Danai Koutra, University of Michigan
- Representation Learning Beyond Homophily and Proximity
David Gleich, Purdue University
- Flow-based Algorithms for Improving Clusters
Yves-Alexandre de Montjoye, Imperial College London
- Is it Proportional? Estimating the detrimental network effects of data collections
Two of the most important privacy scandals of the last decade, the Snowden revelations and Cambridge Analytica, leveraged network effects: using the connections between individuals to access their data. Similarly, contact tracing apps recording close proximity through bluetooth could enable large-scale surveillance. Despite this, the detrimental network effects in privacy have been largely ignored. We argue this has prevented informed discussions and debates on the proportionality of data collection mechanisms. In this talk, we will introduce a graph-theoretic privacy model to study and quantify node-intrusion attacks, as the fraction of a network that an attacker gains access to by compromising a number of random nodes. We will then, in turn, formalize our node and edge-based observability metrics, show important theoretical properties of our metrics relating an attacker’s success to the graph structure, and finally use them to study three node-intrusion attacks..
Rose Yu, UC San Diego
- Understanding Graph Neural Networks in Learning Network Topology and Dynamics
The surprising effectiveness of Graph Neural Networks (GNNs) has led to an explosion of interests in deep learning of networks, leading to applications from particle physics, to molecular biology to robotics. Despite their practical success, most GNNs are deployed as black boxes feature extractors for network data. It is not yet clear to what extent can these models capture different network features. I will discuss the representation power of GNNs to understand what can and cannot be learned. I will showcase the practical implication of our analyses on tasks including distinguishing network models and learning network dynamics..
Caterina De Bacco, Max Planck Institute for Intelligent Systems
- Interdependence Between Network Layers


This page tracks the new paper links made to my list of SPM 2005 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
November 29 2009
- Added ACM DOI link for each paper
- Updated some author and paper links
July 15, 2005
June 27, 2005
May 8, 2005
April 4, 2005
February 14, 2005
kesen.huang@gmail.com


Avoiding painful ribs and heartburn during pregnancy
During pregnancy the ribs become stretched by the expanding uterus, this can sometimes cause pain especially if there is a restriction in the spine where the ribs articulate. Osteopathic treatment can be very helpful in relieving these symptoms. Rib pain and heartburn can also be a result of a restriction in the diaphragm. This can be diagnosed and treated by our osteopaths.
Simple exercise to relieve heartburn and rib pain:
- Stand upright, and reach up over your head holding a broom keep the arms straight. Side bend to 2 o'clock to the left and to 11 o'clock to the right. Breathe into the lower stretched ribs.
If you have heartburn, avoid coffee tea and peppermint. Eat small meals regularly and not too late at night. If necessary eat just soup and ice-cream at night. A walk after dinner can also help.


Class MathHelpers
Static class consisting of mathematical "helper" functions and constants that can be useful for performing operations on a 2D grid.
Namespace: SadRogue.Primitives
Assembly: TheSadRogue.Primitives.dll
Syntax
public static class MathHelpers : Object
Fields
DegreePctOfCircle
Result of 1/360; represents in decimal form a percent of a circle that a degree constitutes.
Declaration
public const double DegreePctOfCircle = 0.0027777777777777779
Field Value
| Type | Description |
|---|---|
| Double |
DegreesToRadiansConstant
Pi / 180. Used to speed up calculations related to circles.
Declaration
public const double DegreesToRadiansConstant = 0.017453292519943295
Field Value
| Type | Description |
|---|---|
| Double |
RadiansToDegreesConstant
180 / Pi. Used to speed up calculations related to circles.
Declaration
public const double RadiansToDegreesConstant = 57.295779513082323
Field Value
| Type | Description |
|---|---|
| Double |
Methods
Clamp(Double, Double, Double)
Restricts a value to a specified range.
Declaration
public static double Clamp(double value, double min, double max)
Parameters
| Type | Name | Description |
|---|---|---|
| Double | value | The value to restrict. |
| Double | min | The minimum to clamp the value to. |
| Double | max | The maximum to clamp the value to. |
Returns
| Type | Description |
|---|---|
| Double | The clamped value. |
Clamp(Int32, Int32, Int32)
Restricts a value to a specified range.
Declaration
public static int Clamp(int value, int min, int max)
Parameters
| Type | Name | Description |
|---|---|---|
| Int32 | value | The value to restrict. |
| Int32 | min | The minimum to clamp the value to. |
| Int32 | max | The maximum to clamp the value to. |
Returns
| Type | Description |
|---|---|
| Int32 | The clamped value. |
Clamp(Single, Single, Single)
Restricts a value to a specified range.
Declaration
public static float Clamp(float value, float min, float max)
Parameters
| Type | Name | Description |
|---|---|---|
| Single | value | The value to restrict. |
| Single | min | The minimum to clamp the value to. |
| Single | max | The maximum to clamp the value to. |
Returns
| Type | Description |
|---|---|
| Single | The clamped value. |
Lerp(Double, Double, Double)
Performs linear interpolation between two values.
Declaration
public static double Lerp(double value1, double value2, double amount)
Parameters
| Type | Name | Description |
|---|---|---|
| Double | value1 | Starting value. |
| Double | value2 | Ending value. |
| Double | amount | The weight to apply to |
Returns
| Type | Description |
|---|---|
| Double |
Lerp(Single, Single, Single)
Performs linear interpolation between two values.
Declaration
public static float Lerp(float value1, float value2, float amount)
Parameters
| Type | Name | Description |
|---|---|---|
| Single | value1 | Starting value. |
| Single | value2 | Ending value. |
| Single | amount | The weight to apply to |
Returns
| Type | Description |
|---|---|
| Single |
ToDegree(Double)
Converts given angle from radians to degrees.
Declaration
public static double ToDegree(double radAngle)
Parameters
| Type | Name | Description |
|---|---|---|
| Double | radAngle | Angle in radians. |
Returns
| Type | Description |
|---|---|
| Double | The given angle in degrees. |
ToRadian(Double)
Converts given angle from degrees to radians.
Declaration
public static double ToRadian(double degAngle)
Parameters
| Type | Name | Description |
|---|---|---|
| Double | degAngle | Angle in degrees. |
Returns
| Type | Description |
|---|---|
| Double | The given angle in radians. |
Wrap(Single, Single, Single)
Wraps a value around the min and max.
Declaration
public static float Wrap(float value, float min, float max)
Parameters
| Type | Name | Description |
|---|---|---|
| Single | value | The value to wrap. |
| Single | min | The minimum value before it transforms into the maximum. |
| Single | max | The maximum value before it transforms into the minimum. |
Returns
| Type | Description |
|---|---|
| Single | A new value if it falls outside the min/max range otherwise, the same value. |


A customizable slider component for Angular >= 9 based on https://github.com/angular-slider/ng5-slider


1.0.1
Client connected to MongoDB
(object)
for the new connection
const client = new Client({db: 'example'})
Create a new collection instance of the given name
(string)
of the collection
const collection = client.collection('foo')
Open connection to db and return collection of provided name
(string)
of the collection to open up connection to in db
Promise
:
collection instance
Close the connection to database
boolean
:
success of closing the connection
client.close()
Represents a a collection in MongoDB
(any)
(any)


--- layout: post title: Mundane Is Interesting date: 2005-12-18 00:00:00 categories: digital-media mobile monkeys ---
stevenberlinjohnson.com:: "It was simply a sign of something that was probably obvious already: there's nothing cool any more about having an iPod. That's not to say it isn't an incredible product. It's just hard to defend the cool factor when the President apparently has two of them..."
Actually, I'd argue that it's when things stop being cool that they start getting interesting. For me, this was the case with mobile phones: when they were something special that yuppies lugged around with them in the 80s: boring. When everyone you knew had one: interesting.
Ditto microprocessors, puzzles, and the internet.


