Rays the roof: Real estate magnate Louise Sunshine selling glam Miami penthouse
by Jennifer Gould KeilReal estate titan Louise Sunshine is in contract to sell her duplex penthouse at 10 Venetian Way in Miami.
The final sale price is not yet available.
Sunshine is now a strategic advisor for Fort Partners, where she has tapped Douglas Elliman’s Fredrik Eklund and John Gomes to rep a soon-to-be-completed $35 million penthouse at the Four Seasons Hotel and Residences at the Surf Club in Surfside, Florida.
We hear Sunshine herself is gearing up for a move to that same oceanfront development.
Sunshine’s soon-to-sell home — a 6,422-square-foot, six-bedroom, six-bathroom pad — was last asking $6.49 million.
The residence is a combo of two separate penthouses, featuring a dramatic glass staircase, an open kitchen, 22-foot ceilings and stunning views of Biscayne Bay and the Miami skyline.
The unit also features three sprawling terraces and comes with five parking spaces.
Building amenities include a “resort-style” pool, a gym, tennis courts and a concierge.
The listing broker of her 10 Venetian Way home is Jill Hertzberg, of Coldwell Banker Residential Real Estate.


There are many ways to contribute to Tide. You can help us champion the adoption of quality testing results in the WordPress project. You can also help by contributing code or documentation to Tide itself.
Note that you can also contribute to WordPress meta to improve the WordPress plugin directory.
Before submitting your contribution, please make sure to take a moment and read through the following guidelines.
This project and everyone participating in it is governed by the Tide Code of Conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to<EMAIL_ADDRESS>develop
branch.develop
and merge back against develop
.
Travis CI will run the unit tests whenever you push changes to your PR. Tests are required to pass successfully for a merge to be considered.
As outlined in the announcement post, badges related to work on Tide are awarded as follows:
The Tide Team badge will be manually assigned to all active Tide maintainers – i.e those who are listed as “Maintainers” on this page (also here).
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Terry O'Neill, photographer who defined swinging 60s, dies at 81
by Cahir O'DohertyBorn the son of Irish immigrants, legendary photographer Terry O’Neill created images that helped define the swinging 1960s.
Terry O’Neill, who died at the age of 81 last week, captured banner name talents at the height of their fame including Paul Newman, Audrey Hepburn, Elizabeth Taylor, Elton John, and perhaps especially David Bowie (whom he shot several times in his two main superstar periods of 1973 and 1983).
Steered early on by his parents towards the Catholic priesthood, all of that ended when he left school at 15 to play drums in a rock and roll band.
Read more: These are the best Irish books of 2019
O'Neill's father Leonard was born on Blarney Street in Cork, where he worked in Henry Ford & Son Ltd before transferring to its plant in Dagenham, England - the start of a new life.
Young O'Neill's big break came in 1963 when he was asked by an editor to go to the Abbey Road studios to photograph a band that turned out to be The Beatles. The ensuing coverage elevated him to the front ranks of his time, in a career that introduced him to the many of the world's most compelling stars.
Taking the world of celebrity photographer by storm in the 1960s, O'Neill was among the most prominent members of a new kind of celebrity portraiture. His secret was his charisma, which put his famous subjects at ease, confident they were in safe hands - and he loved show business, which they felt he understood from the inside.
Known for his candid portraits, his client list is a who's who of the later 20 century. But his shots of David Bowie in his Ziggy Stardust and Thin White Duke heyday are among the most compelling he ever captured.
A consummate professional at all times O’Neill loathed the rise of the paparazzi. “Paparazzi should be shot,” he told the press in 1996. “Most of them can’t even use a camera, and I can see no reason why they should not be banned.”
Read more: This is the new Irish crime drama you have to see


jQuery Events!
jQuery Events!
jQuery Events!
Dont click me
Seriously! Dont do that
This is your last warnin


HTML before
HTML before
HTML before
hello world
foo bar baz
woo yay hoopla
HTML after
HTML after
HTML after


Next: Internal and External Symbols, Previous: Package Names and Nicknames, Up: Introduction to Packages


1. Register an account
2. Find the seat
3. Click JOIN GAME
1. Register an account
2. Navigate to the Events page
3. Click CREATE EVENT
4. Fill in the required fields
5. Click CHOOSE TABLE
6. Select an open table
7. Click CONFIRM TABLE
Whatever you want to make them.
Over 75+ retro arcade games and pinball RPG, board and card tabletop gaming
Console gaming on NES, SNES, Gamecube, PS1, PS2, Genesis, Xbox
Yes, we welcome all tabletop games.
Yes, we host birthdays. We only ask that you do not bring outside food or drinks.


