AIST started operation of ABCI, which is a publicly available cloud infrastructure for AI research and development, from August 2018, and RWBC-OIL uses ABCI for AI research. RWBC-OIL researchers from AIST and TokyoTech attend SC20 to provide presentations and booth exhibition about ABCI and AI/BigData research projects.
Highlights at SC20 is presentations of RWBC-OIL research activities that use ABCI and TSUBAME3.0. This year, a research paper from AIST and TokyoTech is presented. There are also many talks from RWBC-OIL researchers at BoF, panels, workshop and exhibition booths.
AIST booth: https://www.eventscribe.com/2020/SC20/preview.asp?boothTarget=412596
Tokyo-Tech booth: https://www.eventscribe.net/2020/SC20/preview.asp?boothTarget=412822
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, Aleksandr Drozd, Jens Domke, Lingqi Zhang, Ryousei Takano, Satoshi Matsuoka
Date (EDT): Tuesday, 17 November 2020, 1:00pm - 1:30pm
Location: Track 4
TOP500 Supercomputers
Date (EDT): Monday, 16 November 2020, 4pm - 5:30pm
Location: Track 1


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Piyush Goyal: 'Govt Planning Full Electrification Of Railways Within Next 3-4 Years'
by Nisha QureshiGeneral News
Union Minister Piyush Goyal on Sunday said that the Government plans full electrification of Indian Railways in the next 3-4 years to control carbon emission
Written By Nisha Qureshi | Mumbai | Updated On: November 25, 2019 08:02 IST
Union Minister Piyush Goyal on Sunday said that the Government plans full electrification of Indian Railways in the next 3-4 years to control the carbon emissions of the Indian Railway.
'World's first carbon emission-free railway'
"Railways are continuously increasing electrification for a better, pollution-free future. The electrification of the entire railway has been targeted in the next 3-4 years, making it the world's first carbon emission-free railway," he tweeted.
READ | Will work with gems, jewellery industry on import duty demand: Piyush Goyal
READ | WATCH: Union Minister Piyush Goyal speaks about ‘India First’ policy
Piyush Goyal says Indian railways will soon be net-zero
Earlier, Piyush Goyal said that the Government is working towards making Indian railways the world's first net-zero railway to help reduce environmental pollution. This is important because according to NITI Aayog data, carbon dioxide emission from the Indian Railways was around 6.84 million tons in 2014.
"By 2030, we are working to make the Indian railways the world's first net-zero railway. The Government is phasing out the old coal plants, which is going to help reduce environmental pollution, and will also create the demand for new plants and spur the investment cycle," he had said.
In September, the Ministry of Railways and Confederation of Indian Industries signed a Memorandum of Understanding, In order to foster the green initiatives in the country. The major objectives of the MoU signed in between CII and Indian Railways was the efficiency in manufacturing facilities, greening of the railway properties, demonstrative pilots of net-zero energy buildings, development of green procurement policy, waste management policy, solid waste disposal, carbon neutrality, and phytoremediation, among other things.
According to a PIB report, the Ministry of Railways has planned to set up 1000 Mega Watt (MW) solar power by 2020-21 which would help Railways to source about 10% of its electrical energy from renewable sources. Another initiative aimed at the substitution of 20% Compressed Natural Gas (CNG) with solar energy-based DEMU (Diesel Electric Multiple Unit).
READ | Piyush Goyal: India will lead world in combating poverty, climate change, terrorism
READ | Piyush Goyal reacts to Kunal Kohli's 'wonderful' time on train journey
(Inputs from ANI)


{% extends "mxv/base.html" %} {% load static %} {% block title%}Thanks for letting us know!{% endblock %} {% block breadcrumb %}
Click here to return to our main website.


3 Import a dataset
In this part, we will show you how to import a dataset into the application.
This example will use the RICardo dataset provided here: https://github.com/gflowiz/sageo-ricardo
This dataset is about goods exchange between countries from the seventeenth century and early twentieth century.
We will use 2 CSV files :
- SAGEO_RICardo_nodes.csv that contains information on the nodes (position mainly);
- SAGEO_RICardo_edges_small.csv that contains informations on the links (or edges) who connects the nodes.
Arabesque will be able to link nodes and egdes using common IDs between those 2 files. Arabesque will automatically remove links or nodes that are not common to the 2 files.
3.1 Load the link data
To create a new map, you first start to load the link data. To do so, click on the Browse button.
Browse your files and select the SAGEO_RICardo_edges_small.csv
file.
This file is Ricardo data aggregated by decades to make it smaller footprint.
The file SAGEO_RICardo_edges_nona.csv
contains non aggregated data.
Click on Open
to load the data in the application.
When the data has been loaded, the filename will appear in the box and you can click on the Add button.
3.2 Load the nodes data
A popup window will open to help you select the informations needed by the application.
Arabesque works with 3 columns:
- ID of the origin node
- ID of the destination node
- the column containing the information on the transferred volume
The RICardo dataset comes with columns named accordingly but the name is not very important.
You can also choose an aggregation function. Arabesque provides the most common ones (sum, mean, median, maximum, minimum).
Links needs to be spatialized. This is provided by the nodes.
There is 2 ways to add nodes to the map. By providing a node file or by using preset locations.
3.2.1 From a preset dataset
This feature is a work in progress and is not available yet.
3.2.2 From a file
You can provide your own nodes file.
On the previous menu, click on Import Location. This will pop up the following window.
Click on Browse, select the SAGEO_RICardo_nodes.csv
file and click on Open
.
This files need 3 columns:
- a column containing the node IDs
- a column with the latitude of the node
- a column with the longitude of the node
The node is always a point located in space with decimal geographical coordinates.
Before loading the file, Arabesque will need to know which column store IDs and lat/long information.
Use the drop-down lists to assign the right columns. Click on the Arabesque button to launch the map.
3.3 New map
3.3.1 Removed nodes and links
When Arabesque connects nodes and links, it automatically remove orphans nodes (not related to any link) and links (links without nodes). Arabesque will inform you with a popup window.
For example: > 1382 nodes have been removed. > No links have for origin or destination these nodes
Click on OK
3.3.2 Default map
Arabesque will display a default map, with the most significative information and a default theme.
You can modify it like shown in the Functionnalities section.


myPOIs || alert('Points of interest could not be loaded!');
myMapStyles || alert('Map Styling could not be loaded!');
"use strict";
myPOIs || alert('Points of interest could not be loaded!');
myMapStyles || alert('Map Styling could not be loaded!');
"use strict";
We make a POIObject out of all POI (Point of Interest) data. This object should contain the POI’s KO properties and the associated marker in the maps.
function POIObject (POI, map) {
const that = this; // save context
this.location = ko.observable(POI.location);
this.address = ko.observable(POI.address);
this.title = ko.observable(POI.title);
this.mapMarker = new google.maps.Marker({
position: that.location(),
title: that.title(),
animation: google.maps.Animation.DROP,
map: map
});
setMarkerAnimations(this.mapMarker);
Create an onclick event to open the info window at each marker.
this.openInfo = _ => {
this.infowindow.open(map, this.mapMarker);
animateMarker.call(this.mapMarker);
};
this.mapMarker.addListener('click', this.openInfo);
}
function AppViewModel (map) {
const that = this; // save the context for referring later on if need be.
We need a KO observable array of POIs. This helps HTML view bindings and help filter as well using KO.
this.myPOIObjList = ko.observableArray(
myPOIs.map(POI => new POIObject(POI, map)));
this.myPOIObjList().map(POI => getFourSquareData(POI));
Toggle visibility for the list display
this.toggleVisibility = _ => $("#wrapper").toggleClass("active");
The filter string is input by the user via the filter-string input in HTML. It needs to be converted to a regex for matching against the full list later on.
this.POIFilter = ko.observable('');
Do the regex conversion here, and give a graceful alert if the input string is not a valid regex. It would otherwise give a hairy TypeError or something likewise in the console.
this.filterRegex = ko.computed(_ => {
try {
Generate a case-insensitive regex out of user input.
return new RegExp(that.POIFilter(), 'i');
} catch (err) {
Else simply warn the user, and return an empty regex to match all.
alert('Invalid Input!');
return new RegExp('');
}
});
Here we need to filter the myPOIObjList as per the user input above. A new ko.computed needs to be used instead of modifying the original myPOIObjList, else we risk losing the full list we had if there was a bad search with no matches.
this.filteredPOIList = ko.computed(_ => {
Filter the list first
const filteredList = that.myPOIObjList().filter(
POI => that.filterRegex().test(POI.title().toLowerCase())
);
updateMarkers(this.myPOIObjList(), filteredList, map);
return filteredList;
});
}
function init () {
const map = initMap();
Save the context for the viewModel, in case we need to use the properties in some other function. Thereafter, apply KO bindings.
const viewModel = new AppViewModel(map);
ko.applyBindings(viewModel);
}


Paint
Bitmap
OFF
-
mouse drag = move single spot
-
Shift + mouse drag = triangle-move spots
-
Ctrl + mouse drag = zomm in or out
-
double click = incremental paint
-
Shift + double click = backwards incremental paint
-
double click on thumb = toggle paint bitmap
-
Space key = animate
-
digit 1 = full screen


Modal gallery example with different types of loading spinners.
1. Type STANDARD
2. Type CIRCULAR
3. Type BARS
4. Type DOTS
5. Type CUBE_FLIPPING
6. Type CIRCLES
7. Type EXPLODING_SQUARES
| Input | Value |
|---|---|
id |
Value:
from 1 to 7 (one for every instance in this example)Unique id
|
images |
Value:
Image[] Array of
Image objects |
currentImage |
Value:
Image Image object to show |
libConfig |
Value:
LibConfig LibConfig object to config the library. See below. |
| Input | Value |
|---|---|
currentImageConfig |
Value:
{ loadingConfig: { enable: true, type: 1 } } It will show loading spinners of different types, based on the value of 'type' (number from 1 to 7, also available as LoadingType enum)
|