We can help you use technology to hold power to account. Find out how.
Our knowledge, at your disposal
How to make all the right digital decisions
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Talk to us about getting up and running in your country.
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Sample Application
The Couchbase Travel App is a sample web application that demonstrates how to interact with the Couchbase query services via the SDKs.
Each SDK comes with its own implementation of the backend for the application. The backend is a REST API available in the following languages:
Please refer to the travel-app documentation of your SDK of choice for specifics for that particular SDK, like backend dependencies, source-code checkout, and running instructions.
The travel app front-end is the same in each SDK implementation and is built on an Angular2 stack.
While the app runs, you can get a peek of what is happening in the backend via the "Narration" — a console-like UI element that can display N1QL queries, for instance. It is only visible in components that communicate with the server, but can also be collapsed.
The application allows users to find flights by entering airports and travel dates. For the airport entries, the app uses a N1QL query bound to an Angular typeahead directive to suggest airport names based on the first few letters entered.
The focus of this tutorial is the backend. The documentation for the travel app illustrates the data model and walks through the N1QL and FTS queries used to select flights and search for hotels.
Generic set up
You’ll need:
-
Your favorite editor or IDE
-
Your SDK of choice and its specific dependencies
-
A local
Couchbase 5.1
(or later) installation — make sure that thetravel-sample
bucket has been loaded, and that there is at least one node with data, query, index, and full text search services in the cluster -
The sample app source code for your SDK from GitHub — check the instructions and make sure to use the implementation compatible with the v2 API
-
That’s it!
To start, it is easiest if you run Couchbase Server and the travel sample app on the same machine. It’s not required to run your development environment this way, and advanced Multi-Dimensional Scaling (MDS) configurations are supported. It’s just easier to start a development environment with components running locally.
If you do not already have Couchbase Server 5.1 or later installed, download Couchbase Server and install it.
When installed, open a browser and navigate to the Couchbase Web Console URL — by default, <IP_ADDRESS>:8091
.
When prompted, create a new cluster.
Make sure all the services (Data Service, Index Service, Search Service and Query Service) are selected.
It is important to install the travel-sample bucket from Couchbase Server 5.1 or later, as it has been enriched with hotels data.
If you have installed the sample bucket from an earlier version, delete the bucket and follow the instructions below.
|
If you have not installed the travel-sample
bucket from Couchbase Server 5.1, select .
Select the travel-sample checkbox, and then click Load Sample Data.
The activity notification in the upper-right corner shows that the bucket is being added.
When the bucket is ready to use, it is listed under the Installed Samples heading.
In order to be able to search for hotels, you must create an FTS index on the travel-sample
bucket.
Select and create an index named hotels
.
| The index mapping could be better tuned, but a generic mapping will also work fine for this example. |
Finally, download the complete source code for the backend, then build and run the Travel App. See the links above for instructions for your development environment.
The app displays a message showing the login URL for the frontend. Make a note of this so you can log in.
Using the Travel App
To find and book a flight:
-
Open a browser and navigate to the login URL that was displayed when you started the app.
-
Sign in to Couchbase Travel by creating a new account, or providing existing credentials. (New accounts expire after some time depending on the backend used, usually 1 hour.)
-
In the Airport or City section, enter an airport code or city name in the From and To fields.
-
In the Travel Dates section, enter Leave and Return dates in
MM/DD/YYYY
format. -
Click Find Flights. The app displays the available flights for the outbound and return legs of the trip.
-
Select one or more flights to add to your cart by clicking the Choose button next to each flight.
Now you have flights stored locally in your cart. You can see that the number of flights in the cart and the total cost are displayed in the navigation bar at the top right. To review your flights, navigate to the cart page:
-
Click on the cart icon in the navigation bar.
-
Review the flights in your cart, and book one by clicking on the Book button next to it.
The flight is now booked, as confirmed by a message on the page.
The flights that you’ve booked are stored in the backend. The backend uses authentication to control access to the endpoints for booking and listing flights.
To see which flights you’ve booked, navigate to the user page:
-
Click on the user icon in the navigation bar.
To search for a hotel, navigate to the hotel page:
-
Click on the hotel icon to the right of the navigation bar.
-
Don’t enter any search criteria yet. Just click on the Find Hotels button. The first 100 hotels are displayed.
-
Now enter a keyword in the Description box, or a location in the Location box, and click on Find Hotels again to refine your search.
To use the hotel search, you must have created an FTS index named hotels , as described earlier.
The API backend that is used by the hotel page makes use of this FTS index.
|
The Travel App data model
The data model for the travel app uses several distinct document types: airline, route, airport, and hotel.
The model for each kind of document contains:
-
A key that acts as a primary key
-
An
id
field that identifies the document -
A
type
field that identifies the kind of document
The following figure illustrates the relationship between the different kinds of documents in version 5.1 of the Travel App. It shows the primary key, ID, and type fields that each document has, plus a few representative fields in each type of document.
Landmark documents are not used in the travel-sample application, which focuses on a more specific subset of point of interests that have their own type: hotels.
Hotels, like landmarks, don’t have relations to other types.
Airline documents
Airline documents contain details about airlines such as the name of the airline, International Air Transport Association (IATA) two-character airline designator, International Civil Aviation Organization (ICAO) three-character airline designator, and the airline call sign.
For airline documents, the value of the type
field is airline
.
airline_24 ←This is the key, which also acts as a primary key
{
"active": "Y",
"callsign": "AMERICAN",
"country": "United States",
"iata": "AA",
"icao": "AAL",
"id": "24",
"name": "American Airlines",
"type": "airline" ←This is the type identifier for the document
}
Route documents
Route documents contain details about flights such as the name of the airline, departure airport, destination airport, number of stops during the flight, type of aircraft, flight number, and flight schedule.
Route documents also contain a foreign key identifier, airlineid
, that is used to retrieve the document that contains information about the airline that flies the route.
The value of the airlineid
field is identical to the key for the corresponding airline document.
For route documents, the value of the type
field is route
.
route_5966 ←This is the key, which also acts as a primary key
{
"id": "5966",
"type": "route", ←This is the type identifier for the document
"airline": "AA",
"airlineid": "airline_24", ←This is the foreign key identifier to an airline document
"sourceairport": "MCO",
"destinationairport": "SEA",
"stops": "0",
"equipment": "737",
"schedule": [
{"day": 1, "utc": "13:25:00", "flight": "AA788"},
{"day": 4, "utc": "13:25:00", "flight": "AA419"},
{"day": 5, "utc": "13:25:00", "flight": "AA519"}
]
}
Airport documents
Airport documents contain details about airports such as name, location, time zone, ICAO four-character alphanumeric airport code, and Federal Aviation Administration (FAA) location identifier.
For airport documents, the value of the type
field is airport
.
airport_3577 ←This is the key, which also acts as a primary key
{
"travel-sample": {
"airportname": "Seattle Tacoma Intl",
"city": "Seattle",
"country": "United States",
"faa": "SEA",
"geo": {
"alt": 433,
"lat": 47.449,
"lon": -122.309306
},
"icao": "KSEA",
"id": 3577,
"type": "airport", ←This is the type identifier for the document
"tz": "America/Los_Angeles"
}
}
Hotel documents
Hotel documents are all about a specific type of point of interests: hotels. They include information such as a name, description, location (coutry, state, city and address), price and services, and even customer reviews.
For hotel documents, the value of the type
field is hotel
.
hotel_10180 ←This is the key, which also acts as a primary key
{
"address": "8301 Hollister Ave",
"checkin": "12PM",
"checkout": "4PM",
"city": "Santa Barbara",
"country": "United States",
"description": "Located on 78 acres of oceanfront property, this resort is an upscale experience that caters to luxury travelers. There are 354 guest rooms in 19 separate villas, each in a Spanish style. Property amenities include saline infinity pools, a private beach, clay tennis courts, a 42,000 foot spa and fitness center, and nature trails through the adjoining wetland and forest. The onsite Miro restaurant provides great views of the coast with excellent food and service. With all that said, you pay for the experience, and this resort is not for the budget traveler. In addition to quoted rates there is a $25 per day resort fee that includes a bottle of wine in your room, two bottles of water, access to fitness center and spa, and internet access.",
"free_breakfast": true,
"free_internet": false,
"free_parking": false,
"geo": {
"accuracy": "ROOFTOP",
"lat": 34.43429,
"lon": -119.92137
},
"id": 10180,
"name": "Bacara Resort \u0026 Spa",
"pets_ok": false,
"price": "$300-$1000+",
"reviews": [
{
"author": "Orval Lebsack",
"content": "I stayed there with a friend for a girls trip around St. Patricks Day. This was my third time to NOLA, my first at Chateau Lemoyne. The location is excellent....very easy walking distance to everything, without the chaos of staying right on Bourbon Street. Even though its a Holiday Inn, it still has the historical feel and look of NOLA. The pool looked nice too, even though we never used it. The staff was friendly and helpful. Chateau Lemoyne would be hard to top, considering the price.",
"date": "2013-10-26 15:01:39 +0300",
"ratings": {
"Cleanliness": 5,
"Location": 5,
"Overall": 4,
"Rooms": 4,
"Service": 4,
"Sleep Quality": 5,
"Value": 4
}
}
],
"state": "California",
"type": "hotel", ←This is the type identifier for the document
"url": "http://www.bacararesort.com/",
"vacancy": true
}
}
Architecture
The application serves an HTML file named index.html (in an src/app/ folder usually contained in a root folder dedicated to frontend files, e.g.
the public path in the Node.js application).
This file contains references to various included script files for front side JavaScript components such as Bootstrap and jQuery.
It also contains a small script snippet that bootstraps the Angular2 application, as well as an Angular loop ({{#each scripts.polyfills}}
) to load polyfills
(implementation of html5/web component elements that can be used as a backup if the browser doesn’t provide the corresponding feature natively).
Angular2 works in terms of Components
, and the main component that gets displayed at bootstrap is in src/app/try.component.ts.
It is in charge of displaying the common elements of the single-page app, and defines Routes
, determining which component to show depending on the URL.
The place at which said components are shown in the html is represented by a component tag
provided by the angular Router
: <router-outlet></router-outlet>
.
The application is then split into several packages.
Each talks to a specific subset of the REST API provided by the backend.
Each package has a corresponding main Angular Component
that provides the methods to interact with the API or perform work.
Their corresponding views then use these methods to populate data in the application.
The backend REST API, in its version associated with Couchbase Server 4.5 (version 2), can be found in the couchbaselabs/try-cb-frontend repository on GitHub.
The Home component
src/app/+home is the main page, dealing with flight searches.
In the backend, these searches are performed using N1QL
queries.
It has three main methods:
-
Two at
getToAirport
andgetFromAirport
which are both REST API calls to the /api/airports REST endpoint -
And one at
findFlights
, which is a REST API call to the /api/flightPaths/{to}/{from} endpoint
The application attempts to find an airport based on the codes used for the name
(case sensitive), and codes for Federal Aviation Administration (FAA) or International Civil Aviation Organization (ICAO).
The backend method allows the user to search by FAA code, ICAO code or airport name.
It then prepares a N1QL query based on the selection criteria (using bucket information from its configuration).
The query is then sent to the Couchbase query service, all by using the backend’s associated SDK.
Using the data model, if you enter SEA
, KSEA
, or Seattle
, the typeahead directive gives you the option to select Seattle Tacoma Intl. To see the results of the query, watch the terminal window in which the application is running.
The example is showing an output for the N1QL statements:
QUERY: SELECT airportname FROM `travel-sample` WHERE faa ='SEA'
QUERY: SELECT airportname FROM `travel-sample` WHERE icao ='KSEA'
QUERY: SELECT airportname FROM `travel-sample` WHERE airportname LIKE 'Seattle%'
After the airports are selected and a leave date is entered, the application tries to find route and schedule information for an airline that services the requested flight path. The call to the REST API /api/flightPaths/{to}/{from} on the server is initiated when the user clicks the Find Flights button.
The elements in the form bind their controls to corresponding objects in the component, which allows to perform some validation checking among other things.
To learn more about forms and validation in Angular2, please refer to the official documentation.
The submit button calls the component’s findFlights()
function, which in turn invokes the server REST API /api/flightPaths/{to}/{from}/ function either once for a one way flight or twice for a round-trip flight.
When it requests the return flight for a round trip, it just uses the reverse to
and from
sequence.
While to
and from
are made part of the URL, the API also expects an additional query parameter, leaves
, that contains the requested travel date.
In the backend, the code corresponding to the flightPaths
endpoint performs several important steps for processing the request to find flights:
-
It determines the FAA identifier for the
to
andfrom
airports. -
It searches for routes and schedule information based on the source and destination airports and the requested dates. The collection of schedule documents is nested in the route document for each route. They are grouped by day of the week (1-7), and the requested dates are compared to the day of the week to see what flights are available on those particular dates. It returns a list of flights that includes data for the following fields: airline, flight, departure time ("utc" field), from, to, price (randomly assigned by the backend) and aircraft ("equipment" field).
The Query can be seen in the terminal window that is running the backend application:
QUERY:
SELECT a.name, s.flight, s.utc, r.sourceairport, r.destinationairport, r.equipment
FROM `travel-sample` r
UNNEST r.schedule s
JOIN `travel-sample` a
ON KEYS r.airlineid
WHERE r.sourceairport='SEA' AND r.destinationairport='MCO' AND s.day=6
ORDER BY a.name
N1QL query anatomy
The Couchbase Query API is a powerful tool for efficient retrieval of information from a document data store. In each SDK-specific travel application, queries about flights are created by a dedicated module (for example in Node.js, the flightPath.js module). Here’s an example of a query that finds flights between Seattle-Tacoma International Airport (SEA) and Orlando International Airport (MCO), followed by a description of what’s happening in the query:
SELECT a.name, s.flight, s.utc, r.sourceairport, r.destinationairport, r.equipment
FROM `travel-sample` r
UNNEST r.schedule s
JOIN `travel-sample` a ON KEYS r.airlineid
WHERE r.sourceairport='SEA' AND r.destinationairport='MCO' AND s.day=6
ORDER BY a.name
N1QL provides JOIN functionality, something previously not possible in a document database. For two documents to be joined in the result of a SELECT statement, one of them must contain a field whose value is equal to the Couchbase key of the other document. The following example shows two documents that demonstrate that requirement and a SELECT statement that joins them:
"keyA" is the Couchbase KV key for Doc A. Doc A: { some fields } Doc B: { some fields "joinField": "keyA" } SELECT * FROM default b JOIN default a ON KEYS b.joinField
The data model for the travel application includes an airlineid
field in each route
document.
That airlineid
field is used as a foreign key identifier and corresponds to the key for an airline
document.
To select the airline name a.name
, the query uses the following clause: JOIN `travel-sample` a ON KEYS r.airlineid
.
One of the powerful features available in the N1QL query language is the ability to UNNEST
or flatten, the results returned in the SELECT
statement.
This is frequently needed when working with JSON documents, which may have rich heirarchies.
In the data model for the travel application, each route document contains a nested collection of schedule documents.
To alleviate a complicated JSON parsing code pattern for the return results, you can have the query execution UNNEST
the schedule documents, so they become the root-level fields in the returned results.
Standard SQL syntax is used in the WHERE
clause for the SELECT
statement.
The result set is ordered by the a.name
field, which contains the airline name.
Refer to the Querying with N1QL section for more information on using N1QL with the SDKs.
The Hotels component
src/app/+hotels allows to search for hotels.
In the backend these searches are performed using FTS
queries, performed using the /api/hotel/ REST API endpoint.
The form is bound to the component using a FormBuilder
, and it has two optional text inputs: a description and a location field.
When submitting the form, the component’s findHotels
method is called with a JSON representation of the form’s content.
The findHotels
method will use these values to determine if it should hit a more specific endpoint, in the case where a particular description keyword or location are requested by the form.
For instance, to search for a hotel in France with a description matching "golf" (in a full text search sense, possibly including alternative spellings, words with the same root, etc...), one should use the /api/hotel/France/golf endpoint. The possible endpoints are:
-
/api/hotel to find all hotels
-
/api/hotel/{description}/ to find all hotels matching a keyword/phrase in their name or description.
-
/api/hotel/{description}/{location}/ to find all hotels matching the keyword/phrase and a location keyword (in either of the location-related fields: country, city, state, address...)
-
/api/hotel/*/{location}/ to find all hotels within a location (not using keyword)
As a second step, for each hit returned by the FTS service the information that we want to display is collected by the backend using the subdocument API. This allows the client to request that the cluster to only transmit specific parts of the hotel document to be sent back to the client for display.
Refer to the Sub-Document Operations section for more information on using the Subdocument API with the SDKs.
Both parts of the request can be individually seen in the narration component:
FTS query anatomy
The Couchbase Search API (FTS) is a powerful tool for searching text inside a document data store. In each SDK-specific travel application, queries about hotels are created by a dedicated module (for example in Java, the Hotel service). Here’s an example of a query that finds hotels mentioning "golf" in France, followed by a description of what’s happening in the query:
{"query":{"conjuncts": [
{"field":"type","term":"hotel"},
{"disjuncts":[
{"field":"country","match_phrase":"France"},
{"field":"city","match_phrase":"France"},
{"field":"state","match_phrase":"France"},
{"field":"address","match_phrase":"France"}
]},
{"disjuncts":[
{"field":"description","match_phrase":"golf"},
{"field":"name","match_phrase":"golf"}
]}
]},
"size":100}
The query
is actually a composite (conjuncts
) of 3 sub-queries: a term
query and two disjuncts
queries.
-
The term query matches a term as-is, without performing any analysis: it must match exactly what is stored in the index (which could be just the root of a word). Here we use it to restrict our search to the
hotel
type
. -
A disjunction query matches if any of its sub-queries matches. We use the first only when there is a
location
provided, to search for it in any of thecountry
,city
,state
andaddress
fields. We use amatch phrase
sub-query on each, which will attempt to find several keywords, analyzing them to remove stop words, include close results like words from the same root, etc... -
The second disjunction query is added whenever a description is provided for the search. It performs two match phrase queries on the description, respectively in the
name
andcontent
fields of the landmark. -
There are quite a few other FTS query types. For instance, a match query is much like a match phrase query, except it only considers one term to search for, and can apply fuzzyness, e.g. to match close words with a slightly different spelling.
Finally, we notice that at the same level as the query
entry there is also a size
attribute.
This is one of the many tunings applicable to the whole query, a top-level parameter that limits the results to 100 hits.
Refer to the Full Text Search section for more information on using FTS with the SDKs.


