Southampton target Preston’s Joe Savage for sporting director role
Hearts are also taking an interest after Savage's impressive work at Deepdale
by Nick MashiterPreston's head of recruitment Joe Savage is on Southampton's radar as they search for a new sporting director.
Hearts are also taking an interest after Savage's impressive work at Deepdale.
North End have one of the smallest budgets in the Sky Bet Championship but are second in the table, two points behind leaders West Brom.
Savage joined Preston in 2017, following boss Alex Neil to Deepdale after the pair worked together at Norwich and Hamilton.
He was chief scout at Carrow Road for two years having been director of recruitment and analysis at Hamilton.
Boss Neil has also been in demand with Preston rejecting Stoke's request to speak to the Scot at the weekend.
They reported the Potters to the EFL for an illegal approach before withdrawing the complaint.
Southampton are looking for a new sporting director to replace Ross Wilson after he moved to Rangers last month.
The 36-year-old helped bring boss Ralph Hasenhuttl to St Mary's last December following the departure of Mark Hughes.
The Saints are 18th in the Premier League having suffered a record 9-0 defeat to Leicester two weeks ago.
Managerless Hearts, who sacked Craig Levein last week, are looking to create a sole post for a sporting director.
Owner Ann Budge is reorganising the structure of the club, who sit second bottom of the Ladbrokes Premiership, with Levein having also acted as director of football.


Heya,
We are pleased to announce the release of the %(artifact_name)s, version %(release_version)s
%(artifact_description)s.
For questions or comments around this plugin, feel free to use elasticsearch mailing list!
Enjoy,
- The Elastic team


Subdomain information can be had from various OSINT resources on the web (google, shodan, censys, and so much more). Some tools will integrate these features as well.
See tools section for DNS brute forcing


Hello! My name is Lucas Hawk, and I recently graduated from Allegheny College with a B.S in Computer Science and Mathematics, and I completed a thesis in each subject. I am most comfortable working with Java, although I have worked with Python, C#, and some other languages as well. A couple areas of computer science which particularly interest me include multi-agent systems and artificial intelligence.
Outside of programming, I enjoy several sports - particularly Ultimate Frisbee. I have played for about 6 years, and I served as captain of Allegheny College's ultimate frisbee team in 2016. I also love golfing, camping, and most things involving the outdoors. I am an avid fan of all Pittsburgh sports, although the Penguins specifically hold a special place in my heart. If you wish to contact me, please refer to my resume!


This example demonstrates enumerating through a calendar and performing calendar math.
Calendars use local time zone information, which includes periodic adjustments for things like Daylight Saving Time and leap years. Enumerating where necessary helps avoid making assumptions about the duration of various calendar periods.
Calendar math may produce different results depending on the local time of the system. Code which uses the Calendar class should therefore be tested across different time zones and date spans over which it might be used.


THE LITTLE VAMPIRE (New Line) Starring: Jonathan Lipnicki, Rollo Weeks, Richard E. Grant, Jim Carter, Alice Krige, Pamela Gidley, Tommy Hinkley, John Wood. Screenplay: Karey Kirkpatrick and Larry Wilson, based on the series of books by Angela Sommer-Bodenburg. Producer: Richard Claus. Director: Uli Edel. MPAA Rating: PG (adult themes, mild profanity, violence) Running Time: 95 minutes. Reviewed by Scott Renshaw.
THE LITTLE VAMPIRE falls squarely in the center of the "family film" continuum -- a place few family films tend to occupy. On one end, we find those films that appeal to viewers of all ages with imagination and a lack of pandering (TOY STORY, E.T., BEAUTY AND THE BEAST). On the other end, we find those films that appeal either to marketing concerns (POKEMON, MIGHTY MORPHIN POWER RANGERS) or toilet humor (most live-action Disney films of the last decade). True mediocrity in family filmmaking is quite a rarity, which makes THE LITTLE VAMPIRE an intriguing pop culture curiosity: It's a simple and whimsical tale that keeps flirting with inappropriateness.
One-time JERRY MAGUIRE moppet Jonathan Lipnicki stars as Tony Thompson, an 8-year-old dragged by his parents (Tommy Hinkley and Pamela Gidley) from San Diego to Scotland for his dad's assignment designing a new golf course. As though bullying by his xenophobic schoolmates and being a Chargers fan were not horrifying enough, Tony discovers that his new hometown is filthy with vampires. Fortunately, they're a kinder, gentler vampire family that prefers to dine on bovine blood, and actually wishes to be rid of its curse. Among them is Rudolph (Rollo Weeks), a 309-year-old in the form of a 9-year-old who becomes Tony's pal. He also enlists Tony's help to find a lost artifact that can help the vampires regain their humanity -- provided nasty vampire-hunter Rookery (Jim Carter) doesn't find it first and use it to destroy them.
Based on a popular series of children's books, THE LITTLE VAMPIRE comes complete with many of the standard set-ups of the family classics. Like the protagonists in E.T., THE IRON GIANT and A LITTLE PRINCESS, Tony is an isolated only child looking for a friend who finds that friend in an unexpected place. There are the expected scenes of Tony and his new vampire buddy getting back at his tormentors, as well as plenty of shots of the spiky-haired Lipnicki (who hasn't quite outgrown his cuteness yet) yelling "Wow!" and/or "Cool!" while being borne aloft by Rudolph. Jim Carter, in his Tex Cobb-circa-RAISING ARIZONA wardrobe, makes for a good nasty-comic villain, and the script (co-written by CHICKEN RUN and JAMES AND THE GIANT PEACH scribe Karey Kirkpatrick) includes at least a few clever lines like, "It's not easy for a father to hear, but your son is a blood-sucking fiend." Much of it is silly, but there's also a dark side that many of the better children's tales have always had.
There's also a perplexing dose of questionable content. The entire concept of vampirism could be troubling enough for some children, but THE LITTLE VAMPIRE adds to it a strange sub-plot involving Rudolph's younger sister Anna (Anna Popplewell) and her crush on Tony.  In fact, in one scene she sits on Tony's bed and -- in her best Lauren Bacall come-hither tones -- asks Tony, "You know how to whistle, don't you?" Though it's likely to go right over the heads of the very young, there's something icky about using a lascivious pre-pubescent as a punch line -- even ickier than the giant pasture patty dropped from the sky by a flying vampire cow during the film's "Far Side"-meets-John Hughes climax.
It's not terribly surprising to find THE LITTLE VAMPIRE straying from the kid-friendly straight-and-narrow, considering it was directed by Uli Edel, the man behind such innocent entertainments as LAST EXIT TO BROOKLYN and Madonna's 1993 sex stinker BODY OF EVIDENCE. Edel's pacing is fairly sluggish for a children's film, and the rest of it is a bit too bland for parents to get excited about. Yet there's still some fun adventure here, and it's not all designed to get its viewers to buy action figures.  And that, unfortunately, is a sad statement on "family" filmmaking in 2000: Simple mediocrity is starting to look pretty good.
On the Renshaw scale of 0 to 10 bat boys: 5.
Visit Scott Renshaw's Screening Room http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the Screening Room for details, or reply to this message with subject "Subscribe".
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


Your Orders
| ID | Small Base | Medium Base | Large Base | X-Large Base | Cheese Topping | Mushrooms Topping | Ham Topping | Anchovies Topping | Chips Extra | Garlic Bread Extra | Address Line 1 | Postcode | Total Price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LC3issglZC2sCZntCsE | Selected | Selected | Selected | Selected | Selected | Selected | 10.2 |


Complex Analytic Landscapes
f(z) = p(z)/q(z) =
p(z) = z-1
q(z) = z^3+1-i
You can select functions of the following list:
Non-rational functions are approximated through rational functions
Author: Aaron Montag. User defined input is parsed by
Algebrite.


For those who are still unsure, an HIV-positive person can achieve undetectable levels after undergoing antiretroviral therapy (ART). A level of a person's HIV viral load is what causes them to be more or less likely to transmit the disease. An undetectable viral load reduces the likelihood of transmission by 96 percent. Once a person achieves an undetectable status, it is possible to remain at this level provided that the person is compliant with their ART medication.
 A sample of blood is drawn and sent to a lab for testing. Results are expressed as the number of copies of the virus per milliliter of blood. Each virus is called a "copy" because HIV reproduces by making copies of itself (replicating). The viral load test gives a more accurate picture than a CD4+ cell count of what the virus is doing in your body at the moment. (The CD4+ test measures the effect HIV is having on your immune system.) The viral load test is used to help decide when to start treatment with medicines (antiretroviral therapy) and when to change antiretroviral medicines.
CD4 counts are reported as the number of cells in a cubic millimeter of blood. A normal CD4 count is from 500 to 1,500 cells per cubic millimeter of blood. It is more important to pay attention to the pattern of results than to any one test result. In general, HIV disease is progressing if the CD4 count is going down. This means the immune system is getting weaker and you are more likely to get sick. In some people, CD4 counts can drop dramatically, even going down to zero. The test does not always correspond with how well you are feeling. For example, some people can have high CD4 counts and do poorly. Others can have low CD4 counts and have few complications. If your CD4 count goes down over several months, your doctor may recommend: Beginning or changing antiretroviral therapy. Starting preventive treatment for opportunistic infections.


|
AceTime
2.3.0
Date and time classes for Arduino that support timezones from the TZ Database.
|
Representation of a given time zone, implemented as an array of ZoneEra records. More...
#include <ZoneInfoMid.h>
Public Attributes | |
| const char *const | name |
| Full name of zone (e.g. More... | |
| uint32_t const | zoneId |
| Unique, stable ID of the zone name, created from a hash of the name. More... | |
| const ZC *const | zoneContext |
| ZoneContext metadata. | |
| uint8_t const | numEras |
| Number of ZoneEra entries. More... | |
| const ZoneEra< S > *const | eras |
A const ZoneEras* pointer to numEras ZoneEra entries in increasing order of UNTIL time. | |
| const ZoneInfo *const | targetInfo |
| If Link, points to the target zone info. More... | |
Representation of a given time zone, implemented as an array of ZoneEra records.
Definition at line 287 of file ZoneInfoMid.h.
| const char* const ace_time::zoneinfomid::ZoneInfo< S, ZC >::name |
| uint8_t const ace_time::zoneinfomid::ZoneInfo< S, ZC >::numEras |
Number of ZoneEra entries.
If this Zone is a actually a Link to a target Zone, the ZoneEra and numEras of the target Zone is placed here, and the targetInfo pointer below is set to the target ZoneInfo. This allows a Link entry to be self-contained, acting like any other Zone entry, which simplifies some of the code the ZoneProcessor because it does not need to traverse the link to find the required information. In essence, a Link is a "hard link" to a Zone.
An alternative implementation is to set numEras to 0 for a Link, and traverse the targetInfo to find the required numEras and eras. This led to more complicated code.
Definition at line 316 of file ZoneInfoMid.h.
| const ZoneInfo* const ace_time::zoneinfomid::ZoneInfo< S, ZC >::targetInfo |
If Link, points to the target zone info.
If Zone, nullptr.
Definition at line 325 of file ZoneInfoMid.h.
| uint32_t const ace_time::zoneinfomid::ZoneInfo< S, ZC >::zoneId |
Unique, stable ID of the zone name, created from a hash of the name.
This ID will never change once assigned. This can be used for presistence and serialization.
Definition at line 296 of file ZoneInfoMid.h.


