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I am always in awe of how my mother seems to say, "I'm going to make soup tonight!" and then does it. It just magically happens! I see her buy the ingredients, or pick out the leftovers, and I see her cooking things, but I don't fully understand how she combines all the flavors in such a skillful, non-recipe-following way. Soups are a hard thing to make, so I have full respect for my mother, the Soup Master.
Last night, I attempted my own soup. I keep seeing this pin for creamy tortellini soup on Pinterest, but that recipe uses a slow-cooker and a flavoring packet as its base. First, I don't have a slow cooker. If I did, the world would be in trouble, because I would be slow cooking everything in sight. You can make bread in a slow cooker! I need to get one someday, but that day is not today. I need to have a life for a few years before I fully devolve into slow cooker spinsterhood, cooking by myself, all day every day. Second, I don't like the idea of using a packet as a base for a soup - that stuff isn't real, it's just dried up chemicals that have been made to taste good, and I'm pretty sure it's going to give everyone cancer. There have been studies! There's science! I'm just saying.
The original recipe uses spinach but I am obsessed with kale and I figured putting it in a soup would be a good way to soften it up (since kale is normally so stiff and bitter when raw.) I also got half and half instead of regular cream, to pretend this was a healthy soup, and only used one packet of tortellini. I am in love with cheese, so I could have added more, but I held back. Good for me.
First, I chopped up about half of a white onion and sauteed it in some olive oil to soften it up and make it taste sweeter for adding to the soup. When that was done, I took it off the hot burner and set it aside until later.
I put two cartons (gallons? pints? quarts? I think those cardboard boxes of broth are quarts. I think.) of vegetable stock in a soup pot. I thought there wasn't enough liquid, so I added about two cups of water. I would later find this to be a mistake, but... live and learn.
If I'm giving you the real picture of what last night looked like, here it is - I added the whole pint of half and half (told you this wasn't that healthy!), added the kale to cook down for a while, and added my spices (salt, pepper, garlic powder, and red chili pepper flakes.) Then I added the tortellini and cooked it in the broth so it would absorb the flavors and maybe absorb some of the extra liquid, since I wanted this to be thick and creamy like on the Pinterest image.
When that didn't look like it was working, I called my mom. I admit it. She instructed me to take out the tortellini so they didn't fall apart (seen above), and then boil down the rest of the cream/stock combination until it got to a thicker consistency.
This is what that looks like!
I added the tortellini back in, and added two cans of cannellini beans from the pantry for extra protein and to make it more like a tortellini stew. I also added a decent amount of parmesan to really make it cheesy.
You guys, it worked. I am so proud of myself! I know I'm probably not supposed to brag, but this is my blog, so I'm bragging. It turned out as creamy as I wanted it, as cheesy as I wanted it, and the chili flakes added an extra kick that made my eyes water. Thank god I made enough for two days worth of leftovers. The only change I'd make is to pick better tortellini next time - using basil and parmesan pasta in a soup that already has other flavors and kale was kind of weird. I'd want the pasta to have a less flavorful flavor so everything else can shine through!
Listen to me, talking about flavors shining through. Ugh. I'm going to go eat more soup now, don't listen to me any longer. But, go make it yourself! I promise you won't regret it. Unless you don't like cheesy or spicy things, then you're a bit out of luck.


It's more than just a rehearsal. From invites to toasts, here's everything you need to know about this prewedding soirée.
Your sole purpose might be wedding planning right now, but don't forget about rehearsal dinner planning too. The night before your wedding should be about—you guessed it—rehearsing, but also about greeting your guests, giving everyone a taste of what they can expect for the rest of the weekend, and simply celebrating your love in a comfortable, nonchalant setting.
Essentially, a dreamy rehearsal dinner is the perfect way to kick off your weekend (and sport an amazing outfit too). Read all of the answers to your biggest rehearsal dinner inquiries, below.
1. What actually is the rehearsal dinner?
It's a dinner traditionally held the night before the wedding, often on a Friday, and usually starts around dinnertime (to leave time for attendants to get there from work). For a Sunday or holiday wedding, you have more options. Since the rehearsal dinner has become more of a celebration in its own right than just a formality, some couples choose to hold the event two nights before the wedding. This way, there's more time to relax, recuperate and get ready for the main event. If most attendants won't be arriving until late on the eve of your wedding, a breakfast celebration the morning of the wedding can be a fabulous alternative. And, of course, skipping the rehearsal meal altogether is perfectly acceptable (sometimes there's just no way to fit it all in).
The rehearsal dinner is a great opportunity for your two families to spend time together before the wedding day in a slightly less hectic setting. Take advantage of the relaxed environment, full of happy anticipation—come wedding night, you'll most likely be pulled in too many directions to put in quality time with anyone. The ultimate goal is to relieve some prewedding tension and make everyone feel comfortable with the upcoming nuptials, while not upstaging the main event.
Traditionally, the groom's family organizes and pays for the party, but you two can definitely take matters into your own hands—or both sets of parents may choose to share responsibility. While you as the honored couple may have input on the overall direction, if your future in-laws host, you should hand over the title of creative directors to them as much as you can. On the other hand, if you're hosting, you get to make the decisions. So you'll want to give yourselves enough time to scout venues in order to book one four to six months in advance.
4. Who do I have to invite?
At the very least, the rehearsal dinner guest list includes your immediate families, wedding party members and their spouses or significant others, and the parents of any child attendants (inviting the children themselves is up to you). You should also invite the officiant and his or her spouse to the dinner—they may not come, but it's a polite and generous gesture to offer.
5. Who else should I consider inviting?
Turn the dinner into a fabulous welcome party. If you have family or friends that traveled far to be at your wedding, extend the invitation to them as a thank-you for their extra effort. If you're throwing a destination wedding or a party where at least half of the guests are from out of town, show your appreciation by inviting everyone to some kind of night-before festivity. If you're working with a limited budget, stick to a more exclusive group for the rehearsal dinner and consider having an informal welcome cocktail or dessert party for out-of-towners later in the evening.
6. What should it look like?
The great news? You can opt for an event that's formal (banquet or garden party) or casual (outdoor picnic or barbecue). If your wedding is the climax of the weekend, don't let your rehearsal dinner overshadow it—this get-together should be the sneak peek. Since the rehearsal dinner is often more informal than the wedding reception, the food and atmosphere can reflect that. Ultimately, the setting of the rehearsal dinner depends on the budget, how many guests there'll be and what kind of party the host envisions.
7. Where should I have it?
A sit-down dinner at a hotel ballroom or fine restaurant are often go-tos, but there's no reason to feel limited to that. Couples are hosting their rehearsal dinners anywhere from backyards to art galleries and even on the beach. And feel free to use the term "dinner" loosely—cocktails and hors d'oeuvres, a buffet and a barbecue are all great options. Be sure to specify on your invites so guests know what to expect and how to dress.
8. How do I get the word out?
If your rehearsal dinner will be a fancy affair with lots of out-of-town guests, you should send formal invitations. You'll also want people to RSVP so you have a head count for the caterer. If, on the other hand, your rehearsal dinner will be fairly low-key or small (think: a party at a restaurant or an intimate gathering at your future in-laws' home) then you don't need to be as "official" with your invitations. You can send e-vites, use DIY invites, alert people via your wedding website or personally call to ask people to join you. Just make sure it's clear to your guests where they need to be and when.
9. When should I invite them?
If you're sending out invitations, get them out with or shortly after your wedding invitations. This will help everyone keep their schedules straight, book their travel plans and ensure timely RSVPs. Give far-flung attendants the basic plans way in advance so they can book flights with the proper arrival time.
10. What should happen at my rehearsal dinner?
A few elements are generally incorporated into the festivities.
Expect to meet and great your guests—the rehearsal dinner is your chance to welcome everyone. Walk around and catch up with loved ones and meet more of your in-laws throughout the evening, because the wedding night is often a bit of a blur.
You also may hand out the wedding party gifts at this occasion, but do it subtly. No matter what, take a moment to stand and thank your bridal party for their support. Parent gifts can also be presented at the rehearsal dinner (but we prefer a more private time, if you can find it, just in case it gets emotional). The gift exchange can be a nice last moment for you to connect with them before the festivities. Some brides and grooms also use the occasion to present each other with special wedding gifts or surprises.
And traditionally, once dessert comes to a close, the toasts begin. As host of the party, the groom's father (sometimes along with the groom's mother) typically goes first, toasting his soon-to-be daughter-in-law and her family. Next up: The groom also toasts his new wife, the guests and the hosts, but there's no reason both of you can't stand up to thank everyone together.
Lastly, while everyone is still seated and you have their attention, it's also your chance to slip in a few last-minute refreshers about the next day. Before calling it a night, double-check that everyone in the wedding party knows exactly where to go the next day, what they're supposed to bring, and when and where they're expected to arrive to get ready. If you have a broader audience, remind guests about any activities for them the next day, as well as pickup times and locations for transportation to and from the ceremony.
Find your dream rehearsal dinner venue.