Council backs new bicycle rules, including pedestrian-cyclist buffer
by Phil Tank, Saskatoon StarPhoenixSaskatoon councillors say pedestrians complain about being startled by cyclists passing them on trails.
Saskatoon cyclists could soon be required to provide a one-metre buffer when passing pedestrians under a slate of new rules.
City council approved in principle changes to the city’s existing cycling bylaw at Monday’s meeting, including requiring a one-metre buffer between cyclists and motor vehicles and permitting children under 14 to ride on sidewalks.
Coun. Randy Donauer proposed the one-metre pedestrian-cyclist buffer, saying residents have complained about being startled by cyclists passing them on the Meewasin Trail.
“Sometimes a shouting match ensues and vulgarity is exchanged,” Donauer said.
How a buffer would work remains to be seen, but Donauer said he kept his proposal vague so issues like narrow trails that lack sufficient room for a one-metre buffer could be addressed.
Coun. Darren Hill added that he has also heard from residents who have been startled by cyclists passing them while they were walking.
Council voted 9-2 in favour of that idea; Coun. Sarina Gersher and Coun. Mairin Loewen voted against it.
Marina Melchiorre, a senior transportation engineer with the city, told council she had not come across another city with such a pedestrian-cyclist buffer in her research drafting the proposals.
Loewen added that the number of pedestrians wearing earbuds and who fail to hear a cyclist approaching plays a role in these interactions.
Council also voted 7-4 against a proposal to remove a requirement that cyclists use protected bike lanes on streets where they are available. Gersher, Loewen, Coun. Hilary Gough and Mayor Charlie Clark voted in favour of removing the requirement.
Although council unanimously approved most of the new cycling rules, a study of prohibiting youth from riding on sidewalks in business improvement districts and industrial areas was backed by an 8-3 margin.
“We need to make sure we’re looking after pedestrians, as well,” Hill said.
Gersher, Gough and Loewen voted against the study.
Loewen said she would rather see youth riding carefully on sidewalks in busy business districts than on the streets.
“The sidewalks are already too small on 20th Street West,” Randy Pshebylo, executive director of the Riversdale Business Improvement District, told council. Pshebylo had already expressed concerns about the rule allowing youth to cycle on sidewalks after two pedestrians were injured after leaving businesses on 20th.
Pshebylo wondered about lowering the speed limit on 20th Street to 30 kilometres per hour.
The rule changes include requiring cyclists to use hand signals, but do not include mandatory helmet use. The city administration will now draft a revamped bylaw based on the approved changes.


Just a note on some further explorations of some products I've been trying from ...OTJ.
I've had very good results making kava-kava with the cold tea method. In this, I take about 50-60 cc of chopped kava root, wrap it in a white cotton cloth, tie it with dental floss, and dunk it in 1 to 1.5 quarts of water. You must knead it to get good results. It takes a good 20 minutes or more to get the majority of the extract. The suggested 10 minutes is not enough. 30 minutes is probably over kill. The fact that sitting down and quietly kneading and dunking this thing for 20 to 30 minutes might be part of the relaxing effect isn't lost on me. So it goes. I could probably use it.
I've found that I can get a good synergistic effect between this and Calea zacatechichi. I've been adding about a teaspoon of the Calea to the kava in the bag. I can't stomach hot Calea tea. It's too nauseatingly bitter.
The effect is a calming sedative effect without loss of clarity of thought. Sleepiness occurs after about an hour. Sleep is deep and restful, with many vivid dreams. This effect seems to be cumulative, in that it gets stronger with subsequent use. The effect on dreaming also seems to be retained; it continues for a few days after stopping the Calea.
I also seem to get better results when I make the tea with my daily hyperoxidation dose added to the water. I put in 4 oz of 0.5% hydrogen peroxide. The frothing effect that signifies a good kava solution is more pronounced with the H2O2 added. Also, I've not had similar success with cold Calea solutions made without this. This hyperoxidation stuff is a whole story in itself. Not being a drug, I won't go into it here. If you're interested, do a Spider or similar search on the Web for 'oxygen therapy'. Look for the site Amandis. There's well over 100 files there. I suggest starting with #9. Sorry, I never keep track of Web addresses. ...OTJ also sells taheebo, a tea suggested as useful for oxygen therapy. But H2O2 is much cheaper.
To drink this, I add 3 tablespoons of Ovaltine, and drink it quickly. The kava isn't unpleasant, but the Calea is. This helps the taste in that it tastes much more like less sweetened chocolate. A 4 oz package of Calea will mix in well with about three 8 oz. pakages of kava root. This costs about $55.00 and will make at least 30 to 50 doses, maybe more; I tend to go overboard with sedatives since I seem to run at a very high speed much of the time.
Although it's not really very psychoactive, I feel I've been getting good physical results by adding a spoonful of Ashwagandha powder to the solution. It's an Indian medicinal similar to ginseng. The Ashagandha adds a slight carboard taste. This can be countered with a bit more sugar. This stuff seems to gove me more energy available and prevent caffiene/guarana stimulant burn out. But then so does the H2O2, so I can't promise this.
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.


2 Samuel 20:7
And there went out after him Joab's men, and the Cherethites, and the Pelethites, and all the mighty men: and they went out of Jerusalem, to pursue after Sheba the son of Bichri.


User Class
Every client connected to the Gordon Server and joined to a session is represented as a user.
Users can interact with each other by updating their DataObjects, sending chat or custom messages.
Constructor
User
()
Item Index
Properties
Methods
addEventListener
-
event
-
callback
Adds a listener to the given event name. (Substitute for on)
Parameters:
-
event
StringThe event name
-
callback
FunctionCallback function
dispose
()
chainable
Disposes the event instance.
emit
-
eventName
-
...args
Emits a event.
this.emit('eventName', 1, 'status', 5);
Parameters:
-
eventName
String -
...args
Object
Returns:
value
listenerCount
-
eventName
Get the number of listener to the given event name.
Parameters:
-
eventName
String
Returns:
value
listeners
-
eventName
Get all listeners to the given event name.
Parameters:
-
eventName
String
Returns:
list
on
-
event
-
callback
Adds a listener to the given event name.
Parameters:
-
event
StringThe event name
-
callback
FunctionCallback function
removeListener
-
event
-
callback
Adds a listener to the given event name.
Parameters:
-
event
StringThe event name
-
callback
FunctionCallback function
Properties
id
Number
User id
master
Boolean
Master flag. Every room has one dedicated master.
Default: false
Events
closed
Fired when web socket has disconnected
connected
Fired when web socket has connected
customMessage
A custom message has arrived
dataObjectRemoved
A DataObject has been removed
dataObjectUpdate
A DataObject has been updated
joined
Status of a join room request
master
Client has become the room master.
newDataObject
A new DataObject has been created
newUser
Fired when a new user has joined the current room
ping
Ping value updated
privateChatMessage
A private message has been sent
roomChanged
Status of a change room request
userLeft
Fired when a new user has left the current room


Hi, I'm a software engineer
Or atleast, aspiring to be one. I've learned a lot about computers so on this page you'll find a collection of everything I've done.
Built a custom gaming rig
Did a ton of freecodecamp.org coding challenges
Played around with github
Made some Javascript projects


HPL Copyright Notice and Licensing Terms
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions, and the following disclaimer in the
documentation and/or other materials provided with the distribution.
- All advertising materials mentioning features or use of this
software must display the following acknowledgement: This product
includes software developed at the University of Tennessee,
Knoxville, Innovative Computing Laboratories.
- The name of the University, the name of the Laboratory, or the
names of its contributors may not be used to endorse or promote
products derived from this software without specific written
permission.

[Home]
[Copyright and Licensing Terms]
[Algorithm]
[Scalability]
[Performance Results]
[Documentation]
[Software]
[FAQs]
[Tuning]
[Errata-Bugs]
[References]
[Related Links]


Sarin Accounts
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See the latest that is happening around here!
On August 11th, 2017 this page was created using only HTML5 and CSS.
It is a responsive (supposedly) web page, meaning it can re-size itself based on the device used to view it, such as a desktop, tablet, and/or a phone.
Check out the menu to the left side to navigate this website or to go to Hacker News.


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public class Simulation
extends java.lang.Object
implements java.io.Serializable
| Modifier and Type | Class and Description |
|---|---|
private class |
Simulation.enterClient
Entrada Cliente
|
| Modifier and Type | Field and Description |
|---|---|
private int |
checkouts
Number of total checkouts in the sumermarket
|
private SuperMarket |
costco
The object that allows to carry out the operations of a supermarket in this simulation
|
private Lista<Customer> |
customers
List of clients that had been attended during the simulation
|
private int |
largeCheckout
Number of checkouts of type large
|
private int |
quickCheckouts
Number of chekouts of type quick
|
private Serializer |
serializer
This object will provide us the ability to save all simulation data
|
| Constructor and Description |
|---|
Simulation()
Default constructor for the class, initialize the supermarket with 13,13 (Average) checkouts
|
Simulation(int quickCheckouts,
int largeCheckouts)
Main constructor for the simulation, when it is in use allows to specify the number of different checkouts
|
| Modifier and Type | Method and Description |
|---|---|
private int[] |
attendedClientsByItems()
Auxiliary method in calculate the number of items in fast and large checkouts
|
double |
averagingTime()
The numerical main method that allows to know the average for each client by waiting.
|
void |
genRandomProd(int products)
Random generate the number of products indicated
|
SuperMarket |
getCostco()
It allows access to the supermarket
|
void |
getReports()
It generates the reports for this simulation and mkdir for the folders if them aren't created yet
|
void |
loadProductsList(java.lang.String path)
This load the list of products provided to supply the warehouse
|
void |
simulate()
Main operation, it makes the whole job of form up a customer in this simulation
|
java.lang.String |
writeLine()
Allows to observe the information that we need to plot into a graph
|
private int checkouts
private int quickCheckouts
private int largeCheckout
private SuperMarket costco
private Lista<Customer> customers
private Serializer serializer
public Simulation()
public Simulation(int quickCheckouts, int largeCheckouts)
quickCheckouts
- number of quick checkouts that will be generatedlargeCheckouts
- number of large checkouts that will be generatedpublic SuperMarket getCostco()
public void getReports()
public void simulate()
public double averagingTime()
public java.lang.String writeLine()
private int[] attendedClientsByItems()
public void genRandomProd(int products)
products
- number of products to generatepublic void loadProductsList(java.lang.String path)
path
- list to be readed


BotIt cloud is full automation window suite. The system will self size the emulator you wanna Bot. It will self update the Modes and Images with all the cords and functions. one click will allow you to run everything.
Everything is random. not a single run will be the same. Random click areas. Random Sleeps. Random Failsafes and Bio Breakes (bots have to OO too!)
No Mouse Use. No Screen Use. Very Very low System use. So Keep Netflix and Chill While Botit Run the Clicks For you.
Fully Portable (Keep in mind Free user can only reset mac once every 24 hours. only Tier 1 and above can multi on the same account) Many New Features added. BackGround Pixel Scanner Object mapper


An Angular {{name}} component{{plural ? 's' : ''}} {{plural ? 'have' : 'has'}} been created as a native Angular version of Bootstrap {{name}}. {{name}} {{plural ? 'are' : 'is'}} delivered with some new features, variants, and unique design that matches CoreUI Design System requirements.


