graph.Graph¶
Module graph¶
A module containing Graph class. We assume that every graph is simple, i.e., there is no loops or multi edges.
-
class
graph.
Graph
(graph_dict={})[source]¶ -
add_edge
(edge)[source]¶ Add an edge to graph. Assumes that edge is of type set, tuple or list. No loops or multiple edges.
Returns: Returns true if edge is not present, otherwise false.
-
add_edges
(edges)[source]¶ Adds a list of edges to graph.
Parameters: edges – a list of edges to be added Returns: void
-
add_vertex
(vertex)[source]¶ - If vertex is not in self.__graph_dict, a key “vertex” with an empty list
- as a value is added to the dictionary. Otherwise nothing has to be done.
Returns: True if the vertex is not present in the graph, otherwise false.
-
connected_component
()[source]¶ Computes the number of connected components and the size of each componenent
Returns: a pair: thi first entry is the number of components, the second is a list constaining sizes of components
-
connected_components
()[source]¶ Computes connected components.
Returns: a list of lists, each containing vertices from the same conn component.
-
degree_sequence
()[source]¶ Degree sequence is a list of all degrees in non-increasing order.
Returns: a degree sequence of the graph
-
density
()[source]¶ Density is ratio of number of edges and number of edges that complete graph has with the same number of vertices.
Returns: returns the density of the graph
-
diameter
()[source]¶ Computes the diameter of the graph.
Returns: The maximum of distances between any pair of nodes or infinity if the graph is not connected
-
erdos_gallai
(degree_seq=None)[source]¶ Erdos Gallai theorem states that a non-increasing sequence of n numbers d_i is a degree sequence of a simple graph if and only if the sum of sequence is even and the condition $sum_{i=1}^{k}{d_i} leq k(k-1) + sum_{i=k+1}^n min(d_i, k) ext{ for all } k$ This can be implemented in time $O(n)$. However, the current implementation requires O(n^2) time.
Parameters: degree_seq – If it is None, then the degree sequence of the graph is used. Returns: True if the degree sequence satisfies both conditions and False otherwise
-
global_clustering_coefficient
()[source]¶ Global clustering coefficient is the ratio of number of triangles and number of connected triplets of vertices
Returns: float, clustering coefficient of the graph
-
in_neighbourhood
(u, v)[source]¶ Parameters: - u – a vertex of the graph
- v – a vertex of the graph
Returns: Returns True if v is in the neighborhood of u. It raises KeyError if u is not present in the graph.
-
is_vertex_present
(vertex)[source]¶ Checks if the vertex is already created. Returns True if it is otherwise False
Parameters: vertex – name of the vertex Returns: true if vertex is in the graph, false otherwise
-
read_from_file
(filename=None)[source]¶ Reads graph from a file.
Parameters: filename – path to the file Returns: True if the graph is read successfully. If filename is not specified or it does not exist, ValueError and IOError are raised (respectively).
-
shortest_path
(u=None, v=None)[source]¶ Computes the shortest path between u and v. If both of them are None, then computes the shorthest path between all pair of vertices. If u is not None and v is None, then it computes the shortest path between u and all other vertices. Finally, if both of them are not None, it computes the shorthest path between u and v.
Parameters: - u – the source vertex
- v – the end vertex
Returns: the shortest path
-
spanning_tree
()[source]¶ Computes the spanning tree of the graph using Kruskal algorithm and union - find. Complexity: $O(mlog*(n))$
Returns: a list of tuples representing edges of the tree
-
update_neighbors
(u, new_neighbors)[source]¶ Updates the list of vertices of a vertex. :param u: Vertex whose neighborhood is updated :param new_neighbors: (list) New neighbors of u :return: None
-


<EMAIL_ADDRESS><EMAIL_ADDRESS>class is the parent for all Bright classes.
Bright object implement following:
| Method/property | Description |
|---|---|
If you are using global config, please prepend below methods with br()->config()-> |
|
| setAttr(name, value) | Set value of object attribute. |
| getAttr(name, defaultValue, saveDefault) | Get value of object attribute. If value is missing - get default value. If saveDefault = true and value is missing, default value will be stored as object attribute value. |
| clearAttr(name) | Clear value of object attribute |
| isAttrExists(name) | Check is value for some object sttribute exists |
| getAttributes() | Get all object attributes |
| setAttributes() | Set all object attributes at once |
| enable() | Increase internal enable/disable counter. Set enabled flag to true if counter become 0 |
| disable() | Set enabled flag to false and decrease internal enable/disable counter |
| isEnabled() | Check if enabled flag is true |
| getInstance() | Return singleton object for current class |
| before(event, callback) | Register callback which must be called before event triggered |
| after(event, callback) | Register callback which must be called after event triggered |
| on(event, callback) | Register callback which must be called when event triggered |
| trigger(event, ...) | Trigger event |


There are three constants of motion for the guiding center motion, namdely, the canonical toroidal angular momentum Pϕ, the magnetic moment μ, and the total kinetic energey 𝜀. Examining how well the kinetic energy 𝜀 and the toroidal angular momentum Pϕare conserved provides a way to evaluate the accuracy of the numerical code. The kinetic energy 𝜀 and toroidal angular momentum Pϕ are deﬁned by
|
| (57) |
|
| (58) |
Deﬁne 𝜀n = mvn2 and Pϕn = ZeBnLn2, then the normalized forms of 𝜀 and Pϕ are written as
|
| (59) |


About Us |
Acadmic |
Central Facilities |
Vacancies |
People |
Campus |
Student welfare |
Placement |
faculty |
Mission |
Feedback |
|---|
vision"To build a rich intellectual potential embedded with inter-disciplinary knowledge, human values and professional ethics among the youth, aspirant of becoming engineers and technologists, so that they contribute to society and create a niche for a successful career." |
Our Gallery |
|---|


| Interface | Description |
|---|---|
| AuthenticationManagement |
Service methods for user authentication.
|
| CommunoteAuthenticationFilter |
Interface to define filters, which could be provided by plugins.
|
| CommunoteAuthenticationProvider |
Interface for orderable authentication providers, which could be implemented by plugins.
|
| UserIdentification |
The user identification provides information on identifying a particular user.
|
| Class | Description |
|---|---|
| AbstractCommunoteAuthenticationFilter |
Abstract implementation of
CommunoteAuthenticationFilter with order 0 and concrete
methods for getting the AuthenticationManager . |
| AuthenticationFilterManagement | |
| AuthenticationHelper |
Provides functionality to login a user not using the web frontend e.g.
|
| AuthenticationManagementBase |
Spring Service base class for
com.communote.server.core.api.security.AuthenticationManagement , provides access to
all services and entities referenced by this service. |
| AuthenticationManagementImpl | |
| AuthenticationProviderManagement |
Management for handling with
CommunoteAuthenticationProvider . |
| AuthenticationTokenManagement |
This is a service class for managimg authentication tokens.
|
| FieldUserIdentification |
A UserIdentification that is map holding some attributes.
|
| InternalSystemUserDetails |
Represents an internal system user.
|
| PublicUserDetails |
Represents a not logged in public user.
|
| SecurityHelper |
This helper class checks the current user in the acegi system and returns the details
UserDetails . |
| SwitchUserHelper |
This helper contains methods to switch a user context.
|
| UserDetails |
Value object with user details which is compatible to the spring security framework.
|
| Exception | Description |
|---|---|
| AccountNotActivatedException |
Thrown to indicate that the user account is not in state ACTIVE.
|
| AccountPermanentlyDisabledException |
Thrown to indicate that the account is permanently disabled.
|
| AccountPermanentlyLockedException | |
| AccountTemporarilyDisabledException |
Thrown to indicate that the account is temporarily disabled.
|
| AccountTemporarilyLockedException | |
| AttributeMappingException | |
| AuthenticationManagementException |
The default exception thrown for unexpected errors occurring within
AuthenticationManagement . |
| SwitchUserNotAllowedException |
Exception indicating that the current use had no right to change to another user
|
| TermsNotAcceptedException |
Thrown to indicate that the user has not yet accepted the terms of use or the privacy policy.
|
| TermsOfUseNotAcceptedException |
Exception to be thrown if a user was authenticated successfully but has not yet accepted the
Terms Of Use.
|
| UnknownHostAuthenticationServiceException |
This exception is thrown, when it is not possible to connect to the foreign host.
|
Copyright © 2019 Communote team. All rights reserved.


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MASKS (Masques) (director/writer: Claude Chabrol; screenwriter: Odile Barski; cinematographer: Jean Rabier; editor: Monique Fardoulis; cast: Philippe Noiret (Christian Legagneur), Robin Renucci (Roland Wolf ), Anne Brochet (Catherine), Bernadette Lafont (Patricia, masseuse), Roger Dumas (Manu), Monique Chaumette (Colette), Pierre-Francois Dumeniaud (Max); Runtime: 100; MK2; 1987-France)
Reviewed by Dennis Schwartz
A crime thriller exposing the people who pretend they are someone they are not. Director Claude Chabrol has come up with a diverting thriller, that is well-crafted and features a magnificent performance by Philippe Noiret as Christian Legagneur. He's an overbearing, smarmy, popular TV game-show host, who thinks life is a game and he can treat everyone like they're puppets because he's controlling the strings. To the public and those around him he comes off as a generous, caring and kind-hearted man.
Under the ruse of writing a story on the TV celeb, Roland Wolf poses as a journalist and gets an invite to his subject's country home for three days of interviews. He immediately starts snooping around and finds things he's not supposed to. Here he meets all those close to Christian: Max (Dumeniaud), his mute driver and chef. Max's wife Colette (Chaumette), who acts as his secretary on the program and at his home she helps serve the meals. Another couple that serves his needs consists of: Manu (Roger Dumas), who acts as the wine connoisseur and errand boy, and his attractive wife Patricia (Lafont), who serves as his masseuse, tarot reader, and dispenser of herbal remedies for his sickly goddaughter Catherine (Anne Brochet).
Tension picks up when he finds a tennis racquet that the household help tells him was Madeleine's. By prying, he finds out that she was an aspiring actress whom Christian invited to stay over to keep his goddaughter company. But after being a houseguest for three months, she suddenly vanished. Christian said she had a tiff with Catherine and wrote a note saying she was taking off to stay in a remote area.
Roland befriends Catherine and in their confidential talks they learn to trust each other. She is kept constantly drowsy but Christian refuses to have her see a doctor, instead Patricia gives her special plant remedies. Catherine seems upset when Roland tells her that her godfather is planning to kill her, and that she's being drugged by him. Madeleine told her the same thing, which caused a rift in their relationship and her disappearance.
It is learned that Christian became her guardian when she was 5-years-old, and Roland by snooping around has uncovered that he has fleeced her inheritance by siphoning off money from their joint account to buy businesses such as -- a car wrecking one and a retirement home, under only his name.
Things come to head when Catherine sees for herself that she been fooled by her guardian's false kindness. She's also pleased to learn that Roland isn't a journalist but Madeline's sister, and is now only interested in her safety. They have also fallen in love.
He has come here because he expects his sister vanished because of foul play, and he wants to get the proof. It then becomes a chess game between Roland and Christian to see who wins.
What was missing in this better-than-average thriller, was a sense of passion. Everything seemed to be happening in a too matter-of-fact manner.
REVIEWED ON 6/15/2001 GRADE: B-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Welcome to the CSS Page
There are three different types of css styles and they are listed below:
CSS is what makes websites look more appealing to your eyes. First type is External style sheet which is when your css document is seperate from your html one. The second type is called Inline styles which is when you change the styles of some html within the same line of code. Third one is called Internal styles which is when you normally do your css but you do it in your html.
Did you know that css was founded in 1994 by Håkon Wium Lie while working with a computer scientist Tim Berners-Lee.