2 writes to StartExecutionTime
System.Management.Automation (2)
engine\hostifaces\History.cs (2)
39
StartExecutionTime
= startTime;
54
StartExecutionTime
= history.StartExecutionTime;
3 references to StartExecutionTime
System.Management.Automation (3)
engine\hostifaces\History.cs (3)
54
StartExecutionTime = history.
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92
public TimeSpan Duration => EndExecutionTime -
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Watch out for the Car..stay tuned
Fancied combustion engine and automotive mechanics...
If it weren't for the last minute,nothing would get done
When you see a good move,look for a better one.
Be the Ambani of Your Dreams before others hire you.
Ride Hard or stay Home.
It's Time to find another Gear.
If it's not about bidding, don't ask me.
Life without junk is like an unsharped pencil..POINTLESS
If the Apocalypse comes,Beep me.
It's not just a game.
Always remember to Explore


Item 1.
Bar chart with neighborhood issues listed along the horizontal axis and frequency on the vertical axis. Above each neighborhood issue is a bar with height equal to the frequency of responses which were concerned with that issue, as listed in Table 2.6.3.in-context


Check out our features!
Clipp has direct messaging and streamlines the meet-up scheduling process, making it super convenient!
Our fun swiping feature ensures that you never know what items you will see next!
Users don't have to spend a dime to get the item they want on Clipp!
This app is built by college student and for college students, ensuring a safe experience!
Feel free to reach out at leoluo@umich.edu


47-year-old grandmother among 94 people arrested for drug offences
SINGAPORE: A 47-year-old grandmother was among 94 people arrested this week for suspected drug offences in an islandwide operation conducted by the Central Narcotics Bureau (CNB).
The woman, a suspected drug trafficker, had a baby and two other young children with her at the time of arrest on Monday (Nov 4), the police said.
She was babysitting a one-year-old girl, who was subsequently handed over to her mother, CNB said.
The other two children - a five-year-old girl and a six-year-old boy - are the suspect’s grandchildren.
They have since been referred to the Ministry of Social and Family Development’s Child Protective Service after authorities failed to contact their mother.
Two packets of Ice were recovered during the arrest near Jurong West Street 91.
ARRESTS IN MULTIPLE LOCATIONS
Separately, 93 other people were arrested in separate raids across areas such as Ang Mo Kio, Bukit Batok, Bukit Panjang and Pasir Ris this week.
In all, officers seized 314g of Ice, 81g of cannabis, 18g of heroin, 384g of ketamine, 498 Ecstasy tablets, 652 Erimin-5 tablets and 40g of new psychoactive substances during the operation.
In one case, two suspected traffickers were arrested around Jalan Rengkam in Hougang on the evening of Nov 6.
CNB said its officers saw a 38-year-old man - who was accompanied by a 39-year-old man - get into a black car and re-emerge with a blue plastic bag.
Shortly after, the black car left, and a team of officers tailed it.
The two men headed to another car parked around Hougang Street 31. They were arrested just as they got into the car, where a blue plastic bag containing about 119g of Ice was found.
The black car was later intercepted at the junction of Pasir Ris Drive 1 and Loyang Avenue. Two men, aged 27 and 29, were arrested. About 5g of Ice, 10 Ecstasy tablets and S$7,585 in cash were found in the car.
More drugs were found when officers raided the suspects’ flats, CNB said.
Investigations are ongoing.