Before Change
fire_order += ordering
// Add counts for every n-gram
for i in range(1, n+1):
for beg in range(len(fire_order)-i+1):
// For every ordering based on n (i)
if tuple(fire_order[beg : beg+i]) not in ngram_scores:
ngram_scores[tuple(fire_order[beg : beg+i])] = torch.zeros(n_labels)
ngram_scores[tuple(fire_order[beg : beg+i])][int(gold_labels[n_ex])] += 1
return ngram_scores
After Change
// Aggregate all of the firing neurons" indices
for t in range(activity.size()[0]):
ordering = torch.nonzero(activity[t].view(-1))
if ordering.numel() > 0:
ordering = ordering[:, 0].tolist()
fire_order += ordering


--- layout: post title: They said it couldn't be done... published: true date: 2010-09-01 categories: [] posterous_url: http://blog.hungerandthirstdesign.com/post/1047044044/they-said-it-couldnt-be-done posterous_slug: post/1047044044/they-said-it-couldnt-be-done ---
Actually nobody said that. Or maybe I did. In any case…
After much delay and a bit of big talk about trying to launch this deal for real, I at least have the blog together. I’m still very much in the process of putting together the site design on the whole, but I’m liking where it is. Authentication bits are now working although being logged in doesn’t really do anything for you… so yeah. Not much point to that yet. Hang in there though, I promise it will be worth it.
Also looking to soon add ways for you to contact me here at HungerAndThirstDesign.com, as currently, our only method of contact is really Twitter (HaTD). So please drop us a line and we’ll get back to you that way, push updates, or generally tell you when you’ll be able to contact us for reals.
Lastly, just want to say I’m very excited about the upcoming release of Rails 3, and hoping that the Rails team can make good on the hints to push it out late February. Will definitely be looking to make this and future sites work better, faster and more efficient on the new framework. Pumped!!


Provide your slot details below and we'll keep checking if a slot is available in the background.
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{{singleModel}}
{{radioModel || 'null'}}
Model via JsonPipe: {{checkModel | json}}
Left: {{checkModel.left}} Middle: {{checkModel.middle}} Right: {{checkModel.right}}
The selected page no: {{currentPage}}/{{smallnumPages}}
Page: {{bigCurrentPage}} / {{numPages}}
Rate: {{rate}} - Readonly is: {{isReadonly}} - Hovering over: {{overStar || "none"}}
Select a tab by setting active binding to true:
Bootstrap 4 doesn't have justified classes
Model: {{selected | json}}
Model: {{asyncSelected | json}}


Find and Replace
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WITNESS LIST
Health & Human Services
April 28, 2011 11:00 AM - or upon adjournment
FOR:
Camacho, Jose Executive Director (Texas Association of Community Health Centers),
Austin, TX
Stukenberg, Amanda Director (also providing written testimony) (Family Planning of the
Coastal Bend), Corpus Christi, TX
Zuniga, Martha Executive Director (South Texas Family Planning Health Corporation),
Corpus Christi, TX
ON:
Capan, Patrice Executive Director (Self; Family Health Care, Inc.), Little Elm, TX
Dimitry, Lauren Health Policy Coordinator (also providing written testimony) (Texans
Care for Children), Austin, TX
Everett, Carol (also providing written testimony) (Self), Round Rock, TX
Pojman, Joe Executive Director (also providing written testimony) (Texas Alliance for
Life), Austin, TX
Realini, Janet President, Healthy Futures of Texas (also providing written testimony)
(Healthy Futures Alliance, Texas Medical Association., Texas Pediatric Association., Texas
Academy of Family Physicians), San Antonio, TX
Shaw, Tama Executive Director (Hill Country Community Action), San Saba, TX
Turicchi, Paula Senior Vice President (also providing written testimony) (Parkland Health
and Hospital System), Dallas, TX
Registering, but not testifying:
For:
Wilson, Stacy Director of Government Relations (Community Care), Austin, TX
For Bill/For Committee Substitute (CSSB 1854) :
Horne, Emily (Texas Right to Life), Houston, TX
Kebodeaux, Emily Legislative Associate (Texas Right to Life), Houston, TX
On:
Garcia, Imelda Acting Section Director (Department of State Health Services), Austin, TX
Ghahremani, Kay Children's Health Insurance Program/Medicaid Policy Director (Health
and Human Services Commission), Austin, TX
Hagerty, Fran Chief Executive Officer (Women's Health and Family Planning Association
of Texas), Austin, TX
1


Welcome
Here's, our cherish memories throughout our college life; And, all memorable experiences that we all made together, cheers!