One week ago, Samantha Kofer was a third-year associate at New York City's largest law firm. Now she is an unpaid intern in a legal aid clinic deep in small-town Appalachia. When Lehman Brothers collapsed, she lost her job, her security, her future.
As she confronts real clients with real problems, she finds herself a world away from her past life of corporate fat cats and fatter bonuses. This is coal country. Meth country.
Three women, one shared ambition: to join the ATS and do their bit for King and country. Bea has grown up part of a large, boisterous Kent family. But she hasn't heard from her soldier sweetheart in months and her mother is controlling her life.
She needs to take charge of her future. Edie inhabits a world of wealth and privilege, but knows only too well that money can't buy happiness. She wants to be like Winston Churchill's daughter, Mary, to make a difference.
True love sometimes comes at a price. East Kent, 1830 Catherine Rook takes her peaceful life for granted. Her days are spent at the village school and lending a hand on her family's farm.
She thought she knew her past. She thought her past was the truth. Tina Bolotnikov, widowed after her husband, Sasha, is killed in a car accident, relocates back to her hometown on the south coast of the UK, to bring up her young son.
Hannibal Lecter emerges from the nightmare of the Eastern Front, a boy in the snow, mute, with a chain around his neck. He seems utterly alone, but he has brought his demons with him. Hannibal's uncle, a noted painter, finds him in a Soviet orphanage and brings him to France, where Hannibal will live with his uncle and his uncle's beautiful and exotic wife, Lady Murasaki.
While her mother is at home giving birth, eleven-year-old Isa must look after her younger siblings, but when her little sister is killed in an accident on a trainline she carries the guilt through the rest of her life. As Isa grows up, more tragedy strikes: her mother dies of a broken heart after giving birth to a stillborn son, and her father finds solace in heavy drinking. Though Isa finds satisfaction in domestic service for an aristocratic family in London, when her father returns home from the First World War paralysed and suffering memory loss, she must return home to take care of her family once more.
Everyone loves a hero - and the greatest heroes of them all were the ancient Greeks. Stephen Fry's blistering retelling of their fabulous stories makes this the PERFECT GIFT for young and old alike. Mortals and Monsters.
Quests and Adventures . . .
Beatrice Desmond, 55, lives on a remote farm nestled in a deep hollow in southern West Virginia. A native of Boston and a graduate of an Ivy League college, Beatrice is a fish out of water in Seneca County; although she maintains contact with certain friends and family, too often, Beatrice retreats into her work as a translator and editor, or into the bottle of Jack Daniel's she maintains nearby. Fate finally intervenes, requiring Beatrice to befriend and shelter Clara, an abused teenager, and accept the job of ghostwriting the memoir of her dashing but enigmatic neighbor, Tanner Fordyce.
Liverpool 1985 Kathleen, Adam and Jocelyn are three teenage friends who bond over an unconventional nativity play. They all have ambitions, they all have dreams. Adam wants to be a writer, Jocelyn wants to sing and Kathleen - well, she wants to be an embalmer.
The modern horror story grew and developed across the nineteenth century, embracing categories as diverse as ghost stories, the supernatural and psychological horror, medical and scientific horrors, colonial horror, and tales of the uncanny and precognition. This anthology brings together twenty-nine of the greatest horror stories of the period, from 1816 to 1912, from the British, Irish, American, and European traditions. It ranges widely across the sub-genres to encompass authors whose terror-inducing powers remain unsurpassed.
The book includes stories by some of the best writers of the century - Hoffmann, Poe, Balzac, Dickens, Hawthorne, Melville, Zola - as well as established genre classics such as M. R. James, Arthur Machen, Bram Stoker, Algernon Blackwood, Charlotte Perkins Gilman and others.


IF this is anything to go by, chivalry is definitely dead.
While the modern world of dating has done wonders for bridging the gap in gender expectations, surely it is still acceptable for a guy to buy a girl a drink?
Apparently not, based on what happened to Twitter user Abbyfent17 - and it's not what you think.
Abby had enjoyed a night out at The Viper Rooms in Harrogate where she met a young man called Liam.
Obviously hitting it off, Liam bought Abbey a drink and they exchanged numbers.
But then, a few weeks later Abby receives a text from Liam that says "Hi, hope you don't mind love but can you transfer me back £6.50".
I found a dating website on my wife's secret phone... is she leading a double life?
Obviously a little confused by the sudden message asking her for money, Abby asks who she is speaking to.
To which a ballsy Liam replies "Liam from the viper rooms a few weeks ago. I bought yer a drink? Can I have money back for it. Will give you sort code and account no. Thanks".
Most women would have told him where to go but Abby saw the funny side and offered to pay him back before posting the conversation on Twitter.
She even asked for the public's help in deciding what to do about this moral dilemma, creating a poll on the social media site.
In just under 24-hours it has received 16,595 votes, with an outstanding 83 per cent saying no to paying him back.
As if that wasn't enough attention to Liam's slimy move, the sales manager from The Vipers Rooms got involved to offer Abby a free table and bottle of bubbly for her and 7 friends to make up for her experience.
It's safe to say that Abby came out a clear winner in this situation, as well as the club which got some great publicity, but poor Liam just looks like a bit of a tool.


text:
Q: All combinations to split a vector into two groups in R I am trying to figure out an efficient way to obtain all unique combinations that elements in a vector (the vector is always a sequence between 1:n) can be split into two equal-sized groups. Each element in the vector must be represented once (in one of the two groups). For example, when n = 6, the vector will be [1,2,3,4,5,6]. This can be split into ten unique ways to form two equal-sized groups:
123 | 456
124 | 356
125 | 346
126 | 345
134 | 256
135 | 246
136 | 245
145 | 236
146 | 235
156 | 234

Note that the order of the values within the groups does not matter, so:
156 | 234

is the same as:
651 | 342

Also note that symmetrical solutions do not matter either, so:
156 | 234

is the same as:
234 | 156

When n = 4, there are 3 solutions. When n = 6, there are 10 solutions. When n = 8, there are 35 solutions. I believe I came up with a way to obtain these solutions in R. However, it is a bit slow once n becomes larger. For the most part, I am satisfied with what I have, but want to ask if anyone has suggestions on ways to improve its speed or code quality etc. In particular, I begin with a solution where there are lots of repeats, and then I delete repeats. This, I think, makes the algorithm rather slow.
library(combinat)
# Number of elements in array
n = 6
# Array
dat <- 1:n

# All possible permutations for array
ans <- permn(dat)
lengthAns <- length(ans)
ansDF <- data.frame()
# Place first permutation in final answer data frame
ansDF <- rbind(ansDF, ans[[1]])

# Look at the rest of the possible permutations. Determine for each one if it is truly unique from all the previously-listed possible permutations. If it is unique from them, then add it to the final answer data frame
for (i in 2:lengthAns){
  j = i
  k = TRUE
  while (k && j > 1){
    j = j-1
    if(setequal(ans[[i]][1:(n/2)], ans[[j]][1:(n/2)]))
      k = FALSE
    if(setequal(ans[[i]][1:(n/2)], ans[[j]][(n/2+1):(n)]))
      k = FALSE
  }
  if (k){
    ansDF <- rbind(ansDF, ans[[i]])
  }
}

# At this point, ansDF contains all unique possible ways to split the array into two-equally sized groups.

A: N = 6
x = 1:N
x1 = combn(x, N/2) #how many ways can we take half the elements to form the 1st group
NC = NCOL(x1)
x2 = x1[, NC:1] # simplified way to generate the complementary groups that include values not in x1
grp1 = t(x1[,1:(NC/2)]) # We only need half of the rows, the 2nd half containing the same set in reverse order
grp2 = t(x2[,1:(NC/2)])
all.comb = cbind(grp1, grp2)

#      [,1] [,2] [,3] [,4] [,5] [,6]
# [1,]    1    2    3    4    5    6
# [2,]    1    2    4    3    5    6
# [3,]    1    2    5    3    4    6
# [4,]    1    2    6    3    4    5
# [5,]    1    3    4    2    5    6
# [6,]    1    3    5    2    4    6
# [7,]    1    3    6    2    4    5
# [8,]    1    4    5    2    3    6
# [9,]    1    4    6    2    3    5
#[10,]    1    5    6    2    3    4

redpajama_set_name: RedPajamaStackExchange


text:
Australian National Airways Pty Ltd est une compagnie aérienne australienne disparue.

ANA fut constituée à Melbourne en  par regroupement de West Australian Airways, Adelaide Airways et Holymans Airways, qui exploitait déjà un Douglas DC-2.8 DC-2 furent finalement achetés par ANA, suivis d'une importante flotte de DC-3 puis de 12 DC-4.

La mise en service des premiers DC-4 en  permit d'ouvrir une liaison régulière entre Laverton, Victoria et Vancouver, au Canada, puis en octobre d'une liaison Melbourne - Perth sans escale 6 jours par semaine. La ligne Australie-Canada fut prolongée vers Auckland (Nouvelle-Zélande) avec escale à Sydney au lieu de Laverton, mais en  ANA dut céder l'exploitation de la ligne trans-pacifique à British Commonwealth Pacific Airlines.

ANA se recentra donc sur le réseau domestique, avant d'être rachetée en  par Ansett, la compagnie résultant de cette fusion devenant Ansett-ANA.

Flotte
 Douglas DC-2 : VH-ADQ/AEN, VH-ADZ, VH-ARB, VH-ARC, VH-USY, VH-UXJ, VH-UYB, VH-UYC.
 Douglas DC-3 : VH-ABR, VH-ACB, VH-AES, VH-AET, VH-AEV, VH-ANH à -ANZ, VH-BHB, VH-INA à -ING, VH-INI, VH-INM, VH-INN, VH-UZJ, VH-UZK.
 Douglas DC-4 : VH-ANA à -ANG (les lettres VH-ANF et VH-ANG furent attribuées chacune à 2 avions différents), VH-INX à -INZ.

Voir aussi

Compagnie aérienne australienne disparue
Entreprise fondée en 1936
Entreprise disparue en 1957

redpajama_set_name: RedPajamaWikipedia


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text:
Difference between revisions of "Localization Microscopy"
Nico (Talk | contribs)
(→Localization Microscope Window (first window))
'''Fitting Parameters'''
'''Dimensions:''' Determines the model used for the Gaussian. 1 - results in symmetric Gaussian in which only the width can vary, 2 - elliptical Gaussian, 3 - elliptical Gaussian in which the angle (theta) of the blob can vary. See [http://en.wikipedia.org/wiki/Gaussian_function Wikipedia article on Gaussian] for details.

'''Fitter:''' Use either the Simplex or Levenberg-Marquardt fitter. Levenberg Marquardt needs many fewer iterations to converge and is recommended.

'''Fitter:''' Use either the [http://en.wikipedia.org/wiki/Simplex_algorithm Simplex] or [http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm Levenberg-Marquardt] fitter. Levenberg Marquardt needs many fewer iterations to converge and is generally faster.

'''Max Iterations:''' Try 1000 for Simplex Fitter and 50 for Levenberg-Marquardt. Nicely shaped spots will converge much faster (especially using the LM fitter), so setting this number low will avoid spending many cpu cycles on poorly shaped spots.

'''Box Size (pixels):''' This number will be rounded to the next higher odd number. It is used in both the spot finding and Gaussian fitting. Keeping it small (equivalent of about 4 times resolution of the microscope, i.e. about 800 nm total when using a 100x 1.4na objective) will speed up analysis considerably.
This plugin uses Gaussian fitting to localize single fluorescent molecules for tracking or reconstruction of super-resolution images. One window is used to set parameters for fitting and tracking, the second window (which can be opened from the first using the 'Data' button) is used to perform operations (such as rendering, drift correction, etc.) on data-sets of fitting results.
To track a spot of interest in a time-series, draw a box around the spot and press the 'Track' button. After a short while, a row will appear in the Gaussian tracking data Window. Select the row and press Plot to visualize movement of the spot in the x direction.
To obtain a super-resolution image, acquire images using a square ROI with a size that is a power of 2 (256x256 often works best). Acquire a large data series of your blinking molecules (use the show button to get a feel for the number of spots that can be detected in your data). Press Fit to find spots and fit Gaussians to them. After a while, a row will appear in the Gaussian tracking data Window. Selected the row. Make sure the drop-down box in the Localization Microscopy section is set to 8x. Press Render.
Localization Microscope Window (first window)
Imaging Parameters:
Values entered in this section will be used in subsequent calculations. Press the read button to deduce some of these values from the meta-data contained in Micro-Manager datasets. You will need to measure the Photon-conversion factor (pcf) or use the pcf provided on the camera data-sheet. If you do not use EM gain, enter '1' in the field linear EM gain (to calculate the number of photons, digital numbers will be multiplied by pcf / linear EM gain. The Camera Offset (in digital numbers) is the average of an exposure in which no light can reach the camera. The program uses this to calculate the actual number of photons detected in signal and background.
Finding Maxima
To find spots, the application searches the image using a box with the size given in the Fit Parameters section. The minimum difference (in digital numbers) between the center pixel and the average of the four corner pixels should be at least the value of the Noise tolerance field for a putative spot to qualify. The image can optionally be pre-filtered by obtaining the difference image of a Gaussian filter with a width equal to the pixel size and a Gaussian filter 5 times the pixel size. However, such pref-filtering rarely helps and will slow down calculations. The show button will show on the frontmost window what objects will be recognized as spots.
Fitting Parameters
Dimensions: Determines the model used for the Gaussian. 1 - results in symmetric Gaussian in which only the width can vary, 2 - elliptical Gaussian, 3 - elliptical Gaussian in which the angle (theta) of the blob can vary. See Wikipedia article on Gaussian for details.
Fitter: Use either the Simplex or Levenberg-Marquardt fitter. Levenberg Marquardt needs many fewer iterations to converge and is generally faster.
Max Iterations: Try 1000 for Simplex Fitter and 50 for Levenberg-Marquardt. Nicely shaped spots will converge much faster (especially using the LM fitter), so setting this number low will avoid spending many cpu cycles on poorly shaped spots.
Box Size (pixels): This number will be rounded to the next higher odd number. It is used in both the spot finding and Gaussian fitting. Keeping it small (equivalent of about 4 times resolution of the microscope, i.e. about 800 nm total when using a 100x 1.4na objective) will speed up analysis considerably.
MTrack: blank
Data: blank
Stop: blank
Data processing window
Drift correction
2C ref. and cor.
Retrieved from "https://www.micro-manager.org/w/index.php?title=Localization_Microscopy&oldid=7271"

redpajama_set_name: RedPajamaCommonCrawl


I have listed a new property at 2064 RIVERGROVE PL in North Vancouver.
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Upscale living on a large scale! The sun is shining in this exquisite and sprawling 3 bedroom penthouse with 2 breathtaking, massive private decks. Large windows facing North, East & West illuminate the space together with large skylights. A chef's kitchen invites gracious living and entertaining. The serene master bedroom showcases a fireplace, huge walk-in closet and 5-piece bathroom. Enjoy views of the tranquil greenbelt towards Northland Golf Course from your private oasis. The home is complete with 3 gas fireplaces, a luxurious Fisher Paykel washer & dryer and 2 side by side parking spaces, all within a welcoming and neighbourly building on a private cul-de-sac. Enjoy this ultimate recreational neighbourhood with hiking, kayaking, skiing and more.Open house: Thurs April 11th 11am-12.
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text:
Texas Tech University's Engineers Week Events
Schedule of events for upcoming Engineers Week.
tags: College of Engineering, News Releases
Experts Can Speak About Ethanol and Other Biofuels
Texas Tech scientists can speak on a range of alternative energy topics, including their own collaborative investigations into new methods to more cheaply and efficiently produce ethanol and other fuels.
tags: General, News Releases
The Ordinary Turned Extraordinary: Texas Tech Professor Publishes Short Story Collection
Genaro J. Perez, a professor of Hispanic literature at Texas Tech, published his first book of short stories, entitled 'French Quarter Tales.'
tags: College of Arts and Sciences, News Releases
Texas Tech Equestrians Win Honors, Prepare for Upcoming Competitions
Texas Tech Equestrian Team members won honors Sunday in the Intercollegiate Hunt Seat Competition and Regional Championships at the Boulder County Fairgrounds in Colorado.
tags: College of Agricultural Sciences and Natural Resources, News Releases


Are you planning a community service outreach in Mexico this holiday season? Don't forget to review Mexican insurance at MexicanInsuranceStore.com Your travel may be noble in nature, but an accident without enough Mexican auto insurance from MexicanInsuranceStore.com could lead to serious problems. You don't want to spend the holidays in a Mexican jail, and your coverage can make the difference if you do encounter a problem. You now need Mexican insurance at MexicanInsuranceStore.com to drive on any highway in Mexico.
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With Mexican insurance at MexicanInsuranceStore.com you can get in touch with your provider to obtain bilingual help from a reputable professional. This will do a lot to calm your own nerves over the situation as you will have help in dealing with the issues.
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We sell our trees bare-root during the dormant winter months (November through to the end of March). It is advisable to reserve trees before the winter, as our trees are very much in demand and we often sell out of some species quite quickly.
Please use the form below to tell us which trees you would like to reserve for this coming winter of 2019-20.
We will then contact you to check the numbers you require and confirm the reservation.


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Stylish studios and spacious 1- and 2-bedroom suites, Ironing board and iron, and personal safe, Internet access.


text:
Die Ganske-Verlagsgruppe ist die Holding einer mittelständischen Unternehmensgruppe, die 18 Unternehmen mit rund 1.500 Beschäftigten zusammenfasst. Schwerpunkt der Unternehmenstätigkeit sind Bücher, Zeitschriften, elektronische Medien und der Handel. Hauptstandorte sind Hamburg und München.

Der Schwerpunkt der Themen liegt neben dem Literaturverlag in den Bereichen Reise, Gourmet, Lifestyle und Architektur. Lizenzierte Versionen der von der Verlagsgruppe herausgegebenen Publikumszeitschriften sowie Auftragsproduktionen wie beispielsweise das BMW-Magazin und das Evonik-Magazin erscheinen in vielen Ländern der Welt.

Geschichte
Im Jahre 1907 gründete Richard Ganske in Kiel den Lesezirkel "Leserkreis Daheim", den später sein Sohn Kurt Ganske übernahm und ausbaute. Er gründete nach dem Zweiten Weltkrieg den Jahreszeiten Verlag für gehobene Zeitschriften (seit Juni 1948 z. B. die Zeitschrift Merian) und erwarb den Hamburger Traditionsbuchverlag Hoffmann und Campe, in dem einst die Bücher Heinrich Heines erschienen und der heute unter anderem Siegfried Lenz, Angela Merkel, José Saramago, Harald Martenstein, Loki Schmidt, Stephen Hawking, Patricia Cornwell, Doris Lessing und Matthias Politycki verlegt. Seit dem Tod von Kurt Ganske führt dessen Sohn Thomas Ganske an der Spitze eines dreiköpfigen Vorstandes die Unternehmensgruppe.

Die Verlagsgruppe brachte 1986 die damals neuartige Zeitschrift Tempo, inspiriert durch Vanity Fair und New York Magazine, den sogenannten New Journalism nach Deutschland. Auch die Wochenzeitung Die Woche, eingestellt 2002, wurde von der Ganske Verlagsgruppe verlegt.

Im Mai 2011 übernahm die Ganske-Verlagsgruppe den Kunstbuchverlag Hatje Cantz sowie von Langenscheidt die Reiseführermarken Polyglott und APA, die in dem Unternehmen GVG TRAVEL MEDIA weitergeführt werden.

2015 wurde iPUBLISH Ganske Interactive Publishing GmbH und ticcats.de eingestellt.
Im Dezember 2016 wurden mit Wirkung ab Februar 2017 die Produktion und der Vertrieb des Zweitausendeins-Merkhefts übernommen.

Produkte
Die Aktivitäten der Unternehmensgruppe umfassen Bücher und Zeitschriften, elektronische Reise- und Navigationssoftware, einen Versandbuchhandel, einen Lesezirkel und das Relais & Châteaux-Hotel Hohenhaus.

Imprints
APA
Deutscher Taschenbuch Verlag (größter Anteilseigner)
Gräfe und Unzer
Guide Michelin
Hallwag
Hoffmann und Campe
Merian
 Hatje Cantz
Polyglott
Teubner
Travel House Media

Zeitschriften
Siehe: Jahreszeiten Verlag

Electronic Publishing
4Seasons Digital.net

Handel
Leserkreis Daheim
Presse Spezial Handel
Premium Sales Germany
Werbe Merkur
Akzente Versandbuchhandlung
Science Shop
vitaldirekt Versand
Mail:Order:Kaiser
Taubert Versandbuchhandlung
Frölich und Kaufmann Kunstbuchhandlung
Hotel Hohenhaus (Relais & Châteaux)

Siehe auch
 Liste deutschsprachiger Verlage

Weblinks
Ganske-Verlagsgruppe

Einzelnachweise

Buchverlag (Deutschland)
Zeitschriftenverlag (Deutschland)
Reiseverlag (Deutschland)
Verlag (Hamburg)
Versandhändler
Gegründet 1907


text:
Q: Set labelindent for basedescript (mdwlist) I would like to integrate a multiline label for a description environment. Therefor, I already found out that the package mdwlist could do the job for me.
I almost got the result I want, except for the labelindent. Anybody who knows the mdwlist way of putting the label (and following text) with a labelindent?
The result with a standard description environment looks like this:
\begin{description}[labelindent=0.5cm,labelwidth=4cm,leftmargin=!]%
    \item [{\parbox[t]{4cm}{Boutons du vibreur\\en bas}}] L'opération du vibreur en bas est fait par les boutons situés sur le cabinet électrique du vibreur.
    \item [Boutons du système de distribution] Les commandes de base du système de distribution peuvent être données par 2 stations de boutons, une de chaque côté de la machine.
    \item [Écran Beckhoff] Le contrôle de l'installation, à l'exception de la peseuse, est fait par cet écran. Il a le but de visualiser le fonctionnement et de commander le système de distribution.
    \item [Écran Ishida] La peseuse est commandée par cet écran séparé.
\end{description}

You can see that the label is too long, so I tried using mdwlist. I want to keep the labelindent=0.5cm but don't know how to do this...
\usepackage{mdwlist}

\begin{basedescript}{\desclabelwidth{4cm} \desclabelstyle{\multilinelabel}}
    \item [Boutons du vibreur en bas] L'opération du vibreur en bas est fait par les boutons situés sur le cabinet électrique du vibreur.
    \item [Boutons du système de distribution] Les commandes de base du système de distribution peuvent être données par 2 stations de boutons, une de chaque côté de la machine.
    \item [Écran Beckhoff] Le contrôle de l'installation, à l'exception de la peseuse, est fait par cet écran. Il a le but de visualiser le fonctionnement et de commander le système de distribution.
    \item [Écran Ishida] La peseuse est commandée par cet écran séparé.
\end{basedescript}


text:
extern "C" {

ColorConfig* New_ColorConfig() {
	return static_cast<ColorConfig*>(new OIIO::ColorConfig());
}

ColorConfig* New_ColorConfigPath(const char * filename) {
	return static_cast<ColorConfig*>(new OIIO::ColorConfig(filename));
}

bool supportsOpenColorIO() {
	return OIIO::ColorConfig::supportsOpenColorIO();
}

bool ColorConfig_error(ColorConfig* c) {
	return static_cast<OIIO::ColorConfig*>(c)->error();
}

const char* ColorConfig_geterror(ColorConfig* c) {
	if (!ColorConfig_error(c)) {
		return NULL;
	}
	return static_cast<OIIO::ColorConfig*>(c)->geterror().c_str();
}

int ColorConfig_getNumColorSpaces(ColorConfig* c) {
	return static_cast<OIIO::ColorConfig*>(c)->getNumColorSpaces();
}

const char * ColorConfig_getColorSpaceNameByIndex(ColorConfig* c, int index) {
	return static_cast<OIIO::ColorConfig*>(c)->getColorSpaceNameByIndex(index);
}

int ColorConfig_getNumLooks(ColorConfig* c) {
	return static_cast<OIIO::ColorConfig*>(c)->getNumLooks();
}

const char * ColorConfig_getLookNameByIndex(ColorConfig* c, int index) {
	return static_cast<OIIO::ColorConfig*>(c)->getLookNameByIndex(index);
}

int ColorConfig_getNumDisplays(ColorConfig* c) {
	return static_cast<OIIO::ColorConfig*>(c)->getNumDisplays();
}

const char * ColorConfig_getDisplayNameByIndex(ColorConfig* c, int index) {
	return static_cast<OIIO::ColorConfig*>(c)->getDisplayNameByIndex(index);
}

int ColorConfig_getNumViews(ColorConfig* c, const char * display) {
	return static_cast<OIIO::ColorConfig*>(c)->getNumViews(display);
}

const char * ColorConfig_getViewNameByIndex(ColorConfig* c, const char * display, int index) {
	return static_cast<OIIO::ColorConfig*>(c)->getViewNameByIndex(display, index);
}

const char * ColorConfig_getColorSpaceNameByRole(ColorConfig* c, const char *role) {
	return static_cast<OIIO::ColorConfig*>(c)->getColorSpaceNameByRole(role);
}

void deleteColorProcessor(ColorProcessor* processor) {
	OIIO::ColorConfig::deleteColorProcessor(static_cast<OIIO::ColorProcessor*>(processor));
}

ColorProcessor* ColorConfig_createColorProcessor(ColorConfig* c, const char * inputColorSpace,
                                     				             const char * outputColorSpace) {
	OIIO::ColorProcessor *cp;
	cp = static_cast<OIIO::ColorConfig*>(c)->createColorProcessor(inputColorSpace, outputColorSpace);
	return static_cast<ColorProcessor*>(cp);
}

} // extern "C"

redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}
Machine learning leverages data to build models capable of assessing the labels and properties of novel data.  Unfortunately, the available training data frequently contains biases with respect to things that we would rather not use for decision making.  Machine learning builds models faithful to training data and can lead to perpetuating these undesirable biases.  For example, systems designed to predict creditworthiness and systems designed to perform analogy completion have been demonstrated to be biased against racial minorities and women respectively.  Ideally we would be able to build a model which captures exactly those generalizations from the data which are useful for performing some task which are not discriminatory in a way which the people building those models consider unfair.

Work on training machine learning systems that output fair decisions has defined several useful measurements for {\it fairness}:  Demographic Parity, Equality of Odds, and Equality of Opportunity.  These can be imposed as constraints or incorporated into a loss function in order to mitigate disproportional outcomes in the system's output predictions regarding a protected demographic, such as sex.

In this paper, we examine these fairness measures in the context of {\it adversarial debiasing}.  We consider supervised deep learning tasks in which the task is to predict an output variable $Y$ given an input variable $X$, while remaining unbiased with respect to some variable $Z$.  We refer to $Z$ as the {\it protected variable}.  For these learning systems, the predictor $\hat Y = f(X)$ can be constructed as (input, output, protected) tuples $(X, Y, Z)$.
The predictor $f(X)$ is usually given access to the protected variable $Z$, though this is not strictly necessary.  This construction allows the determination of which types of bias are considered undesirable for a particular application to be chosen through the specification of the protected variable.

We speak to the concept of {\it mitigating bias} using the known term {\it debiasing}\footnote{Note that ``debias'' may not be quite the right word, as all bias is not necessarily removed.}, following definitions provided by \incite{hardt2016equality} and refined by \incite{beutel2017data}.

\begin{definition}{{\sc Demographic Parity.}}
A predictor $\hat Y$ satisfies {\it demographic parity} if $\hat Y$ and $Z$ are independent.
\end{definition}

 This means that $P(\hat Y = \hat y)$ is equal for all values of the protected variable $Z$: $P(\hat Y = \hat y) = P(\hat Y = \hat y |Z = z)$.

\begin{definition}{{\sc Equality of Odds.}}
A predictor $\hat Y$ satisfies {\it equality of odds} if $\hat Y$ and $Z$ are conditionally independent given $Y$.
\end{definition}

This means that, for all possible values of the true label $Y$, $P(\hat Y = \hat y)$ is the same for all values of the protected variable: $P(\hat Y = \hat y|Y = y) = P(\hat Y = \hat y|Z = z, Y = y)$

\begin{definition}{{\sc Equality of Opportunity.}}
If the output variable $Y$ is discrete, a predictor $\hat Y$ satisfies {\it equality of opportunity} with respect to a class $y$ if $\hat Y$ and $Z$ are independent conditioned on $Y=y$.
\end{definition}

This means that, for a {\it particular} value of the true label $Y$, $P(\hat Y = \hat y)$ is the same for all values of the protected variable: $P(\hat Y = \hat y|Y = y) = P(\hat Y = \hat y|Z = z, Y = y)$

We present an adversarial technique for achieving whichever one of these definitions is desired.\footnote{Achieving equality of odds and demographic parity are generally incongruent goals.  See also \incite{kleinberg2016inherent} for incongruency between calibration and equalized odds.}
A predictor $f$ will be trained to model $Y$ as accurately as possible while satisfying one of the above equality constraints.  Demographic parity will be achieved by introducing an adversary $g$ which will attempt to predict a value for $Z$ from $\hat Y$.  The gradient of $g$ will then be incorporated into the weight update rule of $f$ so as to reduce the amount of information about $Z$ transmitted through $\hat Y$.  Equality of odds will be achieved by also giving $g$ access to the true label $Y$, thereby limiting any information about $Z$ which $\hat Y$ contains beyond the information already contained in $Y$.

We consider the case where the protected variable is a discrete feature present in the training set as well as the case in which the protected variable must be inferred from latent semantics (in particular, gender from word embeddings).  In order to accomplish the latter we adapt a technique presented by \incite{bolukbasi2016man} to define a subspace capturing the semantics of the protected variable, and then train a model to perform a word analogies task accurately, while unbiased on this protected variable.
A consequence of this technique is that the network learns {\it ``debiased"} embeddings, embeddings that have the semantics of the protected variable removed. These embeddings are still able to perform the analogy task well, but are better at avoiding problematic examples such as those shown in \incite{bolukbasi2016man}.

Results on the UCI Adult Dataset demonstrate the technique we introduce allows us to train a model that  achieves equality of odds to within 1\% on both protected groups.

We also compare with the related previous work of  \incite{beutel2017data}, and find we are able to better equalize the differences between the two groups, measured by both False Positive Rate and False Negative Rate (1 - True Positive Rate), although note that the previous work performs better overall for False Negative Rate.

We provide some discussion on caveats pertaining to this approach, difficulties in training these models that are shared by many adversarial approaches, as well as some discussion on  difficulties that the fairness constraints introduce.

\section{Related Work}

There has been significant work done in the area of debiasing various specific types of data or predictor.

{\it Debiasing word embeddings}: \incite{bolukbasi2016man} devises a method to remove gender bias from word embeddings. The method relies on a lot of human input; namely, it needs a large ``training set'' of gender-specific words.

{\it Simple models}: \incite{lum2016statistical} demonstrate that removing the protected variable from the training data fails to yield a debiased model (since other variables can be highly correlated with the protected variable), and devise a method for learning fair predictive models in cases when the learning model is simple (e.g. linear regression). \incite{hardt2016equality} discuss the shortcomings of focusing solely on {\sc demographic parity}, present alternate definitions of fairness, and devise a method for deriving an unbiased predictor from a biased one, in cases when both the output variable and the protected variable are discrete.

{\it Adversarial training}: \incite{goodfellow2014generative} pioneered the technique of using multiple networks with competing goals to force the first network to ``deceive'' the second network, applying this method to the problem of creating real-life-like pictures. \incite{beutel2017data} apply an adversarial training method to achieve {\sc equality of opportunity} in cases when the output variable is discrete. They also discuss the ability of the adversary to be powerful enough to enforce a fairness constraint even when it has access to a very small training sample.

\section{Adversarial Debiasing} \label{section:adversarial-debiasing}
\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{model2.png}
    \caption{The architecture of the adversarial network.}
    \label{fig:arch}
\end{figure}
We begin with a model, which we call the {\it predictor}, trained to accomplish the task of predicting $Y$ given $X$. As in Figure \ref{fig:arch}, we assume that the model is trained by attempting to modify weights $W$ to minimize some loss $L_P(\hat y, y)$,
using a gradient-based method such as stochastic gradient descent.

The output layer of the predictor is then used as an input to another network called the {\it adversary} which attempts to predict $Z$. This is part of the network corresponds to the {\it discriminator} in a typical GAN \cite{goodfellow2014generative}. We will suppose the adversary has loss term $L_A(\hat z, z)$ and weights $U$.
Depending on the definition of fairness being achieved, the adversary may have other inputs.
\begin{itemize}
\item For {\sc Demographic Parity}, the adversary gets the predicted label $\hat Y$. Intuitively, this allows the adversary to try to predict the protected variable using nothing but the predicted label. The goal of the predictor is to prevent the adversary from doing this.

\item For {\sc Equality of Odds}, the adversary gets $\hat Y$ and the true label $Y$.
\item For {\sc Equality of Opportunity} on a given class $y$, we can restrict the training set of the adversary to training examples where $Y = y$.\footnote{This last technique of restricting the training set is discussed at length by \incite{beutel2017data}, so we only mention it here.}
\end{itemize}

In order for gradients to propagate correctly, $\hat Y$ above refers to the output layer of the network, not to the discrete prediction; for example, for a classification problem, $\hat Y$ could refer to the output of the softmax layer.

We update $U$
to minimize $L_A$ at each training time step, according to the gradient $\nabla_U L_A$.  We modify $W$ according to the expression:
\begin{equation} \label{eq:grads}
    \nabla_W L_P - \text{proj}_{\nabla_W L_A} \nabla_W L_P - \alpha \nabla_W L_A
\end{equation}
where $\alpha$ is a tuneable hyperparameter that
can vary at each time step and we define $\text{proj}_v x = 0$ if $v = 0$.

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{gradients2.png}
    \caption{Diagram illustrating the gradients in Eqn. \ref{eq:grads} and the relevance of the projection term $\text{proj}_h g$. Without the projection term, in the pictured scenario, the predictor would move in the direction labelled $g + h$ in the diagram, which actually \textit{helps} the adversary. With the projection term, the predictor will never move in a direction that helps the adversary.}
    \label{fig:grads}
\end{figure}

The middle term $\text{proj}_{\nabla_W L_A} \nabla_W L_P$ prevents the predictor from moving in a direction that helps the adversary decrease its loss while the last term, $\alpha \nabla_W L_A$, attempts to increase the adversary's loss. Without the projection term, it is possible for the predictor to end up helping the adversary (see Fig. \ref{fig:grads}).  Without the last term, the predictor will never try to {\it hurt} the adversary, and, due to the stochastic nature of many gradient-based methods, will likely end up helping the adversary anyway. The result is that when training is completed the desired definition of equality should be satisfied.

Notice that our definitions and method make no assumptions about the nature of the output and protected variables: in particular, they work with both regression and classification models, as well as with both discrete and continuous protected variables.

\section{Properties}

We note several properties of the above method that we believe distinguish it from past work.

\begin{enumerate}
\item{\bf Generality:} The above method can be used to enforce {\sc demographic parity}, {\sc equality of odds}, or {\sc equality of opportunity} as described in \incite{hardt2016equality}. Further, it applies without modification to the cases when the output variable and/or protected variable are continuous instead of discrete.

\item{\bf Model-agnostic:}  The adversarial approach described can be applied regardless of how simple or complex the predictor's model is, as long as the model is trained using a gradient-based method, as many modern learning models are. Further, as we will discuss later, at least in some situations, we suggest that the adversary does not need to be nearly as complex as the predictor---a simple adversary can be used with a complex predictor.

\item{\bf Optimality:}  Under certain conditions, we show that if the predictor converges, it must converge to a model that satisfies the desired fairness definition. Since the predictor also attempts to decrease the prediction loss $L_P$, the predictor should still perform well on the target task.
\end{enumerate}

\section{Theoretical Guarantees} \label{section:theory}
\begin{proposition}
Let the predictor, the adversary, and their weights $W$,  $U$ be defined according to Section \ref{section:adversarial-debiasing}
Let $L_A(W, U)$ be the adversary's loss, convex in $U$, concave in $W$,\footnote{We understand that these assumptions are not satisfied in most use cases involving neural networks; however, as with most theoretical analyses of machine learning models (see, for example, \incite{goodfellow2014generative} or \incite{kingma2014adam}; the former makes even stronger assumptions), assumptions of concavity are necessary for any proofs to work} and continuously differentiable everywhere.

Suppose that:
\begin{enumerate}
    \item When the predictor's weights are $W_0$, the predictor gives the same output $\hat Y$ regardless of input $X$. (For example, when $W_0 = 0$).
    \item There are some weights $U_0$ that minimize $L_A$ when the weights for $\hat Y$ have no effect on the output: For all $W$, $L_A(W, U_0) = \min_U L_A(W_0, U)$.
    \item Predictor and adversary converge to $W^*$ and $U^*$ respectively.
\end{enumerate}

Then, $L_A(W^*, U^*) = L_A(W^*, U_0)$. That is, the adversary gains no advantage from using the weights for $\hat Y$.

\end{proposition}

\begin{proof}
Since the adversary converges, $L_A(W^*, U^*) \le L_A(W^*, U_0)$: otherwise, since $L_A$ is convex in $U$, the adversary's weights would move toward $U_0$.  In other words, the adversary's minimum is the point at which the adversary gains an advantage from using $\hat Y$.  Similarly, since the predictor converges, $L_A(W^*, U^*) \ge L_A(W_0, U^*)$: Otherwise, the predictor would be able to increase the adversary's loss by moving toward $W_0$, and the projection term and negative weight on $\nabla_W L_A$ in Eqn. \ref{eq:grads} would push the predictor to move towards $0$. Then:
\begin{alignat*}{3}
    L_A(W^*, U_0) &\ge L_A(W^*, U^*)  &\qq{(as stated above)}
    \\&\ge L_A(W_0, U^*) &\qq{(as stated above)}
    \\&\ge L_A(W_0, U_0) &\qq{(by definition of $U_0$)}
    \\&= L_A(W^*, U_0) &\qq{(by definition of $U_0$)}
\end{alignat*}
so we must have $L_A(W^*, U^*) = L_A(W^*, U_0)$.
\end{proof}

Note that, in this proof, the adversary can be operating in a few different ways, as long as it is given $\hat Y$ as one of its inputs; for example, for demographic parity, it could be given only $\hat Y$; for equality of odds, it can be given both $\hat Y$ and $Y$.

We will show in the next propositions that the adversary gaining no advantage from information about $\hat Y$ is exactly the condition needed to guarantee that desired definitions of equality are satisfied.

\begin{proposition} \label{prop:parity}
Let the training data be comprised of triples $(X, Y, Z)$ drawn according to some distribution $D$. Suppose:
\begin{enumerate}
    \item The protected variable $Z$ is discrete.
    \item The adversary is trained for {\sc demographic parity}; i.e. the adversary is given only the prediction $\hat y$.
    \item The adversary is strong enough that, at convergence, it has learned a randomized function A that minimizes the cross-entropy loss $\mathbb E_{(x, y, z) \sim D}[-\log P(A(\hat y) = z)]$; i.e. the adversary in fact achieves the optimal accuracy with which you can predict $Z$ from $\hat Y$
    \item The predictor completely fools the adversary; in particular, the adversary achieves loss $H(Z)$, the entropy of $Z$.
\end{enumerate}
Then the predictor satisfies {\sc demographic parity}; i.e., $\hat Y \perp Z$.
\end{proposition}
\begin{proof}
Notice that if the adversary draws $A(\hat y)$ according to the distribution $Z|\hat Y = \hat y$, then its loss is exactly the conditional entropy
\begin{alignat*}{2}
H(Z|\hat Y) &= \mathbb E[-\log P(Z=z | \hat Y = \hat y)] \\
&= \mathbb E[-\log P(A(\hat y) = z | \hat Y = \hat y)]
\end{alignat*}
where the expectation is taken over $(x, y, z) \sim D$.
Now suppose for contradiction that $\hat Y$ is dependent on $Z$. Then $H(Z|\hat Y) < H(Z)$, so the adversary can achieve loss less than $H(Z)$, contradicting assumption (4).
\end{proof}

\begin{proposition} \label{prop:eq-odds}
If assumptions (2)-(4) above are replaced with the analogous equality of odds assumptions; in particular, that the adversary is given $\hat y$ and $y$, and the adversary cannot achieve loss better than $H(Z|Y)$~then the predictor will satisfy {\sc Equality of Odds}; i.e., $(\hat Y \perp Z) | Y$
\end{proposition}
\begin{proof}
Analogous to the above. Notice that if the adversary draws $A(\hat y, y) \sim (Z|\hat Y = \hat y, Y = y)$, then its loss is exactly the conditional entropy
\begin{alignat*}{2}
H(Z|\hat Y,Y) &= \mathbb E[-\log P(Z=z | \hat Y=\hat y,Y=y)] \\
&= \mathbb E[-\log P(A(\hat y) = z | \hat Y=\hat y,Y=y)]
\end{alignat*}
where the expectation is again taken over $(x, y, z) \sim D$. But if $\hat Y$ is conditionally dependent on $Z$ given $Y$, then $H(Z|\hat Y, Y) < H(Z|Y)$, so the adversary can achieve loss less than $H(Z|Y)$.
\end{proof}

Note that Propositions \ref{prop:parity} and \ref{prop:eq-odds} work analogously in the case of continuous $Y$ and $Z$, with the probability mass function $P$ replaced with the probability density function $p$, and the discrete entropy $H$ replaced by the differential entropy $h(X) = \mathbb E[-\log p(x)]$, since the relevant property ($h(A) = h(A|B)$ iff $A \perp B$) holds for differential entropy as well. They also work analogously when the adversary $A$ is restricted to a limited set of predictors.