ListEventSourceMappings
Lists event source mappings. Specify an EventSourceArn
to only show event source mappings for a
single event source.
Request Syntax
GET /2015-03-31/event-source-mappings/?EventSourceArn=EventSourceArn
&FunctionName=FunctionName
&Marker=Marker
&MaxItems=MaxItems
HTTP/1.1
URI Request Parameters
The request uses the following URI parameters.
- EventSourceArn
-
The Amazon Resource Name (ARN) of the event source.
-
Amazon Kinesis - The ARN of the data stream or a stream consumer.
-
Amazon DynamoDB Streams - The ARN of the stream.
-
Amazon Simple Queue Service - The ARN of the queue.
-
Amazon Managed Streaming for Apache Kafka - The ARN of the cluster.
Pattern:
arn:(aws[a-zA-Z0-9-]*):([a-zA-Z0-9\-])+:([a-z]{2}(-gov)?-[a-z]+-\d{1})?:(\d{12})?:(.*)
-
- FunctionName
-
The name of the Lambda function.
Name formats
-
Function name -
MyFunction
. -
Function ARN -
arn:aws:lambda:us-west-2:123456789012:function:MyFunction
. -
Version or Alias ARN -
arn:aws:lambda:us-west-2:123456789012:function:MyFunction:PROD
. -
Partial ARN -
123456789012:function:MyFunction
.
The length constraint applies only to the full ARN. If you specify only the function name, it's limited to 64 characters in length.
Length Constraints: Minimum length of 1. Maximum length of 140.
Pattern:
(arn:(aws[a-zA-Z-]*)?:lambda:)?([a-z]{2}(-gov)?-[a-z]+-\d{1}:)?(\d{12}:)?(function:)?([a-zA-Z0-9-_]+)(:(\$LATEST|[a-zA-Z0-9-_]+))?
-
- Marker
-
A pagination token returned by a previous call.
- MaxItems
-
The maximum number of event source mappings to return.
Valid Range: Minimum value of 1. Maximum value of 10000.
Request Body
The request does not have a request body.
Response Syntax
HTTP/1.1 200
Content-type: application/json
{
"EventSourceMappings": [
{
"BatchSize": number,
"BisectBatchOnFunctionError": boolean,
"DestinationConfig": {
"OnFailure": {
"Destination": "string"
},
"OnSuccess": {
"Destination": "string"
}
},
"EventSourceArn": "string",
"FunctionArn": "string",
"FunctionResponseTypes": [ "string" ],
"LastModified": number,
"LastProcessingResult": "string",
"MaximumBatchingWindowInSeconds": number,
"MaximumRecordAgeInSeconds": number,
"MaximumRetryAttempts": number,
"ParallelizationFactor": number,
"Queues": [ "string" ],
"SelfManagedEventSource": {
"Endpoints": {
"string" : [ "string" ]
}
},
"SourceAccessConfigurations": [
{
"Type": "string",
"URI": "string"
}
],
"StartingPosition": "string",
"StartingPositionTimestamp": number,
"State": "string",
"StateTransitionReason": "string",
"Topics": [ "string" ],
"TumblingWindowInSeconds": number,
"UUID": "string"
}
],
"NextMarker": "string"
}
Response Elements
If the action is successful, the service sends back an HTTP 200 response.
The following data is returned in JSON format by the service.
- EventSourceMappings
-
A list of event source mappings.
Type: Array of EventSourceMappingConfiguration objects
- NextMarker
-
A pagination token that's returned when the response doesn't contain all event source mappings.
Type: String
Errors
- InvalidParameterValueException
-
One of the parameters in the request is invalid.
HTTP Status Code: 400
- ResourceNotFoundException
-
The resource specified in the request does not exist.
HTTP Status Code: 404
- ServiceException
-
The AWS Lambda service encountered an internal error.
HTTP Status Code: 500
- TooManyRequestsException
-
The request throughput limit was exceeded.
HTTP Status Code: 429
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following:


Secure Infinispan server by default
The Infinispan server we introduced in 10.0 exposes a single port through which both Hot Rod and HTTP clients can connect.
While Infinispan has had very extensive security support since 7.0, the out-of-the-box default configuration did not enable authentication.
Infinispan 11.0’s server’s default configuration, instead, requires authentication. We have made several improvements to how authentication is configured and the tooling we provide to make the experience as smooth as possible.
Automatic authentication mechanism selection
Previously, when enabling authentication, you had to explicitly define which mechanisms had to be enabled per-protocol, with all of the peculiarities specific to each one (i.e. SASL for Hot Rod, HTTP for REST). Here is an example configuration with Infinispan 10.1 that enables DIGEST authentication:
<endpoints socket-binding="default" security-realm="default">
<hotrod-connector name="hotrod">
<authentication>
<sasl mechanisms="DIGEST-MD5" server-name="infinispan"/>
</authentication>
</hotrod-connector>
<rest-connector name="rest">
<authentication mechanisms="DIGEST"/>
</rest-connector>
</endpoints>
In Infinispan 11.0, the mechanisms are automatically selected based on the capabilities of the security realm. Using the following configuration:
<endpoints socket-binding="default" security-realm="default">
<hotrod-connector name="hotrod" />
<rest-connector name="rest"/>
</endpoints>
together with a properties security realm, will enable DIGEST for HTTP and SCRAM-*, DIGEST-* and CRAM-MD5 for Hot Rod. BASIC/PLAIN will only be implicitly enabled when the security realm has a TLS/SSL identity.
The following tables summarize the mapping between realm type and implicitly enabled mechanisms.
| Security Realm | SASL Authentication Mechanism |
|---|---|
Property Realms and LDAP Realms |
SCRAM-*, DIGEST-*, CRAM-MD5 |
Token Realms |
OAUTHBEARER |
Trust Realms |
EXTERNAL |
Kerberos Identities |
GSSAPI, GS2-KRB5 |
SSL/TLS Identities |
PLAIN |
| Security Realm | HTTP Authentication Mechanism |
|---|---|
Property Realms and LDAP Realms |
DIGEST |
Token Realms |
BEARER_TOKEN |
Trust Realms |
CLIENT_CERT |
Kerberos Identities |
SPNEGO |
SSL/TLS Identities |
BASIC |
Automatic encryption
If the security realm has a TLS/SSL identity, the endpoint will automatically enable TLS for all protocols.
Encrypted properties security realm
The properties realm that is part of the default configuration has been greatly improved in Infinispan 11. The passwords are now stored in multiple encrypted formats in order to support the various DIGEST, SCRAM and PLAIN/BASIC mechanisms.
The user
functionality that is now built into the CLI allows easy creation and manipulation of users, passwords and groups:
[disconnected]> user create --password=secret --groups=admin admin
[disconnected]> connect --username=admin --password=secret
[ispn-29934@cluster//containers/default]> user ls
[ "admin" ]
[ispn-29934@cluster//containers/default]> user describe admin
{ username: "admin", realm: "default", groups = [admin] }
[ispn-29934@cluster//containers/default]> user password admin
Set a password for the user: ******
Confirm the password for the user: ******
[ispn-29934@cluster//containers/default]>
Authorization: simplified
Authorization is another security aspect of Infinispan. In previous versions, setting up authorization was complicated by the need to add all the needed roles to each cache:
<infinispan>
<cache-container name="default">
<security>
<authorization>
<identity-role-mapper/>
<role name="AdminRole" permissions="ALL"/>
<role name="ReaderRole" permissions="READ"/>
<role name="WriterRole" permissions="WRITE"/>
<role name="SupervisorRole" permissions="READ WRITE EXEC BULK_READ"/>
</authorization>
</security>
<distributed-cache name="secured">
<security>
<authorization roles="AdminRole ReaderRole WriterRole SupervisorRole"/>
</security>
</distributed-cache>
</cache-container>
...
</infinispan>
With Infinispan 11 you can avoid specifying all the roles at the cache level: just enable authorization and all roles will implicitly apply. As you can see, the cache definition is much more concise:
<infinispan>
<cache-container name="default">
...
<distributed-cache name="secured">
<security>
<authorization/>
</security>
</distributed-cache>
</cache-container>
...
</infinispan>
Conclusions
We hope that the changes we’ve made to improve security will make your servers more secure and easier to configure. For more information read the server security documentation.