Overview
Use this tool to create or modify skins for YUI Widgets, with real-time previewing. A skin is the visual presentation of something, not its features or function. Use YUI Skin Builder.Main Parts of the Tool
There are 2 main parts to the Skin Builder UI.
- Control Tabs
- Skin Preview
Control Tabs
Toward the top of YUI Skin Builder is an area with 5 tabs- Style: This displays buttons for changing the key colors of the skin. You can change the border-radius, padding, and text contrast styles in the skin.
- Schemes: From this tab, you can select one of several color schemes, or start creating your own custom scheme. Color schemes set different relationships between the colors shown in the palette tab. More explanation about schemes later.
- Items: This contains checkboxes for each of the available widgets and modules that are available for skinning.
-
Code: This tab contains text area controls that display the real-time generated CSS code for your skin as you make modifications. You can also change the skin name, which marks all the selectors in the CSS code.
The Code tab also includes a Link button which captures all the current skin settings into a URL. You can save it somewhere or bookmark it to get back to where you left off. You can also share it with others.
Skin Preview
At the bottom of Skin Builder is an array of real instances of each of the YUI 3 Widgets selected in the "Items" tab. Their skin/appearance is continually updated with the latest changes you make in the Control Tabs.A Typical Use Pattern
- During all the following steps, keep an eye on the Widget previews, evaluating the results of your changes.
- Open the Style tab.
- Click Edit Master Color... button. The color you select should match a very dominant color from the app these widgets will appear in. For example if these skinned widgets will go on a site that has green as a high emphasis, attention attracting color, choose that same green from this color picker. You'll notice the other colors in the palette change automatically. They change to relative colors based on relationships to the "highest/master" color. These relationships are called "schemes".
- Click the Edit Page Color... button (if your page background color isn't white). From the Color Picker that pops up, select a color that matches the background color the widgets will display on. This is commonly the page background, but may be a container object that widgets will appear in. You can also enter the color directly with a hex value if you know it.
- Make any changes needed to the padding and border-radius styles. You can adjust the contrast between the text and its background. This adjustment can help maintain readability.
- Click the Schemes tab. Try other schemes to see which works best for your site. If you choose a dark background scheme, you may need to select a different "highest/master" color from the palettes tab
- Edit the Skin Name suffix. In the markup of your site or app where widgets are to use this skin, replace the class "yui3-skin-sam" with the matching new class name, such as, "yui3-skin-myskinnamesuffix".
- Select the contents of the textarea labeled "CSS for all widgets". Paste this code into the file wherever you want your CSS to be referenced from. If you don't need CSS skin code for all the YUI 3 widgets, you can pick out and use only the sections of the CSS you do need.
- Click the Link button shown in step 7. Save this URL somewhere so you can make future edits starting where you left off. Share it with others.
Examples
Here are a couple screenshots of using YUI Skin Builder to edit/create YUI Widget skins to match existing sites.
How Does It Work?
Successfully applying colors to objects (skinning) is a process of applying the colors that will give the objects their correct visual emphasis for their function relative to other objects. Skin Builder provides a palette of colors arranged by emphasis, and then logically maps those colors onto the backgrounds, borders, and text of each part of YUI widgets and components according to their function. It then allows you to manipulate the color palette in several ways that provide infinite variety of colors while giving instant preview feedback.Key color
This is a user-selected color. It's the color that has the most emphasis in the skin. It will be used as the background color of visually dominant widget elements such as, primary or default buttons, or selected tabs. In the Palette, it is presented as the background color of the "highest"Main colors
There are 6 Main Colors used in this tool. From left to right, their names are...- Highest
- High
- Normal
- Low
- Lowest
- Page
Main colors "Highest" through "Lowest" are designed to hold colors of decreasing visual emphasis. The main color "Page" is the background color of the page or container the widgets will be viewed on in the final app or web page. Each of these main colors represents a family of colors that are used to color widgets.
Color scheme
A Color Scheme is a data structure that holds information about the relationship of each main color to the Key Color. The Main color "Highest" always has the same value color value as the Key Color. The value of the main color "high" might be calculated like this: Start with the key color, and adjust its hue by 0, its saturation down by 30%, and its lightness up by 20%. The value of the main color "normal" might be calculated like this: Start with the key color, and adjust its hue by 0, its saturation down by 30%, and its lightness up by 30%. Etc. A Color Scheme data structure looks like this...Y.ColorSpace.schemes.monochrome = { container: {h:0, s:0, l:0}, // the page or container highest: {h:0, s:-30, l:60}, high: {h:0, s:-30, l:60}, normal: {h:0, s:-30, l:75}, low: {h:0, s:-30, l:80}, lowest: {h:0, s:-30, l:90} };
Foreground Colors
Each main color has an associated set of named, foreground colors:- Text-high
- Text-normal
- Text-low
- Rule-high
- Rule-low
- Border-high
- Border-low
- Gradient
The text colors have special algorithms applied to them to keep them readable given any background color.
Hover Colors
Since many widgets have objects that require visual feedback on user interaction, we provide a set of hover colors for the hover state of elements. The hover color is calculated from each of the main colors. It's a child of its parent main color. Each hover color also has a full set of foreground colors.Space/palette
The Space/palette is a nested data structure collection of all the color values available for use in a particular skin. These values include all main colors, hover colors, and foreground colors. The space/palette object literal in JSON form, contains nested key-value pairs. The values are colors in hex format that looks like this.Skin.PALETTE = { block: { highest: { background: '#3355BA', gradient: '', border: { low: '#0B2981', high: '#6680CC' }, text: { low: '#708EE6', normal: '#D9E2FF', high: '#ffffff' }, rule: { low: '#223A80', high: '#4A69C4' }, hover: { // the hover color and its foreground colors background: '#1B3A95', gradient: '', border: { low: '#041D67', high: '#6680CC' }, text: { low: '#5675CF', normal: '#EFF3FF', high: '#fff' }, rule: { low: '#162D6F', high: '#314D9F' } } // next would be the other main colors, // "high", "normal", "low", "lowest", and "page" } };
Color Mapping
Each YUI Widget has a color map file, which defines variable names and set their value equal to a color, padding, or radius value in the skin palette.YUI.add('skin-tabview', function (Y) { Y.Skin.renderers.tabview = function (skin) { var space = skin.colorspace, block = skin.colorspace.block; return { paddingTab: skin.padding(0.3, 0.75), paddingTabSelected: skin.padding(0.4, 0.75), paddingPanel: skin.padding(0.25, 0.5), tabRadius: skin.radius(0.4), // 40% of global radius tabBackground: block.high.background, tabGradient: block.high.gradient, tabText: block.high.text.normal, // Example variable is here tabBorderLow: block.high.border.low, tabBorderHigh: block.high.border.high, tabHoverBackground: block.high.hover.background, tabHoverGradient: block.high.hover.gradient, tabHoverText: block.high.hover.text.normal, tabHoverBorderLow: block.high.hover.border.low, tabHoverBorderHigh: block.high.hover.border.high,For example: The mapping file for the TabView widget is named skin-tabview.js. One of the variables you can see in the snippet above is named "tabText". Its value is equal to (mapped to) the color stored in the skin object "block.high.text.normal". This is the normal text color in the "high" main color that you can see (circled) in the palette of this tool.
YUI Skin Builder has CSS style code templates for each widget. At the bottom of the template snippet below, you can see where the variable "tabText" is used.
... {{prefix}}{{skinName}} {{prefix}}tab-label { {{tabGradient}}; background-color: {{tabBackground}}; border-style: solid; border-color: {{tabBorderHigh}} {{tabBorderLow}} {{tabBorderLow}} {{tabBorderHigh}}; border-width: 1px 1px 0 1px; cursor:pointer; font-size: 85%; padding: {{paddingTab}}; text-decoration:none; border-radius: {{tabRadius}} {{tabRadius}} 0 0; color: {{tabText}}; /****** The variable in the example is here */ } ...When the CSS templates are processed, the variables are replaced.
.yui3-skin-mine .yui3-tab-label { /* omitting a lot of gradient CSS here for this example */ background-color: #b0bcdd; border-style: solid; border-color: #d1d8eb #a6b3d9 #a6b3d9 #d1d8eb; border-width: 1px 1px 0 1px; cursor:pointer; font-size:85%; padding: 0.29em 0.75em; /*0.3em .75em;*/ text-decoration:none; border-radius: 4px 4px 0 0; color: #0e1320; /****** The variable has been replaced */ }The YUI Widgets Preview uses this CSS and it's output in the code tab of the YUI Skin Builder for your use.


Hello {{user.username}},
Your subscription has been approved.
You will be reciving emails whenever a blog has been posted to keep track of the latest blogs.
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for more info or support kindly contact sunnyblog@gmail.com


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Using a grid in a modal popup.
In some cases, and in particular with the bootstrap modal, you may find that your grid renders smaller than the
available width (or sometimes appears to render not at all. This is believed to be because the bootstrap modal
animates the initial render and the grid renders whilst the modal is still animating - the available space isn't
as expected. You can correct this by calling handleWindowResize
. The animation time seems to be somewhat variable,
so the currently recommended approach is to use $interval, and to call every 500ms for the first 5s after modal opening.
In a sense this is similar to what the auto-resize feature does, but it only does it for a short period after modal opening.


"x" "1" "ANKE,H, STERNER,O AND STEGLICH,W, STRUCTURE-ACTIVITY RELATIONSHIPS FORUNSATURATED DIALDEHYDES. 3. MUTAGENIC, ANTIMICROBIAL, CYTOTOXIC, AND PHYTOTOXICACTIVITIES OF MERULIDIAL DERIVATIVES, J. ANTIBIOT. __ 42(5):738-744,1989"


Rain Fall
问题描述 :
Rainfall is measured in millimeters. The rain is collected in a vertical transparent tube with millimeter markings, and once the rain has stopped falling, one can check the height of the water in the tube.
In our problem, the tube unfortunately has a leak at height L millimeters (mm). If the water level is above the leak then water drains from the tube at a rate of K millimeters per hour (mm/h).
We want to figure out how much rain fell during a particular rainfall. We assume that the tube is high enough that it does not overflow. We also assume that rain falls at an (unknown) uniform rate during a rainfall, and that water does not evaporate from the tube. The height of the leak itself is also negligible.
输入:
L is where the leak is (mm)
K is the rate at which water leaks (mm/h)
T1 is the duration of the rainfall (h)
T2 is the time between the end of the rainfall and the observation of the water level (h)
H is the water level in the tube when we observe it (mm)
Each number is at least 0.01 and at most 1000.00, and each is given with two decimals.
输出:
L is where the leak is (mm)
K is the rate at which water leaks (mm/h)
T1 is the duration of the rainfall (h)
T2 is the time between the end of the rainfall and the observation of the water level (h)
H is the water level in the tube when we observe it (mm)
Each number is at least 0.01 and at most 1000.00, and each is given with two decimals.
。。。不是动态规划吧？
能不能说下这个是用的什么算法策略？
原来如此 了解了
很强大啊，佩服
https://coding.net/moon/miracle
红绯鱼：
牛逼牛逼
为什么暴力求解方法循环到sqrt(n) 就可以了呢
int L[m+1][n+1];这样定义出错，这个在gcc上可以编译通过 在VS上不行 应该改为new 或者vector
为什么暴力求解方法循环到sqrt(n) 就可以了呢
顺利测试通过了，多谢
顺利测试通过了，多谢
不错
很给力啊，学习了


--- layout: default title: Colin Scott navbar: about description: Colin Scott gets to work on awesome problems with awesome people ---
I am working as a software engineer for Google's NetInfra org (previousy: Next Billion Users). Prior to Google I spent two wonderful years at Microsoft Research India, where I focused on information and communications technology for development (ICTD) and got to work with amazing people like Bill Thies, Shubranshu Choudhary, Muthian Sivathanu, Amit Sharma, Jacki O'Neill, Indrani Medhi-Thies, and Swami Manohar. I graduated from UC Berkeley, where I was supported by an NSF Graduate Research Fellowship. My dissertation was on testing and debugging networked systems. On the side, I also collaborated with folks in the TIER group. I was advised by Scott Shenker, Arvind Krishnamurthy, and (briefly) George Necula. In a previous life I studied philosophy and computer science at the beautiful University of Washington. I spent most of my time there working on Internet measurement and outage avoidance with Ethan Katz-Bassett, Dave Choffnes, Arvind Krishnamurthy, and Tom Anderson. Several of my blog posts and publications have topped Hacker News, make sure to check them out!