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Yue ZHANG
Address: 1475 Folsom St. Apt 361, Boulder, CO 80302
Tel:<PHONE_NUMBER>, Email<EMAIL_ADDRESS>A pdf version will be sent on request. Or find me at
Linkedin. :)
Education
Technical Qualifications
Experience
Activities


aaa (aaa
bbb)'s Rsssarch Projects
@ University of ccc
ddd eee
version: 202306
Since joining the ddd eee at the University of ccc, I have been working on several successful projects which include:
A multi-disciplinary ggg design ggg system which aims to:
kkk(Design methods for sss), as a part of the sss lll project, aims to extend the capabilities of the eee system on a number of fronts.
sss generation has been implemented in eee. With new added XML tags, user can now guides eee to generate complicated sss networks for sss ssss.
To develop an automated package to optimise a ggg eee to minimise total mass flow by employing a sudddogate modelling based approach while respecting ligament size and thermal temperature constraints.
* Follows ddd scripting best practice
The sss ggg package includes:
This tools has been delivered to and used by ddd sss team.
* Although it was developed for gggs, this package can be applied to all types of sss design gggs.
The aim of this project is to morph a CFD mesh by empoying displacements calculated as a result of a FEA simulation.
Proof-of-concept implementation has been developed using Matlab.
The Matlab implementation worked. However, the performance is poor on large dataset. It uses
Mathworks' proprietary function (pointLocation
).
New implementation in C++ uses the k -D tree approach to map CFD nodes to FEA tetrahedrons. Significant performance improvement has been obtained.
The package has been delivered to ddd to be applied on various geometry test cases.
This approach could be potentially applied to:
This project aims to extend the capabilities of eee to iii geometries. The version 2 of eee has shiftted the selection of feature identification rule combinations from the system to the user. It improved eee's flexsssility significantly without hard-coding the rules for dealing with features that eee never saw before.
The working package 4 (WP4) of jjj project aims to investigate the AI/ML related techniques for geometry reasoning, classification, completion, and/or generation, especially in 3D space.
It involves the use of:
This work can ggg the sss field map around a specific design of sss using a pre-trained CGAN in combination of ANN/CNN models.
gggion accuracy against real CFD:
3D data are more resourse consuming than 2D data so than it won't work well by simply extending the same approach from 2D to 3D models.
Various approaches of reprsssnting 3D data have been proposed:


Aston Martin reveals first SUV
by BloombergThis week at dual events in Los Angeles and Beijing, Aston Martin unveiled the first SUV in its 106-year history.
The $189,000 DBX, one of the last luxury-brand SUV models to hit the market, jumps into a race of automakers intending to capitalize on buyers’ seemingly endless appetite for powerful and expensive SUVs, especially in China and the U.S.
It will compete against Bentley’s $165,000 Bentayga and Rolls-Royce’s $325,000 Cullinan on the high-end side; on the sportier side, Lamborghini’s $200,000 Urus and Porsche’s $126,500 Cayenne Turbo.
DBX sits square in the middle of this set with a 542-horsepower, twin-turbocharged V-8 engine borrowed from the handsome DB11 coupe.
It gets 516 pound-feet of torque and will go zero to 60 mph in 4.3 seconds, with a top speed of 181 mph.
That beats some of the competition but not all: It’s faster than the massive Cullinan, though not as quick as the sexy Urus, which can reach 60 mph in three seconds.
Crunch Time
The DBX comes at a critical time for the Gaydon, England-based brand, whose shares are down 60% in 2019 amid a challenging sales environment and Britain’s Brexit woes.
In September it secured $150 million in new financing at a high 12% interest rate; it can bring in an additional $100 million if it records 1,400 DBX orders within nine months of the first payout.
Aston is hoping DBX will balance out and bolster the million-dollar-plus limited-edition supercars it’s sold to great success recently.
While the Valkyrie and Valhalla are made in minute batches for only a few hundred buyers worldwide, selling out instantly, the SUV brings the brand to the masses, relatively speaking, by appealing to the needs of a wider spectrum of buyers.
“Obviously with an SUV, you start broadening the reach of appeal for both men and women,” says Aston Martin of Americas President Laura Schwab.
“The real trick of this car is that it’s so sporty-looking on the outside, and it drives like a sports car, but you can’t imagine the space [inside]. It’s a blend of performance and hand-built luxury.”
Aston Martin’s five-seat SUV looks vaguely similar to many other recent debuts, as if it were a composite sketch of the Ford Mustang Mach-E, the Jaguar F-Type, and the Urus.
But it packs distinctive air vents in the hood, sculpted sides, and a rear end stacked up high in the back, with a small roof spoiler and a curved line of taillights swooping across it. A full-length panoramic roof floods the interior cabin with light.
A very wide “DB” grille stretches across the front of the vehicle, and frameless doors blend seamlessly into the sides, elongating the look of the car.
The nine-speed automatic SUV weighs a little more than 5,000 pounds—less than a Bentley Continental GT, let alone the Bentley SUV.
The seats are trimmed in full-grain leather sourced from long-term Aston partner Bridge of Weir. A wool fabric cover is also available for the interior, as are other trims, including walnut wood and Alcantara.
The DBX’s connectivity and entertainment technologies are portrayed on a 10.25-inch touchscreen in the center console, while a 12.3-inch touchscreen sits in front of the driver.
Apple CarPlay comes standard, as does a 360-degree camera system and ambient lighting that offers 64 different colors in two zones.
The 40-20-40 split-rear seats leave 54 cubic feet of trunk space when they’re laid flat.
Also, in a move to help customers consider this rig for everyday use—a novel proposition for Aston Martin—it comes with an optional pet washer, so you can clean your muddy dog, as well as an available snow package that includes a heating component to warm ski boots.
The Future Is Friendly
That DBX competitors have exceeded sales estimates bodes well for Aston Martin, which, with an average price of $175,000 per vehicle across its entire product line, will double its output when DBX hits full production levels.
Cullinan is on track to sell 5,500 units in 2019, vs. its original target of 3,500; Bentley has sold 4,000 to 5,000 Bentaygas since it launched in 2015.
By 2023, Aston Martin plans to churn out 14,000 vehicles annually worldwide, a number bolstered largely with the advent of the DBX.
It represents a huge jump: In its century-long history, Aston has sold fewer than 90,000 vehicles total.
A big focus as deliveries start, Schwab says, will be getting the dealers—typically used to selling to customers who own many expensive sports cars they may drive only on weekends—ready to handle the oncoming hoards.
Deliveries of the Aston Martin DBX start in the summer of 2020.


{% extends "base.html" %} {% load bs4 %} {% block content %}
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Create a custom Bulma v<%= htmlWebpackPlugin.options.bulmaVersion %> build by choosing the components and elements you need.


Overview
This module is a part of yocto node modules for NodeJS.
Please see our NPM repository for complete list of available tools (completed day after day).
Motivation
Create a simple ftp client for out tools.
How to use
Please check example directory.
Implemented Method
Only put method is implemented, the other method will be in the future.
Changelog
All history is here