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unsupervised
Contents
5.6. unsupervised#
5.6.1. clustering#
labels are not given
intra-cluster distances are minimized, inter-cluster distances are maximized
distance measures
symmetric D(A,B)=D(B,A)
self-similarity D(A,A)=0
positivity separation D(A,B)=0 iff A=B
triangular inequality D(A,B) <\(\leq\) D(A,C)+D(B,C)
ex. Minkowski Metrics \(d(x,y)=\sqrt[r]{\sum \vert x_i-y_i\vert ^r}\)
r=1 Manhattan distance
r=1 when y is binary -> Hamming distance
r=2 Euclidean
r=\(\infty\) “sup” distance
correlation coefficient - unit independent
edit distance
<IP_ADDRESS>. hierarchical#
two approaches:
bottom-up agglomerative clustering - starts with each object in separate cluster then joins
top-down divisive - starts with 1 cluster then separates
ex. starting with each item in its own cluster, find best pair to merge into a new cluster
repeatedly do this to make a tree (dendrogram)
distances between clusters defined by linkage function
single-link - closest members (long, skinny clusters)
complete-link - furthest members (tight clusters)
average - most widely used
ex. MST - keep linking shortest link
ultrametric distance - tighter than triangle inequality
\(d(x, y) \leq \max[d(x,z), d(y,z)]\)
<IP_ADDRESS>. partitional#
partition n objects into a set of K clusters (must be specified)
globally optimal: exhaustively enumerate all partitions
minimize sum of squared distances from cluster centroid
evaluation w/ labels - purity - ratio between dominant class in cluster and size of cluster
k-means++ - better at not getting stuck in local minima
randomly move centers apart
Complexity: \(O(n^2p)\) for first iteration and then can only get worse
<IP_ADDRESS>. statistical clustering (j 10)#
latent vars - values not specified in the observed data
K-Means
start with random centers
E: assign everything to nearest center: \(O(\|\text{clusters}\|*np) \)
M: recompute centers \(O(np)\) and repeat until nothing changes
partition amounts to Voronoi diagram
can be viewed as minimizing distortion measure \(J=\sum_n \sum_i z_n^i ||x_n - \mu_i||^2\)
GMMs: \(p(x|\theta) = \underset{i}{\Sigma} \pi_i \mathcal{N}(x|\mu_i, \Sigma_i)\)
\(l(\theta|x) = \sum_n \log \: p(x_n|\theta) \\ = \sum_n \log \sum_i \pi_i \mathcal{N}(x_n|\mu_i, \Sigma_i)\)
hard to maximize bcause log acts on a sum
“soft” version of K-means - update means as weighted sums of data instead of just normal mean
sometimes initialize K-means w/ GMMs
<IP_ADDRESS>. conditional mixture models - regression/classification (j 10)#
graph LR;
X-->Y;
X --> Z
Z --> Y
ex.
latent variable Z has multinomial distr.
mixing proportions: \(P(Z^i=1|x, \xi)\)
ex. \( \frac{e^{\xi_i^Tx}}{\sum_je^{\xi_j^Tx}}\)
mixture components: \(p(y|Z^i=1, x, \theta_i)\) ~ different choices
ex. mixture of linear regressions
\(p(y| x, \theta) = \sum_i \underbrace{\pi_i (x, \xi)}_{\text{mixing prop.}} \cdot \underbrace{\mathcal{N}(y|\beta_i^Tx, \sigma_i^2)}_{\text{mixture comp.}}\)
ex. mixtures of logistic regressions
\(p(y|x, \theta_i) = \underbrace{\pi_i (x, \xi)}_{\text{mixing prop.}} \cdot \underbrace{\mu(\theta_i^Tx)^y\cdot[1-\mu(\theta_i^Tx)]^{1-y}}_{\text{mixture comp.}}\) where \(\mu\) is the logistic function
also, nonlinear optimization for this (including EM)
5.6.2. dim reduction#
In general there is some tension between preserving global properties (e.g. PCA) and local peroperties (e.g. nearest neighborhoods)
Method |
Analysis objective |
Temporal smoothing |
Explicit noise model |
Notes |
|---|---|---|---|---|
PCA |
Covariance |
No |
No |
orthogonality |
FA |
Covariance |
No |
Yes |
like PCA, but with errors (not biased by variance) |
LDS/GPFA |
Dynamics |
Yes |
Yes |
|
NLDS |
Dynamics |
Yes |
Yes |
|
LDA |
Classification |
No |
No |
|
Demixed |
Regression |
No |
Yes/No |
|
Isomap/LLE |
Manifold discovery |
No |
No |
|
T-SNE |
…. |
…. |
… |
|
UMAP |
… |
… |
… |
NMF - \(\min_{D \geq 0, A \geq 0} \|\|X-DA\|\|_F^2\)
SEQNMF
LDA/QDA - finds basis that separates classes
reduced to axes which separate classes (perpendicular to the boundaries)
K-means - can be viewed as a linear decomposition
<IP_ADDRESS>. spectral clustering#
spectral clustering - does dim reduction on eigenvalues (spectrum) of similarity matrix before clustering in few dims
uses adjacency matrix
basically like PCA then k-means
performs better with regularization - add small constant to the adjacency matrix
<IP_ADDRESS>. pca#
want new set of axes (linearly combine original axes) in the direction of greatest variability
this is best for visualization, reduction, classification, noise reduction
assume \(X\) (nxp) has zero mean
derivation:
minimize variance of X projection onto a unit vector v
\(\frac{1}{n} \sum (x_i^Tv)^2 = \frac{1}{n}v^TX^TXv\) subject to \(v^T v=1\)
\(\implies v^T(X^TXv-\lambda v)=0\): solution is achieved when \(v\) is eigenvector corresponding to largest eigenvalue
like minimizing perpendicular distance between data points and subspace onto which we project
SVD: let \(U D V^T = SVD(Cov(X))\)
\(Cov(X) = \frac{1}{n}X^TX\), where X has been demeaned
equivalently, eigenvalue decomposition of covariance matrix \(\Sigma = X^TX\)
each eigenvalue represents prop. of explained variance: \(\sum \lambda_i = tr(\Sigma) = \sum Var(X_i)\)
screeplot - eigenvalues in decreasing order, look for num dims with kink
don’t automatically center/normalize, especially for positive data
SVD is easier to solve than eigenvalue decomposition, can also solve other ways
multidimensional scaling (MDS)
based on eigenvalue decomposition
adaptive PCA
extract components sequentially, starting with highest variance so you don’t have to extract them all
good PCA code: http://cs231n.github.io/neural-networks-2/
X -= np.mean(X, axis = 0) ## zero-center data (nxd)
cov = np.dot(X.T, X) / X.shape[0] ## get cov. matrix (dxd)
U, D, V = np.linalg.svd(cov) ## compute svd, (all dxd)
Xrot_reduced = np.dot(X, U[:, :2]) ## project onto first 2 dimensions (n x 2)
nonlinear pca
usually uses an auto-associative neural network
<IP_ADDRESS>. topic modeling#
similar, try to discover topics in a model (which maybe can be linearly combined to produce the original document)
ex. LDA - generative model: posits that each document is a mixture of a small number of topics and that each word’s presence is attributable to one of the document’s topics
<IP_ADDRESS>. sparse coding = sparse dictionary learning#
D is like autoencoder output weight matrix
\(a\) is more complicated - requires solving inner minimization problem
outer loop is not quite lasso - weights are not what is penalized
impose norm \(D\) not too big
algorithms
thresholding (simplest) - do \(D^Ty\) and then threshold this
basis pursuit - change \(l_0\) to \(l_1\)
this will work under certain conditions (with theoretical guarantees)
matching purusuit - greedy, find support one at a time, then look for the next one
<IP_ADDRESS>. ica#
overview
remove correlations and higher order dependence
all components are equally important
like PCA, but instead of the dot product between components being 0, the mutual info between components is 0
goals
minimize statistical dependence between components
maximize information transferred in a network of non-linear units
uses information theoretic unsupervised learning rules for neural networks
problem - doesn’t rank features for us
goal: want to decompose \(X\) into \(z\), where we assume \(X = Az\)
assumptions
independence: \(P(z) = \prod_i P(z_i)\)
non-gaussianity of \(z\)
2 ways to get \(z\) that matches these assumptions
maximize non-gaussianity of \(z\) - use kurtosis, negentropy
minimize mutual info between components of \(z\) - use KL, max entropy
often equivalent
identifiability: \(z\) is identifiable up to a permutation and scaling of sources when
at most one of the sources \(z_k\) is gaussian
\(A\) is full-rank
ICA learns components which are completely independent, whereas PCA learns orthogonal components
non-linear ica: \(X \approx f(z)\), where assumptions on \(z\) are the same, but \(f\) can be nonlinear
to obtain identifiability, we need to restrict \(f\) and/or constrain the distr of the sources \(z\)
bell & sejnowski 1995 original formulation (slightly different)
entropy maximization - try to find a nonlinear function \(g(x)\) which lets you map that distr \(f(x)\) to uniform
then, that function \(g(x)\) is the cdf of \(f(x)\)
in ICA, we do this for higher dims - want to map distr of \(x_1, ..., x_p\) to \(y_1, ..., y_p\) where distr over \(y_i\)’s is uniform (implying that they are independent)
additionally we want the map to be information preserving
mathematically: \(\underset{W} \max I(x; y) = \underset{W} \max H(y)\) since \(H(y|x)\) is zero (there is no randomness)
assume \(y = \sigma (W x)\) where \(\sigma\) is elementwise
(then S = WX, \(W=A^{-1}\))
requires certain assumptions so that \(p(y)\) is still a distr: \(p(y) = p(x) / |J|\) where J is Jacobian
learn W via gradient ascent \(\Delta W \propto \partial / \partial W (\log |J|)\)
there is now something faster called fast ICA
topographic ICA (make nearby coefficient like each other)
interestingly, some types of self-supervised learning perform ICA assuming certain data structure (e.g. time-contrastive learning (hyvarinen et al. 2016))
<IP_ADDRESS>. topological#
multidimensional scaling (MDS)
given a a distance matrix, MDS tries to recover low-dim coordinates s.t. distances are preserved
minimizes goodness-of-fit measure called stress = \(\sqrt{\sum (d_{ij} - \hat{d}_{ij})^2 / \sum d_{ij}^2}\)
visualize in low dims the similarity between individial points in high-dim dataset
classical MDS assumes Euclidean distances and uses eigenvalues
constructing configuration of n points using distances between n objects
uses distance matrix
\(d_{rr} = 0\)
\(d_{rs} \geq 0\)
solns are invariant to translation, rotation, relfection
solutions types
non-metric methods - use rank orders of distances
invariant to uniform expansion / contraction
metric methods - use values
D is Euclidean if there exists points s.t. D gives interpoint Euclidean distances
define B = HAH
D Euclidean iff B is psd
t-sne preserves pairwise neighbors
t-sne tries to match pairwise distances between the original data and the latent space data:
original data
distances are converted to probabilities by assuming points are means of Gaussians, then normalizing over all pairs
variance of each Gaussian is scaled depending on the desired perplexity
latent data
distances are calculated using some kernel function
t-SNE uses heavy-tailed Student’s t-distr kernel (van der Maaten & Hinton, 2008)
SNE use Gausian kernel (Hinton & Roweis, 2003)
kernels have some parameters that can be picked or learned
perplexity - how to balance between local/global aspects of data
