but my current drowsiness from the Xanax is just overpowering it. Right now I'm examing a Xanax XR pill,trying to figure out how to use it.
2:15
- Really I only feel like I'm on a couple milligrams of xanax, but the effects came on faster than they would have if I had eaten 4 pills earlier.
2:45
- The 'middle' part, is here. I have the Xanax high with added drowsiness from the Sonota. I anticipate that this high will last for quite a while
3:00
- I went ahead and snorted an Extended Release Xanax, I'm hoping that becasue it was snorted, it won't time-release.
------------------------------------------------------------------
--------------------------NEXT DAY--------------------------------
------------------------------------------------------------------
I cannot recall what time I actually fell asleep, but I awoke at 9:30 (against my own will) with no negative effects or a 'hangover'. I was extremely groggy though, but that was only because I went to bed late and woke up early.
My final conclusion is that If you snort Xanax, you will almost instantly feel what you would usually feel if you had swallowed the pill whole 30 minutes prior.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Action : Print
This action allows you to print a print stream previously created with the action New printing.
Parameters
| Parameters | Descriptions |
|---|---|
| Variable with the stream to print | Must be equal to a variable (not array) whose value is a print stream (the stream has been created and obtained by the action New printing). |
| Display the print settings | Determines if a dialog box to configure a printer and print settings to be displayed. |
| Do a print preview | Determines if a dialog box allowing you to preview before printing the document to be opened. |
| Variable receiving the result of printing (optional) | If necessary, select the variable (not array) receiving the print result. |
Return value
The return value is determined by the success or otherwise of the document is sent to the printer. This value does not know if printing was done correctly, but only whether there has been no problem before the start of the printing process (which is not supported by your software but by the computer). It will be stored in the variable defined in the parameter "Variable receiving the result of printing". This value is True/False (Boolean).
| Result | Returned values |
|---|---|
| No problems were encountered | True |
| An unknown problem has been encountered | False |
Code Visual Basic.Net generated by SoftwareZator
If the parameter "Show Window print settings" is checked and the parameter "Do a print preview" is unchecked, the generated code will be as follows :
Your_Variable = Your_Stream.Print(True, False)
If the parameter "Show Window print settings" is unchecked and the parameter "Do a print preview" is checked, the generated code will be as follows :
Your_Variable = Your_Stream.Print(False, True)
Runtime errors
Sometimes a computer program encounters a problem during execution. Often, an error message is displayed. The table below allows you to determine why the error you encounter appears and how to correct it :
| Errors types | Descriptions | Resolve |
|---|---|---|
| System.MissingMemberException | This error occurs when the action New printing has not been executed, the variable defined in the parameter "Variable with the stream to print", before this action, or that the value of the variable has changed in the meantime. | To correct this error, make sure the action New printing is executed for the same variable as that specified in the parameter "Variable with the stream to print" and the value thereof has not been changed or reset before the execution of the action "Print". |
Characteristic
| Type | Informations |
|---|---|
| Required level | Beginner |
| Category | Printing |
| Type of return value | True/False (Boolean) |
| Works in the functions | Yes |
| Works in reporting zone | No |
| This action may receive children actions | No |


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"
IPL score predictor
Predict Score
--- Select a Batting team ---
Mumbai Indians
Kolkata Knight Riders
Chennai Super Kings
Rajasthan Royals
Kings XI Punjab
Royal Challengers Bangalore
Delhi Daredevils
Sunrisers Hyderabad
--- Select a Bowling team ---
Mumbai Indians
Kolkata Knight Riders
Chennai Super Kings
Rajasthan Royals
Kings XI Punjab
Royal Challengers Bangalore
Delhi Daredevils
Sunrisers Hyderabad
{{ prediction_text }}


| DOSE: |
1 tablet |
oral |
MDMA |
(pill / tablet) |
Two very close friends and I drove 60 miles to attend a foam party at a seaside resort. We drank copiously - about half a dozen ?boilermakers? each.
Resolve thus softened, a friend who met us at the party revealed that a selection of drugs was available for purchase upstairs. Briefly, we debated as to whether we would invest in a couple of lines of Charlie, or a few pills. (Acid was ruled out immediately; we all consider it a gutter drug on the basis of our experiences). We went for the pills - one each at R80 a pop (that's about $12).
Being fairly responsible adults, and aware of the side effects of any drug before chowing it, we stopped drinking booze and went on to water and energy drinks for the rest of the evening.
Well. Munching that little pill changed me from being a person who didn't enjoy rave music at all, to the friggin Energiser bunny.
I have never felt so close to other people before - the two guys I was with are my best friends, but the addition of a little ecstasy just made me realise all the wonderful qualities they possess as if under a powerful magnifying glass. The whole setting became more intimate (there were about 40 total strangers who were also chowed up) - people were touching one another, sharing water bottles, Tiger balm and drinks. An absolutely incredible feeling of fraternity pervaded the club.
My most remarkable observation was the feeling of absolute crystal clarity of all my senses. Drunk people suddenly seemed so oafish and clumsy. As soon as a drunk person bumped against me, I could feel, without looking, that that person was pissed - realisation dawned, that as a drug, alcohol is totally worthless in comparison to ecstasy.
We walked down to a gas station in the early hours of the morning to buy some gum. The night air was wonderfully cool and soft and alive. We chatted about our intimate feelings and aspirations - such a rewarding experience. (All three of us are degreed professionals)
Needless to say, after such a revelatory experience, we placed an order for a batch of another 20 pills for the coming weekend. The second experience - in a popular rave club, and with a dose of two pills each (staggered at a three hour interval) this time - proved to be every bit as satisfying as the first trip.
My conclusion, after researching the product and its effects/side effects, is that certain elements in society - such as cops, various members of the legal and medical professions and other authority figures, is that a wonderful stimulant with apparently negligible side effects, has been demonised and shrouded in myth. This stuff should be available on demand from every cornerstore, just like cigarettes (I don't smoke) are.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Microsoft competes for popularity with upstart Slack
by Associated PressSAN FRANCISCO (AP) — Microsoft says that its own workplace collaboration service Teams now has more than 20 million daily active users.
That's 8 million more than what upstart work messaging service Slack boasted of having last month. Microsoft is in a fierce popularity contest with Slack.
Slack, which had its initial public offering in June, was in a federally-mandated quiet period when Microsoft first revealed its Teams metrics in early July.
Slack has since gotten louder about downplaying the numbers, saying users are more engaged with its customizable service than "legacy suites of badly connected products" — a thinly veiled swipe at Microsoft.
Microsoft benefits from being able to bundle Teams as part of a software package that includes email and other products.
Slack's shares took a hit after Microsoft's announcement Tuesday.