Hamilton City Council receives White Ribbon Accreditation
by Hamilton City CouncilHamilton City Council has formalised its commitment to preventing family violence in New Zealand.
Last month, the Council was awarded White Ribbon Accreditation to acknowledge its continued support of the White Ribbon kaupapa. To gain the business accreditation, the Council must have policies in place, trained staff and support proactive violence prevention.
Chief Executive Richard Briggs says this is great recognition of the Council’s ongoing commitment to the campaign.
“We’ve been supporting White Ribbon projects and have had support in place for our people for years. This is another great step up in our commitment as an employer and as an important part of our community.”
Along with the accreditation, the Council has two White Ribbon Ambassadors whose role is to influence and support the White Ribbon kaupapa.
“We need strong men who are willing to start a tough conversation about how we have respectful relationships with women and our behaviour towards them,” says Mr Briggs.
 Whether you’re at work for 20 or 40 hours per week, during that time you should feel safe and supported.”
White Ribbon Day (25 November) is the international day when people wear a white ribbon to show they won’t tolerate or condone violence towards women.
On Friday 29 November, a White Ribbon hikoi (walk) will begin at 11am from Meteor Theatre and finish in Garden Place where there will be live entertainment until 1pm.
This year’s theme is to challenge the Unspoken Rules. Unspoken Rules are the expectations boys and young men inherit from society, based on outdated ideas of what a man is, how he acts, and how he should express himself.
Hamilton City Council is now New Zealand’s largest council to receive White Ribbon Accreditation. Upper Hutt City Council, Carterton District Council and Napier City Council are also White Ribbon accredited.
ends


This word generator uses a Markov chain to create words that look weird and random but that are still largely pronounceable. View the GitHub project here or play with the settings below.
The corpus is a sample of real words that's used as a starting point to generate new words. Generated words will be somewhat similar to the original ones from the corpus.


| 66 | (A) | The brode sayle at a brayde brystis in two |
| 66 | (C) | The brode sayles at a brayde brekyn a-twynne |
| 67 | (D) | þe brode sayle at a Brayd brast yn sundur |
| 64 | (E) | the brod seyl at a brayd . brestyth in tweyne |
| (Ex) | ---- no text ---- | |
| 66 | (L) | þe brode sail at o brayd to-bresteþ a-twynne |
| 61 | (P) | þe brade sayle on a brayde brosten in two |
| 66 | (U) | The brode saile at a braid / in sundre brast a-twey |
| (V) | ---- no text ---- |


This HP gaming PC is down to just $470 for Black Friday — Fortnite anyone?
by Cale HuntFortnite, the worldwide battle royale sensation, doesn't take a powerhouse desktop rig to run at a high framerate, but pre-built desktop PCs usually come at a premium price due to the convenience. Thanks to Black Friday, though, this HP Pavilion gaming desktop is seriously discounted, and it has the guts to deliver a solid 1080p gaming experience. If you're looking to slay in Fortnite or other popular games, this is a great PC pick.
Pre-built desktop gaming PC
HP Pavilion 690
Impressive hardware for the price
The HP Pavilion desktop PC includes an AMD Ryzen 5 2400G processor (CPU), AMD Radeon RX 580 dedicated graphics card (GPU) with 8GB of RAM, and a 1TB hard-disk drive (HDD) for plenty of storage.
$300 off for Black Friday
The HP Pavilion desktop gaming PC has a slick brushed aluminum finish and green highlights, with a bunch of ports, including USB-C, two USB-A 3.1, 3.5mm audio, and an SD card reader, on the front for easy access. It also has an optical drive so that older games on CD-ROM can still be enjoyed. On the back, you get another two USB-A 3.0, four USB-A 3.1, HDMI, and three DisplayPort. You should have no issues connecting your favorite PC accessories.
Don't miss out on today's newest and best Black Friday deals
Amazon's Fire Tablet is the one Black Friday purchase you NEED to make!
Recommended system specs for Fortnite include a Core i5 2.8GHz CPU, 8GB of RAM, and a GPU with at least 2GB of VRAM, and anything better is going to allow you to crank up the game settings for a better experience. The Pavilion's AMD Ryzen 5 2400G CPU hits 3.60GHz and has four cores for excellent performance, it hits the 8GB of RAM recommendation (you can upgrade up to 32GB in the future), and the AMD Radeon RX 580 GPU with 4GB of VRAM is more than enough.
If you're looking for a great gift for an aspiring Fortnite champion who's been suffering on integrated graphics, this is a solid PC at a great price for Black Friday. If portability is more what you're looking for, be sure to check out our roundup of the best deals on gaming laptops, or have a look at more pre-built gaming PCs also discounted.
And be sure to check out our enormous (and growing) collection of everything on sale for Black Friday for many more savings on all things tech and beyond.
See all the best Black Friday deals!
Pre-built desktop gaming PC
HP Pavilion 690
Impressive hardware for the price
The HP Pavilion desktop PC includes an AMD Ryzen 5 2400G processor (CPU), AMD Radeon RX 580 dedicated graphics card (GPU) with 8GB of RAM, and a 1TB hard-disk drive (HDD) for plenty of storage.
$300 off for Black Friday
We may earn a commission for purchases using our links. Learn more.


Research compendium (code and data) used for the species distribution modelling analyses in the following journal publication:
Villaverde T, González-Moreno P, Rodríguez-Sánchez F & Escudero M. (2017) Niche shifts after long-distance dispersal events in bipolar sedges (Carex, Cyperaceae). American Journal of Botany, in press.
Compendium DOI: 10.5281/zenodo.896787
CITATION: Francisco Rodriguez-Sanchez (2017) Research compendium for “Niche shifts after long-distance dispersal events in bipolar sedges (Carex, Cyperaceae)” (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.896787
In order to run the analyses you will need to install the package first:
devtools::install_github("Pakillo/Carex.bipolar")
There is a makefile.R
that runs each analysis in the appropriate order. Note these analyses use many GIS layers stored locally. To be able to reproduce the analyses you will then need to download those files and adapt the code to your local path.
A more user-friendly version of the functions and analyses can be browsed at https://pakillo.github.io/Carex.bipolar/index.html.


Never leave your house again.
We’ve got you covered. We’ll send a mountain of waffles to your door every single morning. They won’t look like this, but they’ll probably be pretty good.
It’s okay. Be lazy.
Why waste time moving your lethargic body to do menial tasks like buying groceries or making food?
They’re pretty good.
We’re not out to win a waffle award or anything, but we think our waffles are pretty good. Mediocre waffles are better than no waffles.
That’s a lot of waffles.
Portions are kind of our thing.
They last forever.
These things are so chock-full of preservatives, they’ll never go bad. You won’t find any natural ingredients in these waffles.


| DOSE: T+ 0:00 |
0.5 mg |
oral |
Pharms - Phenazepam |
(liquid) |
| T+ 1:00 |
1 mg |
oral |
Pharms - Phenazepam |
(liquid) |
I felt I had to write this report out of duty to the drugs community and cause I could not find a single decent report online about it. Now I'm all for recreational drug use and advocate the legality of weed, LSD, Shrooms, XTC etc. However, the following substance has no real use to those interested in drug use as a means of spiritual enlightenment or as a means of having a good time. It's anti-anxiety properties are also heavily outweighed by it's negative features.
I recently came into possession of the Soviet developed benzo called 'Phenazepam' in the form of 4x5ml bottles of clear liquid (1ml=1mg). After doing some research, I decided to go ahead and dose 0.5ml/0.5mg. Within 15 mins I felt the familiar feel of a benzo going to work on my GABA receptors (subtle relexation, less agitation, happy doing nothing). After one hour I felt that another 1mg was in order to gain a fuller experience and understanding of this substance.
What was clear was that it relieves anxiety, no doubt about that, and that's good cause I suffer from mild social anxiety. However, it definetely lacks some of the other qualities and characteristics that make benzos so appealing in the first place. Mainly, there was a lack of euphoria. To clarify what I mean, this isn't the sort of euphoria one gets from MDMA or even opiates. It refers more to that feeling you get where you've been feeling anxiety for a prolonged period of time and then you take maybe 20-30mg of diazepam.
Also, I feel that the more traditional benzos relieve my anxiety in such a way that I actually want to interact with people and go out to a bar or a party.  It also makes me grumpy with other people.  To this end phenazepam is not a very practical day-to-day drug to be used at work or in everyday life.
There are other major issues I have with this substance.
Increasing the dosage doesn't provide any euphoria, just amplifies the above negative effects.
Also, the 60 hour half-life raises some issues of going into benzo withdrawal with this substance. Cause it's so potent and lasts so long I'd imagine a tolerance can build up quite quickly if you take it a lot. And let's be honest, the amnesia from a drug like this can make you forget when you last dosed, so you might end up dosing quite a lot in one day!
All in all, a very large pair of thumbs down to Phenazepam with a 2/10 rating. Stay clear, the Russians picked a bummer here. No wonder communism fell if this the type of benzo they gave to their population!
[Erowid Note: Driving while intoxicated or tripping is dangerous and irresponsible because it endangers other people. Don't do it!]
PS I don't normally drive, but had to in an emergency the day after taking 1.5mg of Phenazepam. Not recommended in the slightest - feet were getting mixed up with the wrong pedals and ran a red light!  Peace!!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


I’m a product manager passionate about technology, equity, ethics, and how they intersect and am committed to taking on projects that make an impact.
//TODO: Change me!! Make me better!
Financial accounting (or financial accountancy) is the field of accounting concerned with the sum...
Lorem markdownum, dictis umbrosum dextrum, Lelegeia quamquam distantes pares ignisque quaerit ded...
Lorem markdownum aequalis strigis. Saetigeri iubeas, vultu huic alvum nondum de obside ut laniavi...
Cyanee nec pedicis positi. Esse et diem forte quoque et ieiunia vixque dixit negari ullis stamina...


Ireland set for Euros play-off with Slovakia as Wales seal qualification
Aaron Ramsey’s double led Giggs’ men past Hungary tonight and determined Ireland’s opponents next March.
by AFPIRELAND ARE SET to face Slovakia in the semi-finals of the Uefa Nations League play-offs next March as they seek to grab a place at Euro 2020, courtesy of Wales’ 2-0 win against Hungary tonight.
That tie will be away from home – in either Bratislava or Trnava – on 26 March next year, and is a one-off game in a bid to qualify for the final against either Bosnia or Northern Ireland on 31 March. Home advantage for that final will be determined by a draw on Friday.
The play-off line-up has been finalised by Wales’ qualification for the championships, sealed with an Aaron Ramsey brace in Cardiff tonight.
The Juventus midfielder struck early in both halves to ensure Ryan Giggs’s men secured second place in Group E behind World Cup finalists Croatia.
Wales reached the semi-finals of the European championships in 2016 and while matching that run next year will be a huge ask, there was a glimmer of what they are capable of with Ramsey restored alongside Gareth Bale.
Ramsey was making his first start of the qualifying campaign due to a series of injuries.
And he made an immediate impact by charging into the box to meet Bale’s inch-perfect cross to settle the home side’s nerves on 15 minutes.
Wales still needed a moment of magic at the other end of the field as an incredible double save from Wayne Hennessey denied Dominik Szoboszlai and Roland Sallai an equaliser as Hungary threatened before the break.
However, there was no way back for the visitors once Ramsey controlled Kieffer Moore’s knockdown to smash home from close range two minutes into the second half.
From then on Wales were rampant and could easily have added to their advantage as Bale curled a free-kick inches wide before Daniel James also saw a powerful effort drift just off target.
Only a fine save from Peter Gulacsi denied Ramsey his first international hat-trick 15 minutes from time.
But roared on by a vociferous home support, Wales rarely looked troubled as they produced arguably their best performance since Giggs took charge two years ago at the best possible time.
The former Manchester United winger has had his critics since succeeding Chris Coleman, but can now look forward to leading his country at a major international tournament, a feat he never achieved during a decorated playing career.
With reporting by Gavin Cooney


Hasina urges people to ignore 'disinformation'
by bdnews24.comPrime Minister Sheikh Hasina has urged the people of the country to be wary of 'disinformation' amid reports of a spike in prices of kitchen staples in the market.
"Sometimes, disinformation is spread to create confusion among the people. I would like to urge all of you to ignore those," Hasina said at an event marking the Armed Forces Day on Thursday.
Onion prices crossed Tk 100 per kg once India stopped exporting the commodity in September before skyrocketing to Tk 250 per kg when Cyclone Bulbul hit the country.
Prices began dropping again after the government issued warnings against price hikes warned against it while imports began streaming in.
Amid the crisis in the onion market, rumours of a scarcity of salt started doing the rounds on Monday as traders began raising the price of the product after panicked customer rushed to stores to stock up on it.
The situation worsened within a day prompting the government to dismiss talk of a shortage of salt in the market as just a rumour.
Addressing the 'rumours' surrounding the availability of onions and salts, Hasina said, "There's no onion! No salt...these are the rumours that confuse the people. I know that it is easy to become influenced. But we have to prevent it."


Our mission is to help you live a happy and healthy life by connecting you to the green spaces you need. From finding your next favorite dog park to discovering the best hiking spots in your area, Park Passport will take you there!
Our multi-page marketing website designer turned the theme chosen by your group into a fully responsive website. He ensured the user is well informed about our product in a visually pleasing way.
Our multi-page marketing website designer turned the theme chosen by your group into a fully responsive website. She ensured the user is well informed about our product in a visually pleasing way.
Created functional components and used events in application to add dynamic functionality to app. Ensured code was organized at the component level. Created UI composed of small reusable components, proper usage of useState, and useEffect hooks.
Created functional components and used events in application to add dynamic functionality to app. Ensured code was organized at the component level. Created UI composed of small reusable components, proper usage of useState, and useEffect hooks.
Responsible for deploying the React application and ensuring the static landing page is correctly incorporated with the app. Marcos implemented a state management system that makes sense for the project. The system is organized well and state is not lost as user goes through the application.
Responsible for building and deploying a Web API following the REST architectural pattern with code that is clean and organized using the Express Framework. Sucessfully deployed application with data persistence with a normalized data model that ensures data integrity and consistency.
Our leader who kept us going and made this application possible. With her on our team, there was no merge conflict that could stop us.


# -*- coding: utf-8 -*-
# Copyright 2020 PyePAL authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Core functions for PAL"""
from typing import List, Sequence, Tuple, Union
import numpy as np
from numba import jit
from .utils import (
dominance_check_jitted_2,
dominance_check_jitted_3,
is_pareto_efficient,
)
__all__: List[str] = []
[docs]def _get_uncertainty_region( # pylint:disable=invalid-name
mu: np.array, std: np.array, beta_sqrt: float
) -> Tuple[np.array, np.array]:
"""
Args:
mu (float): mean
std (float): standard deviation
beta_sqrt (float): scaling factor
Returns:
Tuple[float, float]: lower bound, upper bound
"""
low_lim, high_lim = mu - beta_sqrt * std, mu + beta_sqrt * std
return low_lim, high_lim
[docs]def _get_uncertainty_regions(
mus: np.array, stds: np.array, beta_sqrt: float
) -> Union[np.array, np.array]:
"""
Compute the lower and upper bound of the uncertainty region
for each dimension (=target)
Args:
mus (np.array): means
stds (np.array): standard deviations
beta_sqrt (float): scaling factors
Returns:
Union[np.array, np.array]: lower bounds, upper bounds
"""
low_lims, high_lims = [], []
for i in range(0, mus.shape[1]):
low_lim, high_lim = _get_uncertainty_region(mus[:, i], stds[:, i], beta_sqrt)
low_lims.append(low_lim.reshape(-1, 1))
high_lims.append(high_lim.reshape(-1, 1))
return np.hstack(low_lims), np.hstack(high_lims)
[docs]def _union(
lows: np.array, ups: np.array, new_lows: np.array, new_ups: np.array
) -> Union[np.array, np.array]:
"""Performing iterative intersection (eq. 6 in PAL paper) in all dimensions.
Args:
lows (np.array): lower bounds from previous iteration
ups (np.array): upper bounds from previous iteration
new_lows (np.array): lower bounds from current iteration
new_ups (np.array): upper bounds from current iteration
Returns:
Union[np.array, np.array]: lower bounds, upper bounds
"""
out_lows = []
out_ups = []
for i in range(0, lows.shape[1]):
low, up = _union_one_dim( # pylint:disable=invalid-name
lows[:, i], ups[:, i], new_lows[:, i], new_ups[:, i]
)
out_lows.append(low.reshape(-1, 1))
out_ups.append(up.reshape(-1, 1))
out_lows_array, out_ups_array = np.hstack(out_lows), np.hstack(out_ups)
return out_lows_array, out_ups_array
[docs]@jit(nopython=True)
def _union_one_dim(
lows: Sequence, ups: Sequence, new_lows: Sequence, new_ups: Sequence
) -> Tuple[np.array, np.array]:
"""Used to intersect the confidence regions, for eq. 6 of the PAL paper.
The iterative intersection ensures that all uncertainty regions
are non-increasing with t.
We do not check for the ordering in this function.
We really assume that the lower limits are the lower limits
and the upper limits are the upper limits.
All arrays must have the same length.
Args:
lows (Sequence): lower bounds from previous iteration
ups (Sequence): upper bounds from previous iteration
new_lows (Sequence): lower bounds from current iteration
new_ups (Sequence): upper bounds from current iteration
Returns:
Tuple[np.array, np.array]: array of lower limits, array of upper limits
"""
out_lows = []
out_ups = []
for i, low in enumerate(lows):
# In one dimension we can imagine the following cases where there
# is zero intersection
# 1) |--old range--| |--new range--|,
# i.e., lower new limit above old upper limit
# 2) |--new range--| |--old range--|,
# i.e., upper new limit below lower old limit
if (new_lows[i] >= ups[i]) or (new_ups[i] <= low):
out_lows.append(new_lows[i])
out_ups.append(new_ups[i])
# In other cases, we want to intersect the ranges, i.e.
# |---old range-|-|--new-range--| --> |-|
# i.e. we take the max of the lower limits and the min of the upper limits
else:
out_lows.append(max(low, new_lows[i]))
out_ups.append(min(ups[i], new_ups[i]))
return np.array(out_lows), np.array(out_ups)
[docs]def _pareto_classify( # pylint:disable=too-many-arguments, too-many-locals
pareto_optimal_0: np.array,
not_pareto_optimal_0: np.array,
unclassified_0: np.array,
rectangle_lows: np.array,
rectangle_ups: np.array,
epsilon: np.array,
) -> Tuple[np.array, np.array, np.array]:
"""Performs the classification part of the algorithm
(p. 4 of the PAL paper, see algorithm 1/2 of the epsilon-PAL paper)
One core concept is that once a point is classified,
it does no longer change the class.
When we do the comparison with +/- epsilon we always use the absolute values!
Otherwise, we get inconcistent results depending on the sign!
Args:
pareto_optimal_0 (np.array): boolean mask of points classified as Pareto optimal
not_pareto_optimal_0 (np.array): boolean mask of points
classified as non-Pareto optimal
unclassified_0 (np.array): boolean mask of unclassified points
rectangle_lows (np.array): lower uncertainty boundaries
rectangle_ups (np.array): upper uncertainty boundaries
epsilon (np.array): granularity parameter (one per dimension)
Returns:
Tuple[list, list, list]: binary encoded list of Pareto optimal,
non-Pareto optimal and unclassified points
"""
pareto_optimal_t = pareto_optimal_0.copy()
not_pareto_optimal_t = not_pareto_optimal_0.copy()
unclassified_t = unclassified_0.copy()
# This part is only relevant when we have points in set P
# Then we can use those points to discard points from p_pess (P \cup U)
if sum(pareto_optimal_0) > 0:
pareto_indices = np.where(pareto_optimal_0)[0]
pareto_pessimistic_lows = rectangle_lows[pareto_indices] # p_pess(P)
for i in range(0, len(unclassified_0)):
if unclassified_t[i] == 1:
# discard if any lower-bound epsilon dominates the upper bound
if dominance_check_jitted_2(
pareto_pessimistic_lows + np.abs(epsilon * pareto_pessimistic_lows),
rectangle_ups[i],
):
not_pareto_optimal_t[i] = True
unclassified_t[i] = False
pareto_unclassified_indices = np.where(pareto_optimal_0 | unclassified_t)[0]
pareto_unclassified_lows = rectangle_lows[pareto_unclassified_indices]
# assuming maximization
pareto_unclassified_pessimistic_mask = is_pareto_efficient(
-pareto_unclassified_lows
)
original_indices = pareto_unclassified_indices[pareto_unclassified_pessimistic_mask]
pareto_unclassified_pessimistic_points = pareto_unclassified_lows[
pareto_unclassified_pessimistic_mask
]
for i in range(0, len(unclassified_t)): # pylint:disable=consider-using-enumerate
# We can only discard points that are unclassified so far
# We cannot discard points that are part of p_pess(P \cup U)
if unclassified_t[i] and (i not in original_indices):
# discard if any lower-bound epsilon dominates the upper bound
if dominance_check_jitted_2(
epsilon * np.abs(pareto_unclassified_pessimistic_points)
+ pareto_unclassified_pessimistic_points,
rectangle_ups[i],
):
not_pareto_optimal_t[i] = True
unclassified_t[i] = False
# now, update the pareto set
# if there is no other point x' such that max(Rt(x')) >= min(Rt(x))
# move x to Pareto
unclassified_indices = np.where(unclassified_t | pareto_optimal_t)[0]
unclassified_ups = rectangle_ups[unclassified_indices]
index_map = {index: i for i, index in enumerate(unclassified_indices)}
# The index map helps us to mask the current point from the unclassified_ups list
for i in range(0, len(unclassified_t)): # pylint:disable=consider-using-enumerate
# again, we only care about unclassified points
if unclassified_t[i]:
# If there is no other point which up is epsilon dominating
# the low of the current point,
# the current point is epsilon-accurate Pareto optimal
if not dominance_check_jitted_3(
unclassified_ups,
rectangle_lows[i] + epsilon * np.abs(rectangle_lows[i]),
index_map[i],
):
pareto_optimal_t[i] = True
unclassified_t[i] = False
return pareto_optimal_t, not_pareto_optimal_t, unclassified_t
# ToDo: maybe add jitter to avoid issues when the prediction is exactly 0
[docs]@jit(nopython=True)
def _get_max_wt( # pylint:disable=too-many-arguments
rectangle_lows: np.array,
rectangle_ups: np.array,
means: np.array,
pareto_optimal_t: np.array,
unclassified_t: np.array,
sampled: np.array,
) -> int:
"""Returns the index in design space with the maximum size of the hyperrectangle
(scaled by the mean predictions, i.e., effectively,
we use the coefficient of variation).
Samples only from unclassified or Pareto-optimal points.
Args:
rectangle_lows (np.array): Lower, pessimistic, bounds of the hyperrectangles
rectangle_ups (np.array): Upper, optimistic, bounds of the hyperrectangles
means (np.array): Mean predictions
pareto_optimal_t (np.array): Mask array that is True
for the Pareto optimal points
unclassified_t (np.array): Mask array that is True for the unclassified points
sampled (np.array): Mask array that is True for the sampled points
Returns:
int: index with maximum size of hyperrectangle
"""
max_uncertainty = -np.inf
maxid = 0
for i in range(0, len(unclassified_t)): # pylint:disable=consider-using-enumerate
# Among the points x ∈ Pt ∪ Ut, the one with the largest wt(x)
# is chosen as the next sample xt to be evaluated.
# Intuitively, this rule biases the sampling towards exploring,
# and thus improving the model for, the points most likely to be Pareto-optimal.
if ((unclassified_t[i] == 1) or (pareto_optimal_t[i] == 1)) and not sampled[
i
] == 1:
# weight is the length of the diagonal of the uncertainty region
coeff_var = np.divide(
rectangle_ups[i, :] - rectangle_lows[i, :], means[i, :]
)
uncertainty = np.linalg.norm(coeff_var)
if uncertainty > max_uncertainty:
max_uncertainty = uncertainty
maxid = i
return maxid
[docs]def _uncertainty(rectangle_ups, rectangle_lows, means):
"""Hyperrectangle sizes"""
if (rectangle_lows is None) or (rectangle_ups is None):
raise ValueError(
"You must have trained a model and\
run the prediction to calculate hyperrectangle"
)
coeff_var = np.nan_to_num(np.divide(rectangle_ups - rectangle_lows, means))
uncertainty = np.linalg.norm(coeff_var, axis=1)
return uncertainty