optimization - for optimization purposes, this can be decomposed into attractive/repulsive forces
umap: Uniform Manifold Approximation and Projection for Dimension Reduction
pacmap
<IP_ADDRESS>. misc#
NNK-Means: Dictionary Learning using Non-Negative Kernel regression (shekkizhar & ortega, 2021)
data summarization - represent large datasets by a small set of elements (e.g. k-means)
here, use dictionary learning instead of k-means to summarize data
each dictionary element is a sparse combination of inputs
use non-negative kernel regesion (NNK) to measure distances when designing the dictionary (shekkizar & ortega, 2020)
5.6.3. generative models#
notes for deep unsupervised learning
MLE equivalent to minimizing KL for density estimation:
\(\min_\theta KL(p|| p_\theta) =\\ \min_\theta-H(p) + \mathbb E_{x\sim p}[-\log p_\theta(x)] \\ \max_\theta E_p[\log p_\theta(x)]\)
<IP_ADDRESS>. autoregressive models#
model input based on input
\(p(x_1)\) is a histogram (learned prior)
\(p(x_2|x_1)\) is a distr. ouptut by a neural net (output is logits, followed by softmax)
all conditional distrs. can be given by neural net
can model using an RNN: e.g. char-rnn (karpathy, 2015): \(\log p(x) - \sum_i \log p(x_i | x_{1:i-1})\), where each \(x_i\) is a character
can also use masks
masked autoencoder for distr. estimation - mask some weights so that autoencoder output is a factorized distr.
pick an odering for the pixels to be conditioned on
ex. 1d masked convolution on wavenet (use past points to predict future points)
ex. pixelcnn - use masking for pixels to the topleft
ex. gated pixelcnn - fixes issue with blindspot
ex. pixelcnn++ - nearby pixel values are likely to cooccur
ex. pixelSNAIL - uses attention and can get wider receptive field
attention:\(A(q, K, V) = \sum_i \frac{\exp(q \cdot k_i)}{\sum_j \exp (q \cdot k_j)} v_i\)
masked attention can be more flexible than masked convolution
can do super resolution, hierarchical autoregressive model
problems
slow - have to sample each pixel (can speed up by caching activations)
can also speed up by assuming some pixels conditionally independent
hard to get a latent reprsentation
can use Fisher score \(\nabla_\theta \log p_\theta (x)\)
<IP_ADDRESS>.1. flow models#
good intro to implementing invertible neural networks: https://hci.iwr.uni-heidelberg.de/vislearn/inverse-problems-invertible-neural-networks/
input / output dimension need to have same dimension
we can get around this by padding one of the dimensions with noise variables (and we might want to penalize these slightly during training)
ultimate goal: a likelihood-based model with
fast sampling
fast inference (evaluating the likelihood)
fast training
good samples
good compression
transform some \(p(x)\) to some \(p(z)\)
\(x \to z = f_\theta (x)\), where \(z \sim p_Z(z)\)
\(p_\theta (x) dx = p(z)dz\)
\(p_\theta(x) = p(f_\theta(x))|\frac {\partial f_\theta (x)}{\partial x}|\)
autoregressive flows
map \(x\to z\) invertible
\(x \to z\) is same as log-likelihood computation
\(z\to x\) is like sampling
end up being as deep as the number of variables
realnvp (dinh et al. 2017) - can couple layers to preserve invertibility but still be tractable
downsample things and have different latents at different spatial scales
other flows
flow++
glow
FFJORD - continuous time flows
discrete data can be harder to model
dequantization - add noise (uniform) to discrete data
<IP_ADDRESS>. vaes#
-
minimize \(\mathbb E_{q_\phi(z|x)}[\log p_\theta(x|z)- D_{KL}(q_\phi(z|x)\:||\:p(z))]\)
want latent \(z\) to be standard normal - keeps the space smooth
hard to directly calculate \(p(z|x)\), since it includes \(p(x)\), so we approximate it with the variational posterior \(q_\phi (z|x)\), which we assume to be Gaussian
goal: \(\text{argmin}_\phi KL(q_\phi(z|x) \:|| \:p(z|x))\)
still don’t have acess to \(p(x)\), so rewrite \(\log p(x) = ELBO(\phi) + KL(q_\phi(z|x) \: || \: p(z|x))\)
instead of minimizing \(KL\), we can just maximize the \(ELBO=\mathbb E_q [\log p(x, z)] - \mathbb E_q[\log q_\phi (z|x)]\)
mean-field variational inference - each point has its own params (e.g. different encoder DNN) vs amortized inference - same encoder for all points
-
want large evidence \(\log p_\theta (\mathbf x)\) (means model is a good fit to the data)
want good fit to the posterior \(q_\phi(z|x)\)
just an autoencoder where the middle hidden layer is supposed to be unit gaussian
add a kl loss to measure how well it matches a unit gaussian
for calculation purposes, encoder actually produces means / vars of gaussians in hidden layer rather than the continuous values….
this kl loss is not too complicated…https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf
generally less sharp than GANs
uses mse loss instead of gan loss…
intuition: vaes put mass between modes while GANs push mass towards modes
constraint forces the encoder to be very efficient, creating information-rich latent variables. This improves generalization, so latent variables that we either randomly generated, or we got from encoding non-training images, will produce a nicer result when decoded.
<IP_ADDRESS>. gans#
evaluating gans
don’t have explicit objective like likelihood anymore
kernel density = parzen-window density based on samples yields likelihood
inception score \(IS(\mathbf x) = \exp(\underbrace{H(\mathbf y)}_{\text{want to generate diversity of classes}} - \underbrace{H(\mathbf y|\mathbf x)}_{\text{each image should be distinctly recognizable}})\)
FID - Frechet inception score works directly on embedded features from inception v3 model
embed population of images and calculate mean + variance in embedding space
measure distance between these means / variances for real/synthetic images using Frechet distance = Wasseterstein-2 distance
infogan
problems
mode collapse - pick just one mode in the distr.
train network to be loss function
original gan paper (2014)
generative adversarial network
goal: want G to generate distribution that follows data
ex. generate good images
two models
G - generative
D - discriminative
G generates adversarial sample x for D
G has prior z
D gives probability p that x comes from data, not G
like a binary classifier: 1 if from data, 0 from G
adversarial sample - from G, but tricks D to predicting 1
training goals
G wants D(G(z)) = 1
D wants D(G(z)) = 0
D(x) = 1
converge when D(G(z)) = 1/2
G loss function: \(G = \text{argmin}_G \log(1-D(G(Z))\)
overall \(\min_g \max_D\) log(1-D(G(Z))
training algorithm
in the beginning, since G is bad, only train my minimizing G loss function
projecting into gan latent space (=gan inversion)
2 general approaches
learn an encoder to go image -> latent space
In-Domain GAN Inversion for Real Image Editing (zhu et al. 2020)
learn encoder to project image into latent space, with regularizer to make sure it follows the right distr.
optimize latent code wrt image directly
can also learn an encoder to initialize this optimization
some work designing GANs that are intrinsically invertible
stylegan-specific - some works which exploit layer-wise noises
stylegan2 paper: optimize w along with noise maps - need to make sure noise maps don’t include signal
<IP_ADDRESS>. diffusion / energy-based models#
first describe a procedure for gradually turning data into noise
then train a DNN to invert this procedure step-by-step
single model handles many different noise levels with shared parameters
seminal paper: Generative Modeling by Estimating Gradients of the Data Distribution (song & ermon, 2019)
really started earlier: Deep Unsupervised Learning using Nonequilibrium Thermodynamics (sohl-dickstein, …, ganguli, 2015)
[Improved Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2102.0 9672) (2021)
can make this class-conditionnal by incorporating classifier into the model which inverts the noise
5.6.4. self/semi-supervised#
<IP_ADDRESS>. self-supervised#
blog: https://dawn.cs.stanford.edu/2017/07/16/weak-supervision/
training data is hard to get
related paper: https://www.biorxiv.org/content/early/2018/06/16/339630
basics: predict some part of the input (e.g. present from past, bottom from top, etc.)
ex. denoising autoencoder
ex. in-painting (can use adversarial loss)
ex. colorization, split-brain autoencoder
colorization in video given first frame (helps learn tracking)
ex. relative patch prediction
ex. orientation prediction
ex. nlp
word2vec
bert - predict blank word
contrastive predictive coding - translates generative modeling into classification
contrastive loss = InfoNCE loss uses cross-entropy loss to measure how well the model can classify the “future” representation among a set of unrelated “negative” samples
negative samples may be from other batches or other parts of the input
momentum contrast - queue of previous embeddings are “keys”
match new embedding (query) against keys and use contrastive loss
similar idea as memory bank
Unsupervised Visual Representation Learning by Context Prediction (efros 15)
predict relative location of different patches
SimCLR (Chen et al, 2020)
maximize agreement for different points after some augmentation (contrastive loss)
<IP_ADDRESS>. semi-supervised#
make the classifier more confident
entropy minimization - try to minimize the entropy of output predictions (like making confident predictions labels)
pseudo labeling - just take argmax pred as if it were the label
label consistency with data augmentation
Billion-scale semi-supervised learning for image classification (FAIR, 19)
unsupervised learning + model distillation succeeds on imagenet
ensembling
temporal ensembling - ensemble multiple models at different training epochs
mean teachers - learn from exponential moving average of students
unsupervised data augmentation - augment and ensure prediction is the same
distribution alignment - ex. cyclegan - enforce cycle consistency = dual learning = back translation
simpler is marginal matching
<IP_ADDRESS>. compression#
simplest - fixed-length code
variable-length code
could append stop char to each codeword
general prefix-free code = binary tries
codeword is path from froot to leaf
huffman code - higher prob = shorter
arithmetic coding
motivation: coding one symbol at a time incurs penalty of +1 per symbol - more efficient to encode groups of things
can be improved with good autoregressive model
<IP_ADDRESS>. contrastive learning#
What makes for good views for contrastive learning (tian et al. 2020)
how to select views (e.g. transformations we want to be invariant to)?
reduce the mutual information (MI) between views while keeping task-relevant information intact
Supervised Contrastive Learning (khosla et al. 2020)
Data-Efficient Image Recognition with Contrastive Predictive Coding
pre-training with CPC on ImageNet improves accuracy
Automatically Discovering and Learning New Visual Categories with Ranking Statistics