dgm1 package¶
Submodules¶
dgm1.dgm1_nrw module¶
-
class
dgm1.dgm1_nrw.
DGM1NRW
(dgm1_dir, shp_region=None)¶ Bases:
object
The class DGM1NRW downloads and processes dem rasters found in https://www.opengeodata.nrw.de/produkte/geobasis/hm/dgm1_xyz/dgm1_xyz/.
- Parameters
dgm1_dir (str) – directory to save downloaded 1 meter rasters as well as its shapefile and resampled rasters.
shp_region (str) – shapefile with the region of interest. If defined, all processes refer to this shapefile.
-
nodata
= -9999.0¶ nodata for the created rasters
-
url_dgm1_xyz
= 'https://www.opengeodata.nrw.de/produkte/geobasis/hm/dgm1_xyz/dgm1_xyz/'¶ URL of dem rasters with one meter resolution and 2000x2000 \(m^2\) size.
-
compress_options
= ['COMPRESS=LZW', 'PREDICTOR=2']¶ Compress options used in gdal driver.Create().
-
creation_options
= ['BIGTIFF=YES', 'COMPRESS=LZW', 'ZSTD_LEVEL=1', 'TILED=YES', 'PREDICTOR=2']¶
-
shp_region
= None¶ Shapefile with the region of interest. If defined, all processes refer to this shapefile. Otherwise all rasters are downloaded and processed
-
dgm1_dir
= None¶ Directory to save the original 1 meter products as well as its shapefile and resampled rasters.
-
shp_filename_tiles
= None¶ Shapefile dgm1_2_nw.shp contaning all tiles found on the server as 2x2 \(km^2\) squares. It is saved in
dgm1_dir
/gis.
-
tif_dir
(pixel_size)¶ Return the directory name of rasters with the given pixel_size. pixel_size = 1 corresponds to the downloaded rasters with 1 meter resolution.
- Parameters
pixel_size (int) – pixel size in meters. It must be a divisor of 2000 (2, 4, 5, 8, 10, 16, 20, 25, 40, 50, 80, 100, 125, 200, 250, 400, 500, 1000, 2000).
- Returns
(str) full path of the directory.
-
static
vrt_filename
(pixel_size)¶ Return the basename of the vrt-file for the given pixel size.
- Parameters
pixel_size (int) – pixel size in meters. It must be a divisor of 2000 (2, 4, 5, 8, 10, 16, 20, 25, 40, 50, 80, 100, 125, 200, 250, 400, 500, 1000, 2000).
- Returns
(str) basename of the vrt-file
-
tif_filenames
(pixel_size)¶ Return the names of the downloaded files with the given pixel files in tif-format.
- Parameters
pixel_size (int) – pixel size in meters. It must be a divisor of 2000 (2, 4, 5, 8, 10, 16, 20, 25, 40, 50, 80, 100, 125, 200, 250, 400, 500, 1000, 2000).
- Returns
(list) full path file names.
-
static
srs
()¶ Return the compound coordinate reference system using EPSG 25832 for the horizontal component and EPSG 7837 for the vertical component. The compound srs is labeled as
ETRS89 / UTM zone 32N + DHHN2016 height
.- Returns
(osr.SpatialReference) spatial reference system.
-
region_envelope
()¶ Return [x_min, y_min, x_max, y_max] for
shp_region
- Returns
(list) envelope of
shp_region
-
tif_filenames_intersecting
(envelope=False)¶ Return a list of file names *.xyz.gz transformed into *.tif found on the server, which intersect the region
shp_region
. Return an empty list if the region is not defined or it is outside the domainshp_filename_tiles
.- Parameters
envelope – if True use envelopes of regions, otherwise use regions to get intersections. Default False.
- Returns
(list) list of files with the suffix tif.
Example:
>>> dgm1 = DGM1NRW('~/dgm1', '~/study_area/region.shp') >>> dgm1.tif_filenames_intersecting() ['dgm1_32350_5672_2_nw.tif', 'dgm1_32350_5674_2_nw.tif', ...
-
gz_filenames_intersecting
(envelope=False)¶ Return a list of gzipped file names (dgm1_XXXXX_YYYY_2_nw.xyz.gz) found on the server, which intersect the region
shp_region
. Return an empty list if the region is not defined or it is outside the domainshp_filename_tiles
.- Parameters
envelope – if True use envelopes of regions, otherwise use regions to get intersections. Default False.
- Returns
(list) list of files with the suffix xyz.gz.
Example:
>>> dgm1 = DGM1NRW('~/dgm1', '~/study_area/region.shp') >>> dgm1.gz_filenames_intersecting() ['dgm1_32350_5672_2_nw.xyz.gz', 'dgm1_32350_5674_2_nw.xyz.gz', ...
-
create_shapefile
()¶ Create a shapefile with squares of 2x2 km² using raster file names from
url_dgm1_xyz
. The shapefileshp_filename_tiles
is saved indgm1_dir
. The shapefile attribute Filename indicates the gz-file name found on the server. The spatial coordinate system is defined increate_spatial_reference()
.
-
download
(n_cores=1)¶ Download rasters from
url_dgm1_xyz
intersectingshp_region
. Download all rasters ifregion
is not defined.- Parameters
n_cores – number of cores for parallel downloading and transformation into tif-files. n_core is limited to \(n\_cores \leq n - 1\), where n ist the total number of cores found on the computer. n_cores can be intentionally be high in order to use \(n - 1\) cores.
-
resample
(pixel_size, force=False)¶ Resample dowloaded TIF files with 1 meter resolution into TIF files with a resolution of pixel_size.
- Parameters
pixel_size (int) – pixel size in meters. It must be a divisor of 2000 (2, 4, 5, 8, 10, 16, 20, 25, 40, 50, 80, 100, 125, 200, 250, 400, 500, 1000, 2000)
force (bool) – if True, resample also existing files, otherwise skip them. Default force=False
-
create_vrt
(vrt_filename, pixel_size=1, n_cores=1)¶ Create a raster named
vrt_filename
in vrt-format. A folder without the suffix .vrt will be created together with the and populated with rasters of the givenpixel_size
intersecting with the region.Example:
>>> dgm1 = DGM1NRW('~/dgm1', '~/study_area/region.shp') >>> dgm1.create_vrt('~/study_area/dgm1/dgm1_region_02m.vrt', pixel_size=2)
- Parameters
vrt_filename (str) – full path vrt file name (.vrt)
pixel_size – pixel size in meters. It must be a divisor of 2000 (1, 2, 4, 5, 8, 10, 16, 20, 25, 40, 50, 80, 100, 125, 200, 250, 400, 500, 1000, 2000)
n_cores (int) – number of cores for parallel downloading and transformation into TIF files. n_core is limited to \(n\_cores \leq n - 1\), where n ist the total number of cores found on the computer. n_cores can be intentionally be high in order to use \(n - 1\) cores.
-
mosaic
(tif_filename, pixel_size, extent='region', n_cores=1, **kwargs)¶ Mosaic TIF files intersecting with the
shp_region
.- Parameters
tif_filename (str) – output file name (.tif)
pixel_size (int) – pixel size in meters. It must be a divisor of 2000 (1, 2, 4, 5, 8, 10, 16, 20, 25, 40, 50, 80, 100, 125, 200, 250, 400, 500, 1000, 2000)
extent (str) – clip to clip
shp_region
; region for a raster covering the envelope ofshp_region
; rasters for a raster extended to full tiles. Default region. extent=None corresponds to extent=`rasters`n_cores (int) – number of cores for parallel downloading and transformation into TIF files. n_core is limited to \(n\_cores \leq n - 1\), where n ist the total number of cores found on the computer. n_cores can be intentionally be high in order to use \(n - 1\) cores.
- Keyword Arguments
See https://gdal.org/python/osgeo.gdal-module.html#WarpOptions