Path of Light
Sketching with Math and Quasi Physics
Reflection
$${r=i-\frac{2i\cdot n}{\left\|n\right\|^2}n}$$
$${i}$$: vector of the ray to the surface, $${n}$$: normal of the surface, $${r}$$: vector of the reflected ray
Parabola
Parallel rays coming into a parabolic mirror converge at a single focal point


[gecode-users] Sum over matrix slice
Tomas Lidén
tomas.liden at liu.se
Thu May 15 14:41:50 CEST 2014
Argh - my bad... Thanks!
/T
Christian Schulte skrev 2014-05-15 13:39:
>
> Hi Tomas,
>
> According to the documentation slice(i1,i2,j1,j2) extracts the
> elements with coordinates in [i1,i2) and [j1,j2).
>
> So, in your example, slice(i,i+1,i,i+1) extracts the elements with
> coordinates [i,i+1) and [i,i+1) which is the single element with
> coordinates i,i. That means you have to write slice(i,i+2,i,i+2).
>
>
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Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a considerable influence on consumer shopping behavior. An online review typically consists of free-form text and a star rating out of 5. It provides a valuable feedback to the merchant about which products are popular while which are not. It also provides hints to the business so that they can reevaluate their customer policy and make improvement towards certain direction. Specifically, user’s rating usually reflects their opinion towards the products themselves, or the customer service they received. Either way, it gives worthwhile information that the market could rely on. Besides, users’ review text can be utilized as useful feature because of the emotion they convey. The problem of predicting a user’s star rating for a product, given the user’s text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning
In this project, we treat the Review Rating Prediction for restaurant reviews on Yelp as a 5-class classification problem, and build a model which predicts the rating (Classify it into 1-5 star rating classes) given a text review. We examine the performance of models built using three supervised machine learning classification algorithms namely logistic regression, Naive Bayes classification and K Nearest Neighbors. We analyze the performance of each of these models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models
Definitely: Outcome would be a system that predicts rating for a given review text.
Implementation flow starts with separating restaurant reviews from Yelp dataset which has
reviews for various categories, such as restaurants, shopping, hotels and travel, etc. we then
preprocess the text reviews to extract meaningful content from each of them. To do this, we
use standard Python libraries to remove capitalizations, stop words and punctuations. Next, we
use Unigrams model to extract useful features from the review corpus build a feature vector for
each review. Then we implement three supervised machine learning algorithms (logistic
regression, Naive Bayes classification and K Nearest Neighbors) to train our prediction model.
Likely: Using Bigrams model for feature extraction.
Ideally: Implementation of the system in PySpark.
We are using Yelp Open Dataset provided by Yelp. The Yelp dataset is a subset of businesses, reviews, and user data. It consists of mainly 6 files (business, reviews, users, checkins, tips and photos). The algorithms implemented in the project use stars and review text attributes from the reviews file. The size of the reviews data set is 3.4 GB which consists of reviews of several categories of which we will be considering the most reviewed category restaurants.
One of the categories that our project could fall under is definitely the supervised learning. The main reason for this is that a prior knowledge of the target variable is already known , that needs to be predicted at the end of project. To train our prediction models, we used three supervised learning algorithms.
A Naive Bayes (Ng and Jordan, 2002) classifier makes the Naive Bayes assumption (i.e. it assumes conditional independence between any pair of features given some class) to model the joint probability P(r, s) for any feature vector r and star rating s. Then, given a new feature vector r ∗ for a new review r ∗ , the joint probability function is computed for all values of s, and the s value corresponding to the highest probability is output as the final class label for review r ∗.
The multinomial Naive Bayes(which assumes that P(ri |s) is a multinomial distribution for all i) with smoothing has been implemented. To classify, we calculated probabilities of the review belonging to each rating and then selected the class value with highest probability. We have performed computations by summing logs of probabilities rather than multiplying probabilities for underflow prevention. We ran this model for smoothing factor - alpha ranging from 1 to 5 and found that we had better results for alpha = 1; so we’re considering alpha as 1 for our model.
K nearest neighbors is a supervised classification algorithm that uses all available data samples which classifies a new sample based on a similarity measure. There are many such similarity measures namely: Euclidean Distance, Hamming Distance, Cosine Similarity etc. Since we are working with textual data Hamming Distance best suits as the similarity measure. Hamming distance between two strings is the number of positions at which the corresponding character are different. In other words, it measures the minimum number of substitutions required to change one string into the other, or the minimum number of errors that could have transformed one string into the other.
When a new sample is to be classified, we consider a majority vote of its neighbors, with the sample being assigned to the class that is most common amongst its K nearest neighbors measured using Hamming Distance.
Logistic regression is a simple classification algorithm for learning to predict a discrete variable such as predicting whether a grid of pixel intensities represents a “0” digit or a “1” digit. Here we use a hypothesis class to try to predict the probability that a given sample belongs to the class “1” versus the probability that it belongs to the class “0”. Specifically, we will try to learn a function of the form:
P(y=1|x) = hθ(x) = 1/1+exp(-θTx)
P(y=0|x) = 1- P(y=1|x)
The function 1/1+exp(-z) is often called the “sigmoid” or “logistic” function – it is an S-shaped function that “squashes” the value of z into the range [0,1] so that we may interpret hθ(x) as a probability. Our goal is to search for a value of θ so that the probability P(y=1|x)=hθ(x) is large when x belongs to the “1” class and small when x belongs to the “0” class (so that P(y=0|x) is large). We can learn to classify our training data by minimizing the cost function to find the best choice of θ. To estimate the θ we used following equation:
θ = θ + alpha * ∑ xi (hθ(xi) − yi)
To perform multi-class Logistic Regression, we used one Vs all strategy. This strategy involves training a single classifier per class, with the samples of that class as positive samples and all other samples as negatives. While predicting a class for test sample we apply all classifiers to the unseen sample and predict the label k for which the corresponding classifier reports the highest probability score.


Load Balancer Manager for localhost
- Server Version: Apache/2.4.7 (Ubuntu)
- Server Built: Jul 22 2014 14:36:38
- Balancer changes will NOT be persisted on restart.
- Balancers are inherited from main server.
- ProxyPass settings are inherited from main server.
LoadBalancer Status for balancer://cluster1 [pfeb536ef_cluster1]
| MaxMembers | StickySession | DisableFailover | Timeout | FailoverAttempts | Method | Path | Active |
| 2 [2 Used] |
(None) | Off |
0 | 1 |
byrequests |
/ |
Yes |
LoadBalancer Status for balancer://cluster2 [pfeb536ef_cluster2]
| MaxMembers | StickySession | DisableFailover | Timeout | FailoverAttempts | Method | Path | Active |
| 2 [2 Used] |
(None) | Off |
0 | 1 |
byrequests |
/ |
Yes |
Apache/2.4.7 (Ubuntu) Server at localhost Port 8080


Nokia 9 PureView, Nokia 8.1 and Nokia 7.1 also got the new Ambient mode for Google Assistant *Update*
by StipeBesides the Nokia 6.1, other Nokia smartphones with Android One also started receiving a new feature related to Google Assistant. The Ambient Mode was announced with the Nokia 6.2 and Nokia 7.2 in September, as a timed exclusive in the smartphone market, and this week Google started releasing it to more devices.
As Nokioteca.net reported, the new feature is available for the Nokia 9 PureView, Nokia 8.1 and Nokia 7.1, as far as they can confirm. The Ambient Mode is probably rolling out to other Nokia smartphones and smartphones from other manufacturers as well.
If your device received the new feature, do leave a comment so we can update the article. 🙂
Photo: 9to5Google
Update: The Ambient Mode is also available for Nokia 3, Nokia 3.2, Nokia 7 Plus, Nokia 6.1 Plus and others. To check availability you can go to “Settings, Google, Account services, Search
assistant & voice, Google Assistant, Assistant, Phone (Assistant devices)”. Thanks Alessio for the details. 🙂