Hi! I'm Jeannie.
I'm a scientist turned software developer.
A careful and skeptical eye, the perseverance and passion needed to tackle complex problems, and a prowess for working with data.
Experience with React/Redux, Ruby on Rails, and other modern frameworks. Trained in object-oriented and MVC design principles, as well as Agile development practices.


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The slope of the curve y = f(x) at the point P($x_0$,f($x_0$)) is the number
$m$ = $lim_{h \to 0}$ $\dfrac{f(x_0+h)-f(x_0)}{h}$ (provided the limit exists)
The tangent line to the curve at P is the line through P with this slope
(a) Find the slope of the curve $y = \dfrac{1}{x}$ at x = a$\not$=0. What is the slope at the point x=-1?
(b) Where does the slope equal -$\dfrac{1}{4}$?
(c) What happens to the tangent to the curve at the point ($a$,$1/a$) as $a$ changes
The derivative of a function $f$ at a point $x_0$, denoted $f'(x_0)$ , is,
$f'(x_0)$ = $lim_{h \to 0}$ $\dfrac{f(x_0+h)-f(x_0)}{h}$
provided this limit exists.
$f(t)$ = $16t^2$ The average speed of the rock over the interval beween t=1 and t=1+h seconds, for h>0:
$\dfrac{f(1+h)-f(1)}{h}$ = $\dfrac{16(1+h)^2-16(1)^2}{h}$ = $\dfrac{16(h^2+2h)}{h}$ = $16(h+2)$
The rock's speed at the instant $t=1$ is then
$lim_{h \to 0}$ $16(h+2)$ = $16(0+2)$ = 32
The following are all interpretations for the limit of the different quotient,
$lim_{h \to 0}$ $\dfrac{f(x_0+h)-f(x_0)}{h}$
Find an equation for the tangent to the curve at the given point. Then sketch the curve and tangent together.
$y = 4-x^2$ (-1,3)
Find an equation of the straight line having slope 1/4 that is tangent to the curve $y = sqrt(x)$.
$y = 4-x^2$ (-1,3)
Circle's changing area What is the rate of change of the area of a circle ( $A = \pi\ r^2$ )
with respect to the radius when the radius is $r=3$ ?
Speed of a rocket At $t$ sec after liftoff, the height of a rocket is $3t^2$ ft.
How fast is the rocket climbing 10 sec after liftoff?
We say that a continuous curve y = f(x) has a vertical tangent at the point where $x = x_0$
if $lim_{h \to 0}$ $\dfrac{f(x_0+h)-f(x_0)}{h}$ = $\infty\ $ or -$\infty\ $ .
For example , $y$ = $x^{1/3}$ has a vertical tangent at x = 0 (see accompanying figure):
$lim_{h \to 0}$ $\dfrac{f(x_0+h)-f(x_0)}{h}$ = $lim_{h \to 0}$ $\dfrac{h^{1/3}-0}{h}$
= $lim_{h \to 0}$ $\dfrac{1}{h^{2/3}}$
However , $y = x^{2/3}$ has no vertical tangent at x = 0 (see next figure):
$lim_{h \to 0}$ $\dfrac{g(0+h)-g(0)}{h}$ = $lim_{h \to 0}$ $\dfrac{h^{2/3}-0}{h}$
= $lim_{h \to 0}$ $\dfrac{1}{h^{1/3}}$
does not exist , because the limit is $\infty\ $ from the riht and -$\infty\ $ from the left.
In the last section we defined the derivative of $y = f(x)$ at the point $x = x_0$ to be the limit
$f'(x_0)$ = $lim_{h \to 0}$ $\dfrac{f(x_0+h)-f(x_0)}{h}$
Derivative as a function derived from $f$ by consediring the limit at each point $x$
in the domain of $f$.
The derivative of the funtion $f(x)$ with respect to the variable x is the function $f'$ whose value at x is
$f'(x)$ = $lim_{h \to 0}$ $\dfrac{f(x+h)-f(x)}{h}$ ,
proved the limit exists.
Alternative Formula for the Derivative
$f'(x)$ = $lim_{z \to x}$ $\dfrac{f(z)-f(x)}{z-x}$ ,
The process of calculating a derivative is called differentiation.
Example:
$\dfrac{d}{dx}(\dfrac{1}{x})$ = $-\dfrac{1}{x^2}$, $x\not$=0
$\dfrac{d}{dx}f(x)$ $f'(x)$ = $lim_{h \to 0}$ $\dfrac{f(x+h)-f(x)}{h}$ $f'(x)$ = $lim_{z \to x}$ $\dfrac{f(z)-f(x)}{z-x}$
(a) Find the derivative of $f(x) = \sqrt{x}$ for $x > 0$.
(b) Find the tangent line to the curve $y = \sqrt{x}$ at $x = 4$.
$f'(x) = y' = \dfrac{dy}{dx} = \dfrac{df}{dx} = \dfrac{d}{dx}f(x) = D(f)(x) = D_xf(x)$
The "prime" notations $y'$ and $f'$ come from notations that Newton used for derivatives.
The d/dx notations are similar to those used by Leibniz.
To indicate the value of a derivative at a specified number $x = a$ ,we use notation
$f'(a) = \left. \dfrac{dy}{dx} \right| _{x=a}\ $ = $\left. \dfrac{df}{dx} \right| _{x=a}\ $ = $\left. \dfrac{d}{dx}f(x) \right| _{x=a}\ $
$f'(4) = \left. \dfrac{d}{dx}\sqrt{x} \right| _{x=4}\ $ = $\left. \dfrac{1}{2\sqrt{x}} \right| _{x=4}\ $ = $\dfrac{1}{2\sqrt4} = \dfrac{1}{4}$
We can often make a resonable plot of the derivative of $y = f(x)$ by estimating the slopes on the graph of $f$.
Math the fuctions with the derivatives
A function $y = f(x)$ is differentiable on an open interval(finite or infinite) if it has a derivative at each point of the interval.
It is differentiable on a closed interval [a,b] if it is differentiable on the interior(a,b) and if the limits
$lim_{h \to 0^+}$ $\dfrac{f(a+h)-f(a)}{h}$ Right-hand deriative at a
$lim_{h \to 0^-}$ $\dfrac{f(b+h)-f(b)}{h}$ Left-hand deriative at b
Show that the function $y = |x|$ is differentiable on $(-\infty,0)$ and $(0,\infty)$ but has
no derivative at x = 0.
A function has a derivative at a point $x_0$ if the slopes of the secant lines through $P(x_0,y_0)$ and a nearby point
$Q$ on the graph approach a finite limit as $Q$ approaches $P$.
Whenever the secants fail to take up a limiting position or become vertial as $Q$ approaches $P$ ,the derivative does not exist.


Sebastian Benthall, Thomas Maillart and John Chuang
This course is a hands-on exploration of the theory and practice of open online collaboration. Students will engage multi-disciplinary literature about collaboration while contributing to an existing open project (such as open source software, Wikipedia, or OpenStreetMap). Readings will explore business models for open source software organizations, incentives of cooperation and organization design for open source projects. Practical work will be organized around themes of project management infrastructure, community self-governance, and engineering education through open source participation. The goal of the class is to engage students in an existing open source community while developing functionality and expertise that can be part of iSchool Masters projects, faculty-directed research, and beyond.
The course will meet in South Hall 210 once a week, on Fridays from 11am-1pm.
There is also a lab, which meets Tuesday 1-2pm. Labs will meet in South Hall 202 until we move to Internet Relay Chat (IRC).
This course is 3 units. It fulfills the Management requirement for the I School MIMS program.
The general class format is the following:
Our course mailing list is<EMAIL_ADDRESS>Please sign up for it here.
Students of this course will be required to contribute to an open collaborative project of their choosing. This is an important decision; students should come into the class with ideas about projects or fields they are interested in.
List of potential projects : link.
See Assignments page.
Students will be responsible for regular reports on their open collaboration with outside projects in relation with practical and theoretical topics addressed in the class.
The reports will be blog entries to this website. Reports will be handed in using Github Pull Requests to the course website repository. Students will need to sign up for a Github account.
As the final project of the course, students will combine their reports from throughout the semester into a single collaborative report surveying the open collaboration. This report will be made available on the web and, if we agree on it as a class, submitted for publication.
Grading will be based on:
30% on open digital participation (blog posts, issues, mailing list participation, commits) in selected external project. Contributions can be of any of the following forms, or others with instructor approval:
Students are encouraged to work with their selected community to find constructive tasks for mutual benefit.
20% on incremental open participation reports


Sierra gravel explorer
Most of the dirt roads here in the Sierra Nevada range are steep, chunky, and long. I designed this 650b gravel bike to allow me to explore these roads with as much comfort as possible while still maintaining good performance.

So instead of starting with road race geometry and adding dashes of off-road capability, I started with my hardtail design and applied it to a drop bar bike. The longer and slacker front end pushes the front wheel forward, giving the rider more confidence on steep descents. The chainstay length is fairly long to add stability and to keep the rider’s weight distributed properly between the wheels. The internally-routed dropper post allows me to achieve a proper attack stance when descending. All of this adds up to one mean off-road machine.
I also made sure that the bike was optimized for bikepacking. The water bottle mounts inside the main triangle are as low as possible to allow me to add a partial frame bag. While three bottle mounts on the bottom of the down tube accommodate an oversized cage for extra storage. The top tube has mounts for a bolt-on top tube bag, perfect for the Revelate Mag-Tank. This would be one sweet ride for Adventure Cycling’s Arkansas High Country Route and other dirt road adventures.
##Component choices
This bike uses what’s been dubbed a mullet drivetrain. It has the 10-50t Garbaruk cassette combined with the Garbaruk extended derailleur cage on the Sram Force rear derailleur to accommodate the massive range. The White Industries crank uses a 36t chainring. My motto has always been spin to win.
I modified the left Sram Force shifter to control the KS Lev dropper post. This is the best way to use a dropper post with road bike handlebars. It keeps the controls at your fingertips.
Most of the rest of the build features fine USA-made components. Velocity, Paul, and White Industries produce high-quality, durable components.