Chapter 9 - Finding datasets in publications: The University of Paderborn approach¶
Abstract¶
The steadily increasing number of publications available to researchers makes it difficult to keep track of the state of the art. In particular, tracking the datasets used, topics addressed, experiments performed and results achieved by peers becomes increasingly tedious. Current academic search engines render a limited number of entries pertaining to this information. However, having this knowledge would be beneficial for researchers to become acquainted with all results and baselines relevant to the problems they aim to address. With our participation in the NYU Coleridge Initiative’s Rich Context Competition, we aimed to provide approaches to automate the discovery of datasets, research fields and methods used in publications in the domain of Social Sciences. We trained an Entity Extraction model based on Conditional Random Fields and combined it with the results from a Simple Dataset Mention Search to detect datasets in an article. For the identification of Fields and Methods, we used word embeddings. In this chapter, we describe how our approaches performed, their limitations, some of the encountered challenges and our future agenda.
author:
Rricha Jalota
Nikit Srivastava
Daniel Vollmers
René Speck
Michael Röder
Ricardo Usbeck
‘Axel-Cyrille [Ngonga Ngomo]{}’bibliography:
‘references.bib’title: ‘Finding datasets in publications: The University of Paderborn approach’
Literature Review¶
Previous works on information retrieval from scientific articles are mainly seen in the field of Bio-medical Sciences and Computer Science, with systems [@DBLP:journals/ploscb/WestergaardSTJB18] built using the MEDLINE^1 abstracts, full-text articles from PubMed Central^2 or ACL Anthology dataset^3. The documents belonging to the above-mentioned datasets follow a similar format, and thus, several metadata and bibliographical extraction frameworks like CERMINE [@tkaczyk2014cermine] have been built on them. However, since articles belonging to the domain of Social Sciences do not follow a standard format, extracting key sections and metadata using already existing frameworks like GROBID [@lopez2009grobid], ScienceParse^4 or ParsCit [@councill2008parscit] did not seem as viable options, majorly because these systems were still under development and lacked certain desired features. Hence, building upon the approach of Westergaard et. al [@DBLP:journals/ploscb/WestergaardSTJB18], we built our own sections-extraction framework for dataset detection and research fields and methods identification.
Apart from content and metadata extraction, key-phrase or topic extraction from scientific articles has been another emerging research problem in the domain of information retrieval from scientific articles. Jansen et al. [@jansen2016extracting] extracted core claims from scientific articles by first detecting keywords and key-phrases using rule-based, statistical, machine learning and domain-specific approaches and then applying document summarization techniques. For characterizing a research work in terms of its focus, application domain and techniques used, Gupta et al. [@gupta2011analyzing] proposed applying semantic extraction patterns to the dependency trees of sentences in an article’s abstract. On the other hand, to thematically represent scientific articles and for ranking the extracted key-phrases, Mahata et al. [@mahata2018key2vec] devised an approach for processing text documents to train phrase embeddings.
The problem of dataset detection and methods and fields identification is not only different from the ones mentioned above, but also our approach for tackling it is radically disparate. The following sections describe our approach in detail.
Project Architecture¶
Data Flow Pipeline: Red lines depict the flow of given and generated files between components whereas black lines represent the generation of final output files[]{data-label="fig:flowchart"}{width=”\textwidth”}
Our pipeline (shown in Figure [fig:flowchart]) consisted of three main components: 1) Preprocessing, 2) Fields and Methods Identification and 3) Dataset Extraction. The Preprocessing module read the text from publications and generated some additional files (see Section [preprocess] for details). These files along with the given Fields and Methods vocabularies were used to infer Research Fields and Methods from the publications. Then, the information regarding Research Fields was passed onto the Dataset Detection module and using the Dataset Vocabulary, Dataset Citations and Mentions were identified. The following sections provide a detailed overview of each of these components.
Preprocessing¶
As discussed in Chapter 5, the publications were provided in two
formats: PDF and text. For Phase-1, we used the given text files,
however during Phase-2, we came across many articles in the training
files that had not been properly converted to text and contained mostly
non-ASCII characters. To work with such articles, we relied on the open
source tool pdf2text
from poppler suite
^5 to extract text from
PDFs. The pdf2text
command served as the first preprocessing step and
was called as a subprocess from within a python script. It was used with
-nopgbrk
argument to generate the text files.
Once we had the text files, we followed the rule-based approach as proposed by Westergaard et al. [@DBLP:journals/ploscb/WestergaardSTJB18] for pre-processing. The following series of operations based mostly on regular expressions were performed:
Words split by hyphens were de-hyphenated
Irrelevant data was removed (i.e., equations, tables, acknowledgment, references);
Main sections (i.e., abstract, keywords, JEL-Classification, methodology/data, summary, conclusion) were identified and extracted;
Noun phrases from these sections were extracted (using the python library, spaCy^6).
We came up with the heuristics for identifying the main sections after
going through the articles from different domains in the training data.
We collected the surface forms for the headings of all major sections
(abstract, keywords, introduction, data, approach, summary, discussion)
and applied regular expressions to search for them and separate them
from one another. The headings and their corresponding content were
stored as key-value pairs in a file. For generating noun-phrases, this
file was parsed and for all the values (content) in key-value
(heading-content) pairs, a spaCy object, doc
, was created
sentence-wise. Using the built-in function for extracting noun chunks
([doc.noun_chunks
]{}), we generated key-value pairs of heading and
noun-phrases found in the content and stored them in another file. This
file was later used for fields and methods identification.
To determine how well our approach performed in distinguishing sections,
we evaluated it on the articles in the validation dataset. During
evaluation, we figured out the limiting cases of our approach. A section
could not be differentiated either when there was no explicit mention of
any of its surface forms or if there were multiple mentions of the
surface forms in the articles. For instance, in the validation dataset
(see Table [tab:sections]), keywords were not extracted from 13
articles because of no explicit mention of the term ’keywords’ or its
variants. On manual inspection, we found keywords were actually not
mentioned in these 13 articles. In the remaining articles where the
keywords were present, our algorithm could not detect them from 1
article. For brevity, we have reported only four main sections in
Table [tab:sections]: title, abstract, keywords and methodology/data,
since these are the ones getting preferential treatment in methods and
fields identification. If a section was not found in the article
(because of no explicit mention of any of the surface forms), then only
the sections that could be detected were extracted. The remaining
content was saved as reduced_content
after cleaning and noun-phrases
were extracted from it to prevent loss of any meaningful data.
[C[4cm]{} C[3.5cm]{} C[4cm]{}]{} Sections & No explicit mention & Mentioned but not distinguishedTitle & 0 & 0Keywords & 13 & 1Abstract & 0 & 1Methodology/Data & 18 & 4
In addition to the main sections, we also extracted PDF metadata using
pdfinfo
service from the poppler suite
library. The metadata very
often contained the keywords and subject of an article, which was
helpful in those cases where the keywords were not found by the regular
expression.In the end, the preprocessing module generated four text files for a
publication: PDF-converted text, PDF-metadata, processed articles
containing relevant data, and noun phrases from the relevant sections,
respectively. These files were then passed on to the other two
components of the pipeline, which have been discussed below.
Approach¶
Research Fields and Methods Identification¶
Vocabulary Generation and Model Preperation¶
Research Methods Vocabulary: In Phase-1 of the challenge, we used the given methods vocabulary. However, the feedback that we received from Phase-1 evaluation gave more emphasis to statistical methods used by the authors, references to the time scope, unit of observation, and regression equations rather than the means used to compile the data, i.e., surveys. Since the given methods vocabulary was not a complete representation of statistical methods and also consisted terms depicting surveys, in Phase-2, we decided to create our own Research Methods Vocabulary using Wikipedia and DBpedia.^7 We manually curated a list of all the relevant statistical methods from Wikipedia^8 and fetched their descriptions from the corresponding DBpedia resources. For each label in the vocabulary, we extracted noun phrases from its description and added them to the vocabulary. Please refer Table [tab:vocab] for examples.
[C[1.5cm]{} C[5cm]{} C[5cm]{}]{} Label & Description & Noun Phrases from DescriptionPolitical forecasting & Political forecasting aims at predicting the outcome of elections. & Political forecasting, the outcome, electionsNested sampling algorithm & The nested sampling algorithm is a computational approach to the problem of comparing models in Bayesian statistics, developed in 2004 by physicist John Skilling. & algorithm, a computational approach, the problem, comparing models, Bayesian statistics, physicist John Skilling
Research Fields Vocabulary: For both the phases, we used the given research fields vocabulary and, just like the methods vocabulary, supplemented it with the noun phrases from the description of the research field labels. However, since our phase-1 model seemed to confuse fields with methods, for Phase-2, we additionally created a stopword-list of terms that didn’t contain any domain-specific information, such as; Mixed Methods, Meta Analysis, Narrative Analysis and the like.
Word2Vec Model generation: In this pre-processing step, we used the above-mentioned vocabulary files containing noun phrases to generate a vector model for both research fields and methods. The vector model was generated by using the labels and noun phrases from the description of the available research fields and methods to form a sum vector. The sum vector was basically the sum of all the vectors of the words present in a particular noun phrase. 3em [The pre-trained Word2Vec model
GoogleNews-vectors-negative300.bin
[@DBLP:journals/corr/abs-1301-3781] was used to extract the vectors of the individual words.]{}Research Method training results creation: For research methods, we generated an intermediate result file with the publications present in the training data. It was generated using a
naïve finder algorithm
which, for each publication, selected the research method with the highest cosine similarity to any of its noun phrase’s vectors. This file was later used to assign weights to research methods using Inverse Document Frequency.
Processing with Trained Models¶
Finding Research Fields and Methods: To find the research fields and methods for a given list of publications, we performed the following steps: (At first, Step 1 was executed for all the publications, thereafter Step 2 and 3 were executed iteratively for each publication).
Naïve Research Method Finder run - In this step, we executed the
naïve research method finding algorithm
(i.e. selected a research method based on the highest cosine similarity between vectors) against all the current publications and then merged the results with the existing result from theresearch methods’ preprocessing step
. The combined result was then used to generate IDF weight values for eachresearch method
, to compute the significance of recurring terms.IDF-based Research Method Selection - We re-ran the algorithm to find the closest research method to each noun phrase and then sorted the pairs based on their weighted cosine similarity. The weights were taken from the IDF values generated in the first step and the manual weights assigned (section-wise weighting). Here, the noun phrases that came from the methodology section and from the methods listed in JEL-classification (if present) were given a higher preference. The pair with the highest weighted cosine similarity was then chosen as the Research Method of the article.