To get the most out of tidyVA, it is easiest to load the tidyverse. For fetching data from the Census API load tidycensus (this will require you first sign up for an API key).
library(ggplot2)
library(tidycensus)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyVA)
data("county")
countyshift <- shift_geo(county)
#> Please note: Alaska and Hawaii are being shifted and are not to scale.
VA Healthcare Coverage at the county-level
vars <- c(Male18to64 = "C27009_007E", Male65over = "C27009_010E",
Female18to64 = "C27009_017E", Female65over = "C27009_020E")
VAHC <- get_acs(geography = "county",
variables = vars,
output = "wide", year = 2017) %>%
mutate(TotalMale = Male18to64 + Male65over,
TotalFemale = Female18to64 + Female65over,
Total = TotalMale + TotalFemale)
#> Getting data from the 2013-2017 5-year ACS
VAHC <- tigris::geo_join(countyshift, VAHC,
by_sp = "FIPS", by_df = "GEOID")
data("visn")
visn <- visn %>%
shift_geo() %>%
group_by(VISN) %>%
summarise()
#> Please note: Alaska and Hawaii are being shifted and are not to scale.
plot <- function(var){
ggplot() +
geom_sf(data = VAHC, aes(fill = var), color = NA) + #county layer
geom_sf(data = visn, fill = NA,
color = "#ffffff", size = .5) + #visn layer
scico::scale_fill_scico(labels = scales::comma,
palette = "tokyo") +
theme_va(map = TRUE) +
labs(caption = "Source: 2017 ACS 5-year Estimates",
fill = "Count")
}
plot(VAHC$Total) +
ggtitle("Total Veterans with VHA Coverage")
plot(VAHC$TotalFemale) +
ggtitle("Female Veterans with VHA Coverage",
subtitle = "Counts aggregated at the county-level")
Sex ratio in the population tends to be 1:1, but that’s not the case among Veterans.
ggplot() +
geom_sf(data = VAHC,
aes(fill = (TotalFemale/TotalMale)*100), color = NA) +
geom_sf(data = visn,
fill = NA, color = "#ffffff", size = .5) +
scico::scale_fill_scico(labels = scales::comma,
palette = "tokyo") +
labs(caption = "Source: 2017 ACS 5-year Estimates",
fill = "Ratio") +
ggtitle("Sex ratio of Veterans with VA Health Coverage:",
subtitle = "calculated as number of Female Veterans \nper 100 Male Veterans") +
theme_va(map = TRUE)
data("market")
proj <- sf::st_crs(visn)
market <- sf::st_transform(market,proj)
ggplot() +
geom_sf(data = VAHC[VAHC$VISN == "17",],
aes(fill = (TotalFemale/TotalMale)*100), color = NA) +
geom_sf(data = market[market$VISN == "17",],
fill = NA, color = "#ffffff", size = .5) +
scico::scale_fill_scico(labels = scales::comma,
palette = "tokyo") +
labs(caption = "Source: 2017 ACS 5-year Estimates \nGeography: Markets in VISN 17",
fill = "Ratio") +
ggtitle("Sex ratio of Veterans with VA Health Coverage:",
subtitle = "calculated as number of Female Veterans per 100 \nMale Veterans at the county-level") +
theme_va(map = TRUE, grid_col = "transparent")


Copyright (c) 1963 Sid Sackson
This game is played on the following board:
|
| An example
Black's turn. Black has 4 reserve stones, White has 2. Black position is better, he has more reserve stones than white. He can play a reserve stone at b6 taking that stack. White can attack f2 by moving g3-g2 (f2 cannot move to g2). But black can reply then with b6-f6 protecting his f2 stack. |
An hexagonal variant of Focus is Crossfire with the following extra rule: [author words] "the capacity of a cell equals the number of adjacent cells!" Corners will hold at most a stack of two, sides at most three or five, and cells of the inner area at most six. This gives rise to a strategy not possible in Focus: moving big stacks onto 'low-capacity' cells, corners in particular. Thus considerable numbers of prisoners can be made and reserves created on fixed target cells. In Focus for instance one can never make prisoners, nor create reserves, by moving a stack onto a vacant square. In Crossfire, moving a stack of five onto a vacant corner renders three men.


Booking.com Trademark Case Taken Up by High Court
by TIM RYANWASHINGTON (CN) – The Supreme Court agreed Friday to hear a case that will decide whether companies with “.com” or other domains in their names can receive federal trademark protection, even if their names would otherwise be generic.
The case out of the Fourth Circuit concerns hotel site Booking.com, which filed suit in Virginia after the Patent and Trademark Office refused to register trademarks that included the company’s name.
Under the PTO’s logic, the term “booking” is a generic term that is not eligible for trademark protection under federal law and adding the domain “.com” does nothing to change that fact. A federal judge sided with Booking.com, and the Fourth Circuit agreed.
In asking the high court to take up the case, the Patent and Trademark Office argued allowing companies to trademark terms that would otherwise be generic by adding a domain would go against an 1888 decision that held firms could not receive trademark protection simply by adding the word “company” to a generic term.
It also argued the decision goes against the longstanding principle that companies cannot make generic terms eligible for trademarks just by becoming so popular that people begin associating the term with their service.
Booking.com argues the addition of the domain fundamentally changes the meaning of the word and that its trademark could not possibly be confused with a generic term meant to refer to all hotel reservation websites.
Booking.com is represented by Jonathan Moskin, an attorney at the New York firm Foley & Lardner. Neither Moskin nor a spokesperson for Booking.com immediately returned requests for comment.
Per its custom, the Supreme Court did not issue any comment in taking up the case.
Copyright © 2019 courthousenews.com


Presented by: Laura Mueller-Soppart Published on May 17, 2021 "No business which depends for existence on paying less than living wages to its workers has any right to continue in this country." - President Franklin D Roosevelt, 1933
Fifty five years ago, when Dr. King marched on Washington for jobs and freedom, the federal minimum wage was $1.25 an hour.
In today’s dollars, that guaranteed base wage would be $9.54 an hour.
But the federal minimum wage today is just $7.25 an hour.
The minimum wage is stuck in time.
Explore each state's minimum wage adjustments in 2020 dollars.
According to the Economic Policy Institute, the minimum wage adjusted for the increase in productivity since 1950 would come out to roughly $21 per hour in 2020 – triple what it is now at $7.25 per hour.
While productivity has soared since 1938, wages and salaries in the U.S. have been relatively stagnant since 1970, when wages truly began failing to keep up with productivity.Explore how productivity has changed the nature of the true minimum wage. Economic Policy Institute


To get information about how to vote tactically in the (likely) upcoming UK General Election, enter your postcode below.


{% extends "layout.html" %} {% block meta %} {{ super() }} {% endblock %} {% block head %} {{ super() }} {% endblock %} {% block content %}
This page documents the full query language supported by the the Stanford Cable TV News Analyzer. Prior to reading this documentation, we recommend that you read the getting started tutorial.
All queries compute the total time of video segments in the dataset that match the query's filters. Screen time queries are broken in to several parts. A basic query consists of filters separated by "AND"s.
For example, the following query computes the screen time of Kamala Harris on CNN: Likewise, "OR" is also supported. The following query counts the total video time on CNN or MSNBC. Queries can combine "AND" and "OR" using parentheses in order to construct more complex queries. If no parentheses are specified, AND precedes OR. Putting the examples above together, the following query computes the screen time of Kamala Harris on CNN or MSNBC: If the query is left blank, then no filters are applied and all of the data is counted.Filters on Entire Videos | ||
| {{ search_keys.channel }} | description | name of the channel |
| values | CNN, FOX, MSNBC | |
| default | all | |
| example | {{ search_keys.channel }}="CNN" |
|
| {{ search_keys.show }} | description | name of the show |
| values | list of shows | |
| default | all | |
| example | {{ search_keys.show }}="CNN Newsroom" |
|
| {{ search_keys.hour }} | description | Range (inclusive) of hours in 24h format, in US eastern time. (UTC-5:00 in standard time and UTC-4:00 during daylight saving time). |
| values | 0-23 | |
| default | 0-23 | |
| example |
{{ search_keys.hour }}="10"
|
|
| {{ search_keys.day_of_week }} | description | range (inclusive) or a day in the week |
| values | mon, tue, wed, thu, fri, sat, sun | |
| default | mon-sun | |
| example |
{{ search_keys.day_of_week }}="mon"
|
|
Filters on Detected Faces | ||
|
{{ search_keys.face_name }} |
description | face of the person with the specified name is on screen |
| values |
name (See the people page for a complete list of people.) |
|
| default | n/a | |
| example |
{{ search_keys.face_name }}="Kamala Harris"
|
|
|
{{ search_keys.face_tag }} |
description | face with the specified tag(s) is on screen. Multiple tags can be specified with commas. |
| values |
{% if not hide_gender %}(fe){{ global_tags.male }},{% endif %} (non_){{ global_tags.host }}
{% if not hide_person_tags %}, tag on person{% endif %}
(See the tags page for a complete list of tags.) |
|
| default | n/a | |
| example |
{{ search_keys.face_tag }}="{{ global_tags.host }}"
{% if not hide_gender %}
|
|
| {{ search_keys.face_count }} | description | number of faces on screen |
| values | 1 or more | |
| default | n/a | |
| example | {{ search_keys.face_count }}=2 |
|
Filters on Closed Caption Transcripts | ||
| {{ search_keys.text }} | description | segments where the specified text pattern appears in the captions. |
| values | keywords or phrases. Use | for "or"(See Text Filter Syntax for more details of valid text patterns.) |
|
| default | n/a | |
| example |
{{ search_keys.text }}="affordable care act" {{ search_keys.text }}="affordable care act | obamacare | obama care" |
|
| {{ search_keys.text_window }} | description | Specifies how much to dilate the time interval associated with text filter matches. If the text window is 0, then text filter selects exactly the video segment when a word or phase is being said. By increasing the "window" to larger than 0, it is possible to design queries where segments matching one filter need only be within a certain amount of time of a segment matching a text filter. For example, if the word "obamacare" is said and the text window is 1 second, then each instance of "obamacare" is converted to a 1 second interval centered around the time when "obamacare" is said. Note that for long windows, overlapping intervals are merged. |
| values | keyword or phrase | |
| default | {{ default_text_window }} | |
| example | {{ search_keys.text_window }}=10 (treat each text match as 10 seconds) |
Sometimes a topic can be defined with multiple related or synonymous
words/phrases. For example, the "European Union" can be also be referred to
as the EU or E.U. in the captions. When this is the case, use the "|"
character to delimit multiple words and phrases. For example,
{{ search_keys.text}}="European Union | EU | E.U."
will search for video segments where any of these three n-grams appear in the captions.
This can be repeated for an arbitrary
number of words and phrases.
You can also search for instances where words appear nearby using "&" (and).
For example, to find instances of "United" near "Airlines", use
{{ search_keys.text}}="United & Airlines"
.
This can be chained; for example,
{{ search_keys.text}}="United & Airlines & 737 MAX"
.
Not ("\") works similarly; for instance,
{{ search_keys.text}}="United \ States \ Kingdom"
finds instances
of "United" that are not near "States" or not near "Kingdom".
By default, the threshold for nearness is 15 seconds. This can be modified
using the following ("::") syntax: {{ search_keys.text}}="United \ States :: 60"
,
which sets the window for "\" to 60 seconds. Use "//" to change the window
policy to tokens; for example,
{{ search_keys.text}}="United \ States // 100"
finds "United"
with no instances of "States" within 100 tokens.
Text query "&" and "|" operators behave differently from AND and OR. The
latter operate on intervals, while the former give back intervals which the
latter operate on. The query {{ search_keys.text}}="United & Airlines"
finds
separate intervals of "United" and intervals of "Airlines",
which are nearby. These intervals are of duration "textwindow"
(by default, {{ default_text_window }} seconds). In contrast,
{{ search_keys.text}}="United" AND {{ search_keys.text}}="Airlines"
finds intervals of "United" and "Airlines", and returns their exact time overlap.
The text grammar also supports basic composition of "&", "|", and "\".
For example, {{ search_keys.text}}="United \ States \ Kingdom"
is equivalent to {{ search_keys.text}}="United \ (States | Kingdom)"
,
expressing instances of "United" that are not near either "States" or "Kingdom".
Parentheses are necessary to separate clauses and operators may not be
mixed in a clause. The full details of the text query grammar can be found
here.
Words can be used in many inflected forms. The simplest case is
when words are singular or plural. To search for all inflected forms of
a word without specifying them manually, surround the word with
[...]
brackets. For example,
{{ search_keys.text}}="[truck]"
will find instances of
"truck", "trucks", and "trucking". If multiple words are surrounded by
[...]
, then inflections will be found for any of the words in the brackets.
By default, {{ search_keys.text}}
will precisely find
the intervals of time during which a word or phrase is spoken. This means that
each mention will likely contribute only a small fraction of a second of screen time to a query result.
Sometimes it is useful for a caption-text query to match a wider region of time around the utterance
of a word, for example if an query seeks examples where a person is on-screen within a specific amount of time
of a word being stated. The
{{ search_keys.text_window }}
parameter defines how much a time of time is dilated around a
caption-text match. See the "Supported Query Filters" section for details.
Instead of computing screen time estimates in absolute time units (e.g., in minutes or hours), it can be useful to present query results as a proportion of the screen time of another query. The query language supports normalization of one query's computed time by another using NORMALIZE:
For example, the following query computes the fraction of the overall dataset that is from CNN: The following query computes the fraction of time on CNN that a news presenter is on screen:


LOOKBIB
Section: User Commands (1)Updated: 27 June 2001
Index Return to Main Contents
NAME
lookbib - search bibliographic databasesSYNOPSIS
lookbib [ -v ] [ -istring ] [ -tn ] filename...It is possible to have whitespace between a command line option and its parameter.
DESCRIPTION
lookbib prints a prompt on the standard error (unless the standard input is not a terminal), reads from the standard input a line containing a set of keywords, searches the bibliographic databases filename... for references containing those keywords, prints any references found on the standard output, and repeats this process until the end of input. For each database filename to be searched, if an index filename.i created by indxbib(1) exists, then it will be searched instead; each index can cover multiple databases.OPTIONS
- -v
- Print the version number.
- -istring
- When searching files for which no index exists, ignore the contents of fields whose names are in string.
- -tn
- Only require the first n characters of keys to be given. Initially n is 6.
FILES
- filename.i
- Index files.
SEE ALSO
refer(1), lkbib(1), indxbib(1)
Index
This document was created by man2html, using the manual pages.
Time: 05:29:05 GMT, December 24, 2015


Reading Question 4.
If a person has a miscarriage in a state with a total abortion ban, does the burden of proof fall to this person to verify they miscarried and did not have an abortion? https://www.bbc.com/news/world-us-canada-59214544 4
If a person has a miscarriage in a state with a total abortion ban, does the burden of proof fall to this person to verify they miscarried and did not have an abortion? https://www.bbc.com/news/world-us-canada-59214544 4


@Target([AnnotationTarget.FIELD]) class Unmapped
Field annotation for mocks and @RealObjects that tells mockspresso NOT to map the value contained therein.
Unmapped()
Field annotation for mocks and @RealObjects that tells mockspresso NOT to map the value contained therein. |


Core Concepts
Accessibility
Creating sites that everyone can use
Agile workflows
Being able to keep up with ever changing web technologies
Agile Project Management
Test-driven development
Creating tests at the same time you create features
Foundations of Programming: Test-Driven Development
Web development is more than just learning about specific tools or frameworks
Business and leadership skills
Non-technical skills
Develop communication skills
Listen and try alternatives
Be able to present and defend your ideas
Important to gain different perspectives


Photogram is a source code package using the Ionic Framework, TypeScript and Sass to develop its front end, with the Open Source Parse Server to be its backend with NodeJS, Express and MongoDB.
Inspired by Instagram, and has many amazing features, such as sharing photos, friends, facebook login, real-time chat, and more.
It is suitable for developers and startups who have the minimum knowledge of Javascript, Ionic Framework and NodeJS.


Snow White and the Seven Dwarfs
The adaptation of the famous German fairy tale of the Brothers Grimm about Snow White, who lived in the castle with an evil stepmother-queen, who was very proud of her beauty. Having learned from the magic mirror, that Snow White has become the most beautiful girl in the world, the envier decided to destroy the pretty stepdaughter. But the poor girl did not die in the forest, but on the contrary, met real friends there — funny seven gnomes: Professor, Vorchun, Jovial, Modest, Chihoni, Sonya, and Silent ..


Five step Skin Care Routine but Your Skin Still Doesn't Care?
Well we have a new article highlighting common skin care mistakes and what to do right. Our experts have compiled a, surprisingly short, list of products that you cannot go wrong with.
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For hydrated and glowing skin, we have compiled products that will have your skin looking and feeling healthier.
Scarring can be difficult to remove, with the combination of Lactic and other mentioned products, clearing your acne and eliminating scarring can be an easier journey.


{% extends 'base.html' %} {% block content %} How Awesome are your projects?Discover today at projected awards {% for project in projects %} {{project.title}} {{project.description}} {% endfor %} {% endblock %}
{{project.description}}


DriveSafe
An application on your phone that can reduce your insurance costs by upto 50%.
Check below to see compatibility with your insurance provider.
How It Works Get The Android App View Source Code Contact Us
The application communicates with your vehicle through an OBD-II scanner,inbuilt maps and a dash-cam.
It computes the data over the week and provides a detailed driving summary to you and your insurance provider.
You receive or lose points based on this calculated data.
Whenever you renew your insurance, you can use these points and save money!!
You don't need to pay to use the application.
However, you need to purchase a compatible OBD-II scanner and a dash-cam yourself or through your insurance provider.
However, this is a small investment when compared to the amount of money you can save at the end.
There is no catch.
This application promotes you to adopt better driving practices by earning you points which you can use to pay for insurance.
Moreover, it also informs the emergency services if you are involved in an accident, which can save your life.
This application also temporarily hides mobile notifications when you are driving, thus reducing distractions.
Via differential pricing, your insurance company may charge you differently based on your profile score.
This means that the people next door, even though they might be of the same age as yours, may end up paying more because of their driving habits.
Thousands of people die every year in road accidents.
Our initiative originated at YorkUHacks 2018 and is a step towards making Canadian roads safer and decreasing distractions while driving.
It's a win-win situation for you as you save money, for insurance providers as they save money, and us because we are more safer on the roads.
The application alerts you audibly if you have your parking brake on, or if you're running low on fuel.
This causes lesser distraction as compared to looking at the speedometer icons.
The application informs you if your car isn't running right, or if a service is due.
With Google Maps integrated into the app, you can use all the features Google Maps has to offer.
The application automatically contacts the emergency services if a collision is detected which can help save someone's life.
Notifications are disabled temporarily so that the user is not distracted.However, urgent notifications are audibly spoken.
You save money on fuel,repair and insurance costs if you drive safely by using this application.
Is it compulsory to use this app?
If you want to pay less for insurance, it is necessary that you drive with this application opened.
However, it's not required by law to use this app. The usage is totally optional.
Are there any hidden charges?
No, this application is completely free to use. However, your insurance provider may charge a service fee or a one-time fee.
Do I have to buy a membership?
No, there is no membership for this application.
What if I forget my phone at my house/someplace else?
If you forget your phone, you won't get any points for that driving session.You would also not lose any points as there is no distraction while driving.


Anshita Vyas
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THE SPARKS FOUNDATION is working to bring parity in education, making sure that children have equal opportunity at success, irrespective of the financial background with the most marginalised women and girls to alleviate poverty and reduce social injustice through comprehensive programmes in education . We are responding to the second wave of COVID – 19 by setting up temporary COVID Care Facilities and supporting children with PPE kits.
Welcome To The Sparks Foundation
"The smallest act of kindness is worth more than the grandest intention."
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