The rainbowr
package generates LGBT pride flags overlaid with the R logo
The package can be installed from GitHub with:
# install.packages("devtools") devtools::install_github("djnavarro/rainbowr")
df <- data.frame( flag = c("rainbow", "transgender", "bisexual", "bear", "aromantic", "lesbian"), palette1 = c("white", "grey80", "#ccccff", "grey30", "grey50", "grey50"), palette2 = c("black", "black", "white", "brown", "black", "lavender"), row = c(1, 1, 2, 3, 3, 2), col = c(1, 2, 1, 2, 3, 3), stringsAsFactors = FALSE ) hextile <- make_hextile(df) #> adding rainbow hex (1 of 6) #> adding transgender hex (2 of 6) #> adding bisexual hex (3 of 6) #> adding bear hex (4 of 6) #> adding aromantic hex (5 of 6) #> adding lesbian hex (6 of 6) plot(hextile)


Click and Go watch Your Console!
-
Mr. Button :
send every request.
-
Debounce 300ms
postpone sending request until after wait 300ms have elapsed since the last time it was invoked.
-
Throttle 1000ms :
will send request as soon as you call it for the first time, and, if you call it again any number of times during the wait period 1000ms, as soon as that period is over.


My favourite sport is GAA,I played camogie for four years with Douglas GAA. I enjoy going to hurling and football matches,especially Cork GAA. I have been to many hurling finals in Croke Park to watch Cork play however, I am yet to witness Cork win a All Ireland Final in Croke Park.


LORD Cultural Resources Planning &
45360004
2015/07/10
491 Management consulting , except other depts or prog
2015/07/10 to 2016/01/16
2016/01/16
$79,782.90
Green Gables Heritage Place Site Plan


DROP
max 64px × 64px
DROP IMAGE HERE
Drag an image from your desktop
(or just click on one of these guys)
SCALE
× 10
smaller
bigger
PREVIEW
CSS


An Orchestra without a Conductor? Inconceivable!
The Olin Conductorless Orchestra (OCO) - an ensemble, minus conductor - features instrumentalists in leadership, communicative, and collaborative roles. Dedicated to orchestral performance in the concerted spirit of chamber music, the orchestra forges individual participation, active listening, and group-motivation into performances that have established it as the only conductorless orchestra composed of engineers-- in the world!
Since its Fall 2002 inception, OCO has been a Project-Based Learning (PBL) course where student soperate the classroom, and the professor contributes as guide-on-the-side, coach, and re-orchestrator of works chosen by the group. In helping to run rehearsals, students develop leadership, teamwork, and communication skills in a large team environment. With an essentially flat hierarchy that incorporates an ever-evolving sense of culture and cooperation as a working ensemble, OCO functions as a team of musicians -- regardless of position -- comprising students, faculty, and external guests, all dedicated to making good music with intensity, camaraderie, excitement, and humor.
Our Mission:
The purpose of this website is to share the resources that OCO has collected over the years in the hope that it proves useful to others looking to start their own Conductorless Orchestra.
Recent Concerts:
Recent performances outside Olin include The Great Hall at Cooper Union in New York City and Powers Hall at the Needham Town Hall, both of which received standing ovations. On April 2019, OCO performed an invited concert for the American Society for Engineering Education (ASEE) Zone 1 International Conference in Niagara Falls, NY. OCO's performance was the first time ASEE had ever hosted an orchestra composed of student engineers; the concert received a standing ovation.


This document describes how to use H2O's generated POJO models.
About generated models
H2O-generated POJO models are intended to be easily embedded in any Java environment.
The only compile and runtime dependency for a generated model is the h2o-genmodel.jar
file produced as the build output of this package.
If you are just getting started, first look at the Quick start section below, and
then look at the documentation for the hex.genmodel.easy package.
The following topics are covered here:
Quick start
Step 1 (in terminal window 1): Start H2O
$ java -jar h2o.jar
Step 2 (in a web browser): Build model
- Go to http://localhost:54321
- Click on view example Flows near the right edge of the screen. Here is a screenshot of what to look for:
- Click on GBM_Airlines_Classification.flow
- If a confirmation prompt appears asking you to "Load Notebook", click it
- From the "Flow" menu choose the "Run all cells" option
- Scroll down and find the "Model" cell in the notebook. Click on the Download POJO button as shown in the following screenshot:
Step 3 (in terminal window 2): Download model pieces
$ mkdir experiment
$ cd experiment
$ mv ~/Downloads/gbm_pojo_test.java .
$ curl http://localhost:54321/3/h2o-genmodel.jar > h2o-genmodel.jar
Step 4 (in terminal window 2): Create main program
Create a new file called main.java with the following contents:
{@code
import java.io.*;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.prediction.*;
public class main {
private static String modelClassName = "gbm_pojo_test";
public static void main(String[] args) throws Exception {
hex.genmodel.GenModel rawModel;
rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();
EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);
RowData row = new RowData();
row.put("Year", "1987");
row.put("Month", "10");
row.put("DayofMonth", "14");
row.put("DayOfWeek", "3");
row.put("CRSDepTime", "730");
row.put("UniqueCarrier", "PS");
row.put("Origin", "SAN");
row.put("Dest", "SFO");
BinomialModelPrediction p = model.predictBinomial(row);
System.out.println("Label (aka prediction) is flight departure delayed: " + p.label);
System.out.print("Class probabilities: ");
for (int i = 0; i < p.classProbabilities.length; i++) {
if (i > 0) {
System.out.print(",");
}
System.out.print(p.classProbabilities[i]);
}
System.out.println("");
}
}
}
Step 5 (in terminal window 2): Compile and Run
$ javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m gbm_pojo_test.java main.java
$ java -cp .:h2o-genmodel.jar main
Extracting generated models from H2O
Generated models can be extracted from H2O in the following ways:
From the H2O Flow Web UI:
When viewing a model, press the "Download POJO" button at the top of the model cell,
as the example in the Quick start section demonstrated.
You can also Preview the POJO inside Flow, but it will only show the first thousand
lines or so in the web browser, cutting off the bottom of large models.
From R:
The following code snippet shows an example of building a model and downloading its
corresponding POJO from an R script:
{@code
library(h2o)
h2o.init()
path = system.file("extdata", "prostate.csv", package = "h2o")
h2o_df = h2o.importFile(path)
h2o_df$CAPSULE = as.factor(h2o_df$CAPSULE)
model = h2o.glm(y = "CAPSULE",
x = c("AGE", "RACE", "PSA", "GLEASON"),
training_frame = h2o_df,
family = "binomial")
h2o.download_pojo(model)
}
From Python:
The following code snippet shows an example of building a model and downloading its
corresponding POJO from a Python script:
{@code
import h2o
h2o.init()
path = h2o.system_file("prostate.csv")
h2o_df = h2o.import_file(path)
h2o_df['CAPSULE'] = h2o_df['CAPSULE'].asfactor()
model = h2o.glm(y = "CAPSULE",
x = ["AGE", "RACE", "PSA", "GLEASON"],
training_frame = h2o_df,
family = "binomial")
h2o.download_pojo(model)
}
From Java:
TODO: provide pointer of doing this directly from Java
From Sparkling Water:
TODO: provide pointer of doing this from Sparkling Water
Use cases
The following use cases are demonstrated with code examples:
Reading new data from a CSV file and predicting on it
The {@link hex.genmodel.tools.PredictCsv} class is used by the H2O test harness to make
predictions on new data points.
Getting a new observation from a JSON request and returning a prediction
TODO: write an example for this
A user-defined-function called directly from hive
See the H2O-3 training github repository


Pig dice game
Select no. of players
None
Player 1
Player 2
Number of dice
None
1
2
3
Goal Number
None
50
100
200
Lets play!
Player 1
Total:
Round Total:
Roll!
Hold!
Player 2
Total:
Round Total:
Roll!
Hold!


Contactkeyboard_arrow_up
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Hourskeyboard_arrow_up
access_time Hoursclose
| Monday | 9:00am - 7:00pm |
|---|---|
| Tuesday | 9:00am - 7:00pm |
| Wednesday | 9:00am - 7:00pm |
| Thursday | 9:00am - 7:00pm |
| Friday | 9:00am - 7:00pm |
| Saturday | 10:00am - 3:00pm |
| Sunday | 11:00am - 3:00pm |
Please note that we will be closed on Sunday & Monday, June 30 - July 1, for Canada Day. We will be open again on Tuesday the 2nd.
Serviceskeyboard_arrow_up
loyalty Servicesclose
- Walk-in Clinic
- Family Practice & Urgent Care
- Seniors' Care
- Pediatric & Prenatal Care
- On-site Lab
- Flu Shots
- Children Immunization
- Drivers' Medical
- Pre-employment Physicals
- Plus, many more...


We began the implementation of our models by visualizing the time series of monthly income at Core Creative. Visualizing the time series provides necessary context for the modelling we are attempting to do. It appears the time series is trending up over time and may have a component of seasonality. This is important in determining which independent variables are relevant and suggests we should try using indicator variables to model the effects of seasonality.
The red line in the time series plot below is a simple linear regression fitted line of monthly income against time. This simple regression is not good at explaining or predicting monthly income. Furthermore, there seems to be an outlier somewhere around period 64 or 65. We need to identify this month exactly to remove it from our data.


Manual Specs
Camera Test
Camera JS
Camera JS Front (WP8 only)
Camera Ruby
Symbol Device Test
Symbol Device JS
Symbol Device Ruby


Dropdown Menu: Left aligned
This dropdown menu is aligned left using data-alignment="left" overriding the effect of the .top-bar-right
class.
Menu
- Site Title
This dropdown menu is positioned left regardless of the content before it.
Menu
- Site Title
- Some long text to push the menu.
- You might need to resize the window so that the menu just fits in this line...