§576B-703 Relationship of support enforcement agency to United States central authority. The support enforcement agency of this State is recognized as the agency designated by the United States central authority to perform specific functions under the Convention. [L 2015, c 77, pt of §1]


# coding=utf8
"""
Label propagation in the context of this module refers to a set of
semi-supervised classification algorithms. At a high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms perform very well in practice. The cost of running can be very
expensive, at approximately O(N^3) where N is the number of (labeled and
unlabeled) points. The theory (why they perform so well) is motivated by
intuitions from random walk algorithms and geometric relationships in the data.
For more information see the references below.
Model Features
--------------
Label clamping:
The algorithm tries to learn distributions of labels over the dataset given
label assignments over an initial subset. In one variant, the algorithm does
not allow for any errors in the initial assignment (hard-clamping) while
in another variant, the algorithm allows for some wiggle room for the initial
assignments, allowing them to change by a fraction alpha in each iteration
(soft-clamping).
Kernel:
A function which projects a vector into some higher dimensional space. This
implementation supports RBF and KNN kernels. Using the RBF kernel generates
a dense matrix of size O(N^2). KNN kernel will generate a sparse matrix of
size O(k*N) which will run much faster. See the documentation for SVMs for
more info on kernels.
Examples
--------
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> iris = datasets.load_iris()
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
LabelPropagation(...)
Notes
-----
References:
[1] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised
Learning (2006), pp. 193-216
[2] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient
Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005
"""
# Authors: Clay Woolam<EMAIL_ADDRESS># Utkarsh Upadhyay<EMAIL_ADDRESS># License: BSD
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, ClassifierMixin
from ..externals import six
from ..metrics.pairwise import rbf_kernel
from ..neighbors.unsupervised import NearestNeighbors
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_X_y, check_is_fitted, check_array
from ..exceptions import ConvergenceWarning
class BaseLabelPropagation(six.with_metaclass(ABCMeta, BaseEstimator,
ClassifierMixin)):
"""Base class for label propagation module.
Parameters
----------
kernel : {'knn', 'rbf', callable}
String identifier for kernel function to use or the kernel function
itself. Only 'rbf' and 'knn' strings are valid inputs. The function
passed should take two inputs, each of shape [n_samples, n_features],
and return a [n_samples, n_samples] shaped weight matrix
gamma : float
Parameter for rbf kernel
n_neighbors : integer > 0
Parameter for knn kernel
alpha : float
Clamping factor
max_iter : integer
Change maximum number of iterations allowed
tol : float
Convergence tolerance: threshold to consider the system at steady
state
n_jobs : int, optional (default = 1)
The number of parallel jobs to run.
If ``-1``, then the number of jobs is set to the number of CPU cores.
"""
def __init__(self, kernel='rbf', gamma=20, n_neighbors=7,
alpha=1, max_iter=30, tol=1e-3, n_jobs=1):
self.max_iter = max_iter
self.tol = tol
# kernel parameters
self.kernel = kernel
self.gamma = gamma
self.n_neighbors = n_neighbors
# clamping factor
self.alpha = alpha
self.n_jobs = n_jobs
def _get_kernel(self, X, y=None):
if self.kernel == "rbf":
if y is None:
return rbf_kernel(X, X, gamma=self.gamma)
else:
return rbf_kernel(X, y, gamma=self.gamma)
elif self.kernel == "knn":
if self.nn_fit is None:
self.nn_fit = NearestNeighbors(self.n_neighbors,
n_jobs=self.n_jobs).fit(X)
if y is None:
return self.nn_fit.kneighbors_graph(self.nn_fit._fit_X,
self.n_neighbors,
mode='connectivity')
else:
return self.nn_fit.kneighbors(y, return_distance=False)
elif callable(self.kernel):
if y is None:
return self.kernel(X, X)
else:
return self.kernel(X, y)
else:
raise ValueError("%s is not a valid kernel. Only rbf and knn"
" or an explicit function "
" are supported at this time." % self.kernel)
@abstractmethod
def _build_graph(self):
raise NotImplementedError("Graph construction must be implemented"
" to fit a label propagation model.")
def predict(self, X):
"""Performs inductive inference across the model.
Parameters
----------
X : array_like, shape = [n_samples, n_features]
Returns
-------
y : array_like, shape = [n_samples]
Predictions for input data
"""
probas = self.predict_proba(X)
return self.classes_[np.argmax(probas, axis=1)].ravel()
def predict_proba(self, X):
"""Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X
and each possible outcome seen during training (categorical
distribution).
Parameters
----------
X : array_like, shape = [n_samples, n_features]
Returns
-------
probabilities : array, shape = [n_samples, n_classes]
Normalized probability distributions across
class labels
"""
check_is_fitted(self, 'X_')
X_2d = check_array(X, accept_sparse=['csc', 'csr', 'coo', 'dok',
'bsr', 'lil', 'dia'])
weight_matrices = self._get_kernel(self.X_, X_2d)
if self.kernel == 'knn':
probabilities = []
for weight_matrix in weight_matrices:
ine = np.sum(self.label_distributions_[weight_matrix], axis=0)
probabilities.append(ine)
probabilities = np.array(probabilities)
else:
weight_matrices = weight_matrices.T
probabilities = np.dot(weight_matrices, self.label_distributions_)
normalizer = np.atleast_2d(np.sum(probabilities, axis=1)).T
probabilities /= normalizer
return probabilities
def fit(self, X, y):
"""Fit a semi-supervised label propagation model based
All the input data is provided matrix X (labeled and unlabeled)
and corresponding label matrix y with a dedicated marker value for
unlabeled samples.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
A {n_samples by n_samples} size matrix will be created from this
y : array_like, shape = [n_samples]
n_labeled_samples (unlabeled points are marked as -1)
All unlabeled samples will be transductively assigned labels
Returns
-------
self : returns an instance of self.
"""
X, y = check_X_y(X, y)
self.X_ = X
check_classification_targets(y)
# actual graph construction (implementations should override this)
graph_matrix = self._build_graph()
# label construction
# construct a categorical distribution for classification only
classes = np.unique(y)
classes = (classes[classes != -1])
self.classes_ = classes
n_samples, n_classes = len(y), len(classes)
alpha = self.alpha
if self._variant == 'spreading' and \
(alpha is None or alpha <= 0.0 or alpha >= 1.0):
raise ValueError('alpha=%s is invalid: it must be inside '
'the open interval (0, 1)' % alpha)
y = np.asarray(y)
unlabeled = y == -1
# initialize distributions
self.label_distributions_ = np.zeros((n_samples, n_classes))
for label in classes:
self.label_distributions_[y == label, classes == label] = 1
y_static = np.copy(self.label_distributions_)
if self._variant == 'propagation':
# LabelPropagation
y_static[unlabeled] = 0
else:
# LabelSpreading
y_static *= 1 - alpha
l_previous = np.zeros((self.X_.shape[0], n_classes))
unlabeled = unlabeled[:, np.newaxis]
if sparse.isspmatrix(graph_matrix):
graph_matrix = graph_matrix.tocsr()
for self.n_iter_ in range(self.max_iter):
if np.abs(self.label_distributions_ - l_previous).sum() < self.tol:
break
l_previous = self.label_distributions_
self.label_distributions_ = safe_sparse_dot(
graph_matrix, self.label_distributions_)
if self._variant == 'propagation':
normalizer = np.sum(
self.label_distributions_, axis=1)[:, np.newaxis]
self.label_distributions_ /= normalizer
self.label_distributions_ = np.where(unlabeled,
self.label_distributions_,
y_static)
else:
# clamp
self.label_distributions_ = np.multiply(
alpha, self.label_distributions_) + y_static
else:
warnings.warn(
'max_iter=%d was reached without convergence.' % self.max_iter,
category=ConvergenceWarning
)
self.n_iter_ += 1
normalizer = np.sum(self.label_distributions_, axis=1)[:, np.newaxis]
self.label_distributions_ /= normalizer
# set the transduction item
transduction = self.classes_[np.argmax(self.label_distributions_,
axis=1)]
self.transduction_ = transduction.ravel()
return self
class LabelPropagation(BaseLabelPropagation):
"""Label Propagation classifier
Read more in the :ref:`User Guide <label_propagation>`.
Parameters
----------
kernel : {'knn', 'rbf', callable}
String identifier for kernel function to use or the kernel function
itself. Only 'rbf' and 'knn' strings are valid inputs. The function
passed should take two inputs, each of shape [n_samples, n_features],
and return a [n_samples, n_samples] shaped weight matrix.
gamma : float
Parameter for rbf kernel
n_neighbors : integer > 0
Parameter for knn kernel
alpha : float
Clamping factor.
.. deprecated:: 0.19
This parameter will be removed in 0.21.
'alpha' is fixed to zero in 'LabelPropagation'.
max_iter : integer
Change maximum number of iterations allowed
tol : float
Convergence tolerance: threshold to consider the system at steady
state
n_jobs : int, optional (default = 1)
The number of parallel jobs to run.
If ``-1``, then the number of jobs is set to the number of CPU cores.
Attributes
----------
X_ : array, shape = [n_samples, n_features]
Input array.
classes_ : array, shape = [n_classes]
The distinct labels used in classifying instances.
label_distributions_ : array, shape = [n_samples, n_classes]
Categorical distribution for each item.
transduction_ : array, shape = [n_samples]
Label assigned to each item via the transduction.
n_iter_ : int
Number of iterations run.
Examples
--------
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> iris = datasets.load_iris()
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
LabelPropagation(...)
References
----------
Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data
with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon
University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
See Also
--------
LabelSpreading : Alternate label propagation strategy more robust to noise
"""
_variant = 'propagation'
def __init__(self, kernel='rbf', gamma=20, n_neighbors=7,
alpha=None, max_iter=1000, tol=1e-3, n_jobs=1):
super(LabelPropagation, self).__init__(
kernel=kernel, gamma=gamma, n_neighbors=n_neighbors, alpha=alpha,
max_iter=max_iter, tol=tol, n_jobs=n_jobs)
def _build_graph(self):
"""Matrix representing a fully connected graph between each sample
This basic implementation creates a non-stochastic affinity matrix, so
class distributions will exceed 1 (normalization may be desired).
"""
if self.kernel == 'knn':
self.nn_fit = None
affinity_matrix = self._get_kernel(self.X_)
normalizer = affinity_matrix.sum(axis=0)
if sparse.isspmatrix(affinity_matrix):
affinity_matrix.data /= np.diag(np.array(normalizer))
else:
affinity_matrix /= normalizer[:, np.newaxis]
return affinity_matrix
[docs] def fit(self, X, y):
if self.alpha is not None:
warnings.warn(
"alpha is deprecated since 0.19 and will be removed in 0.21.",
DeprecationWarning
)
self.alpha = None
return super(LabelPropagation, self).fit(X, y)
class LabelSpreading(BaseLabelPropagation):
"""LabelSpreading model for semi-supervised learning
This model is similar to the basic Label Propagation algorithm,
but uses affinity matrix based on the normalized graph Laplacian
and soft clamping across the labels.
Read more in the :ref:`User Guide <label_propagation>`.
Parameters
----------
kernel : {'knn', 'rbf', callable}
String identifier for kernel function to use or the kernel function
itself. Only 'rbf' and 'knn' strings are valid inputs. The function
passed should take two inputs, each of shape [n_samples, n_features],
and return a [n_samples, n_samples] shaped weight matrix
gamma : float
parameter for rbf kernel
n_neighbors : integer > 0
parameter for knn kernel
alpha : float
Clamping factor. A value in [0, 1] that specifies the relative amount
that an instance should adopt the information from its neighbors as
opposed to its initial label.
alpha=0 means keeping the initial label information; alpha=1 means
replacing all initial information.
max_iter : integer
maximum number of iterations allowed
tol : float
Convergence tolerance: threshold to consider the system at steady
state
n_jobs : int, optional (default = 1)
The number of parallel jobs to run.
If ``-1``, then the number of jobs is set to the number of CPU cores.
Attributes
----------
X_ : array, shape = [n_samples, n_features]
Input array.
classes_ : array, shape = [n_classes]
The distinct labels used in classifying instances.
label_distributions_ : array, shape = [n_samples, n_classes]
Categorical distribution for each item.
transduction_ : array, shape = [n_samples]
Label assigned to each item via the transduction.
n_iter_ : int
Number of iterations run.
Examples
--------
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelSpreading
>>> label_prop_model = LabelSpreading()
>>> iris = datasets.load_iris()
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
LabelSpreading(...)
References
----------
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
Bernhard Schoelkopf. Learning with local and global consistency (2004)
http://citeseer.ist.psu.edu/viewdoc/summary?doi=<IP_ADDRESS>.3219
See Also
--------
LabelPropagation : Unregularized graph based semi-supervised learning
"""
_variant = 'spreading'
def __init__(self, kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2,
max_iter=30, tol=1e-3, n_jobs=1):
# this one has different base parameters
super(LabelSpreading, self).__init__(kernel=kernel, gamma=gamma,
n_neighbors=n_neighbors,
alpha=alpha, max_iter=max_iter,
tol=tol,
n_jobs=n_jobs)
def _build_graph(self):
"""Graph matrix for Label Spreading computes the graph laplacian"""
# compute affinity matrix (or gram matrix)
if self.kernel == 'knn':
self.nn_fit = None
n_samples = self.X_.shape[0]
affinity_matrix = self._get_kernel(self.X_)
laplacian = sparse.csgraph.laplacian(affinity_matrix, normed=True)
laplacian = -laplacian
if sparse.isspmatrix(laplacian):
diag_mask = (laplacian.row == laplacian.col)
laplacian.data[diag_mask] = 0.0
else:
laplacian.flat[::n_samples + 1] = 0.0 # set diag to 0.0
return laplacian