"Given the species' reputation, you'd think Bugs Bunny would have more relatives."
"I'm the kind of guy who brings his phone charger to the party."
Ohh! That's Salty!
"Before you judge a man, walk a mile in his shoes. After that who cares?… He's a mile away and you've got his shoes!"
– Billy Connolly
"If I were two-faced, would I be wearing this one?"
– Abraham Lincoln
"The difference between stupidity and genius is that genius has its limits."
– Albert Einstein
"Never put off till tomorrow what you can do the day after tomorrow."
– Mark Twain
"People who think they know everything are a great annoyance to those of us who do."
– Isaac Asimov
Want to read the Saltiest, comments the Internet has to offer? Your in the right place! We have ranked the saltiest comments from all over the web right here!
"Baby Yoda's first word probably came after his second word"
"I don't go jogging in the morning because would just end up splashing coffee everywhere."


XRay Flight Data Recorder Trace Format¶
| Version: | 1 as of 2017-07-20 |
|---|
Introduction¶
When gathering XRay traces in Flight Data Recorder mode, each thread of an application will claim buffers to fill with trace data, which at some point is finalized and flushed.
A goal of the profiler is to minimize overhead, so the flushed data directly corresponds to the buffer.
This document describes the format of a trace file.
General¶
Each trace file corresponds to a sequence of events in a particular thread.
The file has a header followed by a sequence of discriminated record types.
The endianness of byte fields matches the endianess of the platform which produced the trace file.
Header Section¶
A trace file begins with a 32 byte header.
| Field | Size (bytes) | Description |
|---|---|---|
| version | 2 |
Anticipates versioned readers. This document describes the format when version == 1 |
| type | 2 |
An enumeration encoding the type of trace. Flight Data Recorder mode traces have type == 1 |
| bitfield | 4 |
Holds parameters that are not aligned to bytes. Further described below. |
| cycle_frequency | 8 |
The frequency in hertz of the CPU oscillator used to measure duration of events in ticks. |
| buffer_size | 8 |
The size in bytes of the data portion of the trace following the header. |
| reserved | 8 |
Reserved for future use. |
The bitfield parameter of the file header is composed of the following fields.
| Field | Size (bits) | Description |
|---|---|---|
| constant_tsc | 1 |
Whether the platform’s timestamp counter used to record ticks between events ticks at a constant frequency despite CPU frequency changes. 0 == non-constant. 1 == constant. |
| nonstop_tsc | 1 |
Whether the tsc continues to count despite whether the CPU is in a low power state. 0 == stop. 1 == non-stop. |
| reserved | 30 |
Not meaningful. |
Data Section¶
Following the header in a trace is a data section with size matching the buffer_size field in the header.
The data section is a stream of elements of different types.
There are a few categories of data in the sequence.
Function Records
: Function Records contain the timing of entry into and exit from function execution. Function Records have 8 bytes each.Metadata Records
: Metadata records serve many purposes. Mostly, they capture information that may be too costly to record for each function, but that is required to contextualize the fine-grained timings. They also are used as markers for user-defined Event Data payloads. Metadata records have 16 bytes each.Event Data
: Free form data may be associated with events that are traced by the binary and encode data defined by a handler function. Event data is always preceded with a marker record which indicates how large it is.Function Arguments
: The arguments to some functions are included in the trace. These are either pointer addresses or primitives that are read and logged independently of their types in a high level language. To the tracer, they are all simply numbers. Function Records that have attached arguments will indicate their presence on the function entry record. We only support logging contiguous function argument sequences starting with argument zero, which will be the “this” pointer for member function invocations. For example, we don’t support logging the first and third argument.
A reader of the memory format must maintain a state machine. The format makes no attempt to pad for alignment, and it is not seekable.
Function Records¶
Function Records have an 8 byte layout. This layout encodes information to reconstruct a call stack of instrumented function and their durations.
| Field | Size (bits) | Description |
|---|---|---|
| discriminant | 1 |
Indicates whether a reader should read a
Function or Metadata record. Set to 0 for
Function records. |
| action | 3 |
Specifies whether the function is being entered, exited, or is a non-standard entry or exit produced by optimizations. |
| function_id | 28 |
A numeric ID for the function. Resolved to a name via the xray instrumentation map. The instrumentation map is built by xray at compile time into an object file and pairs the function ids to addresses. It is used for patching and as a lookup into the binary’s symbols to obtain names. |
| tsc_delta | 32 |
The number of ticks of the timestamp counter since a previous record recorded a delta or other TSC resetting event. |
On little-endian machines, the bitfields are ordered from least significant bit
bit to most significant bit. A reader can read an 8 bit value and apply the mask
0x01
for the discriminant. Similarly, they can read 32 bits and unsigned
shift right by 0x04
to obtain the function_id field.
On big-endian machine, the bitfields are written in order from most significant
bit to least significant bit. A reader would read an 8 bit value and unsigned
shift right by 7 bits for the discriminant. The function_id field could be
obtained by reading a 32 bit value and applying the mask 0x0FFFFFFF
.
Function action types are as follows.
| Type | Number | Description |
|---|---|---|
| Entry | 0 |
Typical function entry. |
| Exit | 1 |
Typical function exit. |
| Tail_Exit | 2 |
An exit from a function due to tail call optimization. |
| Entry_Args | 3 |
A function entry that records arguments. |
Entry_Args records do not contain the arguments themselves. Instead, metadata records for each of the logged args follow the function record in the stream.
Metadata Records¶
Interspersed throughout the buffer are 16 byte Metadata records. For typically instrumented binaries, they will be sparser than Function records, and they provide a fuller picture of the binary execution state.
Metadata record layout is partially record dependent, but they share a common structure.
The same bit field rules described for function records apply to the first byte of MetadataRecords. Within this byte, little endian machines use lsb to msb ordering and big endian machines use msb to lsb ordering.
| Field | Size | Description |
|---|---|---|
| discriminant | 1 bit |
Indicates whether a reader should read a
Function or Metadata record. Set to 1 for
Metadata records. |
| record_kind | 7 bits |
The type of Metadata record. |
| data | 15 bytes |
A data field used differently for each record type. |
Here is a table of the enumerated record kinds.
| Number | Type |
|---|---|
| 0 | NewBuffer |
| 1 | EndOfBuffer |
| 2 | NewCPUId |
| 3 | TSCWrap |
| 4 | WallTimeMarker |
| 5 | CustomEventMarker |
| 6 | CallArgument |
NewBuffer Records¶
Each buffer begins with a NewBuffer record immediately after the header. It records the thread ID of the thread that the trace belongs to.
Its data segment is as follows.
| Field | Size (bytes) | Description |
|---|---|---|
| thread_Id | 2 |
Thread ID for buffer. |
| reserved | 13 |
Unused. |
WallClockTime Records¶
Following the NewBuffer record, each buffer records an absolute time as a frame of reference for the durations recorded by timestamp counter deltas.
Its data segment is as follows.
| Field | Size (bytes) | Description |
|---|---|---|
| seconds | 8 |
Seconds on absolute timescale. The starting point is unspecified and depends on the implementation and platform configured by the tracer. |
| microseconds | 4 |
The microsecond component of the time. |
| reserved | 3 |
Unused. |
NewCpuId Records¶
Each function entry invokes a routine to determine what CPU is executing. Typically, this is done with readtscp, which reads the timestamp counter at the same time.
If the tracing detects that the execution has switched CPUs or if this is the first instrumented entry point, the tracer will output a NewCpuId record.
Its data segment is as follows.
| Field | Size (bytes) | Description |
|---|---|---|
| cpu_id | 2 |
CPU Id. |
| absolute_tsc | 8 |
The absolute value of the timestamp counter. |
| reserved | 5 |
Unused. |
TSCWrap Records¶
Since each function record uses a 32 bit value to represent the number of ticks of the timestamp counter since the last reference, it is possible for this value to overflow, particularly for sparsely instrumented binaries.
When this delta would not fit into a 32 bit representation, a reference absolute timestamp counter record is written in the form of a TSCWrap record.
Its data segment is as follows.
| Field | Size (bytes) | Description |
|---|---|---|
| absolute_tsc | 8 |
Timestamp counter value. |
| reserved | 7 |
Unused. |
CallArgument Records¶
Immediately following an Entry_Args type function record, there may be one or more CallArgument records that contain the traced function’s parameter values.
The order of the CallArgument Record sequency corresponds one to one with the order of the function parameters.
CallArgument data segment:
| Field | Size (bytes) | Description |
|---|---|---|
| argument | 8 |
Numeric argument (may be pointer address). |
| reserved | 7 |
Unused. |
CustomEventMarker Records¶
XRay provides the feature of logging custom events. This may be leveraged to record tracing info for RPCs or similarly trace data that is application specific.
Custom Events themselves are an unstructured (application defined) segment of memory with arbitrary size within the buffer. They are preceded by CustomEventMarkers to indicate their presence and size.
CustomEventMarker data segment:
| Field | Size (bytes) | Description |
|---|---|---|
| event_size | 4 |
Size of preceded event. |
| absolute_tsc | 8 |
A timestamp counter of the event. |
| reserved | 3 |
Unused. |
EndOfBuffer Records¶
An EndOfBuffer record type indicates that there is no more trace data in this buffer. The reader is expected to seek past the remaining buffer_size expressed before the start of buffer and look for either another header or EOF.
Format Grammar and Invariants¶
Not all sequences of Metadata records and Function records are valid data. A sequence should be parsed as a state machine. The expectations for a valid format can be expressed as a context free grammar.
This is an attempt to explain the format with statements in EBNF format.
- Format := Header ThreadBuffer* EOF
- ThreadBuffer := NewBuffer WallClockTime NewCPUId BodySequence* End
- BodySequence := NewCPUId | TSCWrap | Function | CustomEvent
- Function := (Function_Entry_Args CallArgument*) | Function_Other_Type
- CustomEvent := CustomEventMarker CustomEventUnstructuredMemory
- End := EndOfBuffer RemainingBufferSizeToSkip
Function Record Order¶
There are a few clarifications that may help understand what is expected of Function records.
- Functions with an Exit are expected to have a corresponding Entry or Entry_Args function record precede them in the trace.
- Tail_Exit Function records record the Function ID of the function whose return address the program counter will take. In other words, the final function that would be popped off of the call stack if tail call optimization was not used.
- Not all functions marked for instrumentation are necessarily in the trace. The tracer uses heuristics to preserve the trace for non-trivial functions.
- Not every entry must have a traced Exit or Tail Exit. The buffer may run out of space or the program may request for the tracer to finalize toreturn the buffer before an instrumented function exits.


This document is the API specification for the Deep Java Library (DJL) PyTorch Model Zoo.
The PyTorch Model Zoo module contains PyTorch pretrained traced modules that can be used for inference. See here for more details.


Project Title: Internet
Censorship Lab (ICLab)
Principal Investigators: Prof. Phillipa Gill (University
of Massachusetts -- Amherst)
Sponsor: National Science Foundation (USA)
You are being asked to be a volunteer in a research study
that seeks to characterize worldwide deployments of
information controls technology. This research project is
led by researchers at University of Massachusetts Amherst,
Stony Brook University, and University of Toronto.
The purpose of this study is: To document and analyze the practice of national-level Internet censorship through the creation of a global network of researchers running censorship measurement software.
This measurement software will run tests from your computer or dedicated hardware device which attempt to access a list of sensitive URLs. These sensitive URLs include political, social and religious content, and can include such material as oppositional political content, pornography and content critical of religion. The software will collect data from these tests and send it back to servers maintained by Phillipa Gill’s research group at University of Massachusetts Amherst. This data will be used to determine what content is being censored, what technical methods are used for censorship and how censorship changes over time.
If you decide to be in this study, you will help run network measurement experiment software from network locations you have access to. You will be asked to run the software from either a dedicated hardware device that the researchers will supply to you or from your personal computer.
Specific participation requirements (personal computer):
Downloading a software client and installing it on to your computer. This should take approximately 5 minutes and only needs to be done once.
Running the software on your computer. The software will run as a background process on your computer. It will run tests for network censorship which may take up to one hour, but do not require your interaction.
Providing feedback on the software and testing process when requested by the research team
Specific participation requirements (dedicated hardware):
Providing an Internet connection for the dedicated hardware device and ensuring the device has connectivity.
Provided feedback on the testing process when requested by the research team.
On average it is expected that participation will require 1-3 hours a week over the course of 12 months.
Throughout the duration of the project you will have access to a web-based data explorer to examine the data from the measurement experiments you have participated in. These reports may help you gain a better understanding of the prevalence, depth, and breadth of Internet filtering in your country.
There are no other direct benefits from participation in this research study, although your participation may help contribute to the public good by educating society about the prevalence, depth, and breadth of Internet filtering around the world.
Every effort will be made to maintain the strictest confidentiality. Data will only be used for the purposes outlined in the information and consent forms. Only members of the research team will have access to personally identifiable data. Only aggregated data that does not include information about research participants, or provided by research participants during the research, will be disclosed to others without the participants prior formal written consent.
Electronic data will be stored in a secure server environment maintained maintained by Phillipa Gill’s research group at University of Massachusetts Amherst. Any personally identifiable data kept on computers outside of this secure server environment will be encrypted at all times using strong encryption except to the extent researchers need to decrypt them during use.
Experiment data may include issuing requests for URLs found in the URL list (similar to what a user does manually with a Web browser, such as Firefox, but automated), DNS queries (mapping hostnames such as Google.com to IP addresses, such as <IP_ADDRESS>), or traceroutes (measuring network paths). Experiments may log network traffic associated with the experiment (e.g., network packets and timestamps). The software will also contain a unique user ID to allow us to map users back to their informed consent and identify faulty installation.
Once an experiment has completed the resulting data will be sent, along with the experiment ID and user ID to the data management system. Once the data management system confirms receipt of the data it will be removed from the client software. Personally identifiable data that will be collected and transmitted will be your IP address. Experiment results will be encrypted to ensure they are not observable in transit and this personally identifiable data (as well as the unique user ID) will only be accessible to ICLab researchers.
Whenever the client communicates with the experiment controller or the data management system the data will be encrypted and may also be sent to a proxy (e.g., in Amazon EC2 data centers) before being forwarded on to the target system. The proxy will not store the data and will not contain the key required to decrypt any data it forwards.
The following risks may occur as a result of you being in this study:
Your ISP or network administrator may be able to see the list of websites being accessed by the measurement software if they are monitoring the network. There is a risk that accessing such websites and running these tests violates network policies.
The potential risks posed by conducted these tests is highly specific to the context in which the tests take place. These risks are highly dependent on the domestic political and legal context in your country. While it is challenging to definitively identify what the risks of conducting these tests may be in your country, we have compiled a number of metrics to help inform you of the potential risks in your country.
Freedom House "Freedom on the Net score"
Economist "Democracy Index score"
Local reports from NGOs and our research partners


Before Change
// Replace all layer activations with ReLU.
// We also don"t want to mutate the original model as it will have unexpected consequences on upstream callers.
// For this reason we will maintain the set of original activations and restore it.
original_activations = []
for layer in model.layers[1:]:
if hasattr(layer, "activation"):
original_activations.append(layer.activation)
layer.activation = tf.nn.relu
// Save model. This model should save with modified activation names.
// Upon loading, keras should rebuild the graph with modified activations.
model_path = "/tmp/" + next(tempfile._get_candidate_names()) + ".h5"
model.save(model_path)
// Restore original model to keep upstream callers unaffected.
After Change
modified_model.layers[i] = modified_layer
// 3. Save model with modifications.
modified_model.save(model_path)
// 4. Register modifier and load modified model under custom context.
modifier_fn = _BACKPROP_MODIFIERS.get(backprop_modifier)
if modifier_fn is None:


The repetition/delineate_trailing_params.h
header defines an algorithm that delineates a sequence of preprocessing tokens a specified number of times.
#include <chaos/preprocessor/repetition/delineate_trailing_params.h>
The CHAOS_PP_DELINEATE_TRAILING_PARAMS
macro delineates its third argument the number of times specified by its first argument.
CHAOS_PP_DELINEATE_TRAILING_PARAMS
(count, sep, ...)
The number of times that __VA_ARGS__
is to be repeated.
Valid values range from 0
to CHAOS_PP_LIMIT_MAG
.
The delineator to be situated between each repetition of __VA_ARGS__
.
This delineator must be a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
sep()
The sequence of preprocessing tokens to be repeated.
CHAOS_PP_DELINEATE_TRAILING_PARAMS
(count, sep, param)
The number of times that param is to be repeated.
Valid values range from 0
to CHAOS_PP_LIMIT_MAG
.
The delineator to be situated between each repetition of param. This delineator must be a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
sep()
The sequence of preprocessing tokens to be repeated.
This macro behaves identically to CHAOS_PP_DELINEATE_PARAMS
except that it inserts a leading delineator if any repetitions exist.
This macro uses bypass semantics.
If bypass semantics are already in effect, CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
must be used instead.
CHAOS_PP_DELINEATE_PARAMS
CHAOS_PP_DELINEATE_SHIFTED_PARAMS
CHAOS_PP_DELINEATE_SHIFTED_TRAILING_PARAMS
CHAOS_PP_ENUM_PARAMS
CHAOS_PP_ENUM_SHIFTED_PARAMS
CHAOS_PP_ENUM_SHIFTED_TRAILING_PARAMS
CHAOS_PP_ENUM_TRAILING_PARAMS
The CHAOS_PP_DELINEATE_TRAILING_PARAMS_ID
macro indirectly expands to CHAOS_PP_DELINEATE_TRAILING_PARAMS
.
CHAOS_PP_DELINEATE_TRAILING_PARAMS_ID
()
This macro effectively allows CHAOS_PP_DELINEATE_TRAILING_PARAMS
to pass through itself without becoming disabled.
The CHAOS_PP_DELINEATE_TRAILING_PARAMS_
macro expands to a λ expression representing CHAOS_PP_DELINEATE_TRAILING_PARAMS
.
CHAOS_PP_DELINEATE_TRAILING_PARAMS_
This macro is a shorthand form of the following λ binding:
The CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
macro behaves identically to CHAOS_PP_DELINEATE_TRAILING_PARAMS
except that it is parametized by the bypass recursion state.
CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
(s, count, sep, ...)
The current bypass recursion state.
The number of times that __VA_ARGS__
is to be repeated.
Valid values range from 0
to CHAOS_PP_LIMIT_MAG
.
The delineator to be situated between each repetition of __VA_ARGS__
.
This delineator must be a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
sep()
The sequence of preprocessing tokens to be repeated.
CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
(s, count, sep, param)
The current bypass recursion state.
The number of times that param is to be repeated.
Valid values range from 0
to CHAOS_PP_LIMIT_MAG
.
The delineator to be situated between each repetition of param. This delineator must be a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
sep()
The sequence of preprocessing tokens to be repeated.
If bypass semantics are already in effect, this macro must be used instead of CHAOS_PP_DELINEATE_TRAILING_PARAMS
.
The CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS_ID
macro indirectly expands to CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
.
CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS_ID
()
This macro effectively allows CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
to pass through itself without becoming disabled.
The CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS_
macro expands to a λ expression representing CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS
.
CHAOS_PP_DELINEATE_TRAILING_PARAMS_BYPASS_
This macro is a shorthand form of the following λ binding:


CHIKV (.) DENV individuals confirmed pain using doi: infection positive blood Chikungunya RT-qPCR patients (%) count virus Trinidad cell reported CHIKF cases fever joint study KR, Table clinical sequences data recent white dengue chikungunya IgM likely negative result < having onset Caribbean features number performed persons probable symptoms & .–. /μl) Health nucleotide post analyses days evidence infections values (<x (Dec Dengue ELISA University acid acute adaptive additional distinguish febrile lower major specificity –Nov (i.e. .), APCF Americas Asian CHIKV. Click Joint Public T&T Table). Trop analysis associations care compared complete file. illness p<. sample samples significantly used vs. (DOCX) .). /μl Bivariable Fig IgG Med NPV Oxford PPV Panbio Press; RNA RT-qPCR. SC, White characteristics considered detected emergence enrolled epidemic generated genotype hospital isolated mean mutations p-value platelet polypeptide predictive presence primary prior rash rash, ratios reads secondary sequence travel variables ./journal.pntd..t DENV, Defined Dis. EWMSC, IOL Illumina KA, Rash Tobago Tsetsarkin Weaver [Internet]. abdominal albopictus amino antibodies available bivariable case clade combination common counts current designated determined different duplicates final fitness genome genotypes including infection. instead laboratory outbreak pain, position presentation presented respectively. results screened significant strain study. sufficient suspected viral weeks > (< (Illumina (aOR: ./journal.pntd. /ml) AUFIs AUFIs. Abdominal Adult August BVI Bioinformatics. CA) CHIKV, CI: Care Central Clinical DENV- Diego, Drummond ECSA Eric Facility Hyg. IgM/IgG Inc., MA, Medical Nat Pearson’s Platelet Primary RT-qPCR-confirmed Rambaut Samples San Sciences Sequences Virus Williams accession alignment analysis. and, assay based beginning bleed, collection differences differentiate disease documented estimated factors genomic haemorrhagic health history included infection, infections. labelled level likelihood manufacturer’s model multivariable newly phylogenetic potential pp. public rapid recently respectively returned sensitivity sequencing sera spread strains structural substitutions symptomatic test throat total value variation years


Input your data
Please make sure your {{ client.session_key }} and participation code match the ones provided in the email sent to you by the {{ client.organization }}. Drag and drop your completed template file to encrypt and include your submission in the aggregate data.
—or—
View your data
Your data will appear here after you drag/drop or browse to find your completed Excel template file above.
Entered Data
Red cells indicate an error. Click on the cell to see the error message. Yellow cells indicate the value might be outside of the expected range. Please double-check to make sure the data is correct. You will still be able to submit your data.Answer additional questions
We have included these questions to get instant feedback as to how this process went in order to improve the process in future years. Please know that the answers to these questions will be anonymous, and they will be considered separately from the encrypted and aggregated data above.
Verify and submit your data
Please ensure that all entered data is accurate.
Errors
- No Errors!
Submission history
- You have not submitted yet.


--- ---
Press Start to start the simulation.
When a simulation starts, the transmitter sends a packet to the receiver every time unit unless its window is full, i.e. there are W unacknowledged packets in the queue.
Following assumptions and simplifications are made in this simulation:
| Node named Name | |
| Packet with a sequence number of SN | |
| Erroneous packet with a sequence number of SN | |
| ACK packet acknowledging the packet with a sequence number SN | |
| NAK packet requesting the packet with a sequence number SN | |
| SN min | Sequence number of the first unacknowledged packet |
| SN max | Sequence number of the last packet that can be sent without further acknowledgement from the receiver. The difference between SN min and SN max is the window size. |
| SN next | Sequence number of the first unsent packet |


| Parameter | Value | Description |
| add-index-toc |
Allowed values are: 'yes' and
'no'.
Default value: 'yes'.
|
Specifies whether an A-Z list should be added at the
beginning of the back-of-the-book index.
|
| chain-pages |
Allowed values are: 'none', 'top',
'bottom' or 'both'.
Default value:
'none'.
|
Specifies whether a header and/or a footer containing
navigation icons should be generated in order to link together all
the HTML pages.
NoteThere is no need to specify a value other
than 'none' when generating Web Help, HTML Help, Eclipse
Help, EPUB and Java™ Help.
|
| chain-topics |
Allowed values are: 'yes' and
'no'.
Default value: 'no'.
|
Specifies whether navigation icons should be generated
in order to link together all the topics.
See also related
parameter: ignore-navigation-links.
NoteThere
is no need to specify a value other than 'no' when
generating Web Help, HTML Help, Eclipse Help, EPUB and Java™ Help.
|
| css |
URL.
Default value: ''.
|
Low-level parameter specifying which CSS stylesheet to
use to style the generated (X)HTML pages.
When neither
css nor custom-css is specified, the
default CSS stylesheet being used is xsl-resources-directory/base.css.
RestrictionNot supported by the stylesheets that generate
EPUB.
|
| cssResourceName |
URL basename relative to the directory specified by
parameter xsl-resources-directory.
Default
value: 'base.css'.
|
Very low-level parameter specifying which CSS stylesheet
to use. This CSS stylesheet is expected to be found in the resources
directory.
NoteThis parameter is not useful unless you
develop a plug-in
implementing a DITA specialization. More information in https://www.xmlmind.com/ditac/_distrib/doc/manual/webhelp/specialize.html.
|
| custom-css |
URL.
Default value: ''.
|
Specifies the custom CSS stylesheet used to style the
generated (X)HTML pages. This high-level parameter has priority over
low-level parameter css.
This custom CSS stylesheet
is copied to directory xsl-resources-directory.
Therefore custom-css requires directory
xsl-resources-directory to be specified as an URL which is
relative to the output directory (e.g.
xsl-resources-directory='res').
How to use
custom-css is explained in https://www.xmlmind.com/ditac/_distrib/doc/manual/webhelp/manual-6.html#customCSS.
|
| default-table-width |
A percentage, typically something like '100%'
or '90%'.
Default value: '' (as narrow as
possible).
|
The default width of

 and
<simpletable> elements. |
| external-link-icon-height |
Length. A length may have a unit. Default is
px.
Default value: '10'.
|
The height of the “opens in new window”
icon.
|
| external-link-icon-name |
Basename.
Default value:
'new_window.png'.
|
The basename of the “opens in new window” icon. This
icon is found in the resources directory.
|
| external-link-icon-width |
Length. A length may have a unit. Default is
px.
Default value: '10'.
|
The width of the “opens in new window”'
icon.
|
| format-to-type |
Zero or more DITA format/MIME type pairs. Example:
"txt text/plain xml application/xml html text/html".
Default value: '', which means that DITA
xref/@format is not converted to XHTML
a/@type.
|
Allows to map DITA xref/@format to XHTML
a/@type.
Using default empty value, <xref
scope="external" format="txt"
href="http://acme.com/info.xyz"> is converted to . The fact
that file extension ".xyz" is unknown may cause problems
when attempting to navigate or download file "info.xyz"
using a Web browser.
If
-p format-to-type "txt text/plain" is passed to
ditac then <xref scope="external"
format="txt" href="http://acme.com/info.xyz"> is converted
to , which is better. |
| generator-info |
String
Default value: 'XMLmind DITA Converter
VERSION'.
|
The name of the software which has been used to create
the HTML pages.
Specify an empty string if you don't want to
have a <meta name="generator" content="XXX"/>
element added to your HTML pages.
|
| ignore-navigation-links |
Allowed values are: 'yes', 'no' and
'auto'.
Default value: 'auto' for XHTML and
its variants; 'yes' for Web Help, Java Help, HTML Help,
Eclipse Help and EPUB
|
If 'yes', do not generate the navigation links
corresponding to topicref attribute
@collection-type .If 'no', generate
the navigation links corresponding to topicref attribute
@collection-type .If 'auto', generate
the navigation links corresponding to topicref attribute
@collection-type , unless chain-topics=yes. |
| javascripts |
String. List of URLs separated by
whitespace.
Default value: ''.
|
The URLs specified in this parameter must point to
JavaScript files. These URLs are converted to
 |
| mathjax |
Allowed values are: 'yes', 'no' and
'auto'.
Default value: 'no'.
|
Very few web browsers (Firefox) can natively render
MathML. Fortunately, there is MathJax.
MathJax is a JavaScript display engine
for mathematics that works in all browsers.
Ignored by all XHTML-based formats but XHTML and Web
Help.
|
| mathjax-url |
String.
Default value: the URL pointing to the
MathJax CDN, as recommended in the MathJax
documentation.
|
|
| mark-external-links |
Allowed values are: 'yes' and
'no'.
Default value: 'no'.
|
Specifies whether an external link should be marked
using a “opens in new window” icon.
|
| navigation-icon-height |
Length. A length may have a unit. Default is
px.
Default value: '16'.
|
The height of a navigation icon.
|
| navigation-icon-suffix |
String.
Default value:
'.png'.
|
The suffix of a navigation icon.
The root names of
navigation icons are fixed:
For example, if note-icon-suffix='.svg', the
default resources directory is expected to contain
first.svg ,
first_disabled.svg ,
last.svg , etc.In principle, there is no
need for an end-user to specify any of the
navigation-icon-suffix, navigation-icon-width or
navigation-icon-height parameters.
|
| navigation-icon-width |
Length. A length may have a unit. Default is
px.
Default value: '16'.
|
The width of a navigation icon.
|
| screen-resolution |
Positive integer.
Default value:
'96'.
|
The resolution of the screen in dot per inch (DPI). This
resolution is used to convert image dimensions such as 3cm
to pixels.
|
| xhtml-mime-type |
A MIME type without a parameter such as
'text/html', 'application/xhtml+xml',
'application/xml' or the empty string
('').
Default value: see prose.
|
Low-level parameter. Do not change default value unless
you know what you are doing.
|


Compile-Time loops
We want to develop a compile-time loop structure that is flexible and allows to print templates and to unroll arbitrary loops index-wise.
The classical example for C++ meta-programming is to define a recursion formula by template parameter specialization. Implementing a meta-program to calculate the Nth Fibonacci number (see Wikipedia) could look like:
template <long I>
struct Fibo // recursion formula
{
static const long value = Fibo<I-1>::value + Fibo<I-2>::value;
};
template <> struct Fibo<1> { static const long value = 1; };
template <> struct Fibo<0> { static const long value = 0; };
The primary template defines the recursion formula and two template-specializations
for I=0 and I=1 introduce break conditions. A static constant value is defined
for the template Fibo, to access the resulting value of the Fibonacci calculations.
Thus, the Nth Fibonacci number can be read by Fibo<N>::value
.
The same can be written using concepts-lite technique (in C++17, Technical specification, Tutorial) by formulating the recursion formula using a requires clause:
template <long I> struct Fibo { static constexpr long value = 0; };
template <> struct Fibo<1> { static constexpr long value = 1; };
template <long I>
requires (I > 1)
struct Fibo<I> // recursion formula
{
static constexpr long value = Fibo<I-1>::value + Fibo<I-2>::value;
};
Here the order of definition has changed, i.e. at first we have implemented the break conditions and finally the recursion formula for all integer greater than 1. Thus, for negative indices we set the Fibonacci number to 0. To compile this code, currently you have to use the trunk version of gcc (HowTo: build gcc with concepts), with
g++ -std=c++1z SOURCE.cc
In order to print all Fibonacci numbers from 0 to 50 one has to write a compile-time loop. By explicitly implementing a print-loop for the Fibo class this can be solved:
template <int I, int N>
struct Print {
static void run() {
std::cout << "Fibo<" << I << ">::value = " << Fibo<I>::value << "n";
Print<I+1,N>::run();
}
};
template <int N>
struct Print<N,N> { static void run() {} };
It instantiates the template Print<I,N>
from an initial I to the (not included)
upper bound N. Thus, we have to call
Print<0,51>::run();
to print all Fibonacci numbers from 0 to 50. This loop is specialized for the Fibo class. A more general implementation takes a functor for the output operation instead:
template <int I, int N, class F>
struct Loop {
static void run() {
F::template eval<I>();
Loop<I+1,N,F>::run();
}
};
template <int N, class F>
struct Loop<N,N,F> { static void run() {} };
struct PrintFibo
{
template <int I>
static void eval() {
std::cout << "Fibo<" << I << ">::value = " << Fibo<I>::value << "n";
}
};
The static functor PrintFibo implements a static method eval, that takes an integer template argument. In order to call this method in a templated Loop class, we have to explicitly specify that the method is templated:
F::template eval<I>();
where F corresponds to the PrintFibo class. The printing loop can be instantiated, by
Loop<0,51, PrintFibo>::run();
Instead of calling a templated eval method by explicitly specifying the template
parameter, we could implement a classical static functor, that takes an argument
that represents the integral integer parameter, by using a helper class int_
:
template <int I> struct int_ {};
template <int I, int N, class F>
struct Loop {
static void run() {
F::eval(int_<I>()); // pass a templated struct to the eval method
Loop<I+1,N,F>::run();
}
};
template <int N, class F>
struct Loop<N,N,F> { static void run() {} };
struct PrintFibo
{
template <int I>
static void eval(int_<I>) {
std::cout << "Fibo<" << I << ">::value = " << Fibo<I>::value << "n";
}
};
This is slightly simpler to use, by passing an instance of the int_<I>
class
to the eval method. So far, the class PrintFibo is passed as template argument.
Thus, we have to use a static eval method in the functor. Classically functors
implement an operator()
to be called. In order to allow (dynamic) functors to
be used in the compile-time loop, we modify the implementation slightly:
template <int I> struct int_ {};
template <int I, int N>
struct Loop {
template <class F>
static void run(F f) {
f(int_<I>()); // pass an templated struct to the eval method
Loop<I+1,N>::run(f);
}
};
template <int N>
struct Loop<N,N> { template <class F> static void run(F) {} };
struct PrintFibo
{
template <int I>
void operator()(int_<I>) const {
std::cout << "Fibo<" << I << ">::value = " << Fibo<I>::value << "n";
}
};
by passing the functor as argument to the run()
method.
Now, the loop can be instantiated by
Loop<0,51>::run(PrintFibo());
This is much more flexible, but requires a functor that has a templated
operator()
with integer non-type parameter. So, we can not simply use
lambda-expressions as functors. This is a heavy restriction and should be
overcome. In order to do so, we change the implementation of the int_
class
slightly, by adding a constexpr cast operator:
template <int I> struct int_ { constexpr operator int() const { return I; } };
This now allows to use int_<I>
as template parameter for integer arguments and
the loop can be instantiated using a generic lambda, that does not need to know
its template parameter explicitly:
Loop<0,51>::run([](auto I)
{
std::cout << "Fibo<" << I << ">::value = " << Fibo<I>::value << "n";
});
Adding some more tests and the flexibility to iterate forward, backward, or in stepsize greater than 1, and using concepts-lite as a tool for simplified implementation, we arrive at the final version:
// class that behaves like an integer and stores an integral constant
// Here, simply using std::integral_constant from <type_traits>
template <int I> using int_ = std::integral_constant<int, I>;
// default behavior (i>=N): do nothing
template <int I, int Step, int N>
struct ForImpl
{
static_assert( Step != 0, "Stepsize 0 not allowed!" );
template <class F> static void loop(F) {}
};
// class that performs the loop over all indices [I, N), for i < N
template <int I, int Step, int N>
requires (Step > 0 && I < N) || (Step < 0 && I > N)
struct ForImpl<I, Step, N>
{
template <class F>
static void loop(F&& f) // using universal references
{
f(int_<I>());
ForImpl<I+Step, Step, N>::loop(std::forward<F>(f));
}
};
template <int I, int Step, int N = INT_MAX>
struct For : ForImpl<I, Step, N>
{
static_assert( (Step > 0 && I <= N) || (Step < 0 && I >= N),
"Illegal range definition!" );
};
// if N not given explicitly, use Stepsize=1 or -1
template <int I, int N>
struct For<I, N, INT_MAX> : ForImpl<I, (N >= I ? 1 : -1), N> {};
Here, we have implemented the break condition with boolean template constraints
for upper and lower bound. Instead of passing the functor by copy to the next
iterate, it is now passed by universal reference. The int_
template is
changed to the standard compatible std::integral_constant
, since this provides
the used cast operator and some more attributes. But also the user-defined int_
class can be used.
The loop can be used as follows:
// loop backward from 10 down to one and print all Fibonacci-numbers
For<10,-1,0>::loop([](auto I)
{
std::cout << "Fibo<" << I << ">::value = " << Fibo<I>::value << "\n";
});
Output:
Fibo<10>::value = 55
Fibo<9>::value = 34
Fibo<8>::value = 21
Fibo<7>::value = 13
Fibo<6>::value = 8
Fibo<5>::value = 5
Fibo<4>::value = 3
Fibo<3>::value = 2
Fibo<2>::value = 1
Fibo<1>::value = 1
Download Source
You can download the source of the final implementation from here.


Call Users
Create New Row
ID
Name
Email
Phone
Authorization Roles
History of Last Actions
Last login
Actions


WITNESS LIST Finance Committee March 12, 2001 - 8:00A Oyster Rider ON: Nelson, Joe Oyster Industry (Fisherman's Harvest Inc.), Anahuac, TX Osburn, Hal Fishery Manager (Texas Parks and Wildlife), Austin, TX Registering, but not testifying: On: Ivic, Michael Oyster Industry (Self), San Leon, TX Nelson, Ben Oyster Industry (Jeri's Seafood, Inc.) Nelson, Doris Oyster industry (Texas Oyster Growers & dealers Assc.) SBOE On: McLeroy, Don Dentist (SBOE), Bryan, TX Tarleton State University On: McCabe, Dennis President (Tarleton State University) Texas A&M Kingsville On: Cisneros, Marc President (Texas A&M Kingsville), Kingsville, TX Texas A&M University On: Bowen, Dr. Ray M. President (Texas A&M University Texas A&M University), College Station, TX Texas A&M University - Galveston On: Kemp, Dr. Mike CEO & VP (Texas A&M University - Galveston), Galveston, TX Texas A&M University - Texarkana On: Henry, Stephen President (Texas A&M University - Texarkana), Texarkana, TX Texas A&M University System On: Gaston, Jerry Deputy Chancellor (Texas A&M University System) Hiler, Edward University Administrator (Texas A&M University System), Bryan, TX Powell, Don (Texas A&M University System), Amarillo, TX Texas Food and Fibers Commission On: Avant, Bob Executive Director (Texas Food and Fibers Commission), Pflugerville, TX Texas Wildlife Damage Management Se On: Nunley, Gary Director (Texas Wildlife Damage Management Service), San Antonio, TX West Texas A&M University On: Long, Russell President (West Texas A&M University), Canyon, TX SB 1037 Program to permit certain certified ON: Ancira, Jesse (Comptroller), Austin, TX Registering, but not testifying: On: Scogin, James Project Coordinator (Comptroller Comptroller), Austin, TX SB 1038 waive penalties and interest relati ON: Ancira, Jesse (Comptroller), Austin, TX Registering, but not testifying: On: Scogin, James Project Coordinator (Comptroller), Austin, TX SB 1263 Relating to state taxes FOR: Doody, Kevin Wholesale Grocary (Groceas Supply Company), Woodlands, TX Johnson, Rick President (Texas Food Industry Assn.), Austin, TX Koch, Kevin J. Executive (McLane Company, Inc), Temple, TX Kolkhorst Jr., Robert Vice President Sales (Brenham Wholesale Grocery Co) Wade, James Vice President (Huntsville Wholesalde Groups Inc) AGAINST: Triplett, Mark C. Consultant (Smokeless Tobacco Council), Olympia, WA ON: Fergusan, Dean Revenue Analyst (Comptroller), Austin, TX Hamilton, Billy (Comptroller), Austin, TX LeBas, James (Comptroller), Austin, TX Navarro, Tim Supervisor, Tobacco Tax (Comptroller Comptroller), Austin, TX Registering, but not testifying: For: Wade, Jack HWG Inc (HWG Inc), Huntsville, TX Against: Short, Jim lobbyist (Swisher International), Fulshear, TX


Team Behind Free Bear Hugs
Saf is a Senior Music student at NYUAD interested in sound art, experimental composition and electronic music. She is currently working on completing her IM minor and capstone performance installation project.
Rakan is an NYUSH sophomore majoring in IMB (interactive media + Business). He is part of the Go Local Program in NYUAD this semester.
Muhammad is a Sophomore doing a double major in CS and IM. Interested in game development and animation. Currently just taking required courses and enjoying life.
Yoki is a Sophomore majoring in IM with a minor in CS. She is pretty interested in web development and game design.