Smartsoft Redmine settings
Work hours
Reminder time
Reminder recurrency (in minutes)
Enable reminder (on this PC)
Save


MVC allows for a clean and testable separation between the behavior (controller) and the view (HTML template). A Controller is just a JavaScript class which is grafted onto the scope of the view. This makes it very easy for the controller and the view to share the model.
The model is a set of objects and primitives that are referenced from the Scope ($scope) object. This makes it very easy to test the controller in isolation since one can simply instantiate the controller and test without a view, because there is no connection between the controller and the view.
setUrl()
and readUrl()
functions copy the game state to/from the URL's
hash so the browser's back button will undo game steps. See deep-linking. This example calls $watch()
to set up a listener that invokes readUrl()
when needed.


This page will send HTTP requests to demonstate additional features of Raw routing.
They will show the user of multiple handlers, redirecting and custom path parameters.
View the code
Send an HTTP GET request with multiple handlers:
Send an HTTP GET request to a redirecting handler:
Send an HTTP GET request to a handler that parses the URL path as a parameter:


Yeah...about that.
The only way out is to wake up. At this point, the only way to wake up it to be woken up. And you're the only one in living memory who has a key to the not-time time zone, you made sure of that a long time ago.
THE END
-
play again?


Brute Beauty And Valour And Act, Oh, Air, Pride, Plume, Here Buckle!
Frank Key
© Frank Key 2012
The right of Frank Key to be identified as the author of this work has been asserted by him in accordance with the Copyright, Designs and Patents Act 1988
Published by Hooting Yard
hooting.yard@gmail.com


Can you answer the 1 million dollar question?

You can be sure.
Where the streets of gold are. The place everyone wants to go. Will you?
We are all dead in our sin, because we broke God's law.

Jesus' blood was shed instead of your blood. God's son paid your fine in full.
Repent and trust in Jesus Christ for the forgiveness of your sins and be washed white as snow.
By this you will know that Jesus is our assurance.
 You have been washed white as snow and cleaned by Jesus' blood.
Pursue God in His word and seek Him daily and you will grow.


How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How old was Captain Nemo when he died?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
sadsadHow is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How old was Captain Nemo when he died?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
sadsadHow is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
sadsadHow is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
Howsadasd is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
Howw is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft
How is the fee allocated?
Retro occupy organic, stumptown shabby chic pour-over roof party DIY normcore. Actually artisan organic occupy, Wes Anderson ugh whatever pour-over gastropub selvage. Chillwave craft beer tote bag stumptown quinoa hashtag. Dreamcatcher locavore iPhone. Anderson ugh whatever pour-over gastropub selvage. Chillwave craft


Jaewoo Yang
My name is Jaewoo Yang and my major is software engineering. I love programming.
My Courses @ HYU
- CUL2019: Understanding of Psychology
- AIX0005: AI+X:Machine Learning
- VCC3003: IC-PBL & Competency development
- CSE3026: WEB APPLICATION DEVELOPMENT
- CSE1008: SOFTWARE CAPSTONE DESIGN I
- CSE4007: Artificial Intelligence
My Favorite Movies
- Minions (Naver Movie)
- The Secret Life of Pets 2 (Naver Movie)
- Shutter Island (Naver Movie)
- Director
- Martin Scorsese
- Genre
- Drama / Mystery / thriller
- Stars
- Leonardo Dicaprio, Mark Ruffalo
My Favorite Drinks
(I usually drink Monster when I have test.)
Monster: CocaCola: RedBull:
My Favorite Quote
"I'd rather live with a good question than a bad answer."
- Aryeh Frimer
Fun Facts About My Friends
College Friends
| First Name | Facts |
| Jaewoo | plays League of Legend |
| Hansae | plays LOL Chess |
| Taeo | plays LOL Chess Underlords |
Waffle video from YouTube
Waffle falling over
One of My Favorite Piano Songs
Summer by Joe Hisaishi


WELCOME TO VHA ACCOUNTING SOLUTIONS
We are a young group of Registered Accountants and Tax Practitioners providing simple solutions to your business problems.
- Mission - To be the best in our field of expertise and provide the client with a great overall service!
- Skills - Our skill base not only includes intellectual knowledge but the use of the latest Tax and Accounting software.
- Clients - Satisfied clients thanks to our experience.
We take time to understand the nature of your business, environment and the challenges that your
business face with. We also understand that in South Africa we face changing times and we are
proud to be a BEE level 1 contributor.
Our initial consultant with you is free of charge as we see this as a "value add service."
"I have been a client of VHA Accounting for two years now. I must say their service has been exemplary, They have genuine concern and commitment for their client's needs. I will gladly recommend VHA Accounting to any business or person requiring Accounting or tax services."
~Ronnie Naidoo - Owner,Umlingo Group Pty Ltd
"people you can rely on and professional service that you can trust"
~ Sue Raghnandan - Mr Exhaust Mr Tyre.
"We have been using VHA Accounting for a number of years and have always had great service. Queries are answered very promptly and then dealt with efficiently. Would have no hesitation in recommending them to friends"
~ Michelle Coyne


Firefox (1996)
Grade: 81
"Firefox" is a drama about five teenage girls who become close friends but get into trouble with authority figures. Though critically ignored and maligned, it is actually an excellent film. I liked the cinematography, the characters, and even the eroticism.
The plot of "Firefox" has an odious high school biology teacher sexually harassing several of his students. Led by teenaged drifter Legs (then-unknown Angelina Jolie), five girls become involved in an after school altercation with the teacher. He gets his, but the girls are expelled. They begin hanging together in an abandoned house, but Jolie's hostile attitudes towards men and authority disrupts their lives and friendships.
Some spoilers may follow.
Critical reaction to "Foxfire" is all over the map, and evenly spread between condemnation, hesitation, and praise. Common themes for criticism include the lesbian character Legs, a lengthy frontal nudity scene considered gratitious, a few male characters who are complete jerks, and the similarity of the film's plot to any number of other films. My only criticism is the abandoned house: how could such a lovely house, surrounded by woods, remain empty and open. It is a plot contrivance to give the young women a private hangout.
Too much is made of the lesbian themes of the movie. Of the five girls, only Legs is a lesbian. Maddy has a boyfriend, Violet is promiscuous, Rita is shy but fantasizes about men, while druggie Goldie is asexual. Jolie's character has an unresolved anger towards men, perhaps because of a troubled relationship with her own father. The film's occasional homoeroticism is limited to Legs, and whatever interest is returned to her by the other girls is from friendship.
There are two male characters in "Foxfire" that are jerks: the predatory teacher, and a jock who for some reason wants to revenge that teacher's downfall. The teacher character is too interesting to be a stereotype, while the hateful jock helps provide needed tension in the film. I don't have problems with either character. There is also Goldie's father, who attempts toughlove to reform his troubled daughter. He clearly cares for her, and in the end you feel sorry for him.
One theme that I liked about the movie is that reckless behaviour has negative consequences. A teacher is assaulted; those responsible are expelled. Stop signs and red lights are run; the car gets flipped. They break into a school; they unintentionally start a fire. A student experiments with drugs; she becomes a heroin addict.
I liked cinematography as well. The close-ups all seem appropriate, and the camera follows the action nicely.
http://members.tripod.com/~Brian_Koller/movies.html
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


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I have no undo button


Inherited by Nakama.ApiValidatedSubscription.
|
| bool | Active [get] |
| | Whether the subscription is currently active or not.
|
| |
| string | CreateTime [get] |
| | UNIX Timestamp when the receipt validation was stored in DB.
|
| |
| ApiStoreEnvironment | Environment [get] |
| | Whether the purchase was done in production or sandbox environment.
|
| |
| string | ExpiryTime [get] |
| | Subscription expiration time. The subscription can still be auto-renewed to extend the expiration time further.
|
| |
| string | OriginalTransactionId [get] |
| | Purchase Original transaction ID (we only keep track of the original subscription, not subsequent renewals).
|
| |
| string | ProductId [get] |
| | Purchase Product ID.
|
| |
| string | ProviderNotification [get] |
| | Raw provider notification body.
|
| |
| string | ProviderResponse [get] |
| | Raw provider validation response body.
|
| |
| string | PurchaseTime [get] |
| | UNIX Timestamp when the purchase was done.
|
| |
| string | RefundTime [get] |
| | Subscription refund time. If this time is set, the subscription was refunded.
|
| |
| ApiStoreProvider | Store [get] |
| | Store identifier.
|
| |
| string | UpdateTime [get] |
| | UNIX Timestamp when the receipt validation was updated in DB.
|
| |
| string | UserId [get] |
| | Subscription User ID.
|
| |
◆ Active
| bool Nakama.IApiValidatedSubscription.Active |
|
get |
Whether the subscription is currently active or not.
◆ CreateTime
| string Nakama.IApiValidatedSubscription.CreateTime |
|
get |
UNIX Timestamp when the receipt validation was stored in DB.
◆ Environment
Whether the purchase was done in production or sandbox environment.
◆ ExpiryTime
| string Nakama.IApiValidatedSubscription.ExpiryTime |
|
get |
Subscription expiration time. The subscription can still be auto-renewed to extend the expiration time further.
◆ OriginalTransactionId
| string Nakama.IApiValidatedSubscription.OriginalTransactionId |
|
get |
Purchase Original transaction ID (we only keep track of the original subscription, not subsequent renewals).
◆ ProductId
| string Nakama.IApiValidatedSubscription.ProductId |
|
get |
◆ ProviderNotification
| string Nakama.IApiValidatedSubscription.ProviderNotification |
|
get |
Raw provider notification body.
◆ ProviderResponse
| string Nakama.IApiValidatedSubscription.ProviderResponse |
|
get |
Raw provider validation response body.
◆ PurchaseTime
| string Nakama.IApiValidatedSubscription.PurchaseTime |
|
get |
UNIX Timestamp when the purchase was done.
◆ RefundTime
| string Nakama.IApiValidatedSubscription.RefundTime |
|
get |
Subscription refund time. If this time is set, the subscription was refunded.
◆ Store
◆ UpdateTime
| string Nakama.IApiValidatedSubscription.UpdateTime |
|
get |
UNIX Timestamp when the receipt validation was updated in DB.
◆ UserId
| string Nakama.IApiValidatedSubscription.UserId |
|
get |
The documentation for this interface was generated from the following file: