text:
Dylan, Bob / Cutting Edge: 1965-1966 [The Bootleg Series, Vol. 12 Sampler], The
Album: Cutting Edge: 1965-1966 [The Bootleg Series, Vol. 12 Sampler], The Collection: General
Artist: Dylan, Bob Added: 01/2016
Week Ending: 10 Apr 3 Apr 27 Mar 20 Mar 13 Mar 6 Mar 28 Feb 21 Feb
1. Tue, 12 Sep 17: SOL·MAX·ION
Clean Copper Radio & Hot Topics 4. Thu, 20 Jul 17: SOL·MAX·ION
Clean Copper Radio
2. Thu, 07 Sep 17: SOL·MAX·ION
Clean Copper Radio Talk & Hot Topics 5. Tue, 07 Feb 17: Sarah Bellum
That's Not Bluegrass.
3. Tue, 22 Aug 17: Dj Arid & DJ Bear
Waste FM 6. Thu, 02 Feb 17: The Twisted Kid
The Nucleus
Warren Beer
Review: Bob Dylan 1965 - 1966, The Cutting Edge Sampler
Label: Columbia Legacy
Reviewed by: Warren Beer
This sampler is a set of recordings from 1965 and 1966 by master Rock and Folk lyricist Bob Dylan. A collection of unreleased demos and outtakes from rehearsal sessions. Each one is a gem but I highly recommend Tracks 1, 2, 8, 9.
No FCC issues
1. (2:56) Solo acoustic guitar rendition of this swaying song about a perplexing woman
2. (2:37) Fun and raucous early Dylan electric rock & roll hit single; very peppy
3. (1:43) Partial rehearsal recording of "Like a Rolling Stone"
4. (3:28) An electric blues about a frustrated traveler; lazy shuffling tempo rehearsal version
5. (1:59) Piano folk ballad with beautiful harmonica solo; partial rehearsal version
6. (5:37) Slow shuffling ballad about a nightmarish day in Juarez, Mexico
7. (3:28) A full-blown rock & roll attack against Dylan's folkie persona
8. (6:01) A garage band rock & roll sound sneering at a controversial female character
9. (7:39) One of Dylan's best raucous rocker with him howling about a love who isn't there
10. (4:56) A slow song with beautiful underlying organ and guitar parts, same chord progression as "Like a Rolling Stone." Has a San Francisco reference
11. (3:26) A sarcastic up tempo view of a popular female high fashion item of the early 1960's
12. (4:01) A speedier version than the "Blonde on Blonde" rendition with lyrics tightly compressed into the music's fast-paced rhythm
13. (5:04) One of only two versions of this song recorded by Dylan, very up-tempo with great harmonica solo
14. (3:21) Fast paced 8 bar blues with lively harmonica solo
15. (2:51) Very fast upbeat bouncy song which was a big Dylan hit
1. She Belongs To Me - Take 1 (1/13/1965) 8. Queen Jane Approximately - Take 5 (8/2/1965)
2. Subterranean Homesick Blues - Take 1 (1/14/1965) 9. Visions Of Johanna - Take 5 (11/30/1965)
3. Like A Rolling Stone - Take 5 (6/15/1965) 10. She's Your Lover Now - Take 6 (1/21/1966)
4. It Takes A Lot To Laugh, It Takes A Train To Cry - Take 8 (6/15/1965) 11. Leopard-Skin Pill-Box Hat - Take 8 (2/14/1966)
5. Desolution Row - Take 2 (8/4/1965) 12. Stuck Inside Of Mobile With The Memphis Blues Again - Take 13 (2/17/1966)
6. Just Like Tom Thumb's Blues - Take 3 (3/8/1965) 13. Absolutely Sweet Marie - Take 1 (3/8/1966)
7. Highway 61 Revisited - Take 3 (8/2/1965) 14. Pledging My Time - Take 1 (3/8/1966)
15. I Want You - Take 4 (3/10/1966)


text:
Längsee is a lake of Tyrol, Austria.

See also
 Egelsee (Tyrol)
 Hechtsee

Lakes of Tyrol (state)
Kufstein

redpajama_set_name: RedPajamaWikipedia


Following his release from jail after a successful habeus corpus application just hours before his fight with Frenchman Hassan Amzile, Namibian sexual assault-accused boxer Jonas Junias Jonas has since remained in the custody of the Namibian Embassy in Brazil. Parallel to the boxer's legal representatives dealing with the case on the ground, the Olympics' team Chef de Mission at the Rio 2016 games, Jesse Schickerling, has called on the Namibian government to intervene in order to see the athlete vindicated.
"We believe that as an ambassador of Namibia through the sports, which he (Jonas) has excelled, it is not only the duty of the Namibia National Olympic Committee (NNOC) to assist Jonas in his quest for his vindication, but also other entities responsible for sports in the Republic of Namibia. In as much as Jonas now finds himself in the custody and care of the embassy, and to that extent is on Namibian soil, we believe that it is time the government of the Republic of Namibia intervene and assist, and start negotiations for his release and return to the Republic of Namibia," said Schickerling through media personnel in Rio.
Via the same medium, Schickerling also narrated the events following Jonas' release in reference to his case.
"In a notice dated 14 August 2016, Jonas was advised to appear before the Ethics Commission of the International Olympics Committee on 15 August at 17h30. The notice was only delivered to the Chef de Mission on 15 August at approximately 10h00. During the course of the day, we urgently attempted to prepare and finalise a statement on behalf of Jonas. During the hearing, which occurred at 17h30, we requested for an extension of time from the Ethics Commission to discuss the draft statement with Jonas and to also finalise that statement.
"The Ethics Commission granted our request for an extension and the matter was adjourned for 10h00 on 16 August 2016. At that hearing, we entered the statement with the commission and in that statement, we in essence recorded the history of the criminal proceedings and the three court applications and stressed that Jonas exercised his right to remain silent in the criminal proceedings and that as such he will not at this moment in time make a statement on the facts or the merits of the matter.
"We have requested the Ethics Commission to please or kindly postpone the proceedings until such time as Jonas enters his plea and plea explanation in the criminal proceedings at which time he will make a statement to the Ethics Commission. On that basis, we requested the Ethics Commission to make a ruling at our request.
With regards to the criminal proceedings surrounding the case, the NNOC has scheduled a consultation with the legal practitioners today where they will tailor out a way forward in pursuit of Jonas' vindication. It is believed that the Brazil courts are on recess until 23 August. While adding to the fact that the delegation firmly believes in the innocence of the accused, the NNOC shared their experience over the past eight days that points to the extreme exposure and vulnerability acclaimed athletes face.
While the Court for Arbitration in sports does provide a forum for the protection of athletes' interest in and during games and in general sports-related disputes. When it comes to accusations or charges of the nature levelled against Jonas, during game times at games venue or the Olympics village, no structure is in place to assist such an athlete and to ensure that in such circumstances, the athlete is protected.


BFT School Trippers are special bus routes designed to account for overload at the beginning and end of the school day. This service allows students to get home without having to wait for a second bus if the first one they see is full. This service is accessible to all customers, not just students.
The Route 123 Tripper does not run when the Richland School District (RSD) is out of session whether it be for a teacher work day, holiday, conference or early release.
This tripper route is discontinued; please use Route 63D.
This tripper route is discontinued; please use Route 68D.


text:
Investigation ongoing in Monday robbery at Lanark LCBO
On Oct. 2 at approximately 12:20 p.m. officers from the Ontario Provincial Police (OPP) Lanark County detachment responded to the report of a robbery at the LCBO on Clarence Street in the Village of Lanark.
Preliminary details of the investigation indicate that two males entered the store demanding money and are believed to have left on foot from the scene.
Nobody was injured in the incident.
Sacred Heart Catholic School and Maple Grove Public School went into a hold and secure mode for precautionary measures without incident.
This investigation is in its early stages and an update will be issued when confirmed and accurate information becomes available.
The Lanark County OPP Crime Unit are continuing with the investigation and are urging any person who in the area at the time of the robbery or has any information regarding the person(s) responsible for this crime you are asked to contact the Lanark County OPP detachment at 613-267-2626 during business hours or 24/7 at 1-888-310-1122.
Should you wish to remain anonymous, you can call Crime Stoppers at 1-800-222-8477 (TIPS) where you may be eligible to receive a cash reward of up to $2000
OPP search for two males involved area home invasion
Lanark County OPP charge man in Lanark Village robbery
OPP to be conducting community satisfaction surveys by phone
OPP East Region detachment has a new commander

redpajama_set_name: RedPajamaCommonCrawl


After breakfast, we drove down to the Lizard Peninsula and visited Goonhilly Earth Station, the biggest satellite earth station in the world.
Merlin: With a diameter of 32 meters, this is the biggest dish at Goonhilly.
Guinevere: Usually the only dish facing east. Connects Europe with Australia and several Asian countries via an Intelsat satellite positioned over the Indian Ocean.
Arthur: First dish opened at Goonhilly in 1962 to track the fast-moving Telstar satellite. Nowadays, all communication satellites are in geostationary orbits, i.e. from the ground they appear to remain at a fixed position in the sky.
Our next stop was Falmouth. After a quick walk down to the harbour, we drove up to Pendennis Castle which overlooks the mouth of the River Fal. It was originally built during the reign of Henry VIII to protect the coast against French and Spanish attacks. It was used by the military till the 1950s. During World War II, it served as quarters for British and American troops preparing for the invasion of Normandy.


text:
By Andrew Lynch 6th Medical Recruiting Battalion
DOWNLOAD HI-RES / PHOTO DETAILS
Bruce Schamburek, education services specialist for the 6th Medical Recruiting Battalion, and Captain Katie Flatt, U.S. Army health care recruiter from the Aurora Medical Recruiting Station, speak to attendees of the Trauma, Critical Care & Acute Care Surgery Conference at Caesars Palace on 10 April. Schamburek and Flatt were on hand, along with members of the Las Vegas Medical Recruiting Station, to explain the benefits and opportunities of a career in Army Medicine. For more information on the Army's more than 80 medical specialties go to healthcare.goarmy.com.
Captain Katie Flatt, U.S. Army health care recruiter for the Aurora Medical Recruiting Station, speaks to attendees of the Trauma, Critical Care & Acute Care Surgery Conference at Caesars Palace on 10 April. Flatt was on hand, along with members of the 6th Medical Recruiting Brigade and Las Vegas Medical Recruiting Station, to explain the benefits and opportunities of a career in Army Medicine. For more information on the Army's more than 80 medical specialties go to healthcare.goarmy.com.
Captain Katie Flatt, U.S. Army health care recruiter for the Aurora Medical Recruiting Station, speaks to attendees of the Trauma, Critical Care & Acute Care Surgery Conference at Caesars Palace on 10 April. Flatt was on hand, along with members of the 6th Medical Recruiting Brigade and Las Vegas Medical Recruiting Station, to explain the benefits and opportunities of a career in Army Medicine. Find more information on the Army's more than 80 medical specialties at healthcare.goarmy.com.
Bruce Schamburek, education services specialist for the 6th Medical Recruiting Battalion, speaks to an attendee of the Trauma, Critical Care & Acute Care Surgery Conference at Caesars Palace on 10 April. Schamburek was on hand, along with members of the Las Vegas Medical Recruiting Station and Aurora Medical Recruiting Station, to explain the benefits and opportunities of a career in Army Medicine. For more information on the Army's more than 80 medical specialties go to healthcare.goarmy.com.
LAS VEGAS, April 10, 2018 — U.S. Army health care recruiters explained the benefits of a career in Army Medicine to attendees of the Trauma, Critical Care & Acute Care Surgery Conference at Caesars Palace on 10 April in Las Vegas, Nev. Soldiers and Army Civilians from the 6th Medical Recruiting Battalion and Las Vegas and Aurora Medical Recruiting Stations were on hand to discuss the Army's more than 80 medical specialties for both active duty and reserve opportunities.


Tap The Thumbnail Bellow to See Related Gallery of "French Style Comforter Sets Bedding 2065 16"
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For several years we have been aware that the plant compound, rosmarinic acid has anti-Alzheimer's properties. Now, a recently published research paper has described how carnosic acid (a compound that occurs in the common culinary herbs, rosemary and sage) also has the capacity to prevent and possibly treat this distressing disease.
In ground-breaking research, scientists from Iwate University in Japan and the Burnham Institute for Medical Research in California have found that the antioxidant, carnosic acid protects the brain from free radical damage.
Oxidative damage, caused by excess free radicals is a major cause of neurodegenerative diseases such as stroke, Alzheimer's and Parkinson's diseases.
Writing in the November 8 edition of the Journal of Neurochemistry the scientists involved in this research describe the novel way in which carnosic acid works to protect the brain from free radical damage.
Unlike many other plant compounds, antioxidants and drugs, carnosic acid is able to cross the blood-brain barrier – a physiological barricade that protects the brain from harmful agents such as microorganisms and toxins.
Moreover, carnosic acid does not have any undesirable effects on normal brain cells and is only activated in the presence of free radical damage. In other words, this extraordinary compound lies dormant until sparked into action by the same disease process that it then neutralizes!
This remarkable feature of carnosic acid stands in stark contrast to that of many other naturally occurring antioxidants that do not have this phenomenal property. The others all tend to neutralize free radicals as they come into contact with them.
The problem here is that the body actually uses some free radicals to kill cancer cells and invading microbes. A high concentration of one or more antioxidants may eliminate too many free radicals leaving none available for the cell's defensive processes to use to destroy tumor cells, bacteria, viruses and other organisms.
Carnosic acid, on the other hand waits innocuously until the damage caused by the excess free radicals triggers its protective response.
The property whereby a therapeutic agent works only in the presence of the pathological process that it is capable of acting against is called pathological-activated-therapy (PAT). It is one of the goals of modern science to develop drugs that exhibit PAT properties as they will produce far fewer toxic side effects than medicines that do not have this attribute. Carnosic acid is one of the first naturally occurring compounds to demonstrate this valuable characteristic.
1. It protects against the narrowing of the left and right middle cerebral arteries – two of the major arteries carrying blood to the brain. Narrowing of these arteries with age is a common and important factor contributing to the development of neurodegenerative diseases.
2. Carnosic acid increases the body's production of the antioxidant, glutathione. Glutathione is one of the most important antioxidants that help to protect the brain against free radical damage.
This landmark research has unearthed yet more pieces of a fascinating jigsaw puzzle. The picture that is emerging is one composed of many common culinary herbs and spices that can help to prevent and treat these ubiquitous neurodegenerative diseases.
While scientists continue to look for ways to produce more powerful drugs based on phytochemicals like carnosic acid, we should avail ourselves of herbs and spices like rosemary, sage and others that contain carnosic acid, rosemarinic acid and many other compounds whose neuro-protective properties have yet to be discovered by the scientific world.
Furthermore, as many of the compounds in culinary herbs and spices work synergistically with one another, we need to eat a wide variety of these vitally important foods on a daily basis.
For those who do not eat a diet rich in herbs and spices it is imperative that they use a balanced spice supplement in order to reduce the risk of developing Alzheimer's and other degenerative diseases like cancer, heart disease and diabetes that are all associated with free radical damage, inflammation and the aging process.


Having more than 10 years of Java and Golang experience. Building large scale software infrastructure during the last 4 years. Maintaining big Selenium cluster with 5000+ browsers being run in parallel. One of the core contributors of the Selenoid project and related tools.
Selenoid: get rid of Selenium server!
Browser tests are known to be the flakiest ones. This is partly because browser infrastructure is complicated to maintain. But the second reason is – main stream browser automation tools such as Selenium server are far from being efficient.
As your company grows – your browser automation infrastructure should easily adapt to increased loads and new requirements. Unfortunately this is not so easy to get really scalable Selenium cluster using only the main stream tools. But there are some new open-source tools and approaches that can be used to deliver really scalable Selenium cluster.
This tutorial covers an emerging browser automation tool – Selenoid, a truly efficient replacement of the standard Selenium server. I will explain how it works and why it is so fast. I will show in details features like video recording, sending logs and statistics to centralized storage and how to easily visualize this data.
Reliable. Scalable. Lightning fast. Running 1 000 000 Android tests with Selenoid.


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Previous Story Pro Bowlers want better officiating, more accountability Next Story With voodoo dolls, cookies Saints fans protest missed call
Lock, Jones among quarterback prospects at Senior Bowl
By Associated Press January 25, 2019 12:10 pm
MOBILE, Ala. (AP) — Drew Lock, Daniel Jones and the other Senior Bowl quarterbacks have spent the week practicing with new teammates and learning a simplified NFL offense.
Now, the NFL prospects get to play an actual game Saturday, even though most of the league's coaches, scouts and executives high-tailed it out of town long before kickoff.
That doesn't mean it won't be part of the evaluation process for teams considering using a draft pick on one of the quarterbacks, or any of their teammates, in the showcase game for seniors and junior graduates.
"It'll certainly weigh in, because it's full-speed, live," said Jon Gruden, head coach of the Oakland Raiders and the North team. "When you're live, that's the only evaluation that we're lacking. We could have all these controlled environments all we want, but the best evaluation is live."
Lock and Jones, North teammates, are considered likely first-round draft picks. Other top quarterback prospects, like Ohio State's Dwayne Haskins and Oklahoma Heisman Trophy winner Kyler Murray , have also declared for the draft.
The game might not be the biggest part of the equation this week, but it can definitely be a factor.
"I think it matters a lot how well we lead the team down the field, how well you throw the ball," Jones said. "I think that matters a lot. Winning the game is important for me."
The game also features two of the top pass rushers in the draft, Mississippi State's Montez Sweat and Louisiana Tech's Jaylon Ferguson , and a number of other likely early-round picks on the offensive line, like Washington State's Andre Dillard. Plus playmakers such as wide receivers Deebo Samuel of South Carolina and Georgia State's Penny Hart.
The game's eight quarterbacks took different paths to this point. Lock's career as a four-year starter for the Tigers is almost mundane compared to Washington State's Gardner Minshew , whose breakout season came at school No. 4.
West Virginia's Will Grier (Florida), North Carolina State's Ryan Finley (Boise State) and Auburn's Jarrett Stidham (Baylor) also moved on from their initial schools. Stidham, Jones and Buffalo's 6-foot-7, 249-pound Tyree Jackson skipped their senior seasons to enter the draft. Finley and Penn State's Trace McSorley are also playing for the North.
They all bring confidence to the weeklong NFL audition.
"From a competitor standpoint, I'm going to have to tell you I do believe I'm the best quarterback in the draft," said Lock, who passed for 10,000-plus yards at Missouri and set a Southeastern Conference record with 44 touchdown passes as a junior.
Grier offers no caveats in his own self-belief and scoffs at any notion that his arm strength might be an issue.
"I don't understand some of the things that have been put out there," he said. "I'm the best quarterback in this draft. I think my arm talent's there and my play will speak for itself.
"Ask my receivers. Ask the guys at Florida. Ask the guys I played against if my arm strength was a problem."
There's no doubting his production. Grier passed for 300-plus yards in 19 of his 22 games at West Virginia and was fourth in the Heisman voting. Minshew was fifth after leading the nation in passing yards per game.
Stidham didn't put up such big numbers and had a drop-off in production last season while the Tigers struggled on the offensive line and in developing consistent playmakers.
He finished with a 373-yard, five-touchdown performance in a Music City Bowl romp over Purdue and tried to build on that momentum this week.
"You get a chance to meet and play in front of every type of NFL personnel that you can think of," Stidham said. "It's a great opportunity to really showcase your skills. My job is to come out here and execute a new offense and do it at a high level."
More AP college football: https://apnews.com/Collegefootball and https://twitter.com/AP_Top25
Andre Dillard AP Online - Sports AP Online College Sports News Athlete recruiting Auburn Tigers football College football College sports Daniel Jones drew lock Dwayne Haskins Football Gardner Minshew Jarrett Stidham Jaylon Ferguson Jon Gruden Missouri Tigers football Montez Sweat National Football League National Football League NFL football Penny Hart Professional football Ryan Finley s Southeastern Conference football Sports Trace McSorley Tyree Jackson Tyshun Samuel
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Take-Out > Tickets are still available for the Martin Luther movie!
Tickets are still available for the Martin Luther movie!
Martin Luther: The Idea That Changed the World.
Now you can see this compelling new movie at a special screening at your local movie theater hosted by Trinity.
Order your tickets by clicking on the link below. We still have 65 tickets available and must sell all of them in order to guarantee the showing. Get your tickets early. The cost is $11.00 per ticket. Tickets can be ordered and paid for online OR you can reserve your tickets by paying at the front desk during the week or Sunday after worship services.


text:
Q: Google Maps in Grayscale Is there any way to display a Google Map (embedded via the Javascript API) in grayscale without losing any other functionality?

A: There's a little shorter way (comparing to @Roatin Marth's best answer) to make your Google map grayscale with Google Maps JavaScript API v3 by directly including styles you generated with Google Maps API Styled Map Wizard into google.maps.MapOptions object:
var container = document.getElementById('map_canvas');
var mapOptions = {
  zoom: 11,
  center: new google.maps.LatLng(40.6743890, -73.9455),
  styles: [{
    stylers: [{
      saturation: -100
    }]
  }]
};

var map = new google.maps.Map(container, mapOptions);

You get the array set under styles property in mapOptions variable by clicking onto "Show JSON" button inside Map Style panel when finishing your styles customisation using Google Maps API Styled Map Wizard.

A: Use the CSS filter property to convert the google map to black and white. 100% is completely black and white
selector {
  -webkit-filter: grayscale(100%); /* Safari 6.0 - 9.0 */
  filter: grayscale(100%);
}

A: Just use CSS3 grayscale() filter effect for the Google maps iframe element! It works because the js code prints images.
iframe {
  -webkit-filter: grayscale(100%);
  -moz-filter: grayscale(100%);
  -ms-filter: grayscale(100%);
  -o-filter: grayscale(100%);
  filter: grayscale(100%);
  filter: url(grayscale.svg); /* Firefox 4+ */
  filter: gray; /* IE 6-9 */
}

Try also to target only the background images by applying this to the very first div element under the div.gm-style (or any other wrapper). I don't know if Google changes the dom structure often but at the moment speaking (25 Nov 2014) this works fine.
iframe .gm-style > div:first-child {
  // Same code here
}

A: Yes, in V3 of the api they have introduced StyledMaps.
They've even provided a tool for you to generate the code for the styles you like. Slide the "Saturation" all the way down and you've got grayscale going on!
The following example displays a grayscale map of Brooklyn:
var map;
var brooklyn = new google.maps.LatLng(40.6743890, -73.9455);

var stylez = [
    {
      featureType: "all",
      elementType: "all",
      stylers: [
        { saturation: -100 } // <-- THIS
      ]
    }
];

var mapOptions = {
    zoom: 11,
    center: brooklyn,
    mapTypeControlOptions: {
         mapTypeIds: [google.maps.MapTypeId.ROADMAP, 'tehgrayz']
    }
};

map = new google.maps.Map(document.getElementById("map_canvas"), mapOptions);

var mapType = new google.maps.StyledMapType(stylez, { name:"Grayscale" });
map.mapTypes.set('tehgrayz', mapType);
map.setMapTypeId('tehgrayz');

A: IE has the filter: gray directive.
It renders any HTML element in grayscale. JSFiddle here.
You may be able to apply this to the map's parent DIV. It may turn the map it contains in to a grayscale represtenation. I can't say whether this will work without side effects - you'd have to try. But it's well possible.
IE only, though, but supported since Version 4.
Grayscale Filter docs at MSDN
Other than that, I think there is a Flash API for Maps, isn't there? It might be easier to achieve there.

A: Check out this:
http://snazzymaps.com/style/15/subtle-grayscale
Works like a charm :)

A: Yes, there is a third party API for Google maps that provides gray-scale (snazzymaps).
Its very simple. You can use this website to get the different color scheme for google maps.
http://snazzymaps.com

A: Apart from writing the good people at Google and asking them to create grey-scale versions of all of their image tiles and an optional API parameter, no.

redpajama_set_name: RedPajamaStackExchange


The day we founded Roadmunk we didn't talk business plans or product strategy. Both me and Tomas, my co-founder, only had one topic on our minds: culture.
It was a freezing winter day in 2012, so we holed up at Tomas' apartment, made tea, and talked about the values we wanted to bring to our company.
What kind of team did we want to build? How would our personal beliefs be reflected in our business? By the end of the conversation, we knew we wanted to create a company with culture at its core.
When we talked about culture, we didn't just mean pingpong and snacks. (Although those are fun to have.) We wanted a culture that went deeper than a bag of trail mix.
For us, it's about balance: between career and fun, between hard work and freedom, between business and friendship. It can be tough, because although our team is small, it's split. Our operations staff works from Toronto, while our dev team works in Waterloo. I'll often go weeks without a face-to-face with some of my employees.
The decision to ship our whole company to Costa Rica for a month came from a desire to truly live by our culture-first mandate. I admit, I wasn't immediately on board. There was the cost, and the optics — to our investors, to our clients. But we didn't want culture to be something that we said we cared about, but didn't actually live and breathe.
I'd never been big on formal team-building — it implies you don't have a team to begin with. (Plus, it's corny.) We were also very aware that so much intense time with coworkers would come with its share of challenges. But that actually reinforced why we were going.
This was about getting to a deeper level — even if that meant uncovering some muck along the way. It was about finding an even more intuitive cadence within a team that was already in sync.
Step one: Find a house with a strong internet connection.
Step two: GO FOR IT.
Okay, it wasn't that simple. First, this didn't happen overnight. Although we're still a young startup, it took three years to get our company to a place where an extended remote work excursion was feasible. After browsing Nomad List and considering several countries in Central America, we settled on Costa Rica. The beach was too hot and distracting, so we found an amazing Airbnb in Monteverde, a town located inland, where the weather is more temperate.
Technical considerations included security, privacy and (most importantly) internet. We made each potential host send us a screenshot of an internet speed test. Maybe they thought we were neurotic. (Maybe we are neurotic?) But it was better than having to turn around and come home because we couldn't do any work.
Roadmunk covered room and board, plus the cost of transportation to and from Monteverde. Employees just had to cover their flight and any after-hours activities. Some non-work highlights included a crazy, mile-and-a-half-long zipline over the canopy, trips to the coast for the weekend, and a gorgeous hike through Monteverde.
The house itself was big enough to comfortably sleep our team of more than 10 full-timers. It had several guest cabins, so people could have their own space to sleep. And there was an enormous amphitheatre on the property, where we did yoga every day. All in all, an awesome spot to find balance between working hard and relaxing hard.
There were, obviously, some questions going into the trip. Would we actually be productive? Would we drive each other nuts? What if we all got sunburns?! In the end, the advantages definitely outweighed the anxieties.
There's a lot you can understand from cooking with another person. During the trip, two people were assigned to cook each meal. Which meant that employees who rarely overlap had the chance to collaborate on a fun project: making food!
When it was my turn to cook with Sameena, our head of product, something very cool happened. We chatted for about two minutes at the beginning of the process, then completely immersed ourselves in chopping and prepping. I don't think we said another word until the meal was complete. We were completely in tune.
At the office, things are hectic. But by cooking together and living together, we got on the same wavelength. Employees who don't regularly work together were able to reach a level of trust and understanding they never would have otherwise, while employees from the same teams were able to strengthen their bond.
We broke through a few stubborn business roadblocks during our time in Costa Rica. Getting out of our office — and far away from our usual frustrations — gave us some serious clarity. We nailed down our mission statement. We cracked a design conundrum on an upcoming feature that had been nagging us for months. We had a lot of high-level conversations about where the company will be going in the next 2, 3, 5, 10 years. And we proved that remote work can be just as effective as staying put in your hometown office.
Which isn't to say it was all clear skies. One UX dilemma, in particular, caused a lot of friction: there were definitely some tense back-and-forths as we worked towards a solution. That's where walks came in useful. (I've always loved the idea of 1-on-1 walking meetings.) By making space for daily walk-and-talks, we were able to face stubborn problems from a more relaxed perspective. And yeah, the scenery didn't hurt.
This was my first time in Costa Rica, and I was completely taken with the warmth of the community where we stayed. My impression of the Costa Rican culture is that it is based heavily around relationship-building. Canadians may be known for being polite, but we also keep our distance. By immersing myself in a very different milieu, I gained a new perspective on my relationships — both with my team and Roadmunk users.
Key word here: empathy. Spending a month in a house with your coworkers, you inevitably get closer. Guards come down, stories get told, you learn what makes them tick and what irritates them. And that rubs off on our users.
At the end of the day, building a world-class product is about understanding your customers' motivations and needs, then putting yourself in their shoes and delivering a tool they will love using. Taking a month to focus deeply on the human side of our business offered a profound sense of empathy — which inevitably boosts the bottom line.
Culture isn't a gimmick or a "nice to have." On a business level, our trip was entirely justified: during our month away, we surpassed several sales milestones. It was the strongest month we'd had up to that point.
This is why putting culture first doesn't mean putting business second. I strongly believe that everything flows from culture. As Roadmunk gets bigger, it won't always be possible to take our whole team to another country for a period of remote work. But the fact that our core team shared this experience will positively impact the company for years to come.
Only good things can result from a team that knows each other and grows together. That's why I took my team to Costa Rica for a month. Not just to build an exceptional culture. But to build an exceptional culture that would translate into an exceptional business.
This post was originally published on the Roadmunk blog.
Latif Nanji is the founder of Roadmunk.


text:
What phenomenon/situation would thwart the development of electronics by a society?
I'm interested in exploring an advanced society where electronic technology and infrastructure were not developed. What plausible natural phenomenon or context would prevent, or at least strongly discourage, the widespread utilization of electrical energy?
Some possible ideas that come to my mind are disruptive magnetic fields or frequent geomagnetic storms. I'm sure there are many other possibilities as well, perhaps including aquatic civilization or inaccessibility of suitable mineral resources.
Bonus points for considering:
Technological alternatives that might emerge more prominently in the electrical void (e.g., fluidics)
The phenomenon's other potential impacts on humanoid life
For reference, see these two semi-related questions:
What would be the state of society without semiconductors?
What kind of event could stop electricity?
Edit: In response to concerns about broadness, I'm happy to provide more structure to guide answers. Ideally, let's imagine a terrestrial society that develops similarly to human civilization through the dawn of electricity. Needless to say, some complications may arise from the phenomenon that curtails electrical technology. While I think this question could help other related questions, the "best" answer will be one that yields a society most similar, at least temporarily, to 19th Century Europe.
science-based planets
ZeroFluxZeroFlux
$\begingroup$ As I don't have enough experience nor the time at the moment to flesh this out for a full answer, I'll suggest it here. Some kind of charged atmosphere or a change in the magnetic field of the planet, or especially increased solar activity could cause electrical components to be unreliable at best. If there was enough interference the field may be considered upon discovery as being largely useless given their respective environment. Therefore no further development would take place, and instead research might be done in harnessing the interceding force. $\endgroup$ – Michael May 13 '15 at 20:37
$\begingroup$ This is very broad. You may want to start with something more defined, like whether the society creating the electronics is aquatic or not. There are so many possible equally valid solutions when we're creating the entire world in which electronics don't work. $\endgroup$ – Samuel May 13 '15 at 21:00
$\begingroup$ @ZeroFlux Understandable. That's called idea generation, which isn't really on topic for this site. You should create the world and then ask us what needs to be tweaked inside that world which disallows electronics. $\endgroup$ – Samuel May 13 '15 at 21:12
$\begingroup$ @Samuel - Fair point! I'm simply hoping to encounter a few new ideas. If it helps, I would probably prefer a terrestrial society. $\endgroup$ – ZeroFlux May 13 '15 at 21:12
$\begingroup$ @Samuel I think a little broadness is okay, per the related Area51 discussion, but I've provided some additional (optional) guidance to the question to help ease that concern. discuss.area51.stackexchange.com/questions/14298/… $\endgroup$ – ZeroFlux May 13 '15 at 21:51
Absence of electricity: Religion
The Thunder is sacred and thou shall not harness It.
It's not a far stretch - actually, it's a minimal one, considering how the huge Aztec empire did away with the wheel, an object so common and idea so trivial it's impossible it could not be invented - but being the sacred Face of the Sun, it was forbidden from such mundane use as transport.
Absence of electonics: Solar Flares
Solar storm of 1859 disrupted all electricity-based communication but didn't destroy it.
Telegraph systems all over Europe and North America failed, in some cases giving telegraph operators electric shocks. Telegraph pylons threw sparks.[8] Some telegraph operators could continue to send and receive messages despite having disconnected their power supplies.
Had it happened today, most of electronics would have fried. Electron lamp based circuitry would probably survive in major part, but the computers, satellites, even airplane avionics up in the air, would have suffered greatly.
If solar flares of such magnitude were a monthly occurrence, development of integrated circuit would be stopped in its tracks. We'd still have a standard circuitry with discrete transistors, but we'd never achieve the scale of miniaturization of today's computers: instead of several nanometres of raster, it would be several millimetres, a million times bigger circuits to do the same job; and top speed (currently severely crippled by speed of light) at least a million times lower too - not to mention total size restrictions - make the lines too long and the solar flares will fry them.
SF.SF.
$\begingroup$ No, solar flares do not fry electronics, that is completely wrong. Electricity 101: a change in a magnetic field over a conductor (such as a power line or telegraph wire) induces a voltage. Solar flares can cause geomagnetic storms. Geomagntic storms give long-duration, but slow changes to the Earth's magnetic field. This means that in long transmission lines, there can be over/under-voltages in case of a geomagnetic storm, and these can be troublesome. But very short conductors — such as the insides of electronics — are not affected by this. $\endgroup$ – MichaelK Nov 22 '17 at 8:50
$\begingroup$ What you are thinking about are Electromagnetic Pulses. A pulse is a very short-duration and intense event. So that even the tiny conductors in electronics generate enough voltage to cause problems. But EMP's and Geomagnetric Storms are two different things. $\endgroup$ – MichaelK Nov 22 '17 at 8:52
$\begingroup$ @MichaelK: while disconnected portable electronics would be merely disturbed (radio wave noise suppressing signal but no direct damage) about all of infrastructure is based on devices connected to wired energy and data networks. Cell phones are useless with cell towers fried due to overvoltage in power grid; surge protection usually helps against momentary disturbances, not ones lasting for days. Never mind if a couple miles of line can shock the operator, a couple meters of PSU wire can easily damage a 5V+-5% USB device. And events like those repeating monthly, would cripple progress. $\endgroup$ – SF. Nov 22 '17 at 9:43
$\begingroup$ The only reason we do not have surge protection now is because we have always been spoiled with a relatively well-behaved star. If we change the conditions of that and makes Sol constantly spew CME's at us, this means that we start developing electricity under those conditions, and so we already from the start engineer solutions to that problem. We may even use that phenomena to make electricity. $\endgroup$ – MichaelK Nov 22 '17 at 9:49
Absence of metals
One thing that all electronics that have been developed on Earth rely on is metal conductors. What if your society didn't have access to the right materials to make these things?
Your humanoids, for instance, could have evolved on giant floating islands of vegetation on the surface of a world covered with water. These islands, floating miles above the sea floor far beneath the waves, have all of the elements that humanoids need to evolve, but without any solid ground, the humanoids that evolved on them don't have any metal mines. Without metal mines, no metal. Without metal, no wires. Without wires, no electronics.
It's possible, even probable, that said humanoids would one day develop technology to reach the bottom and then start developing metallurgy, but a lack of surface from which to mine metals would significantly delay the development of electronics.
It may also be possible to have a planet that simply doesn't have appreciable quantities of metal, though I'm not entirely sure how such a planet could form.
ckerschckersch
I thought of two options immediately after this (the second is better because it fits with the fact you want the cause to be natural):
1 - Moving backwards after devastating consequences of a technological singularity:
Example: Technology has evolved so much, robots took over and there were a lot of deaths. Humanity prevailed and has sworn never to go back that route again by forbidding anything electronics related. Everything went back to purely mechanical machines.
2 - Not having made the technological leap in the first place: This is the better option according to what you asked in your later comments. Humans never made the jump to electronics in the first place and still lived in a 19th century setting. This can be done by thinking about the chain of events that has actually led to the advent of electronics, and breaking a link here, another there...
Technology is what it is today partly because some materials violated Ohm's law, and some people noticed it: What if the semiconductor technology has never been what it is? There can be a bunch of reasons for that, but let's screw with the people side of Physics:
Antoine Becquerel died in Spain and never had his son, Edmond Becquerel (who'd never discover the photovoltaic effect, neither have his son, Henri Becquerel, who'd never work on radioactivity with the Curies).
The University of Göttingen was never built (can you imagine the dramatic effect on humanity? All the great people who'd never set foot there, or meet.)
Bernoulli never taught Euler, who'd have made great contributions to religion I suppose.
But you'll have to do a lot of that.. And since technology followed a hockey stick curve (which is funny, since it's the non-linearity in semiconductors and the fact they're characterized by a hockey-stick curve that makes them interesting), the difference between 19th century Europe and the 20th century would've been marginal if we didn't hit the inflection point... Which brings me to this:
You can toy with it and set History to a major fail that would have wiped clean most of the talent that was required to get to the wafer. Maybe a disturbed person put a bomb at the Solvay Conference. Can you imagine the consequences? Most of the big names would have died and we'd never have integrated circuits. If you want to go hardcore (and lamer), you can put a population terrified by science who'd try their best to sabotage any progress.
You can force it by going the "What if there was no silicon in our galaxy?", but since it's one of the most abundant elements, this would be pushing too far.
That's what came to my mind for now. Maybe I'll have something better
Jugurtha HadjarJugurtha Hadjar
By some freak occurrence the inhabitants of your civilisation develop EHS or Electromagnetic Hypersensitivity. This makes them unable to work with or be near electronic devices. Being an advanced civilisation they look for and find alternatives.
AbhishekAbhishek
Not quite an answer to your question, but perhaps close: A world without digital electronics.
Give analog computers, both mechanical and electronic, a better start. Digital computers could be overshadowed in the way that VHS defeated Betamax. It might help if there was no world war at the time to push code-breaking applications. Punch card machines for digital sorting would remain incompatible with computers for analog calculations.
o.m.o.m.
Not the answer you're looking for? Browse other questions tagged science-based planets or ask your own question.
What planetary conditions would make computer and complex electronics useless, or at least difficult to maintain?
The growth and development of the fox's nine tails?
What are the specific effects of an EMP on electronics?
What would the impact of 2 suns have on the development of humanoid ocular organs?
What phenomenon (gravitational?) in space would allow the transfer of humans rapidly between two planets or system-wide?
A non-technological, repeating, phenomenon in the sky, holding its position in the sky for hours


text:
Divisions Mark a Discredited Establishment
Bryan Koulouris
También en español
Robert Reich, the former labor secretary under the Clinton administration, warned the political establishment in a recent article:
"Political insiders don't see that the biggest political phenomenon in America today is a revolt against the 'ruling class' of insiders that have dominated Washington for more than three decades" (robertreich.org 2 Aug 2015).
There is a revolt against racism, a revolt against low wages, a revolt against "politics as usual." Despite a prolonged economic recovery, young people face dead-end jobs, piles of debt, and environmental destruction. The political establishment, bought off by big corporations, is widely distrusted in poll after poll.
The world, meanwhile, is a mess. Ethnic and religious division and war dominate much of the Middle East. China's currency devaluations and stock market crash sent tremors through the global economy. Europe faces austerity policies and the potential to be broken apart. In short, many can see that this system isn't working.
How Could a Racist, Sexist Billionaire Be So Popular?
In this situation, how can a racist, sexist billionaire like Donald Trump get such an echo? Trump gains a hearing because he attacks both parties and all politicians. He openly says that the political system is bought, and he can prove it – he's bought politicians himself! He will not talk in careful tones, and some of the Republican base finds this refreshing. He's also a monster created by the right-wing establishment, who have been stoking up racism and grandstanding with slanders and polarization for over six years.
What's striking is that Trump is gathering support and enthusiasm that mainstream Republicans are unable to mobilize. They were also slow to attack Trump's outlandish and objectionable statements. The Republicans face a fundamental problem: They need to excite a right-wing base that is increasingly out of touch with an electorate that is shifting to the left.
Clinton Unable to Excite People
In the Democratic field, Sanders gathers momentum while Clinton is unable to excite people. This again shows a distrust in the establishment. Both parties' primaries are gaining attention for candidates the big corporations that rule U.S. politics won't get behind. These tremors in the political system and implicit possible independent runs for the presidency foreshadow a bigger coming earthquake in U.S. society.
Reich notes:
"America has long had a ruling class but the public was willing to tolerate it during the three decades after World War II, when prosperity was widely shared and when the Soviet Union posed a palpable threat. Then, the ruling class seemed benevolent and wise.
"Yet in the last three decades – when almost all the nation's economic gains have gone to the top while the wages of most people have gone nowhere – the ruling class has seemed to pad its own pockets at the expense of the rest of America."
Organize to Cut Across Empty Populism
This system is no longer delivering, no longer legitimizing itself as a new generation enters the workforce expecting to do worse than its parents. Movements for jobs, equality, and freedom will come to the forefront and find a political expression. But the Donald Trump phenomenon is a warning to the labor movement and progressive organizations that anti-establishment sentiment can be mobilized behind right-wing and sometimes racist and sexist causes.
In this context, we must organize around the issues that matter to people in our communities, campuses, and workplaces: higher wages, affordable homes, quality schools, and an end to racist policing. This can cut across the empty populism of billionaires while also building a movement that can inspire and motivate people to get active.
Previous articleUniversity of California Introduces $15 Minimum Wage
Next articleSocialism and the Struggle Against Racism
Francesca Gomes


text:
Cloudy with showers. High 78F. Winds SE at 5 to 10 mph. Chance of rain 40%..
Showers this evening then scattered thunderstorms developing overnight. Low 71F. Winds SSE at 5 to 10 mph. Chance of rain 50%.
Hayes resigns as golf center manager
Less than a year and a half after being named the new general manager of the Providence Municipal Golf and Recreation Center (PMGRC), Kenny Hayes gave his two weeks notice to mayor Doug Hammers last week.
Hayes, a Slaughters native, is a graduate of both Webster County High School and the Golf Academy of America, where he earned a degree in Golf Center Management. He was hired in March of 2018 to replace general manager Jon Garrett, who had been with the facility since it was purchased by the city.
Hammers said that Hayes had accepted a job with the Walmart Corporation.
"He's bettering himself," Hammers said. "I wish him the best."
The city is currently accepting and reviewing application to fill the opening.
Reach MATT HUGHES
at 270-667-2068 or
matt@journalenterprise.com


text:
Repeat destination? 🏝 Traveling for merch? What you're owed ✈ Lost, damaged? Tell us
Ohio's Kings Island joins giga coaster club with 300-foot Orion, opening in spring 2020
Madeline Mitchell
Cincinnati Enquirer
Come spring 2020, Ohio will boast two of the world's seven giga coasters, an elite group of full-circuit roller coasters that tower over 300 feet.
Kings Island, located north of Cincinnati, announced Thursday that spring 2020 will mark the debut of Orion, its largest coaster at 300 feet, and its longest at 5,321 feet.
Orion's cost is comparable to what it took to build the entire park in 1972. The budget? $30 million.
"I expect to see a lot of people coming from all over the world to ride," said Kings Island general manager Mike Koontz.
The coaster will plunge riders down an exhilarating 300-foot drop before sending them on a high-speed journey over seven airtime hills at speeds of up to 91 miles per hour, making it North America's seventh-fastest coaster.
Racing along 5,321 feet of track, it will be one of only seven giga coasters in the world – having a height or drop of 300-399 feet.
Cedar Point, located in northeast Ohio, became the first theme park to feature a 300-foot roller coaster when it opened Millennium Force in August 2000.
The other three North American members of the giga coaster club: Intimidator 305 at Virginia's Kings Dominion, Fury 325 at North Carolina's Carowinds and the 306-foot Leviathan at Canada's Wonderland. (Orion was designed by Switzerland-based Bolliger & Mabillard, the same firm behind Leviathan.)
Red Force, located at Spain's Ferrari Land, is the world's tallest at 367 feet. Japan also boasts a giga coaster: Steel Dragon 2000, which stands at 318 feet, according to CoasterCritic.com, a ride-review website.
Koontz said Kings Island was waiting for the right moment to introduce a 300-footer to Cincinnati.
"There's a right time for everything," he said. "So this is our time."
Barrel roll at 197 feet: New Steelers-themed coaster at Kennywood sets records, thrills riders
Follow @maddiemitch_ or on Instagram: @Enquirer on Twitter for more


The classic film The Warriors has been recreated in GTAOnline by awesome guys over at GTA Series and the GTA Community. The video is is a remake of the original Warriors intro of the movie which various New York street gangs come together in order to jointly take over the city. If you didn't know in 2005, Rockstar Games released The Warriors video game on the PS2 and Xbox – be sure you grab a copy of eBay! a must for any Rockstar Games Fan!


text:
\section{Introduction}
\label{sec:introduction}

Face recognition is the method of choice behind some of the most widely deployed biometric authentication systems, currently supporting a range of applications, from passport control at airports, to mobile phone or laptop login. A key weaknesses of the technology, preventing it from being employed in security sensitive applications in uncontrolled environments, as for example ATM machines for money withdrawal, is its vulnerability to {\em presentation attacks}, where imposters attempt to gain wrongful access by presenting in front of the system's camera a photo, or a video, or by wearing a mask resembling a registered person. As a solution to this problem, algorithms for presentation attack detection (PAD) are developed, that is, binary classifiers trained to distinguish between the bona fide samples coming from live subjects, and those coming from imposters.

The large variety in the types of possible presentation attacks, and the large variation in the environmental conditions under which they might take place, make PAD a particularly challenging problem. However, the current state-of-the-art, utilising the power of deep learning, comprises classifiers with excellent accuracy rates, and a satisfactory generalisation power to at least a limited number of previously unseen attacks. Cross-database generalisation is still problematic, however, it is debatable if this is a real obstacle to the deployment of PAD algorithms in practical applications, since such algorithms as usually embedded in specific face recognition systems, with given camera specifications and configurations.

Here, we deal with the problem of race bias in face anti-spoofing algorithms. It is a topic that has attracted considerably less research interest than accuracy and generalisation power, despite the fact that it raises ethical, legal, and regulatory considerations, which, by their own, can prevent adoption in specific applications. Addressing this gap, the aim of this paper is to provide a framework for studying the question: {\em Does the classifier work equally well on people from all races?}.

The proposed race bias analysis process has three key characteristics. First, the focus is on the bona fide error, that is, on genuine people wrongly classified as imposters. Bias in this type of error has significant ethical, legal and regulatory ramifications, and as it has recently point out ``creates customer annoyance and inconvenience, and this is also where bias can occur in PAD systems'', \cite{idrd2022}. Secondly, we analyse various stages of the classification process. Not just the final binary outcome, but also the scalar responses of the network prior to thresholding, and before that the representation of the face image in the network's latent space. Thirdly, we treat the value of the threshold that determines the classifier's operating point on the ROC curve as a user-defined variable. We do not assume it is fixed by the vendor of the biometric verification system through a black-box process.

In the rest of the paper, we demonstrate the application of the proposed bias analysis approach on a face anti-spoofing algorithm based on the recently proposed Vector Quantized Variational Autoencoder (VQ-VAE) architecture, \cite{van2017neural}. The network is trained and validated on the Replay Attack and the SiW databases, and tested for racial bias on bona fide samples from the SiW and the RFW databases. Hypotheses are tested using the chi-squared test on the binary outcomes, the Mann–Whitney U test on the scalar responses, and Hartigan's Dip for testing bimodality in the response distributions. To test for bias in the latent space of the VQ-VAE network, we train an SVM with encoding vectors from two races, and measure its performance as a binary classifier.

The contributions of the paper are summarised as follows:
\begin{itemize}
    \item A demonstration that race bias can be attributed to several characteristics of the response distributions: different means; different variances; bimodality; outliers.
    \item A demonstration that non-specialised databases, such as RFW, can be used to analyse face anti-spoofing algorithms.
    \item A VQ-VAE based network for face anti-spoofing.
\end{itemize}

The rest of the paper is organised as follows. In Section~\ref{sec:sec2}, we review the relevant literature. In Section~\ref{sec:sec3}, we describe the VQ-VAE face anti-spoofing algorithm and the databases we used. In Section~\ref{sec:sec4}, we present the bias analysis on the SiW database, and in Section~\ref{sec:sec5} the bias analysis on the RFW database. We briefly conclude in Section~\ref{sec:sec6}.

\section{Background}
\label{sec:sec2}

We briefly review the area of face anti-spoofing, and then studies of bias in machine learning, and PAD in particular.

\subsection{Face anti-spoofing}

The earlier machine learning approaches to PAD were based on handcrafted features \cite{chingovska2012,boulkenafet2015face}, with Histogram of Oriented Gradient (HOG) \cite{albiol2008face}, and Local Binary Patterns (LBP) \cite{chingovska2012}, among the most popular.

More recent approaches were based on CNNs, \cite{jourabloo2018face,nagpal2019performance}, or combinations of various deep network types \cite{cai2020drl}, leading to the current state of the art being based on various forms of deep learning \cite{zhang2020celeba,yu2020fas,Yu_2020_CVPR,yu2020auto,zhang2020face,cai2020drl,liu2021face,yu2021revisiting}, such as Central Difference Convolutional Networks (CDCN) \cite{Yu_2020_CVPR,yu2020fas}, or transformers \cite{wang2022}. Following some earlier approaches \cite{atoum2017face,liu2018}, the current state of the art algorithms also utilise depth information \cite{wang2020deep,zhang2020face,yu2021revisiting,wu2022}, which can be estimated by a independently trained neural network, while the use of GANs to estimate Near Infrared (NIR) information was proposed \cite{liu2021face}.

The experiment presented in this paper is based on a one class trained autoencoder. Anomaly detection is a popular approach \cite{xiong2018unknown,jimenezdeep,Nikisins2019,Zhang2020CNNBasedAD,baweja2020anomaly}, offering good generalisation to unseen attacks. In \cite{mohammadi2020}, images from face recognition datasets were added to the two-class training set of an autoencoder, and improved cross-database generalisation was reported. A similar behaviour was reported in \cite{abduh2021training} when images from the in-the-wild were added to the training set.

\subsection{Databases}

In this paper, the first training set is from {\em Replay-Attack} \cite{chingovska2012}, a database consisting of 50 subjects of three types of ethnicities, 76\% Caucasian, 22\% Asian, and 2\% African. Our second training set is from {\em SiW} \cite{liu2018}, a database consisting of 165 subjects, of four types of ethnicities, 35\% of Asian and 35\% Caucasian and 23\% Indian, and 7\% African American. The bias analysis is performed on SiW with the subject annotated for ethnicity type by us, and the already annotated RFW database \cite{wang2019rfw}.

Regarding other databases, {\em NUAA} \cite{tan2010face} was one of the first face anti-spoofing large databases. It is rarely used these days as its low quality of imagery poses an unfair challenge in the cross-database validation of algorithms. {\em MSU MFSD} \cite{7031384} consists of 55 subjects, captured by four different devices, while {\em OULU} \cite{OULU_NPU_2017} has again 55 subjects captured by six different mobile devices. {\em WMCA} \cite{george2019biometric} contains 72 subjects and information is captured in RGB, depth, infrared, and thermal. {\em CASIA-SURF}  \cite{zhang2020casia} consists of 1000 subjects captured in RGB, depth, and infrared.

The first face anti-spoofing database to include explicit ethnic labels was {\em CASIA-SURF CeFA} \cite{Liu_2021_WACV}, which has 1,607 in three ethnicities, captured in three modalities. In this paper, for bias analysis we use the RFW \cite{wang2019rfw}, which includes four types of ethnicities, Caucasian, Asian, Indian, and African. RFW does not specialise in face anti-spoofing, and it is more widely used in the bias analysis literature.

\subsection{Bias in machine learning}

In \cite{yapo2018ethical}, several high profile cases of machine learning bias are documented; Google search results appeared to be biased towards women in 2015; Hewlett-Packard's software for web cameras struggled to recognize dark skin tones; and Nikon's camera software was inaccurately identifying Asian people as blinking.

Thus, given also the ethical, legal, and regulatory issues associated with the problem of bias within human populations, there is a considerable amount of research on the subject, especially in face recognition (FR). A recent comprehensive survey can be found in \cite{mehrabi2021survey}, where the significant sources of bias \cite{suresh2019framework,olteanu2019social} are categorised and discussed, and the negative effect of bias on downstream learning tasks is pointed out. We also note that while the current deep learning based FR algorithms are under intense scrutiny for potential bias \cite{singh2020robustness}, this is due to their wider deployment in real life applications, rather than any evidence that they are more biased than traditional approaches.

In one of the earliest studies of bias in FR, predating deep learning, \cite{phillips2011} reported differences in the performance on humans of Caucasian and East Asian decent between Western and East Asia developed algorithms. In \cite{garcia2019harms}, several deep learning based FR algorithms are analysed and a small amount of bias is detected in all of them. Then, the authors show how this bias can be exploited to enhance the power of malicious morphing attacks to FR based security systems.

In \cite{gluge2020not}, the authors compute cluster validation measures on the clusters of the various demographics inside the whole population, aiming at measuring the algorithm's potential for bias. Their result is negative, and they argue for the need of more sophisticated clustering approaches. We note that in our paper, an investigation in the latent space of the potential for bias, by measuring the discriminative power of SVMs over the various ethnicities, returned a similarly negative result. In \cite{serna2021insidebias}, the aim is the detection of bias by analysing the activation ratios at the various layers of the network. Similarly to our work, their target application is the detection of race bias on a binary classification problem, gender classification in their case. Their result is positive in that they report a correlation between the measured activation ratios and bias in the final outcomes of the classifier. However, it is not clear if their method can be used to measure and assess the statistical significance of the expected bias.

In Cavazos et al. \cite{cavazos2020accuracy}, similarly to our approach, most of the analysis assumes a one-sided error cost, in their case the false acceptance rate, and the decision thresholds are treated as user defined variables. However, the analytical tools they used, mostly visual inspection of ROC curves, do not allow for a deep study of the distributions of the similarity scores, while, here, we give a more detailed analysis of the distribution of the responses, which is the equivalent of the similarity scores. In Pereira and Marcel \cite{pereira2021}, a fairness metric is proposed, which can be optimised over the decision thresholds, but again, there is no in-depth statistical analysis of the scores, as we do here for the responses, and thus they offer a more limited insight.

\subsubsection{Bias in Presentation Attack Detection}

The literature on bias in presentation attacks is more sparse. Race bias was the key theme in the competition of face anti-spoofing algorithm on the CASIA-SURF CeFA database \cite{liu2020crossethnicity}. Bias was assessed by the performance of the algorithm under a cross-ethnicity validation scenario. Standard performance metrics, such as APCER, BPCER and ACER we reported. In \cite{alshareef2021study}, the standard CNN models Resnet 50 and VGG16, were compared for gender bias against the debiasing-VAE proposed in \cite{amini2019uncovering}, and several performance metrics were reported. In a recent white paper by the ID R\&D company, which develops face anti-spoofing software, the results of a large scale bias assessment experiment conducted by Bixelab, a NIST accredited independent laboratory \cite{idrd2022}. Similarly to our approach, they focus on the bona fide errors, and their aim is the BPCER error metric to be below a prespecified threshold across all demographics.

Regarding other biometric identification modalities, \cite{fang2021demographic} studied gender bias in iris PAD algorithms. They reported three error metrics, APCER, BPCER, and HTER, finding that female users would be less protected against iris PAD attacks.

\section{Experimental setup}
\label{sec:sec3}

In this section, we briefly describe and validate the two classifiers that will be analysed for bias in the subsequent sections. They have the same VQ-VAE based architecture \cite{van2017neural}, and their only difference is in the training; in the first, the network is trained on Replay Attack, and in the second on SiW. We chose the VQ-VAE architecture because of some recently reported impressive results, on various computer vision problems, even though it has not been employed yet in the field of face anti-spoofing.

\subsection{The VQ-VAE network}

The VQ-VAE architecture comprises three parts. A learned {\em codebook}, which is used to discretise the continuous latent vectors to a set of discrete latent variables; each continuous vector is replaced with its nearest vector in the codebook. An {\em encoder} that maps the input to a sequence $z$ of discrete codes. A {\em decoder} that transforms the sequence $z$ of discrete vectors back to an image. The final binary classification decision is taken by comparing the MSE between the input image and the reconstructed one against a threshold determining the operating point of the algorithm.

Our encoder consists of two convolutional layers with kernel size 4, stride step 2, padding 1, and followed by a ReLU, one convolutional layer with kernel size 3, stride step 1, padding 1 and followed by two residual blocks, which are implemented as ReLU, $3 \times 3$ conv, ReLU, $1 \times 1$ conv for each block. The decoder is a symmetrical structure of the encoder that uses transposed convolutions. The encoder may output a 16x16 grid of vectors, and each one is quantized, using a codebook of size 512, before being fed to the decoder. The weight factor $\beta$ was set to 0.25.

The ADAM optimiser was used with a learning rate of 1e-3, and the model was trained for 100 epochs with a batch size of 16. The total loss consisted of three components: reconstruction loss, codebook loss, and commitment loss. The code was written entirely in Python, on the Pytorch platform. The experiments were run on an Intel Core i7 CPU, with 64 GB of RAM and an Nvidia GTX 1650.

\begin{figure*}[ht]
\includegraphics[width=\linewidth]{figs/Slide1.PNG}
\caption{The architecture of the proposed VQ-VAE.}
\label{fig:VQ-VAE-architecture}
\end{figure*}

\begin{figure*}[ht]
\includegraphics[width=0.24\textwidth]{figs/replay-model/siw-1/mse/AFreplay.png} \hfill
\includegraphics[width=0.24\textwidth]{figs/replay-model/siw-1/mse/As-replay.png} \hfill
\includegraphics[width=0.24\textwidth]{figs/replay-model/siw-1/mse/Ca-replay.png} \hfill
\includegraphics[width=0.24\textwidth]{figs/replay-model/siw-1/mse/IN-replay.png}
\includegraphics[width=0.24\textwidth]{figs/SIW-model/siw/mse/AFsiw.png} \hfill
\includegraphics[width=0.24\textwidth]{figs/SIW-model/siw/mse/As-siw.png} \hfill
\includegraphics[width=0.24\textwidth]{figs/SIW-model/siw/mse/Ca-siw.png} \hfill
\includegraphics[width=0.24\textwidth]{figs/SIW-model/siw/mse/IN-siw.png}
\caption{{\bf Top:} The responses of the RA trained network on each race of the SiW testset. From left to right: African, Asian, Caucasian, Indian. {\bf Bottom:} The responses of the SiW trained network.}
\label{fig:siw-clientImposter}
\end{figure*}

\subsection{Data preparation}

For faces detection we used the Multi-Task Cascade Convolutional Neural Network (MTCNN) \cite{zhang2016joint}. The detected faces were rotationally aligned, making the line between the two eye centres horizontal, and cropped at $64 \times 64$ resolution. Table~\ref{tbl:dataSets} summarises the sizes of the datasets. Notice that the training sets consisted of bona fide data, only.

\begin{table}[h]
\caption{Sizes of training and test datasets. The test sets are equally split between clients and imposters. The SiW test set, consists of 400 samples from each race.}
\centering
\begin{tabular}{|l|c|l|c|} \hline
& \textbf{Training} & \textbf{Test} \\ \hline
RA & 7465 & 400\\ \hline
SiW & 124,000 & 1600\\ \hline
\end{tabular}
\label{tbl:dataSets}
\end{table}

\subsection{Network validation}

We validated the classifiers on RA and SiW, first under an intra-database protocol, and then with a cross-database protocol. When testing on SiW, we also report error rates for each race separately.
\begin{itemize}
\item[] {\bf Intra-database protocol:} on an independent validation set we compute the threshold corresponding to the Equal Error Rate (EER). We use this threshold to compute the Half-Total Error Rate (HTER) on the test set. On SiW, we use a single threshold for all races.
\item[] {\bf Cross-database protocol:} here, thresholds estimated on one database do not work on the other. So, we just report the EERs computed directly on the test sets.
\end{itemize}

\noindent Table~\ref{tbl:validation-all} shows the error computed under the testing protocol. The intra-database error is significantly smaller in RA than in SiW (.055 vs .169), indicating an easier classification task, as RA has less diversity in poses and expressions. However, as expected, the error on SiW is smaller when we train on SiW rather than RA (.169 vs .208).

\begin{table}[h]
\caption{Error rates computed under the testing protocol.}
\centering
\begin{tabular}{|c||l||l|} \hline
 & RA test & SiW test \\ \hline \hline
RAtr & .055 [thr = .015 (HTER)] & .208 [thr = .031 (EER)]   \\ \hline
SiWtr & .180  [thr = .065  (EER)] & .169 [thr = .054 (HTER)] \\ \hline
\end{tabular}
\label{tbl:validation-all}
\end{table}

\noindent Table~\ref{tbl:cross-perRace} shows HTERs for each race of the SiW testset. The thresholds are taken from the corresponding tests of Table~\ref{tbl:validation-all}. We note that the SiW trained network is performing better on the SiW testset, not only in total, but also on each race separately. For each classifier and each race, Figure~\ref{fig:siw-clientImposter} shows the histograms of the responses and the corresponding thresholds.

\begin{table}[h]
\caption{HTERs computed for each race separately on the SiW testset.}
\centering
\begin{tabular}{|c|c|c|c|c|} \hline
 & \textbf{Af} & \textbf{As} & \textbf{Ca} & \textbf{In} \\ \hline \hline
HTER RAtr (thr = .031) & .135 & .247 & .260 & .192 \\ \hline
HTER SiWtr (thr = .054) & .115 & .202 & .210 & .150 \\ \hline
\end{tabular}
\label{tbl:cross-perRace}
\end{table}

\section{Bias analysis on SiW}
\label{sec:sec4}

Here, we use the SiW testset of Section~\ref{sec:sec3}. The binary outcomes of the classifiers are analysed with the chi-squared test, the scalar responses with the Mann–Whitney U test \cite{10.1214/aoms/1177730491}, and finally, the encoding vectors are analysed by using them to train and test an SVM on the task of race classification. The bias analysis process is summarised in Fig.~\ref{fig:Bias_analysis}.

\begin{figure*}[h]
\includegraphics[width=\textwidth]{Picture4.png}
\caption{The bias analysis process. The binary outcome analysis is shown in purple, the scalar responses analysis in blue, and the latent space analysis in orange.}
\label{fig:Bias_analysis}
\end{figure*}

\subsection{Statistical analysis of the binary outcomes}

First, we analyse the binary outcomes corresponding to the thresholds of Section~\ref{sec:sec3}, that is, 0.031 for the RA-trained classifier and 0.054 for the SiW trained classifier. For each pair of races, we formed the $2\times 2$ contingency tables, and applied the chi-squared test, computing p-values for the hypothesis that the probability of misclassification of a bona fide sample from the race with the most misclassifications is higher.

The results are summarised in Table~\ref{tbl:siw-binaryTest}. In several cases, the p-values are low, meaning that for any reasonable threshold of statistical significance the bias hypothesis is accepted. In other cases, p-values above 0.05 mean that bias has not been detected.

\begin{table}[h]
\caption{p-values of the chi-squared tests for the thresholds in Section~\ref{sec:sec3}.}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
          &  Af-As & Af-Ca & Af-In & As-Ca & As-In & Ca-In \\ \hline\hline
RAtr  &  .0000 & .0001 & .1465 & .2532& .0000 & .0000\\ \hline
SiWtr & .1158 & .0104 & .0147 & .0000 & .0000 & 1.0 \\ \hline
\end{tabular}
\label{tbl:siw-binaryTest}
\end{table}

Next, we treat the thresholds, that is, the operating points on the ROC curve, as a variable. In Figs.~\ref{fig:SiWp-values-RA},~\ref{fig:SiWp-values-SiW}, we plot the p-value as a function of the response, for the two classifiers and the six pairs of races. We note that, over the range of thresholds, there are several disconnected intervals corresponding to high bias, which means that threshold optimisation for low bias should not assume unique solutions, as it is often implicit in the literature.

\begin{figure*}[h]
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/line/African-Asian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/line/African-caucasian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/line/African-Indian.png}
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/line/Asian-caucasian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/line/Asian-Indian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/line/caucasian-Indian.png}
\caption{For each pair of races, graphs of the p-value as a function of the threshold. The classifier was trained on RA.}
\label{fig:SiWp-values-RA}
\end{figure*}
\begin{figure*}[ht]
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/line/African-Asian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/line/African-caucasian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/line/African-Indian.png}
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/line/Asian-caucasian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/line/Asian-Indian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/line/caucasian-Indian.png}
\caption{For each pair of races, graphs of the p-value as a function of the threshold. The classifier was trained on RA.}
\label{fig:SiWp-values-SiW}
\end{figure*}

\subsection{Statistical analysis of the scalar responses}

For an insight in the behaviour of the graphs of the p-values we analyse the scalar responses of the algorithm. Figs.~\ref{fig:SiW-Histograms-RA},~\ref{fig:SiW-Histograms-SiW} show a comparison of the histograms of the responses for each pair of races. We note the complex behaviour of the density functions, which induce a complex bias behaviour. In particular, bias at certain thresholds can be caused by differences in the means of the responses; differences in their variances; response bimodality; or outliers.

\begin{figure*}[ht]
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/p-values/AAS.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/p-values/AC.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/p-values/AF-IN.png}
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/p-values/ASC.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/p-values/CI.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/siw-1/p-values/ASI.png}
\caption{For each pair of races in SiW, the histogram of the responses on bona-fide images. The classifier was trained on RA.}
\label{fig:SiW-Histograms-RA}
\end{figure*}

\begin{figure*}[ht]
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/races/AAS.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/races/AC.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/races/AF-IN.png}
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/races/ASC.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/races/ASI.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/siw/races/CI.png}
\caption{For each pair of races in SiW, the histogram of the responses of bona-fide images. The classifier was trained on SiW.}
\label{fig:SiW-Histograms-SiW}
\end{figure*}

Regarding the means of the responses, since the Shapiro-Wilk test rejected the hypothesis of normal distributions, we opted for the Mann–Whitney U test.
Table~\ref{tbl:tTest-SiW} shows the computed p-values on each pair of races, for the hypothesis that the values of randomly selected responses from the two populations are different.
\begin{table}[ht]
\caption{p-values of the Mann–Whitney U test on each pair of races.}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
            &  Af-As & Af-Ca & Af-In & As-Ca & As-In & Ca-In \\ \hline\hline
RAtr  &  .0000&  .0015 & .0085 & .0052   & .0000 & .0000 \\ \hline
SiWtr &  .0001 & .0078  &.0000& .1560 & .0000  & .0000 \\ \hline
\end{tabular}
\label{tbl:tTest-SiW}
\end{table}

Regarding bimodality, we used Hartigan's Dip Test \cite{hartigan1985dip}, with 50 bins, to test each race for bimodality. We note that for the 200 samples we have from each race, a statistical significance of 95\% corresponds to a critical value of 0.037. Table~\ref{tbl:meansVAR-responses-SiW} shows the computed dip values, together with the means and standard deviations for each population.

We note that the biggest difference in mean response is between Asian and Indian from the SiW trained classifier, while the corresponding standard deviations are similar. Thus, the bias we observe in the corresponding diagram in Fig~\ref{fig:SiWp-values-SiW} is due to the classifier's higher mean response on Asians compared to Indians.

On the other hand, the smallest difference in mean response is between Asians and Caucasians, again under the SiW trained classifier. Thus, the bias we observe in the corresponding diagram in Fig~\ref{fig:SiWp-values-SiW}, which for very small threshold values is statistically significant, is due to different variances. We note here, that while at such low threshold values a standalone classifier wouldn't be very useful, it is still an interesting case when constructing ensembles of weak classifiers.

Finally, we notice that all Hartigan's Dip Test values are below the significance threshold, and thus, all populations should be considered unimodal. In particular, that means that some very high responses on African people, especially from the RA trained classifier, should be treated as outliers. We note that, nevertheless, against all the other three races, these outliers create a second, or third region of high bias, and in those regions samples from the African population are treated less favourably.

\clearpage

\begin{table}[ht]
\caption{SiW testset: means, standard deviations, and Hartigan's dip values for the responses of the RA and SiW trained classifiers.}
\centering
\begin{tabular}{|c|c|c|c|c|} \hline
 & \textbf{Af} & \textbf{As} & \textbf{Ca} & \textbf{In} \\ \hline \hline
RAtr $\mu$ & .0257 & .0291 & .0274 & .0223 \\ \hline
SiWtr $\mu$ & .0418 & .0446 & .0438 & .0355 \\ \hline \hline
RAtr $s.d.$ & .0126 & .0084 & .0104 & .0079 \\ \hline
SiWtr $s.d.$ & .0142 & .0109 & .0122 & .0096\\ \hline \hline
RAtr dip & .0221 & .0360& .0335 & .0349 \\ \hline
SiWtr dip & .0299 & .0233 & .0366 & .0324 \\ \hline
\end{tabular}
\label{tbl:meansVAR-responses-SiW}
\end{table}

\subsection{Discrete latent space}

The proposed VQ-VAE encodes each image with a 32768 number long sequence of integers in [0..1023], corresponding to indices of the vectors in the codebook. Using these latent space representations of the images as inputs, for each pair of races, we train an SVM with an RBF kernel of Gaussian type. Table~\ref{tbl:QV-SVM-SiW} shows AUC values for these SVM classifiers.

\begin{table}[h]
\caption{AUC values for an SVM trained on the VQ-VAE's latent space encodings of the images. SiW testset.}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
      &  Af-As & Af-Ca & Af-In & As-Ca & As-In & Ca-In \\ \hline\hline
RAtr  &  .88 & .87 & .74 &  .71 & .78 &  .83 \\ \hline
SiWtr &  .74 & .85 & .76 & .72  &  .76 & .83\\ \hline
\end{tabular}
\label{tbl:QV-SVM-SiW}
\end{table}

We note that in some cases the SVMs perform quite well, meaning that the VQ-VAE encodings of the face images contain enough information to discriminate between races, despite the fact that VQ-VAEs themselves were not trained with race labels. That means that there is a potential for biased behaviour, however, on the other hand, the statistical significance of the result and its practical implications are not clear, and a deeper investigation is required.

\section{Bias analysis on RFW}
\label{sec:sec5}

In this section, we apply the same bias analysis on a testset from the RFW database, consisting of 200 images from each race. We note that this time the race labels come as part of the database, rather than being annotated by us. Another difference from SiW is that the RFW database is not a specialised face anti-spoofing database. Thus, as we do not have imposter images, we do not have empirically established thresholds, as for example the ones corresponding to EER values. For the part of the analysis requiring specific thresholds, we use thresholds corresponding to some predetermined TPR values.

\subsubsection{Statistical analysis of the binary outcomes}

Table~\ref{tbl:thresholdsRfW} shows the thresholds corresponding to TPR values of 1\%, 2\%, 5\%, 10\%, 20\%.
\begin{table}[h]
\caption{Threshold values corresponding to some predetermined TPR values.}
\centering
\begin{tabular}{|c||c|c|c|c|c|} \hline
          & 1\%   & 2\%   & 5\%   & 10\%  & 20\% \\ \hline\hline
RAtr  & .0460 & .0440 & .0389 & .0336 & .0279  \\ \hline
SiWtr & .1206 & .1079 & .0971 & .0838 & .0712  \\ \hline
\end{tabular}
\label{tbl:thresholdsRfW}
\end{table}

Table~\ref{tbl:tTest-RfW} shows the results of the chi-squared tests on the binary outcomes. We note that bias can be detected for some of the thresholds, for some pairs of races. In Figs.~\ref{fig:rfwBinaryRA},~\ref{fig:rfwBinarySIW}, for each classifier, and for each pair of races, we show the histograms of responses on bona fide images, and the p-values of the chi-squared test as a function of the threshold. We observe behaviours similar to those from the tests on the SiW database.

\begin{table}[h]
\caption{p-values of the chi-squared tests for the thresholds shown in Table~\ref{tbl:thresholdsRfW}.}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
            &  Af-As & Af-Ca & Af-In & As-Ca & As-In & Ca-In \\ \hline\hline
RAtr 1\% & 1.0 & .0718 & .2171 & .1316 & .3680 & 1.0 \\ \hline
RAtr 2\% & 1.0 & .1271 & .2839 & .0741 & .1774 & 1.0 \\ \hline
RAtr 5\% & .8494 & .0299 & .1082 & .0116 & .0483 & .7487 \\ \hline
RAtr 10\% & .0356 & .0069 & .3938 & .0000 & .0021 & .0857 \\ \hline
RAtr 20\% & .1333 & .0025 & 1.0 & .0000 & .1047 & .0037 \\ \hline \hline
SiWtr 1\% & 1.0 & 1.0 & 1.0 & 1.0 & .3680 & .6153 \\ \hline
SiWtr 2\% & .2839 & .4456& 1.0 &1.0& .5001 & .7209 \\ \hline
SiWtr 5\% & .2924 & .0249 & .4143& .31053 &1.0& .2152 \\ \hline
SiWtr 10\% & .1158 & .0302 & .3567 & .6501 & .6246 & .2725 \\ \hline
SiWtr 20\% & .0236& .0062 & .01241 & .7194 & 0.9039 & .9054 \\ \hline
\end{tabular}
\label{tbl:tTest-RfW}
\end{table}
\begin{figure*}[h]
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/line/African-Asian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/line/African-caucasian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/line/African-Indian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/line/Asian-caucasian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/line/Asian-Indian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/line/caucasian-Indian.png} \hfill
\caption{For each pair of races, graphs of the p-value as a function of the threshold. The classifier was trained on RA.}
\label{fig:rfwBinaryRA}
\end{figure*}
\begin{figure*}[h]
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/line/African-Asian.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/line/African-caucasian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/line/African-Indian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/line/Asian-caucasian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/line/Asian-Indian.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/line/caucasian-Indian.png} \hfill
\caption{For each pair of races, graphs of the p-value as a function of the threshold. The classifier was trained on SiW.}
\label{fig:rfwBinarySIW}
\end{figure*}

\subsection{Statistical analysis of the scalar responses}

Table~\ref{tbl:tTest-RFW} shows the p-values of the Mann–Whitney U test for each pair of races, and Table~\ref{tbl:meansVar-responses-RFW} shows the means, standard deviations and dip values for each population.

\clearpage

We note that Hartigan's Dip Test detects a bimodality in the responses of the SiW trained algorithm on Indian people, having a dip value of 0.055, above the significance threshold of 0.037. This can also be verified by visual inspection of the corresponding histograms of responses, which, for each pair of races, are shown in Figs.~\ref{fig:rfwHistogramsRA}, \ref{fig:rfwHistogramsSIW}. We also note that this bimodality can be detected in the behaviour of corresponding graphs of the p-values of the chi-squared test. Indeed, in the three graphs in Fig.~\ref{fig:rfwBinarySIW} corresponding to Indian people, we can detect two distinct regions of higher bias, even though the second one does not reach the level of statistical significance.

\begin{table}[h]
\caption{p-values of the Mann–Whitney U test on each pair of races.}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
            &  Af-As & Af-Ca & Af-In & As-Ca & As-In & Ca-In \\ \hline\hline
RAtr  & .0067 & .0253 & .3216 &  .0000 & .0137  & .0044 \\ \hline
SiWtr & .0004 & .0058  & .0062 & .2509  & .2743 & .4805 \\ \hline
\end{tabular}
\label{tbl:tTest-RFW}
\end{table}

\begin{table}[h]
\caption{RFW testset: means, standard deviations, and Hartigan's dip values for the responses of the RA and SiW trained classifiers.}
\centering
\begin{tabular}{|c|c|c|c|c|} \hline
 & \textbf{Af} & \textbf{As} & \textbf{Ca} & \textbf{In} \\ \hline \hline
RAtr $\mu$ & .0217 & .0239 & .0194 & .0214 \\ \hline
SiWtr $\mu$ & .0509 & .0579 & .0569 & .0579 \\ \hline \hline
RAtr $s.d.$ & .0094 & .0101 & .0068 & .0082 \\ \hline
SiWtr $s.d.$ & .0175 & .0220 & .0223 & .0220\\ \hline
RAtr dip & .0175 & .0178& .0299 & .0216 \\ \hline \hline
SIWtr dip & .0114 & .0225 & .0149& .0550 \\ \hline
\end{tabular}
\label{tbl:meansVar-responses-RFW}
\end{table}

\begin{figure*}[h]
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/races/AAS.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/races/AC.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/races/AF-IN.png}
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/races/ASC.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/races/ASI.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/replay-model/rfw-1/races/CI.png}
\caption{For each pair of races in RFW, the histogram of the responses of bona-fide images. The classifier was trained on RA.}
\label{fig:rfwHistogramsRA}
\end{figure*}

\begin{figure*}[h]
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/races/AAS.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/races/AC.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/races/AF-IN.png}
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/races/ASC.png} \hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/races/ASI.png}\hfill
\includegraphics[width=0.33\textwidth]{figs/SIW-model/rfw/races/CI.png}
\caption{For each pair of races in RFW, the histogram of the responses of bona-fide images. The classifier was trained on SiW.}
\label{fig:rfwHistogramsSIW}
\end{figure*}

\subsection{Discrete latent space}

Table~\ref{tbl:QV-SVM-RFW} shows the AUC values of SVM classifiers trained and tested, on each pair of races, with the Quantised Vectors. We note an even better performance than in the case of the SiW testset, implying an even higher potential for bias.
\begin{table}[h]
\caption{AUC values for an SVM trained on the VQ-VAE's latent space encodings of the images. RFW testset.}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
            &  Af-As & Af-Ca & Af-In & As-Ca & As-In & Ca-In \\ \hline\hline
RAtr  &  .95 & .97 & .94 & .89 & .89 & .86 \\ \hline
SiWtr &  .96 & .98  & .94 & .89  & .86 & .85 \\ \hline
\end{tabular}
\label{tbl:QV-SVM-RFW}
\end{table}

\section{Conclusion}
\label{sec:sec6}

We conducted a systematic, largely empirical, study of race bias in face anti-spoofing, based on a series of statistical and algorithmic tests. Our premises were that we are interested in the bona fide error; that binary outcomes, scalar responses, and latent space should all be analysed for bias; and that the threshold determining the classifier's operating point should not be assumed fixed by a black-box procedure.

Our main finding is that the behaviour of race bias depends on several characteristics of the distributions of the corresponding classifier's responses: differences in the means; differences in the variance; bimodal responses; outliers. The main implication of this finding is that the behaviour of bias can be quite complex and, for example, methods optimising for low race bias should not assume unique solutions to the problem. More generally, in our context, bias should be treated as something more complex than the difference in the means of two populations, a misconception that might be reinforced by the fact that in statistics, colloquially, the term bias is often used to describe the component of the error attributed to the difference in the means.

Our recommendations for future work include the theoretical study of bias, especially under the reasonable assumption of log-normality in the distributions of the responses. We also propose conducting an empirical but more systematic study of the relation between the behaviour of bias detecting algorithmic tests on the latent space, and statistically significant bias in the binary outcomes of the classifier.

redpajama_set_name: RedPajamaArXiv


Last night I had a dream. I was in therapy. It wasn't a great dream because I had to explain to the therapist what my anxiety and depression felt like. How I described it was quite deep and I'd like to share it with you.
To me, it's like being trapped within myself. It's like I have some sort of armour or suit on and my actual self is trapped inside, and I can't escape.
The actual me is someone who is happy and wants to try new things, wants to meet new people, and I'm confident in myself in many ways.
But the armour is so suffocating and there's no way to get it off. It weighs me down, it's heavy and wearing this armour every single day is exhausting.
In my dream, my therapist then asked me "Can you ask someone to help you take it off?"
It was quite an eyeopening dream for me.


text:
Q: Can you add a different size RAIDZ1 to an exists zpool? For example, I have a 4x2TB RaidZ1 set up right now, but I would like to add only a 3X4TB RaidZ1 in addition to it. Is there a way to do this with zdevs/another Zpool?
Thanks

A: While as already answered it is technically doable, beware that what you want to do is not a best practice as you end up with an unbalanced pool with sub optimal performance.
All disks in a pool should be of the same size (outside log and cache devices) and all striped vdevs should have the same organization.
You break both of these rules with mixing 2 TB and 4 TB drives, and striping a four disk raidz with a three disk one. You'll have to use zpool add -f option (-force) for the latter to succeed otherwise this message would be output and the add command would fail:
mismatched replication level: pool uses 4-way raidz and new vdev uses 3-way raidz

A: Yes and Yes. You can add multiple VDEVs to a pool, they need not be similar. You can add multiple pools to a system, each consisting of whatever VDEVs.
Note: Once you've added a VDEV to a pool you can not remove it. The only things you can do are replace the disks in a VDEV and add/remove disks from a mirror. If you want to change the way the VDEVs are organized you have to create a whole new pool with the new arrangement then send/receive the data.