25
1 Then Bildad also replied:
2 "God is very powerful; everyone should honor him very much;
he causes everything to be peaceful high up in heaven, with no confusion at all.
3 Can anyone count the angels who are in his army in heaven?
Is there any person on whom his light does not shine?
4 So how can God consider any person to be righteous?
How can he accept any human being?
5 Consider this: God does not even think that the full moon is bright,
and he does not even accept the stars in the heavens as worthy of him.
6 So what about humans?
They are as insignificant as maggots.
God does not think more highly of people than he thinks of worms."


WITNESS LIST
Finance-S/C on Fiscal Matters
April 11, 2013 7:30 AM
Registering, but not testifying:
FOR:
Longoria Jr., Manuel City Manager (City of Leon Valley), San Antonio, TX
Reyna Jr, Arthur C. Attorney - Councilman (City of Leon Valley), SA, TX
FOR:
Bieterman, Tom Chief Acounting Officer Baptist Health System (Healthcare Cost
Coalition), SA, TX
ON:
Christian, John Consultant (Healthcare Cost Containment Coalition), Austin, TX
Registering, but not testifying:
FOR:
Bittick, Susan Tax Consultant (Ryan, LLC), Austin
Cepelo, Jaime attorney (Texas Chapter American College of Cardiology)
Cepelo, Jaime attorney at law (Texas Ambulatory Surgery Center Society)
Girard, Chuck Director, Government Relations (HCA- Hospital Corporation of America),
Austin
Jones, Marsha Consultant (United Surgical Partners Inc), Austin
Providing written testimony:
FOR:
Whitehurst, David VP - Advocacy (Texas Medical Association), Austin, TX
FOR:
Enloe, Brad Commercial Real Estate (Capstar Real Estate Advisors & 3000 Skyline
Dallas), Dallas, TX
Hogan, Patrick W (Texas Technology Consortium), Austin
Kang, Sylvia site acquisitions (Cyrus One)
Patterson, John Commercial Real Estate (Cassidy Turley), Dallas
Registering, but not testifying:
FOR:
Connett, Brent (Texas Conservative Coalition), Austin, TX
Flores, Robert Gov't Affairs Consultant (Texas Association of Mexican-American
Chambers of Commerce), Austin, TX
Giles, Deborah Government Relations (SH1 Int'l)
Johnson, Michael (Hillwood Properties)
LeBas, James fiscal analyst (Rackspace Hosting)
Reilly, Wendy Director, State Government Affairs (The Technology Association of
America (Tech America)), Austin, TX
Schwab, Carlton President/ CEO (Texas Economic Development Council)
Wise, Andrew Software (Microsoft), Austin, TX
WITNESS LIST
Finance-S/C on Fiscal Matters
April 11, 2013 7:30 AM
FOR:
Newton, Chris Association staff (Texas Food & Fuel Association), Austin, TX
Registering, but not testifying:
FOR:
Calcote, ML Consultant (7-Eleven), Austin, TX
Gibson, Stephanie consultant (Texas Retailers Association), Austin
Volkening, Ronnie President (Texas Retailers Association), Austin, TX
FOR:
Booker, Bruce E. Chief Operating Officer (Lake Truck Lines, Inc), San Antonio, TX
Registering, but not testifying:
FOR:
Flores, Robert Gov't Affairs Consultant (Texas Association of Mexican-American
Chambers of Commerce), Austin, TX
Leal, Gloria Attorney & Consultant (Texas Alliance of Energy Producers), Austin, TX
FOR:
Dunn, David Exec Director (TX Charter Schools Assn), Austin, TX
Goodman, Jennifer Superintendent (of Educ.) (Odyssey Academy), Galveston, TX
Harp, Ken Director of Finance (Responsive Education), Lewisville
FOR:
Brown, Bryan Executive (also providing written testimony) (Pinnacle Entertainment and
KTAG Downs Holding), SA, TX
Graham, Tyler D Horse owner / breeder (TQHA (Texas Quarter Horse Assoc)), Elgin, TX
ON:
Schmidt, Robert Physician (Texas Racing Commission), Fort Worth
Registering, but not testifying:
FOR:
Cavazos, Eddie Lobbyist (Penn. Natl.), Austin
Coleman, Katherine Attorney (Sam Houston Race Park), Austin, TX
James, Nick Lobbyist / Exec. Dir. (Texas Greyhound Association), Austin, TX
Providing written testimony:
AGAINST:
Kohler, Rob Consultant (Christian Life Commission), Austin, TX
FOR:
Rao, Navin Vice President & Assistant General Counsel (Michaels Stores, Inc.), Irving, TX
Registering, but not testifying:
FOR:
Kroll, John Economist (Michaels Stores Inc), Austin, TX
WITNESS LIST
Finance-S/C on Fiscal Matters
April 11, 2013 7:30 AM
Volkening, Ronnie President (Texas Retailers Association), Austin, TX
FOR:
Burleson, Robert School Fundraising (AFRDS), Hou, TX
Colombo, Russ Professional Fundraising (Myself- School Sponsors- AFRDS), Houston,
TX
Kitchens, Terence President/ USA Fundraisers (USA Fundraisers), Mont Belvieu, TX
Tomblin, Reginald W. Fundraising Distribution (S'Kool Smartz Inc.), Terrell, TX
Registering, but not testifying:
FOR:
Arnold, Ellen (Texas PTA), Austin
Casey, Daniel Partner, Moak, Casey & Associates (Fast Growth School Coalition), Austin,
TX
McCraw, Ken Ex. Dir (Tx Assn of Community Schools)
McCreary, Casey Assistant Executive Director (1) Texas Association of School
Administrators, 2) TX Ass. of School Boards, ; 3) TX Elementary Principals & Supervisors
Association)
Registering, but not testifying:
FOR:
Harp, Ken Director of Finance (Responsive Education), Lewisville
Registering, but not testifying:
FOR:
Dunn, David Exec Director (TX Charter Schools), Austin, TX
Goodman, Jennifer Education (Superintendent) (Mosbacher Odyssey Academy),
Galveston, TX


I am Game developer by Passion and Profession. I make all types of game for multicross Platform. By Education i am an Electrical, Electronics and Communication (Antenna designer) engineer. but my intrest in Video game attracted me and diverted my life into gaming industry. I really enjoy developing games. Who doesn't like Playing Games and most importantly the physics behind the all those games takes you to another dimension.
while developing games i came to know that physics is not only in real world but also in virtual world its existance has significant presence. There's a lot to learn and implement using unity and and its physics system. A Game Engine Like Unity and Unreal Engine are the best Engine available in game development industry for indie developer to build new games. you can check out my games below. I hope You are going to love it.
A representation of my proficiency in each skill
Used in most of my Games
All the unity games build on C#
Artificial Intelligence in AR Projects
Website development
Website Development
Skinning and Animating 2d Character in unity
© Copyright Nikhil Deshmukh 2021


|
FNMeteoParthenope
1.0
Give power to your app, with FNMeteoParthenope - by Fabio Nisci
|
This is a guide to help developers get up to speed with FNMeteoParthenope. It is geared primarily towards anyone who is new to Mac or iOS development, or has not worked extensively with 3rd-party libraries before.
These step-by-step instructions are written for Xcode 4.6+, using either the iOS 6 or Mac OS 10.7 SDK. If you are using a previous version of Xcode, you may want to update before starting.
First, we need to download the latest source code from the FNMeteoParthenope repository.
Click here to download FNMeteoParthenope in a zip archive**
Once the download has finished, unarchive the zip file and open the resulting directory. The folder we'll be interested in later is named "FNMeteoParthenope", but hold off on doing anything with that for a moment.
Once we have the latest version of FNMeteoParthenope, we're ready to add it to our project.
Now, on a side of screen go to that unarchived folder from the code we downloaded a few minutes ago, select and drag FNMeteoParthenope.framework
and Networking.framework
into Framework folder.
Check that the options below the file browser are as follows:
Finally, a new "FNMeteoParthenope" framework will have been added to your project, which corresponds to the folder that is now in your project directory. You can also choose to reference to the folder, but don't forget to bring all with you if you backup.
Now, a very important step: we need to add the _-ObjC_ to Other Linker Flag. If you don't know how to do that follows this image.
It's not a my choose, Apple say to do that when you are some categories into the project. See it here a detailed explaination by Apple.
Click on the Targets → Your app name → and then the 'Build Phases' tab and then expand 'Link Binary With Libraries' as shown.
Add the following libraries:
Now that our project is all set up with FNMeteoParthenope, let's try building the project. Select "Project > Build", or use its keyboard shortcut, ⌘B
.
At this point, everything's in place for you to start using FNMeteoParthenope. Just #import
the headers for the classes you need and get to it! Move to -Prefix.pch file and add these lines inside #ifdef __OBJC__ statement
Don't know where to start, exactly? Well, that really depends on what your app does. Listed below are a few common tasks, with an explanation of how to use FNMeteoParthenope to accomplish them.
getWeatherForecastForNowPlace
is the perfect method/tool for this job. All you need to do is ask it for a particular FNPlace
, and it'll give you an FNFeedBase
object containig all entries.
First, we construct an FNPlace
object. It encapsulates pretty much everything you need for make a server request.
We then pass that object to place object into FNMeteoClient sharedInstance-getWeatherForecastForNowPlace:success:failure
.
The success
and failure
parameters take block arguments. Blocks are a relatively new and complex feature in Objective-C, so if you're unfamiliar with them, think about reading up on them. Blocks are objects that contain a specified behavior–code that can be executed later.
In the case of our success
and failure
callback blocks, we're specifying what should happen after the request finishes. The request happens in the background, without locking up or slowing down the app. Depending on whether the response from the server indicates success (e.g. 200
status code and JSON Content-Type
), or failure (e.g. 404
status code or unknown MIME type), the code in the corresponding block will be executed.


Alway write business logic in the models. Keep the number of lines in controller to a minimum.
Use Patient.update_attributes(patient_params)
instead ofPatient.update_attributes(first_name: 'Arun',...)
Always use update_attributes to update a record.
If required make a copy or clone of the params and use as per requirement.
Prefer has_many: , through
instead ofhas_and belongs_to_many
Allows us to have a model and more control over the data in intermediate table.
While looping through a large number of records and doing a process over each record, always use find_each insead of each.
ExampleClient.where('created_at >= ? AND created_at <= ?',params[:start_date], params[:end_date])
better useClient.where('created_at >= :start_date AND created_at <= :end_date',start_date: params[:start_date], end_date: params[:end_date])
Example


Winter 2017 Problem Roulette User Login Count
reset all
Course Number
reset
Day of Week
reset
Hour of Day
reset
Date
reset


Blockage of Umm Qasr port has cost Iraq over $6 billion: PM's spokesman
BAGHDAD (Reuters) - The blocking of Iraq’s major Umm Qasr port on the Gulf by anti-government protesters has cost the country over $6 billion so far, a spokesman for Prime Minister Adel Abdul Mahdi said on Wednesday.
“The closure of Umm Qasr port harms the country. Hundreds of trucks are still parked. This issue is causing massive damage to the nation. Iraq’s losses have exceeded $6 billion until now,” said Abdul Kareem Khalaf in a news conference.


This paragraph has some inline math, a Diophantine equation, \(x^2 + \doubler{y^2} = z^2\text{.}\) And some display math about infinite series:
Look at the XML source to see how LaTeX macros are employed universally across all possible output formats.
in-context


http://bit.ly/spruce-may19
@nmaehler
Spruce assembly update
Group meeting 2019-05-17
Niklas Mähler
Norway spruce
Picea abies
20 Gbp genome
~80% repeat content
30,000–60,000 genes
wtdbg2
Fast
Requires a lot of memory
Run by Manfred
MARVEL
Open questions
Why is the repeat masking different between the masking server and the repeat annotation?


{% load static %}
Over the past {{ period }} days, Cortexatron has rolled {{ total_rolls }} individual dice. {% if activity %} The most recent player action occurred at {{ activity }}. {% endif %}
The chart below shows, for each die type: the number of times players rolled that die, the number of times each die face came up, and what percentage of the time each die face came up.
D{{ die.faces }}
{{ die.rolls }} rolls
{% for tally in die.tallies %} [{{ tally.result }}] {{ tally.tally }}x, {{ tally.percent }}%


Money Play. How should Red play 43?
pips Red=151 White=131
(CorrectAnswer= 0/0, ErrorScore= 0)
Anything involving splitting the back checkers is clearly wrong, giving White good big numbers and good little numbers.
11/7, 9/6 doesn’t really do much to improve Red’s position and gives White free licence.
Many would try 9/2*, keeping White off balance but with only one cover number, even if White fans, it’s not ideal.
At the table 13/10, 13/9 was tried but with 3 blots and 22 shots it’s not surprising that is nearly a double blunder. Correct is following the old age: “put them where you want them” and moving 13/9, 11/8. This creates a five and a half-point prime and although it leaves 20 shots there is only one blot.
Any other move is an error and all bar 9/2* are blunders or worse.
11/7, 9/6 doesn’t really do much to improve Red’s position and gives White free licence.
Many would try 9/2*, keeping White off balance but with only one cover number, even if White fans, it’s not ideal.
At the table 13/10, 13/9 was tried but with 3 blots and 22 shots it’s not surprising that is nearly a double blunder. Correct is following the old age: “put them where you want them” and moving 13/9, 11/8. This creates a five and a half-point prime and although it leaves 20 shots there is only one blot.
Any other move is an error and all bar 9/2* are blunders or worse.
1. Cubeful 2-ply 13/9 11/8 Eq.: -0.003 0.513 0.151 0.007 - 0.487 0.186 0.017 2. Cubeful 2-ply 9/2* Eq.: -0.047 (-0.044) 0.496 0.164 0.006 - 0.504 0.182 0.016 3. Cubeful 2-ply 11/7 9/6 Eq.: -0.102 (-0.099) 0.474 0.158 0.007 - 0.526 0.168 0.011 4. Cubeful 2-ply 13/6 Eq.: -0.133 (-0.129) 0.482 0.154 0.007 - 0.518 0.193 0.020 5. Cubeful 2-ply 13/10 13/9 Eq.: -0.143 (-0.139) 0.488 0.150 0.008 - 0.512 0.204 0.024


An Enthusiastic Electronics and Instrumentation Engineer from Government College of Technology, Coimbatore from the graduating batch of 2022. Optimistic and Jouvial person with interest and passion towards learning new tools and technologies. A fast grasper with good knowledge on C, Structured Query Language (SQL) and Internet Of Things (IOT). I love to self learn things, which made me to create this Portfolio.
My ResumePassionate Electronics and Instrumentation Engineer, who has a deep interest in C programming and IOT
Techie! who is also good in Datascience and have a good knowledge on Python, C, Django and Web development and He is a Youtuber and Owner of the Channel DEEPMATRIX
Also an Instrumentation Engineer from my batch, he is developer in C++ and a Content Creater
A Well versed ELectronics and Instrumentation engineer who has high Competency skills on domain Electronics
Professionalistic and Performance-oriented person and he have a good knowledge on Electronics And IOT as well as C, Python from my batch.And He is a Video Editor too.


Following up on a popular, but somewhat outdated list of my “must-have software for OS X”, some applications remains the same and some have been replaced.
Without further ado.
Bonecho (Firefox) - Nothing to say really, Firefox native build, some usefull plugins I use Adblock, Google Browser Sync, Google Gears, PDF Download, Server Spy, Stylish, Better Gmail, del.icio.us Bookmarks ) CoreDuoTemp Disco MagicDraw UML MailActOn Mailplane MailTags MenuCalendarClock iCal OmniDazzle Quicksilver SlimBatteryMonitor Skype SpamSieve SCPlugin Textmate The Unarchiver UNO Yep VMWare Fusion


Workflow¶
Provides a full pipeline from fetching the training data, training the model to submitting the predictions.
-
class
workflow.
Workflow
(*args, **kwargs)[source]¶ A luigi task pipeline that fetches the most recent data, trains a model on said data and does a submission to the numer.ai website.
Param: output_path (str): path where the data shall be stored, defaults to ./data/
.Param: public_id (str): public_id from the API credentials Param: secret (str): secret from the API credentials -
requires
()[source]¶ Formulates the incoming dependencies, in this case the retrieval of the data for the current tournament. Returns the results of the
tasks.numerai_train_and_predict.TrainAndPredict
task as a way to forcefully call the task without depending on it.
-
run
()[source]¶ After the incoming dependencies (
tasks.numerai_fetch_training_data.FetchAndExtractData
, andtasks.numerai_train_and_predict.TrainAndPredict
) are done, the only thing that’s left is the submission of the predictions. This method is taking care of this.
-


RT @cczona: Confs, pls invite me to give Consequences of an Insightful Algorithm. I still regularly hear of its impact. There's so much mat…
5/30/2016, 9:51:47 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @cczona: Confs, pls invite me to give Consequences of an Insightful Algorithm. I still regularly hear of its impact. There's so much mat…
5/30/2016, 9:51:47 PM
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link


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DAILY WATEVER FORUM !
Topic:
<%=topic.title %>
Author:
<%=topic.username%>
Likes:
<%=likes.counter%>
Comments
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<%= comment.comment%>
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ERPNext Foundation Fellows have shown outstanding leadership in contributing features and/or helping out new members on the forum. Lets take a moment to recognize their efforts.
{{ media( "Charles-Henri Decultot", "The success of an open-source project depends on the quality of its core team but also on the quality of its community. As community members, it is our duty to contribute back to this great project in any way we can. That's the reason why, at Dokos, we always try to enhance the core model before making any custom developments. Everybody's contributions leads to making ERPNext better every day: it's a win-win situation", "https://erpnext.org/files/charles_henri.jpeg", "Dokos.io", "https://dokos.io" )}}


Generate cryptocurrency keypairs in your browser. 100% open source and client-side.
Passphrase-protect keypairs. Divide keypairs into parts.


AQP water AQPs aegypti Ae. & mosquitoes blood expression oocytes mosquito knockdown significantly transport expressed using Aedes CTA S., channel uptake AaAQP TAC Xenopus desiccation GAC TAG TCA solute trehalose Fig. TAA meal Biol insect L., MTs P., adult aquaglyceroporin female oocyte significant A., CGG K., M., RNAi-mediated analysis injected levels yellow Aquaporins Insect R., alimentary aquaporin channels different fever membranes performed permeability protein regulation role solutes tissues used hrs mM AgAQP Agre Anopheles B., C., CTG GGG J., Mosquito N., assay cDNA canal control did excretion glycerol group incubated laevis mosquito, observed previously qPCR shown structure survival vector CA). E., Figure Malpighian RNA Rev aquaporins cell changes characterization confirmed data days demonstrate dry dsRNA family flow function glycerol, hemolymph meal. multifunctional oocytes. primary transporter trend tubes water-injected < -expressing AQPs. Acad Aquaporin CCC Drake Drosophila Exp GCA GCT GGA GGT H., MTs, Means Mol Natl PBM. PE, Physiol Proc Sci Supplementary T., Trop Water Y., aegypti, assays brain buffer cells compared conditions demonstrated described diuresis downregulated females gambiae gene genes highly important involved large parts previous rate results similar studies synthesis temperature transporters transports tsetse urine various water,


With a design & build approach to the construction process, continuous and collaborative production is at the heart of who we are.
With ample time spent in the planning phase of your project we ensure a smooth transition from office to field, start to finish, and most importantly a product that you love. We understand that proper planning allows for optimum performance.
As we collaborate to tailor a project to your budget, establish a schedule, and clarify design specifications, we understand it remains essential to streamline communication without compromise or assumptions. A simple and transparent process allows us to be in and out of your home as soon as possible with a finished product that you love.
The planning process starts the day we speak or the day we meet with you for a no-obligation/no cost in-home consultation resulting in a rough estimate and a high level scope of work.
Dimensions are gathered as well as a quick design discovery session to ensure we have mutual understanding and agreement to reach both your aesthetic and functional needs.
Phone call pre-consult to determine next steps
In-home or onsite consultation with probable cost range, options, and alternatives
High level scope definition and timeline
Cost approved and project contract executed
With the initial scope and contract executed, we dive right into your project design. Based on field dimensions and plans we have created or received from you we begin exploring the possibilities and potential of your space. This exercise results in floor plans (layouts) as well as elevation views associated with design elements and fixtures.
These are not final designs, but rather general designs and directions that we could take your layout/floor plan with color schemes and materials. Coupling our imagination, creativity and your input we will work with selected layouts and designs you pick to make edits, changes and enhancements to ensure it is entirely within your vision while tailored to your budget.
More detailed designs will be developed upon the approval of layout, design and materials. During this final design phase, all materials, fixtures, appliances, custom furniture, etc. will be identified to an extent we can place all necessary orders and get ready for the building phase.
Official approval and sign-off are required before any material is ordered, these signatures and contracts will be managed within our database.
As materials are on their way, communicating material drop off locations to meet your needs will be important as materials are expected to arrive on site. If your project requires demolition, a trailer or dumpster bin location can be worked out prior to the project start date. You should expect us to be on a home based schedule that has been communicated and approved by you.
Our project timeline once built, will be communicated and created with the potential to absorb some changes or unexpected change orders. Upon the possibility and discovery of a change order, you can expect us to provide you with an immediate report on project status and potential changes in budget. All change orders will require necessary signatures to move forward with a change order.
A final walk-through of the finished project is the last in-home activity where any questions or concerns may be fully addressed right before you are ready to enjoy your new space.
Pre-construction on site kick-off meeting with office coordinators and crew leaders
Demolition
Construction and/or Installation
Clean-up, reveal and final walk-through
Enjoy your final product!


A rock solid Node.js/Express server using Mongoose to organise models and query
A popular frontend js framework for building user interface, easy to use and learn check docs
Full featured authentication using JSON web tokens for login and store user data. Learn more


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:: com :: sun :: star :: sdbc ::
|
| interface XBlob |
|
- Description
- is the representation (mapping) of an SQL
BLOB
.
A SQL
BLOB
is a built-in type that stores a
Binary Large Object as a column value in a row of a database table.
The driver implements
BLOB
using a
SQL
locator(BLOB)
, which means that a
Blob
object contains a logical pointer to the SQL
BLOB
data rather than the data itself.
A
Blob
object is valid for the duration of the transaction in which is was created.
Methods in the interfaces
XResultSet
,
and
XPreparedStatement
, such as
getBlob
and
setBlob
allow a programmer to access the SQL
BLOB
.
The
Blob
interface provides methods for getting the length of a SQL
BLOB
(Binary Large Object) value, for materializing a
BLOB
value on the client and for determining the position of a pattern of bytes within a
BLOB
value.
|
| Methods' Summary |
| length |
returns the number of bytes in the
BLOB
value
designated by this
Blob
object.
|
| getBytes |
returns as an array of bytes part or all of the
BLOB
value that this
Blob
object designates. The byte
array contains up to
length
consecutive bytes
starting at position
pos .
|
| getBinaryStream |
retrieves the
BLOB
designated by this
Blob
instance as a stream.
|
| position |
determines the byte position at which the specified byte
pattern
begins within the
BLOB
value that this
Blob
object represents. The
search for
pattern
begins at position
start
.
|
| positionOfBlob |
determines the byte position in the
BLOB
value
designated by this
Blob
object at which
pattern
begins. The search begins at position
start
.
|
| Methods' Details |
length
- Description
- returns the number of bytes in the
BLOB
value
designated by this
Blob
object.
- Returns
- the length
- Throws
- SQLException
if a database access error occurs.
|
|
getBytes
| sequence< byte > |
| getBytes( |
[in] hyper |
pos, |
| [in] long |
length ) |
| raises( |
SQLException ); |
- Description
- returns as an array of bytes part or all of the
BLOB
value that this
Blob
object designates. The byte
array contains up to
length
consecutive bytes
starting at position
pos .
- Parameter pos
- is the ordinal position of the first byte in the
BLOB
value to be extracted; the first byte is at
position 1.
- Parameter length
- is the number of consecutive bytes to be copied.
- Returns
- a byte array containing up to
length
consecutive bytes from the
BLOB
value designated
by this
Blob
object, starting with the byte at position
pos
.
- Throws
- SQLException
if there is an error accessing the
BLOB
.
|
|
getBinaryStream
- Description
- retrieves the
BLOB
designated by this
Blob
instance as a stream.
- Returns
- the stream
- Throws
- SQLException
if a database access error occurs.
|
|
position
| hyper |
| position( |
[in] sequence< byte > |
pattern, |
| [in] hyper |
start ) |
| raises( |
SQLException ); |
- Description
- determines the byte position at which the specified byte
pattern
begins within the
BLOB
value that this
Blob
object represents. The
search for
pattern
begins at position
start
.
- Parameter pattern
- the pattern to search
- Returns
- the position
- Throws
- SQLException
if a database access error occurs.
|
|
positionOfBlob
- Description
- determines the byte position in the
BLOB
value
designated by this
Blob
object at which
pattern
begins. The search begins at position
start
.
- Parameter pattern
- the pattern to search
- Parameter start
- position to start
- Returns
- the position
- Throws
- SQLException
if a database access error occurs.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Java Performance
Byte Code Compiler (written in Java)
Byte Code Interpreter (10x Compiled C/C++)
Native Code Generator (~ Optimized C/C++).


Pixal's DC500 is designed for the serious amateur or professional photographer. The DC500 supports a progressive scan 1.5 square pixel CCD with image resolutions up to 1344x1022. A manual zoom lens and focus ring allow for more accurate images.
For low-light conditions, you can set the attached pop-up flash to work in one of four different modes: Automatic, On, Off, or Hot Flash. The automatic mode will turn on the flash automatically when the DC500 senses low-light conditions. The Hot Flash mode allows you to work with an external flash and strobe lights.


Knockout.Autocomplete
Introduction
Usage
Examples
Introduction
Getting started with the plugin
TODO
Usage
Configuration options and common use cases
TODO
Examples
Working samples to get you started
TODO


Before Change
To prevent misuse, the script passed to utils.hooks.exec_script
must be located in the `PyInstaller/utils/hooks/subproc` directory.
script_filename = os.path.basename(script_filename)
script_filename = os.path.join(os.path.dirname(__file__), "subproc", script_filename)
if not os.path.exists(script_filename):
raise SystemError("To prevent misuse, the script passed to "
"PyInstaller.utils.hooks.exec_script must be located "
"in the `PyInstaller/utils/hooks/subproc` directory.")
cmd = [script_filename]
cmd.extend(args)
return __exec_python_cmd(cmd, env=env)
After Change
To prevent misuse, the script passed to utils.hooks.exec_script
must be located in the `PyInstaller/utils/hooks/subproc` directory.
return __exec_script(script_filename, *args, env=env, capture_stdout=True)
def exec_script_rc(script_filename, *args, env=None):


Last updated: 10/15/2013 10:37:40 am
project1
PROD #63
10/15/2013 @ 9:16:59 am
QA #70
10/15/2013 @ 9:16:29 am
TRUNK #75
10/15/2013 @ 9:15:15 am
project2
PROD #319
10/15/2013 @ 9:08:38 am
QA #330
10/15/2013 @ 9:07:46 am
TRUNK #447
10/15/2013 @ 10:19:41 am
project3
PROD #7
10/15/2013 @ 9:34:35 am
QA #12
10/15/2013 @ 9:11:11 am
TRUNK #93
10/15/2013 @ 9:11:00 am
project4
PROD #53
10/15/2013 @ 9:10:29 am
QA #76
10/15/2013 @ 9:09:46 am
TRUNK #104
10/15/2013 @ 9:09:11 am


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ASA Packet Tracer
28 June 2014
Cisco ASA includes a very nice debugging feature called packet-tracer
. You can inject and trace a packet as it travels through different phases and quickly determine whether or not the packet will pass through. I'm using this quite often to verify traffic passing through ACL rules, NAT rules and VPN but its uses is not limited to these.
Command syntax
packet-tracer input <source interface> <protocol> <source IP> <source port> <destination IP> <destination port> [detailed]
Sample output
ASA-5505# packet-tracer input inside tcp x.x.x.x 80 y.y.y.y 80
Phase: 1
Type: ROUTE-LOOKUP
Subtype: input
Result: ALLOW
Phase: 2
Type: UN-NAT
Subtype: static
Result: ALLOW
Phase: 3
Type: ACCESS-LIST
Subtype: log
Result: ALLOW
Phase: 4
Type: CONN-SETTINGS
Subtype:
Result: ALLOW
Phase: 5
Type: IP-OPTIONS
Subtype:
Result: ALLOW
Phase: 6
Type: NAT
Subtype:
Result: ALLOW
Phase: 7
Type: HOST-LIMIT
Subtype:
Result: ALLOW
Phase: 8
Type: VPN
Subtype: encrypt
Result: ALLOW
Phase: 9
Type: NAT
Subtype: rpf-check
Result: ALLOW
Phase: 10
Type: VPN
Subtype: ipsec-tunnel-flow
Result: ALLOW
Phase: 11
Type: IP-OPTIONS
Subtype:
Result: ALLOW
Phase: 12
Type: FLOW-CREATION
Subtype:
Result: ALLOW
New flow created with id 3256277, packet dispatched to next module
Result:
input-interface: inside
input-status: up
input-line-status: up
output-interface: outside
output-status: up
output-line-status: up
Action: allow


WEB
HTML
CSS
JavaScript
HTML
Hypertext Markup Language(HTML)
is the standard markup language for
creating
web
pages
and web applications.