Research Field Finder run - In this step, we first found the closest research field from each noun phrase in the publication. Then we selected the Top N (= 10) pairs that had the highest weighted cosine similarity. Afterwards, the noun phrases that had a similarity score less than a given threshold (= 0.9) were filtered out. The end-result was then passed on to a post-processing algorithm.For weighted cosine similarity, the weights were assigned manually based on the section of publication from which the noun phrases came. In general, noun phrases from title and keywords (if present) were given a higher preference than other sections, since usually these two sections hold the crux of an article. Note, if sections could not be discerned from an article, then noun phrases from the section, reduced_content (see section [preprocess]), were used to find both fields and methods.
Research Field Selection - The top-ranked term from the result of step 3, which was not present in the stopword-list of irrelevant terms, was marked as the research field of the article.
The experimental set-up and average training times (ATT) have been reported in Table [tab:setup]:
[|C[5cm]{} | C[7cm]{} |]{} Computing Infrastructure & macOS, 2 GHz Intel Core i7 processor, 4 cores RAM 16 GB 1600 MHzATT - RF model & 3m 21sATT - RM model & 3m 19sLink to Implemented Code & https://github.com/nikit91/Jword2vec/tree/rich-context
Dataset Extraction¶
For identifying the datasets in a publication, we followed two approaches and later combined results from both. Both the approaches have been described below.
Simple Dataset Mention Search: We chose the dataset citations from the given Dataset Vocabulary that occurred for one dataset only and used these unique mentions to search for the corresponding datasets using regular expressions in the text documents. Then, we computed a frequency distribution of the datasets. As can be seen from Figure [fig:graph], certain dataset citations occurred more often than others. This is because while searching for dataset citations, apart from the dataset title, the corresponding mention_list from Dataset Vocabulary was also considered, which contained many commonly occurring terms like ‘time’, ‘series’, ‘time series’, ‘population’ etc. Therefore, we filtered out those dataset citations that occurred more than a certain threshold value (=1.20) multiplied by the median of the frequency distribution and that had less than 3 distinct mentions in a publication. The remaining citations were written to an interim result file. Table [tab:simple] depicts the improvement in performance of Simple Dataset Mention Search with the inclusion of filtering. The filtering process improved the F1-measure by 42.86%. Note, as the validation data consisted of only 100 articles, changing the threshold value to 1.10 or 1.30 didn’t result in any significant change, hence we have maintained a constant threshold value of 1.20 in our comparison table.
[C[1.5cm]{} C[3.5cm]{} C[3.5cm]{} C[3.5cm]{}]{} Metrics & without filtering & Threshold=1.20, mentions $<$ 3 & Threshold=1.20, mentions $<$ 4Precision & 0.09 & 0.71 & 0.09Recall & 0.28 & 0.12 & 0.28F1-score & 0.14 & 0.20 & 0.14
Frequency Distribution of Dataset Citations[]{data-label="fig:graph"}
Rasa-based Dataset Detection: In our second approach, we trained an entity extraction model based on conditional random fields (CRF) using Rasa NLU [@DBLP:journals/corr/abs-1712-05181]. For training the model we used the Spacy Tokenizer^9 for the preprocessing step. For Entity Recognition we used BILOU tagging and used 50 iterations to train the CRF. We used the Part of Speech tags, the case of the input tokens and the suffixes of the tokens as input features for the CRF model. We particularly tested two configurations for training the CRF-based Named Entity Recognition (NER) model. In Phase-1, the 2500 labeled publications from the training dataset were used for training the Rasa NLU^10 model. Later in Phase-2, when the Phase-1 holdout corpus was released, we combined its 5000 labeled publications with the previously given 2500 labeled publications and then retrained the model again with these 7500 labeled publications.Running the CRF-Model: The trained model was run against the preprocessed data to detect dataset citations and mentions. Only the entities that had a confidence score greater than a certain threshold value (= 0.72) were considered as dataset mentions. A dataset mention was considered as a citation only if it was found in the given Dataset Vocabulary (via string matching either with a dataset title or any of the terms in a dataset ‘mention_list’) and if it belonged to the research field of the article. To check if a dataset belonged to the field of research, we found the cosine similarity of the terms in the ‘subjects’ field of the Dataset Vocabulary with the keywords and the identified Research Field of the article.
Combining the two approaches: The output generated by the Rasa-based approach was first checked for irrelevant citations before a union was performed to combine the results. This was done by checking if a given dataset_id occured more than a threshold value (= 1.20) multiplied by the median of the frequency distribution (same as the filtering process of the Simple Dataset Mention Search).
Note that, the threshold values mentioned above were set after some experiments of trial and testing. For dataset extraction, the goal was to keep the number of false positives low while not compromising the true positives. For research methods and fields, a manual evaluation (see the next section for details) was done to test if the results made sense with the articles.
Evaluation¶
We performed a quantitative evaluation for Dataset Extraction using the evaluation script provided by the competition organizers (refer Chapter 5 for more details). This evaluation (see Table [tab:dataset]) was carried out against the validation data, wherein we compared four different configurations. As can be inferred from the table, there was only a slight increase in performance for the Rasa-based model, when the training samples were increased. However, combining it with the Simple Dataset Mention Search, increased the performance by 19.42%. Interestingly, there was no improvement in performance in the combined approach even when the training samples for the Rasa-based model were increased. This might be because of the removal of frequently-occuring terms from the Rasa-generated output, based on the frequency distribution of dataset mentions as computed in the Simple Dataset Mention Search.
[ M[2.2cm]{} | M[2.3cm]{} | M[2.2cm]{} M[2.2cm]{} M[2.2cm]{} ]{} & &Metrics& Rasa-based Approach (2500) & Rasa-based Approach (7500) & Combined Approach (2500) & Combined Approach (7500)Precision & 0.382 & 0.388 & 0.456 & 0.456Recall & 0.26 & 0.26 & 0.31 & 0.31F1 & 0.309 & 0.311 & 0.369 & 0.369
For Research Fields and Methods, we carried out a qualitative evaluation against 10 randomly selected articles from Phase-1 holdout corpus. Tables [tab:field] and [tab:method] depict a comparison between the predicted fields and methods in Phase-1 and Phase-2. In general, our models returned a more granular output in the second phase, solely because of the modifications we made in the vocabularies.
[C[1cm]{} C[4.5cm]{} C[3cm]{} C[3.5cm]{}]{} pubid & Keywords & Phase-1 & Phase-210328 & Cycling for transport, leisure and sport cyclists & Health evaluation & Public health and health promotion7270 & Older adult drug users, harm reduction & Health Education & Correctional health care6053 & Economic conditions - crime relationship, homicide & Homicide & Gangs and crime
[C[1cm]{} C[4.5cm]{} C[3cm]{} C[3.5cm]{}]{} pubid & Keywords & Phase-1 & Phase-210328 & Thematic content analysis & Thematic analysis & Sidak correction7270 & Interviews conducted face to face, finding systematic patterns or relationships among categories identified by reading the interview transcript & Qualitative interviewing & Sampling design6053 & Autoregressive integrated moving average (ARIMA) time-series model & Methodological pluralism & Multivariate statistics
Discussion¶
Throughout the course of this competition, we encountered several
challenges and limitations in all the three stages of the pipeline. In
the preprocessing step, the appropriate extraction of text from PDFs
turned out to be rather challenging. This was especially due to the
varied formats of the publications, which made the extraction of
specific sections—that contained all data relevant to our
work—demanding. As mentioned before, if there was no explicit mention of
the key-terms like
Abstract, Keywords, Introduction, Methodology/Data, Summary, Conclusion
in the text, then the content was saved as ‘reduced_content’ after
applying all other preprocessing steps and filtering out any irrelevant
data.Our experiments suggest that the labeled publications we received for
dataset detection were not uniform in the dataset mentions provided,
which made it difficult to train an entity extraction model even with an
increased number of training samples. Hence, there was only a slight
improvement in performance when the Rasa-model was trained with 7500
publications instead of 2500. This was also why we combined the
Rasa-based approach with the Simple Dataset Mention Search, so that at
least the datasets that were present in the vocabulary do not get
missed.
Regarding the fields and methods, vocabularies played an immense role in their identification. The vocabularies that were provided by the SAGE publications contained some terms that were either polysemous or very high-level and therefore, were picked up by our model very often. Hence, for research methods, we created our own vocabulary containing all the relevant statistical methods, and for fields, we introduced a stopword-list of irrelevant terms and looked it up each time, before writing the result to the output file. The goal of stopword-list generation was to filter the terms that did not carry domain-specific information and sounded more like research methods than fields. Since the focus was on more granulated results, we tried to look for open ontologies for Social Science Fields and Methods and unfortunately, could not find any. It is worth mentioning that since our approach for Fields and Methods identification relied heavily upon vocabularies, it could not find any new methods or fields from the publications.
Based on the final evaluation feedback, since our Phase-2 models did not perform as good as we expected, following are a few things that we could have done differently.
For research methods, merging the given SAGE methods vocabulary with our manually curated vocabulary, could have resulted in methods that would have been both granular and statistical while still being relevant to the publications. Introducing a stopword-list just as we did for research field identification, could also have been another workaround.
For both fields and methods identification, we could have also tried pre-trained embeddings from glove^11 and fastText^12.
As our entity-extraction approach for Dataset Detection suffered from a limitation of inconsistent labels (i.e. datasets mentioned in the form of abbreviation, full-name, collection procedure, and keywords) in training data, we could have extended the Simple Dataset Mention Search to a pattern-oriented search based on handcrafted rules derived from sentence structure and other heuristics.
Future Agenda¶
The data provided to us in the competition displayed a cornucopia of inconsistencies even after human processing. We hence propose that machine-aided methods for computing correct and complete structured representation of publications are of central importance for scientific research such as an Open Research Knowledge Graph [@DBLP:journals/corr/abs-1901-10816][@DBLP:conf/esws/BuscaldiDMOR19]. Previous works on never-ending learning have shown how humans and extraction algorithms can work together to achieve high-precision and high-recall knowledge extraction from unstructured sources. In our future work, we hence aim to populate a scientific knowledge graph based on never-ending learning. The methodology we plan to develop will be domain-independent and rely on active learning to classify, extract, link and publish scientific research artifacts extracted from open-access papers. Inconsistency will be remedied by ontology-based checks learned from other publications such as SHACL constraints which can be manually or automatically added.^13 The resulting graphs will
rely on advanced distributed storage for RDF to scale to the large number of publications available;
be self-feeding, i.e., crawl the web for potentially relevant content and make this content available for processing;
be self-repairing, i.e., be able to update previous extraction results based on insights gathered from new content;
be weakly supervised by humans (e.g. authors of publications), who would assist in correcting wrong hypotheses, thereby leveraging semi-supervised learning;
provide standardized access via W3C Standards such as SPARQL.
Having such knowledge graphs would make it easier for the researchers (both young and veteran) to easily follow along with their domain of fast-paced research and eliminate the need to manually update the domain-specific ontologies for fields, methods and other metadata as new research innovations come up.
Appendix¶
The code and documentation for all our submissions can be found here: https://github.com/dice-group/rich-context-competition.


Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences. The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs. The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another. Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures.
| Version: | 1.0 |
| Depends: | R (≥ 2.10) |
| Published: | 2013-11-19 |
| Author: | Taryn B. T. Athey and Paul D. McNicholas |
| Maintainer: | Taryn B. T. Athey <tathey at uoguelph.ca> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | VLF results |
| Reference manual: | VLF.pdf |
| Package source: | VLF_1.0.tar.gz |
| Windows binaries: | r-devel: VLF_1.0.zip, r-release: VLF_1.0.zip, r-oldrel: VLF_1.0.zip |
| OS X binaries: | r-release: VLF_1.0.tgz, r-oldrel: VLF_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=VLF to link to this page.


Compared to a European director,
the average US director is . . .
4
. . .
years older
61
57
Average age
2
. . . serving a tenure
years
6
8
Average TENURE
10%
. . .
more likely to be a man
75
85
MALE (%)
17%
. . .
more likely to be over 65
18
35
OVER 65 (%)
Source: FT analysis of ISS Analytics data


In this section we discuss a few other ways of storing things in one or more bytes..
Flags
Rather than use a byte (or word etc) as a number you can use each bit independently to store a flag value (ie a yes/no, on/off, true/false etc) value. Computer languages that support a Boolean (true/false) type typically use a whole byte (or more) when a single bit will do. This is for speed, but if storing a large number of flags then storing 8 values in a byte can save a lot of space (in memory or on disk).
Most processors do not provide instructions for manipulating individual bits. The boolean operations AND and OR are used to set or clear a certain bit without affecting the other bits in a byte. You can use XOR to toggle a certain bit. See the above Boolean Operations section for more about using Boolean masks. Similarly most computer languages do not provide a mechanism for manipulating individual bits, although bit-fields provided by C/C++ can be used to manipulate an individual bit if it is part of a struct (or class).
Bit-fields
Somewhere in between using a byte or word for a whole number and using independent bits as flags is the bit-field. The term "bit-field" was popularised by the C language where individual bits or groups of bits within a struct can be used to store a small numeric value (signed or unsigned). For example, if you are storing employee records in a structure you might store sex in a single bit bit-field (unless you want to allow for more than just male and female). But marital status would require a 2 or 3 bit bit-field, since there are more than 2 alternatives.
A good example of bit-fields is the way MSDOS stores the date and time of files (actually this is a characteristic of the FAT filesystem not MSDOS itself). For example, the date is stored in a 16 bit number, using 7 bits for the year (bits 15 to 9), 4 bits for the month (bits 8 to 5), and 5 bits for the day (bits 5 to 0). In some ways this is a bit wasteful as there are only 12 months but this system allows for up to 16. (An alternative would be to store a date as the number of days from a certain day.) However, it makes some operations, like extracting the month, very easy.
To manipulate bit-fields, Boolean masking and shifts are required. Of course, if you are using bit-fields in a C/C++ struct (or HexEdit template bit-fields, to be available soon) then this is handled for you automatically. In fact, for signed bit-fields sign extension is also performed. Using our MSDOS data example again, to get the month from a date field you would first shift the value right by 5 bits so that the month is in the bottom 4 bits then mask off the top bits (AND with 000Fh) to be just left with the month number. (Sign extension is not necessary as month numbers are always positive.)
If you are working with data created by a C/C++ program that uses bit-fields, there are several variations that you should be aware of.
First the compiler will use an underlying "storage unit" to store bit-fields. This is typically the same size as an "int" (the "natural" integer size for the processor) but could be as small as a byte. Consecutive bit-fields are packed into a single storage unit until one is encountered that is too large to fit which then starts the next storage unit.
For example, if you have five 7-bit bit-fields in a row and the storage unit is a 32-bit integer then the first four will be packed into one storage unit and the fifth one will start in the next storage unit, giving a total storage of 8 bytes (two 32 bit storage units). In this case there are 29 wasted bits (4 in the first storage unit and 25 in the second). Generally programmers try to arrange bit-fields so that there are few wasted bits.
Where the bits of a bit-field are positioned in each storage unit also depends on the compiler. The byte order of the storage units (see Numeric Operations for an explanation of big- and little-endian byte order) depends on the target environment of the compiler. The order of placement of the bit-fields (top to bottom or bottom to top) is entirely up to the compiler, but most fill starting at the bottom.
Finally, occasionally bit packing into an indefinite number of consecutive bytes is used to save space. For example, you might want to pack a large number of 5-bit numbers consecutively so that each group of 5 bytes stores 8 values. This can generally not be handled in C/C++ using bit-fields (but will be handled in HexEdit template bit-fields). In C/C++ you would need to use shifts and masking operations to handle this type of data.
BCD
In most computers numeric data is typically converted from text in the local character set (normally decimal numbers in ASCII) to binary. Processing is done using binary arithmetic (see Binary Arithmetic in the Numbers section above). Results are then converted back to the decimal characters for display to the user.
However, in computers with little processing power (older systems and very small systems) arithmetic was (and still is) done using decimal arithmetic. This was done for simplicity and to save time converting to and from binary. The most common system was packed binary coded decimal (often called packed BCD or even just BCD). In this system each nybble stored one decimal digit, so a byte could store 100 values (00 to 99 decimal). This meant that only 10 of the 16 possible values for the nybble were valid, ie only 100 (10 x 10) of the possible 256 (16 x 16) byte values were used.
Another way to think of packed BCD is as two 4-bit bitfields stored in each byte. Each bitfield stores a decimal digit, so only 10 of the possible 16 values for each bitfield are used.
Many early microprocessors had direct support for working directly with BCD numbers. Some used special processor instructions while others had a special mode where arithmetic instructions working in BCD rather than binary.
Many embedded systems still work this way. For example, a memory address or I/O address may be directly connected to hardware which displays a 2 digit decimal number (for example minutes or seconds on a microwave oven), with each nybble displaying one digit in an LCD display. To add to or subtract from this number it is simply a matter of reading the current value, then using instructions to do a BCD addition or subtraction, and storing the number back.
This is faster and simpler than performing the tedious conversion to and from binary. In other words it simplifies I/O of numbers.
Next topic: Character Sets


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{% extends "base.html" %} {% load crispy_forms_tags account_permissions %} {% block title %}Team{% endblock %} {% block content %} {% include "accounts/_account_header.html" with account=account tab="teams" %} {% include "accounts/_team_header.html" with account=account team=team team_tab="metadata" %}
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atg.payment.creditcard
Interfaces
CreditCardInfo
CreditCardProcessor
CreditCardStatus
CreditCardTypes
Classes
AuthorizationAddressVerificationStatus
CreditCardStatusImpl
CreditCardTools
GenericCreditCardInfo


Mother Nornil:
Great one...
I remember your parents.
I remember your birth.
I remember when you left these shores for the Outer World.
And I remember when you returned to this island of ours.

But you are unafraid to step boldy into the dangerous and unclear future. Truly you are Kamael of the Kamael!
.......
"I am ready."


The Scala Development tool provides several automated refactorings. They can all be found in the Refactoring menu:
Note that they are still under development, so all changes should be verified before accepting them. Please report all bugs you find.


Filter(sqlStatement: String)
Stores part of a SQLite selection statement, used to filter results in database queries.
See Also
Filters
Query


Geometry
Simplified
(a projective space)
The above finite projective
space is the simplest nontrivial example
of a Galois geometry (i.e., a finite geometry
with coordinates in a
finite (that is, Galois) field.)
The vertical (Euclidean)
line represents a (Galois) point, as does the horizontal line
and also the vertical-and-horizontal cross that represents the first
two points'
binary sum (i.e., symmetric difference, if the lines are regarded as
sets).
Homogeneous coordinates for the points of this line —
(1,0), (0,1), (1,1).
Here 0 and 1 stand for the elements of the two-element Galois field GF(2).
The 3-point line is the projective
space corresponding to the affine space
(a plane, not a line) with four points —
(an affine space)
The (Galois) points of this
affine plane are not the single and combined (Euclidean)
line segments that play the role
of points in the 3-point projective line,
but rather the four subsquares that the line
segments separate.
For further details, see Galois Geometry.
There are, of course, also
the trivial two-point affine space
and the corresponding trivial one-point projective
space —
Here again, the points of
the affine space are represented by squares,
and the point of the projective space is represented by a line segment
separating the affine-space squares.


Load Test Dashboard
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Choose a test plan to run:
Start
Nodes status:
Test status:
Terminate
Start new
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Start time:
Stop time:
Time left:
Total error count:
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Total transactions count:
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Donate
When you donate you are not only helping us change lives you are also helping us keep everything up and running smoothly. Donating allows us to continue to do our work throughout New Zealand, regardless of age, gender or race!
By donating you automatically go into the draw for prizes when you attend one of our events. You also are able to claim special rewards at any of our fundraising events at the marked locations! All you need to do is show the QR code that you can access here.
What benefits you get:
- $5 : Free Wristband
- $10 : Entry into monthly raffle
- $20 : Free Drinks
- $50 : Blind + Low Vision T shirt
- $100+ : Free entry into our paid events + special invites and special notification regarding events
All teirs higher than the previous have the benefits of the previous


air
control
Organization
IAQ
Sensorinfo Management
Room Management
Sensortype Management
Sensor Management
Warnings and Solutions Management
Warnings Management
Solutions Management


Drag a ball with the mouse, then release. You can also drag a ball back to table after it goes in a pocket.
CRAZY Mode!
Rack 8 ball
Rack 9 ball
Clear table
shots:
0
,
Written by Luis Montes. Follow me on twitter:
@monteslu
polygon
polygonZone
rectangle
rectangleZone
circle
circleZone
None
done
show hidden
Load JSON


Mr. Roboger's Neighborhood
Hi! My name is Mr. Roboger. Welcome to my neighborhood! I like to play a counting game, where I can count as high as the number you tell me to. Type a number from 1 to 50 below to hear me count!
Number:
Let's begin!
Whew, that was a lot!
Please type only numbers
Please type only numbers greater than 1


class static
qm. Iterator
Source: qminerdoc.
Method
Iterator()
Store Iterators allows you to iterate through the records in the store.
Factory pattern: this class cannot be construced using the new keyword. It is constructed by calling
a specific method or attribute, e.g. calling module:qm.Store#forwardIter to construct the Iterator.
Example
// import qm module
qm = require('qminer');
// create a new base with a simple store
var base = new qm.Base({ mode: "createClean" });
base.createStore({
name: "People",
fields: [
{ name: "Name", type: "string" },
{ name: "Gender", type: "string" }
]
});
// add new records to the store
base.store("People").push({ Name: "Geronimo", Gender: "Male" });
base.store("People").push({ Name: "Pochahontas", Gender: "Female" });
base.store("People").push({ Name: "John Rolfe", Gender: "Male" });
base.store("People").push({ Name: "John Smith", Gender: "Male"});
// factory based construction with forwardIter
var iter = base.store("People").forwardIter;
base.close();
Properties
record
Gives the current record. Type module:qm.Record.
store
Gives the store of the iterator. Type module:qm.Store.
Method
next() → boolean
Moves to the next record.
Example
// import qm module
var qm = require('qminer');
// create a new base containing one store
var base = new qm.Base({
mode: "createClean",
schema: [{
name: "TheWitcherSaga",
fields: [
{ name: "Title", type: "string" },
{ name: "YearOfRelease", type: "int" },
{ name: "EnglishEdition", type: "bool" }
]
}]
});
// put some records in the store
base.store("TheWitcherSaga").push({ Title: "Blood of Elves", YearOfRelease: 1994, EnglishEdition: true });
base.store("TheWitcherSaga").push({ Title: "Time of Contempt", YearOfRelease: 1995, EnglishEdition: true });
base.store("TheWitcherSaga").push({ Title: "Baptism of Fire", YearOfRelease: 1996, EnglishEdition: true });
base.store("TheWitcherSaga").push({ Title: "The Swallow's Tower", YearOfRelease: 1997, EnglishEdition: false });
base.store("TheWitcherSaga").push({ Title: "Lady of the Lake", YearOfRelease: 1999, EnglishEdition: false });
base.store("TheWitcherSaga").push({ Title: "Season of Storms", YearOfRelease: 2013, EnglishEdition: false });
// create an iterator for the store
var iter = base.store("TheWitcherSaga").forwardIter;
// go to the first record in the store
iter.next(); // returns true
base.close();
- Returns
-
boolean
B
1.True
, if the iteration successfully moves to the next record.
2.False
, if there is no record left.


 Instead, every household has a log, which children beat with sticks while chanting until the log "shits" presents.
This is not a joke.
Don't believe us?  The lyrics are worth translating in full:
Again, this isn't a joke.
Yes. Everyone in Catalonia. Catalans love the poop log so much that, in addition to celebrating it at home, they also often gather in large groups, with a massive log, beat it relentlessly, and then delight at the site of it shitting many presents:
We're making a bold prediction. In 100 years, Santa Claus will be a relic of the past, and people everywhere will be celebrating in this, ahem, unique Catalan fashion.
What's our rationale? Data, of course. We've analyzed global interest in variations of Christmas celebrations, and the reality is that the Catalan poop log is attracting more and more interest every year. Meanwhile, Santa Claus is flatlining.
Ultimately, holidays compete in a Darwinian contest, in which only the superior thrive (which is why you've probably never celebrated National Catfish Day). Before now, the poop log was almost a secret, its awesomeness only known to Catalans.
Nice try, Santa, but your reign is over. Bring on the poop log!
As always, the code and data for this post (including source files, cleaning script, visualizations, etc.) can all be found on github.
The trends data come from Google Trends, which we accessed using the gtrendsR package.


This is a collection of tools to ease working with eyetracker data. I’m slowly migrating stuff from my collection of project specific code into this package. FDBeye is about post-acquisition data processing. What’s here now is focused on my own workflows involving data from SRR Eyelink systems, although some stuff may be more generally useful. This package is most definitely a work in progress. Keep that in mind if you make use of it. Pitch in and help with development if you like (see Contributing).
Before you try FDBeye, you might want to look at what else is available. See the list of eye tracking tools on the project wiki.
To get the very latest from FDBeye, install it via devtools::install_github()
. Some people have reported problems with the installation due to the vignettes failing to build. If that happens to you, you can try again with build_vignettes=FALSE
, although the vignettes won’t be available.
install.packages("devtools") ## if you don't already have it
library(devtools)
install_github("davebraze/FDBeye", build_vignettes=TRUE)
On the other hand, if you want a (possibly) more stable experience. Download the most current release. Then install it like this:
install.packages(pathToFile, repos = NULL)
Where pathToFile is the full path and file name for the file you downloaded.
FDBeye is released under the MIT License.
FDBeye development proceeds sporadically. If you want to be notified of any updates, then you can “watch” the github repository. If you already have a github account, this is as easy as clicking a button. If you don’t have a github account, maybe it’s time to sign up for one.
Contributions are welcome, but you might want to review the “issues” page and send me an email before wading in too deep (davebraze@gmail.com). You might also have look at the file CONTRIBUTING.md for pointers on the mechanical details of contributing to a package on github.
IMPORTANT: In opening a pull request to FDBeye, you (a) affirm that you are the copyright owner for the material contained in the pull request, and (b) agree to apply the MIT license to the material contained in the pull request. Contributors retain copyright to their own code.
The list of (mostly FOSS) tools that used to be a part of this README file has been moved to the FDBeye Wiki.


Provides the necessary classes to build the settings window - the settings window is where the user can change the properities of the sprite that was most recently clicked on. These classes are: ExampleReflection, SettingsWindow, and VisulaFactory.


Carbon 2.5.0 is out. You can download Carbon as a .ZIP archive, NuGet package, Chocolatey package, or from the PowerShell Gallery. It may take a week or two for the package to show up at chocolatey.org.
Enhancements
- Added
DelayedAutoStart
boolean property to objects returned byGet-ServiceConfiguration
. This flag istrue
when a service is set to start automatically, delayed.False
otherwise. Added extended - The
Install-Service
function and theCarbon_Service
DSC resource can now set a service’s startup type toAutomatic (Delayed)
. PassAutomatic
to theStartupType
parameter and use the newDelayed
switch. TheDelayed
switch is ignored unlessStartupType
isAutomatic
. Fixes issue #216. - The
Uninstall-Certificate
function can now delete a certificate by just its thumbprint. The certificate will be uninstalled from all stores. You can pipe the thumbprint or a certificate object toUninstall-Certificate
. - Added an
EnsureRunning
switch toInstall-Service
function to ensure that any service is started after configuring. (By default,Install-Service
leaves a service stopped if it was stopped whenInstall-Service
begins.) - Added
IsSymbolicLink
extended type property to directory and file objects (i.e.System.IO.DirectoryInfo
andSystem.IO.FileInfo
objects). - Added
TargetPath
extended type property to file (i.e.System.IO.FileInfo
) objects. If a file is a symbolic link, this property will return the file the link points to. - The
TargetPath
extended type property on directory (i.e. `System.IO.DirectoryInfo) objects now returns target paths when a directory is a symbolic link. Initiazlie-Lcm
can’t be used on Windows Server 2016 and later to put the DSC local configuration manager into pull mode.Initialize-Lcm
now writes an error when you try.
Bug Fixes
Install-Service
and theCarbon_Service
DSC resource write errors when a service is running and its startup type is changed toDisabled
.- The
Carbon_ScheduledTask
DSC resource writes incorrect information to verbose log when the current and desired credential for the scheduled task are different. - The
Carbon_ScheduledTask
DSC resource doesn’t correctly detect when a task’s identity has changed (it wasn’t converting usernames to their canonical representation before comparing the existing identity with the desired identity). - Fixed: Importing Carbon in 32-bit PowerShell fails on a 64-bit operating system. DSC isn’t available so the
Initialize-Lcm
function can’t be exported. Thanks to Anders Andersson for contribuging the fix. - Fixed:
Install-Service
andCarbon_Service
DSC resource fail to change the identity a service runs as if switching from a custom account to the defaultNetworkService
account. - Fixed:
Get-PowerShellModuleInstallPath
returns nothing when run under 32-bit (x86) PowerShell on 64-bit Windows.Fixed:Get-PowerShellModuleInstallPath
returns nothing when run under 32-bit (x86) PowerShell on 64-bit Windows.


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Robin Schwegler<EMAIL_ADDRESS>Abstract: HRworks is developing one of the leading SaaS HR solutions in Germany. Smalltalk and its unique features made our success story possible as the primary programming language at HRworks. Smalltalk helps our developers to work as efficient and profitable as possible. Among other functions, the developers profit from the language’s self reflection, the brilliant class library and ENVY, a version control system written within Smalltalk. Thereby Smalltalk empowers HRworks to keep development costs down and as a consequence thereof also to offer the software at very attractive conditions. Over 1.300 customers with over 150.000 users rely on our Smalltalk based all-in-one HR software day by day. The number of customers and users are rising continuously. Within my presentation I would like to give an overview on Smalltalk’s unique characteristics and illustrate the audience how they empower business success stories, such as HRworks.
Bio: After finishing my masters’ degree in General Management successfully, I decided to join HRworks for several reasons. The company is located in Freiburg, which is my beautiful and beloved hometown. The growth of HRworks promises challenging and exciting fields of work across all departments. In addition to that, the possibility of flexible working hours enables me to engage in my various hobbies, such as tennis, gaming and photography. As HR specialist, I act in a unique function at HRworks. Due to the close contact with the development department, I learn a lot about Smalltalk and I’m deeply impressed about its unique features and possibilities that make it the one and only programming language to help us being in great demand and highly successful. Equipped with this knowledge, I’m jointly responsible for expanding the development team and ensuring the company’s long-term success.


About the Team
Members of the team, from left to right: Evan Porter, Matt Simms, Michael Thompson, Gaetan Savoie, Nick Fitzpatrick, Leah Belyea
Members of the team, from left to right: Evan Porter, Matt Simms, Michael Thompson, Gaetan Savoie, Nick Fitzpatrick, Leah Belyea


Unauthorized Access !!

Caused :
Halo
Lalah
Access denied!
Go back to login page


This is the course website for IS590 Data Visualization in Spring 2019 (Online)
This week, we covered how colors work, how transformations work, and how we can choose colors and transforms to better suit our data.


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Advancing North East | January 2023
"Career in Space and Art"
Indians in France - Toulouse | March 2021
"Journey from India to Space | Gems of India Series"
OneIndia | December 2020
Adventure in Antarctica to 'break the ice', forge ties & to grow
Smartrezo | August 2020
(In French)
Femmes d’exception : Priyanka DAS ingénieure spatiale
Oneindia News | July 2020
"Living in space - Aerospace engineer & artist talks about living her dreams"


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...
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Hi Howard and thank you very much for your purchase! There are many reasons this can happen. One is the incorrect information in wp-config.html file. Please open up wp-config.html and ensure that the following are correct:
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Damian Johns
Time stops counting, and things turn sharp and fuzzy.
GitHub
GitHub Learning Lab
InnerSource Toolkit
GitHub Actions
Slideshow


Two whirlwind weeks of a 7 week adventure around Spain and France.
Play the travel game.
Language is power.
Student budget mindset works wonders.
One: Play the travel game.
Upon arrival at Aeropuerto BCN, I hadn’t slept in almost 36 hours and sported fanny pack and a huge hardcore backpacking bag.
Challenge one: Buy my month-long T-Mes, 1-zone, youth discounted super special transport pass in broken Spanglish.
Challenge two: Figure out how to use trains, and miss one because you don’t realize you need to press a button to open the doors (and the locals laugh at you).

Challenge thirty-one: With a phone almost as dead as you, sprint like Usain Bolt to catch the hourly night bus which may or may not take you home.
Challenge one hundred and one: Explain to Spanish security guards why you need your swiss army tool inside the museum.
Thinking of travel like a game helps me position the challenges I face as a (solo) traveller and adventures instead of obstacles. It’s all about your viewpoint of whatever life throws at you.
Two: Language is power.
Being submerged in Spanish culture and colloquial language helped me realize that languages you learn in class are a world of difference from that same language in daily life. Knowing how to speak someone’s language gives you the power to build relationships and gain perspective on culture understand and communicate with them on a level of their thoughts.
Though out of my comfort zone, I attempt all purchases and interactions in Spanish. Though it doesn’t always go as planned, locals really appreciate your effort to speak their language even when you forget a phrase or two. Royal Palace security guards instantly become nicer. Service is faster at the cheese shop. They are generally excited that a turista puts forth the effort and it’s far easier to get discounts, directions, or explanations even with language fundamentals.
Though my day is split equally between Spanish and English, I also occasionally speak French and Ukrainian to help other tourists. The ability to communicate with a wide range of individuals has afforded me an array of fascinating stories, recommendations, and hilarious moments with other tourists. Language has given me a power of making a fascinating group of acquaintances, including (but not limited to) the night bus driver who also works as a standup comedian, the shopkeeper with indie music recommendations, and the Americans pretending to be Canadian.
Three: Student budget mindset works wonders.
My sister has a motto: if it’s free, it’s for me, and maybe for you too. Essentially, spend your money the right way and get as much free stuff as you can.
Student discounts have saved me over 200 euros on attractions alone. Just about every museum or park has publicized or hidden discounts for certain dates and age groups. Research before you go and take advantage of them - never buy tickets before calling or checking online first for pricing! Timing discounts are also fantastic because just about every historical & artistic attraction has a free afternoon or night admission. In Barcelona, Sundays are free museum day, parks are always open, and there’s so many low-cost or free cultural expositions. Figure out how to make your spending worthwhile by asking people and researching before hand (Sagrada Familia for 20 euros is amazing!)
Food is without a doubt my biggest expense thus far after train transport. Personally, I go for a balance of good local specialities (with the budget filter on) and delicious grocery store handmade meals. Treating yourself and getting a feel for the area’s specialities for cheap means spend 10 minutes on TripAdvisor and city blog sites cross referencing for a great place. For a filling and budget friendly lunch, I generally go for the supermercado shopping adventure. My current travel companions know all about how to make food coma- worthy bocadillos (large spanish sandwich) for three people with ten euros and a swiss army knife.
Places: Barcelona, Madrid, Tarragona, Pamplona, Montserrat & more adventures to come! Thanks for reading.