Market Update
Friday Shows Bloodbath for Crypto Markets, Bitcoin Price At $7,300
by Joeri CantFriday, Nov. 22 — Cryptocurrency prices are bleeding heavily, with some of the top-20 coins showing losses of close to 10% on the day.
Cryptocurrency market daily overview. Source: Coin360
Bitcoin (BTC) started the day just above the $7,600 price mark, when it suddenly started on another sell-off towards the $7,000 price level. The coin bounced off a local low of $6,800 in recent hours, before moving up to its current trading price at $7,300, showing a loss of close to 4% on the day.
The world’s largest cryptocurrency is down around 15% from the weekly open of $8,500 and has dragged the entire crypto market into red territory.
Bitcoin daily price chart. Source: Coin360
BTC could be testing below the $7,000 price mark
Popular crypto analyst and regular Cointelegraph contributor Filb Filb recently said that after looking at the daily chart, “it is clear that Bitcoin has hit the bottom of the downward channel in which it has been trading since July.”
However, Filb Filb pointed out that the price point of $7,600 will be a key level for the bulls to reclaim, which might turn out to be a challenge, especially in the short term. He said:
“Probability would suggest BTC/USD will need to spend a little more time testing below $7,000 before there is a significant bullish reversal.”
Previously Cointelegraph reported that Bitcoin tanked due to the multiple rumors currently going around about a crackdown on legitimate Chinese outposts of cryptocurrency businesses. The news appeared to rattle the crypto markets, which slipped into a downwards spiral ever since.
Ether (ETH), meanwhile, saw a painful drop in sync with BTC earlier today. The number one altcoin bounced of its daily low at $141, showing double-digit losses of more than 10% on the day, before moving up to its current trading price of $154 per coin.
Ether 7-day price chart. Source: Coin360
XRP, the third-largest coin by market capitalization, has continued to lose ground and currently sits at $0.232 per coin, showing a loss of more than 3% at press time.
XRP 7-day price chart. Source: Coin360
Top 20 coins are bleeding hard
All of the top 20 coins are showing red candlesticks, with NEO taking the title of biggest loser, seeing a loss of nearly 10% on the day. Huobi Token (HT) follows closely with a loss of 9% at press time.
The overall cryptocurrency market cap dropped below the 200 billion mark. It currently sits around $198.7 billion, with Bitcoin making up 66.3% of the total.
Keep track of top crypto markets in real time here


<menucascade>
The <menucascade>
element is used to document a series of menu choices.
The <menucascade>
element contains one or more user interface control
(<uicontrol>
) elements, for example: Start > Programs >
Accessories > Notepad. If there is more than one
<uicontrol>
element, a formatter might show connecting characters
between the menu items to represent the menu cascade. This element is part of the DITA user
interface domain, a special set of DITA elements designed to document user interface tasks,
concepts and reference information.
Category: User interface elements


Welcome back! Check out your pipe activity.
The Simple Data Pipe is an app that moves your Salesforce or Stripe data to Cloudant, which is IBM's NoSQL database.
This app is straightforward to use, but if you've landed here without having already done so, you should check out the accompanying tutorial.


Provides rendering context interface for the WorldOfParticles
.
More...
Public Member Functions | |
| abstract void | onParticleCountChanged (int newParticleCount) |
| abstract Barrier | getBarrier () |
| abstract GraphicsConfiguration | getGraphicsConfiguration () |
Provides rendering context interface for the WorldOfParticles
.
Definition at line 20 of file WorldOfParticles.java.
| abstract Barrier WorldOfParticles.RenderingContext.getBarrier | ( | ) | [pure virtual] |
Implemented in JPWorld.
Referenced by WorldOfParticles.getBarrier().
| abstract GraphicsConfiguration WorldOfParticles.RenderingContext.getGraphicsConfiguration | ( | ) | [pure virtual] |
Implemented in JPWorld.
Referenced by WorldOfParticles.getGraphicsConfiguration().
| abstract void WorldOfParticles.RenderingContext.onParticleCountChanged | ( | int | newParticleCount ) | [pure virtual] |
Implemented in JPWorld.
Referenced by WorldOfParticles.addNewParticle(), and WorldOfParticles.removeParticle().


Map Services Enhanced
REST Search
Search for map services, layers, fields, etc. with the following relevant text:
Search
Locations searched:
Results found:


{% extends "layout.html" %} {% block page_title %} Choose your assessment date - GOV.UK {% endblock %} {% block content %} Choose your assessment date Only available days can be selected. There are no assessments are held on Fridays. Which date would you like your assessment on? Which date would you like your assessment on? {% for idx, week in dates %} Week {{idx}} {% for days in week %} {{days}} {% endfor %} {% endfor %} Save and continue {% endblock %}
Only available days can be selected. There are no assessments are held on Fridays.
Save and continue


Streaming data arrives continuously and in volumes and rates that are ever increasing. Timely processing of streaming data is computationally challenging due to limited resources. These include limited CPU operations that can be performed on a datum before the next one arrives, limited memory for storing state information about the stream, limited disk storage for the data itself so that the datums may only be seen once, and limited budgets for energy consumption or CPU/memory/disk resources.
In some settings, it's not known in advance what interesting features are embedded in the stream of data. In an algorithmic sense, this makes the above limitations even more challenging. In addition, the hardware options for collecting streaming data are increasing in variety and capability from multicore CPUs, to GPUs, to special-purpose processors like Tilera chips, to solid-state (Flash) disks. While choices are a good thing, they also challenge developers of stream-processing software to support a growing range of hardware.
Computing on streaming data has been referred to as "drinking from a firehose". Hence the name FireHose for this suite of stream processing benchmarks.
The main purpose of FireHose is to enable comparison of streaming software and hardware, both quantitatively vis-a-vis the rate at which they can process data, and qualitatively by judging the effort involved to implement and run the benchmarks. Because we are both users and designers of software for stream processing, we have wanted for some time to define useful metrics for the speed and flexibility various software/hardware solutions can offer. FireHose represents our initial effort in this regard.
We plan to release the benchmark definitions and sample implementation code openly on the FireHose web site, so that anyone can implement them with their own stream-processing software and/or run them on their own hardware. Our hope is that this effort will generate interest and feedback from the academic and commercial streaming communities. With their help in refining and exanding the suite of benchmarks, and their contributions of performance results, we hope the FireHose benchmarks can become a valuable resource for those interested in stream processing.
The initial benchmarks in the FireHose suite reflect common tasks typical of real-time processing of network packets arriving at high rates as a network is monitored, e.g. for cybersecurity purposes. We plan to augment the suite with benchmarks that process streaming graph edges, and are open to additional suggestions as well.


Methods
(static) createAction(action, overwrite, packageName, token) → {ActionResponse}
Represents a Create Faas Action.
Parameters:
| Name | Type | Description |
|---|---|---|
action |
string | The action being updated |
overwrite |
int | Overwrite item if it exists. Default is false |
packageName |
string | Name of package that contains action |
token |
string | faas access token. |
- Source:
Returns:
Action Model
- Type
- ActionResponse
(static) createRule(namespace, ruleName, overwrite, rule, token) → {Rule}
Represents a create new Faas Rule.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
ruleName |
int | Name of rule to update |
overwrite |
string | Overwrite item if it exists. Default is false. |
rule |
Rule | The rule being updated |
token |
string | faas access token. |
- Source:
Returns:
Rule
- Type
- Rule
(static) createTrigger(namespace, triggerName, overwrite, trigger, token) → {Trigger}
Represents a create new Faas Trigger.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
triggerName |
int | Name of trigger to update |
overwrite |
string | Overwrite item if it exists. Default is false. |
trigger |
Trigger | The trigger being updated |
token |
string | faas access token. |
- Source:
Returns:
Trigger
- Type
- Trigger
(static) deleteAction(namespace, actionName, packageName, token) → {string}
Represents a Delete Faas Action.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace |
actionName |
int | Name of action to fetch |
packageName |
string | Name of package that contains action |
token |
string | faas access token. |
- Source:
Returns:
200 Deleted Item
- Type
- string
(static) deleteRule(namespace, ruleName) → {Rule}
Represents a delete Faas Rule.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
ruleName |
int | Name of rule to delete |
- Source:
Returns:
Rule
- Type
- Rule
(static) deleteTrigger(namespace, triggerName) → {Trigger}
Represents a delete Faas Trigger.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
triggerName |
int | Name of trigger to delete |
- Source:
Returns:
Trigger
- Type
- Trigger
(static) getActions(namespace, limit, skip, token) → {array.<ActionResponse>}
Represents a Faas Action List.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
limit |
int | Number of entities to include in the result (0-200). The default limit is 30. A value of 0 sets the limit to the maximum. |
skip |
boolean | Number of entities to skip in the result. |
token |
string | faas access token. |
- Source:
Returns:
Array of Action Model
- Type
- array.<ActionResponse>
(static) getRules(namespace, limit, skip, token) → {array.<Rule>}
Represents a requested Faas Rules.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
limit |
int | Number of entities to include in the result (0-200). The default limit is 30. A value of 0 sets the limit to the maximum. |
skip |
boolean | Number of entities to skip in the result. |
token |
string | faas access token. |
- Source:
Returns:
Rule
- Type
- array.<Rule>
(static) getSingleAction(namespace, actionName, code, packageName, token) → {ActionResponse}
Represents a requested Faas Action.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
actionName |
int | Name of action to fetch |
code |
boolean | Include action code in the result |
packageName |
string | Name of package that contains action |
token |
string | faas access token. |
- Source:
Returns:
Action Model
- Type
- ActionResponse
(static) getSingleRule(namespace, ruleName) → {Rule}
Represents a requested Faas Rule.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
ruleName |
int | Name of rule to fetch |
- Source:
Returns:
Rule
- Type
- Rule
(static) getSingleTrigger(namespace, triggerName) → {Trigger}
Represents a requested Faas Trigger.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
triggerName |
int | Name of trigger to fetch |
- Source:
Returns:
Trigger
- Type
- Trigger
(static) getTriggers(namespace, limit, skip, token) → {array.<Trigger>}
Represents a requested Faas Triggers.
Parameters:
| Name | Type | Description |
|---|---|---|
namespace |
string | The entity namespace. |
limit |
int | Number of entities to include in the result (0-200). The default limit is 30. A value of 0 sets the limit to the maximum. |
skip |
boolean | Number of entities to skip in the result. |
token |
string | faas access token. |
- Source:
Returns:
Trigger
- Type
- array.<Trigger>
Type Definitions
PathName
Path Name model.
Type:
- Object
Properties:
| Name | Type | Description |
|---|---|---|
path |
string | |
name |
string |
- Source:
Rule
Rules response model.
Type:
- Object
Properties:
| Name | Type | Description |
|---|---|---|
namespace |
string | |
name |
string | |
version |
string | |
publish |
boolean | |
annotations |
array.<string> | |
status |
string | |
trigger |
PathName | |
action |
PathName |
- Source:
Trigger
Trigger response model.
Type:
- Object
Properties:
| Name | Type | Description |
|---|---|---|
namespace |
string | |
name |
string | |
version |
string | |
publish |
boolean | |
annotations |
array.<string> | |
parameters |
array.<string> | |
trigger |
TriggerLimits | |
rules |
Rule | |
updated |
integer |
- Source:
TriggerLimits
Trigger Limits model.
Type:
- Object
Properties:
| Name | Type | Description |
|---|---|---|
description |
string |
- Source:


@(user: models.users.User) @main("The Clayton Hotel", user){
This hotel is conveniently situated close to the Central Business Park and Leopardstown racecourse and serviced by the Luas light rail system and Aircoach. The bedrooms are spacious and well appointed. Contemporary in style, the open-plan public areas include a spacious lounge bar with snack food available throughout the day and lunch and dinner is served in the Restaurant. There is a gym and a choice of conference rooms including the Power Suite. Underground parking is available.


{{ page.title | escape }}
Hi, I'm a Web developer living in Melbourne, born in London with Polish heritage. I like to write and read about anything Design, Code and User Experiece (UX) related. ✌
{{ content }}