COMPUTER HARDWARE
We see computer hardware everywhere in almost every walk of life,from the smart phones we use for our day to day convenience to gaming consoles to card readers in supermarkets and retail stores.Computer hardware has progressed exponentially over the years since 1936 when the first programmable computer(The Z1) was invented .Now there have been various innovative inventions such as Virtual Reality , smart home devices such as Amazon echo , drones and much more . Who knows where new advancements in technology will take us in the future.
CONTENTS
SMARTPHONES
Smartphones are type of mobile phones with multipurpose computing with many features like high hardware and operating system, Software, Internet and Multimedia functionality with basic phone functions and many more upgrades. Smartphones comes with all features and are everywhere in every part of the world now days every person is using a mobile phone as mobile phone comes with a variety of features and different prices. In short mobile phones are taking over our lives.
Benefits:
- Web surfing
- Instant Communication
- GPS
- Privacy
- Entertainment
- Data Transfer
PC(Personal Computer)
We are all around computers. Computers play an important play in our day to day lives, like we are very much dependent them for doing many things such as giving users the ability to quickly and easily store and process data and the ability to create new software for increased productivity. Since the invention of computer they have evolved in terms of increased computing power and decreased size. Computer Generations has reached a different height with the use of a computer tasks which looked difficult before are made simpler by the use of a computer.
Benefits:
- Multitasking
- High Computational speed for complex tasks
- Can store huge amounts of data
- Accuracy in calculations
- Data Security
- Increases your productivity
- Internet Access
SMART HOME DEVICES
Smart home technology generally refers to any suite of devices, appliances, or systems that connect into a common network that can be independently and remotely controlled. When your home technology works together in one system, it can also be referred more loosely as a “connected home”. For example, your home's thermostat, lights, audio speakers, TVs, security cameras, locks, appliances, and more are all connected into a common system, which can be controlled from your smart phone or through a mobile touch screen device. Smart home automation allows you to tap into high-tech functionality and luxury that wasn’t possible in the past. As technology development continues to expand, so will the possibilities for consumer home automation to make life easier and more enjoyable.
Benefits:
- Managing all home devices from 1 place
- Flexibility
- Maximizing home security
- Remote Control of home functions
- Increased energy efficiency
- Improved appliance functionality
- Home Management Insights
SMART TV
Smart TVs, much like smartphones and smart home devices, offer internet connectivity and support for a range of apps. This opens up a world of new entertainment options, from streaming video on Netflix and Hulu to playing games, checking social media, and controlling a whole house full of connected gadgets, including devices that work with Alexa and Google Home-compatible products.A growing number of models now include voice recognition tools, like Amazon Alexa and Google Assistant, for switching channels and searching for programs. Most smart TVs will work with the smart speaker and a few will offer some or all of that same functionality built right into the TV.
Benefits:
- Convenience
- Interactive
- Keyboard Remote Control
- Device Connections
- Internet Connection
VIRTUAL REALITY
Virtual Reality (VR) is the use of computer technology to create a simulated environment. Unlike traditional user interfaces, VR places the user inside an experience. Instead of viewing a screen in front of them, users are immersed and able to interact with 3D worlds. By simulating as many senses as possible, such as vision, hearing, touch, even smell, the computer is transformed into a gatekeeper to this artificial world. The only limits to near-real VR experiences are the availability of content and cheap computing power. Virtual Reality’s most immediately-recognizable component is the head-mounted display (HMD). Human beings are visual creatures, and display technology is often the single biggest difference between immersive Virtual Reality systems and traditional user interfaces. The use of virtual reality as a training tool is well known, in particular in the field of surgery. Medical schools have adopted this technology as a way of teaching the next generation of surgeons.
Benefits:
- Simulation of realistic senarios
- Contextualized Learning
- Innovative and enjoyable
- Can be used in Conferencing
- Multi Sensory Experiences


{% extends "layout.html" %} {% block content %} {% include "includes/phase_banner_alpha.html" %} {% include "includes/breadcrumbs.html" %} {% include "includes/entity_header.html" %} Which user rights would you like to give {{username}}? Data admin System admin Manager or Staff {% endblock %}
Which user rights would you like to give {{username}}?
or


--- title: Getting Started With Sass ---
We recommend using the Command Line Interface. We've perfected the command line for quick and easy power, but you can use Foundation with just the files or other applications, like Mixture or CodeKit.
Using this Command Line Interface will make updating a Foundation project super easy and help speed up your workflow. The CLI updates your project while leaving your customized styles in tact. We recommend this method since the CLI does many tasks for you.
Git is GitHub's version control system, this install basically commands the tools you'll use in the command line to work within GitHub.
Node allows JavaScript to run outside of the browser, and is necessary for Bower to package all the files into the project and link them together.
We wrote a Ruby script that downloads the Sass files and scaffolds the project. If you have a Mac, you probably already have Ruby installed.
With the release of Foundation 6, we created a new CLI that supports all three Foundation frameworks. However, it does not create new Foundation 5 projects.
If you still need to create new Foundation 5 projects, stick with the old CLI, the install instructions for which are below. If you're ready to move to Foundation 6, head over to the installation docs for Foundation 6 and the new CLI.
We use Bower to manage updates to Foundation and the third-party libraries that Foundation is built on.
npm install -g bower grunt-cli
Installing the Foundation CLI on your computer only takes one command function.
gem install foundation
Note: If you run into permission errors running these commands in the CLI, you can add the word 'sudo' before the line inside the interface:
sudo npm install -g bower grunt-cli
sudo gem install foundation
If you use rbenv, rehash your shims after installing:
rbenv rehash
SCSS (Sassy CSS) syntax was introduced as "the new main syntax" for Sass and builds on the existing syntax of CSS. We created a SCSS version to allow more friendly and dynamic styling. This version also allows for greater customization than the HTML/CSS version. There are two easy ways our CLI helps you compile SCSS: Compass or Libsass with Grunt.
If it's not installed already, you can install Compass using the following command:
gem install compass
Next we'll use the Foundation CLI to create a new project:
foundation new MY_PROJECT
Boom, your project is created! Now Foundation will be easy.
cd MY_PROJECT
Bundler is used to ensure all the correct libraries are used. If you don't have it, run `gem install bundler`
Then run bundle (only once per project)
Be sure to run compass watch in order to make changes.
bundle exec compass watch
First, navigate into the directory where you want to create your project:
cd path/to/sites
Next we'll use the Foundation CLI to create a new project:
foundation new project_name --libsass
Boom, your project is created! Now you need to use Grunt to compile everything before you start working:
cd project_name
grunt build
Be sure to run grunt in order to make changes.
grunt
To update, 'cd' into the project folder in the CLI and the run 'bower update.'
bower update
Note: To verify the update has been made, you can see the version number in the bower.json file located in the bower-components folder under foundation.
Note: Alternatively you can place this somewhere else or move the files around.
You'll need to install NodeJS and Bower before updating with Compass.
Note: You can install bower by running the following command:
npm install -g bower
We use bower to keep Foundation assets up to date within your project. Here's how you can update your project:
First, you need create a file named bower.json in the root of your Compass project. This should contain the following:
{
"name": "PROJECT_NAME",
"dependencies": {
"foundation": "~5.4.7"
}
}
Next you will run bower update.
bower update
This will create a directory called bower_components/ in your project. Foundation's Sass and JavaScript files will be placed in the bower_components/foundation folder.
Using Bower and a task runner like Grunt or Gulp, you can add our Sass files to your build process.
In a project with an existing bower.json
, add Foundation to your bower components:
bower install foundation --save
Next, add bower_components/foundation/scss
to your Sass compiler's import paths. The exact setting you need to change will depend on the plugin you're using. For example, this is how to add Foundation to a Grunt task using grunt-contrib-sass
:
sass: {
dist: {
options: {
style: 'expanded'
loadPath: ['bower_components/foundation/scss']
},
files: {
'main.css': 'main.scss',
'widgets.css': 'widgets.scss'
}
}
}
This configuration will work for `gulp-sass`:
gulp.src('./scss/*.scss')
.pipe(sass({
includePaths: ['bower_components/foundation/scss']
}))
.pipe(gulp.dest('./css'));
Publish your project online. It’s as easy as one click. Share your published URL, password protect, get feedback, client sign-off or even add your own domain and host your site.
Learn MoreWith zero setup and Bower built right into the system, CodeKit can compile any language with one click updates. From auto-refresh browsers to built-in minifiers, CodeKit has everything inside to help you get started.
Learn MoreUsing Cactus allows you to put the components of your website together with your local server without having to set up a server environment like Ruby. You can use it on your Mac to compile your SASS, start building templates, or for directing visual feedback.
Learn MoreThe complete package! We'll cover the important files that are included when you download Sass and where to make customizations.
Harness SCSS's variable features easily through the included _settings.scss file. We cover important tips on the settings file, writing sass, and using mixins.
Every Foundation project needs to include these styles in order to work properly. These include things like basic formatting and global utility classes.


RT @ToriWieldt: Looking forward to Real World #Docker for the Rubyist by @jasonrclark session tomorrow! #railsconf http://ow.ly/4ntmsB
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RT @ToriWieldt: Looking forward to Real World #Docker for the Rubyist by @jasonrclark session tomorrow! #railsconf http://ow.ly/4ntmsB
5/5/2016, 1:48:45 PM
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From QC Circle: Exit to Commonwealth Avenue and take the first U-turn slot.
From Fairview: Turn right on the Blue Gardens signage past Don Antonio Heights.
Formal Invitation
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