For example, an adversary using least-squares regression trying to enforce equality of odds can be thought of as one that outputs $A(\hat y, y) \sim N(\mu(\hat y, y), \sigma^2)$ where $\mu(\hat y, y)$ is the output of the regressor, and $\sigma^2 > 0$ is a fixed constant.
Note now that the differential entropy $h(Z|\hat Y, Y) = \mathbb{E}
[-\log p(z|\hat y, y)]$ is nothing more than the expected log-likelihood, and so the function $\mu$ that minimizes this quantity is the optimal least-squares regressor. Thus, for example, if we restrict $\mu$ to be a linear function of $(\hat y, y)$, and the other conditions of Proposition \ref{prop:eq-odds} hold, then an analogous argument to the above propositions shows that $\hat Y$ has no linear relationship with $Z$ after conditioning on $Y$.

These claims together illustrate that a sufficiently powerful adversary trained on a sufficiently large training set can indeed accurately enforce the demographic parity or equality of odds constraints on the predictor, if the adversary and predictor converge. Guaranteed convergence is harder to achieve, both in theory and practice. In the practical scenarios below we discuss methods to encourage the training algorithm to converge, as well as reasonable choices of the adversary model that are both powerful and easy to train.

\section{Experiments} \label{section:experiments}

All models were trained using the Adam optimizer \cite{kingma2014adam} for both predictor and adversary.

\subsection{Toy Scenario}

We generate a training sample ${(x^{(i)},y^{(i)},z^{(i)})}_{i=1}^{n}$ (where $z$ is the protected variable) as follows. For each $i$, let $r \in {0,1}$ be picked uniformly at random, and let $v \sim N(r_{i},1)$. Let $u,w \sim N(v_{i},1)$ vary independently. Then $x^{(i)}=(r,u),y^{(i)}=[w>0],z^{(i)}=r$. (where $[~]$ denotes an indicator function). Intuitively, the variable that we are trying to predict, $y$, depends directly on $v$ and $r$. We are given as inputs the protected variable $r$, and a noisy measurement of $v$.  The end goal would be to train a model that predicts $y$ while being unbiased on $r$, effectively removing the direct signal for $r$ from the learned model.

If one trains generically a logistic regression model to predict $y$ given $x$, it outputs something like $y=\sigma(0.7u+0.7r)$, which is a reasonable model, but heavily incorporates the protected variable $r$. To debias, We now train a model that achieves {\sc demographic parity}. Note that removing the variable $r$ from the training data is insuffucient for debiasing: the model will still learn to use $u$ to predict $y$, and $u$ is correlated with $r$. If we use the described technique and add in another logistic model that tries to predict $z$ given $y$, we find that the predictor model outputs something like $y=\sigma(0.6u-0.6r+0.6)$. Notice that not only is $r$ not included with a positive weight anymore, the model actually learns to use a negative weight on $r$ in order to balance out the effect of $r$ on $u$  Notice that $u-r \sim N(0,2)$; i.e., it is not dependent on $r$, so we have successfully trained a model to predict $y$ independently of $r$.

\subsection{Word Embeddings}

We train a model to perform the analogy task (i.e., fill in the blank: \texttt{man : woman :: he : ?}).

It is known that word embeddings reflect or amplify problematic biases from the data they are trained on, for example, gender \cite{bolukbasi2016man}. We seek to train a model that can still solve analogies well, but is less prone to these gender biases. We first calculate a ``gender direction'' $g$ using a method based on \incite{bolukbasi2016man} which gives a method for defining the protected variable. We will use this technique in the context of defining gender for word embeddings, but, as discussed in \incite{bolukbasi2016man}, the technique generalizes to other protected variables and other forms of embeddings. Following \incite{bolukbasi2016man}, we pick 10 (male, female) word pairs, and define the and define the {\it bias subspace} to be the space spanned by the top $k$ principal components of the differences, where $k$ is a tuneable parameter. In our experiments, we find that $k = 1$ gives reasonable results, so we did not experiment further.

We use embeddings trained from Wikipedia to generate input data from the Google analogy data set \cite{mikolov2013distributed}.  For each analogy in the dataset, we let $x=(x_{1},x_{2},x_{3}) \in \mathbb{R}^{3d}$ comprise the word vectors for the first three words, $y$ be the word vector of the fourth word, and $z$ be $\text{proj}_{g}y$.  It is worth noting that these word vectors computed from the original embeddings are never updated nor is there projection onto the {\it bias subspace} and therefore the original word embeddings are never modified.  What is learned is a tranform from a biased embedding space to a debiased embedding space.

\begin{table}
\begin{center}
  \begin{tabular}{| l | c | l | c |}
    \hline
    \multicolumn{2}{|c|}{biased} & \multicolumn{2}{c|}{debiased} \\ \hline
    neighbor & similarity & neighbor & similarity \\
    \hline \hline
    nurse & 1.0121 & nurse & 0.7056 \\ \hline
    nanny & 0.9035 & obstetrician & 0.6861 \\ \hline
    fianc\'{e}e & 0.8700 & pediatrician & 0.6447 \\ \hline
    maid & 0.8674 & dentist & 0.6367 \\ \hline
    fianc\'{e} & 0.8617 & surgeon & 0.6303 \\ \hline
    mother & 0.8612 & physician & 0.6254 \\ \hline
    fiance & 0.8611 & cardiologist & 0.6088 \\ \hline
    dentist & 0.8569 & pharmacist & 0.6081 \\ \hline
    woman & 0.8564 & hospital & 0.5969 \\ \hline
  \end{tabular}
  \caption{Completions for \texttt{\small he : she :: doctor : ?}}
  \label{table:heshedoctor}
\end{center}
\end{table}

As a model, we use the following: let $v=x_{2}+x_{3}-x_{1}$, and output $\hat{y}=v-ww^{T}v$, where our model parameter is $w$. Intuitively, $v$ is the ``generic'' analogy vector as is commonly\footnote{see e.g. \incite{mikolov2013distributed}}
used for the analogy task. If left to its own devices ({\it i.e.}, if not told to be unbiased on anything), the model should either learn $w=0$ or else learn $w$ as a useless vector.

By contrast, if we add the adversarial discriminator network (here, simply $\hat{z}=w_{2}^{T}\hat{y}$), we expect the debiased prediction model to learn that $w$ should be something close to $g$ (or $-g$), so that the discriminator cannot predict $z=proj_{g}y$. Indeed, both of these expectations hold: Without debiasing, the trained vector $w$ is approximately a unit vector nearly perpendicular to $g: w^{T}g=0.08,||w||=0.82$; with debiasing, $w$ is approximately a unit vector pointing in a direction highly correlated with $g: w^{T}g=0.55,||w||=0.96$. Even after debiasing, gendered analogies such as \texttt{man : woman :: he : she} are still preserved; however, many biased analogies go away, suggesting that the adversarial training process was indeed successful. An example of the kinds of changes in analogy completions observed after debiasing are illustrated in Table \ref{table:heshedoctor}\footnote{The presence of {\it nurse} in the second position may seem worrying, but it should be noted that in this particular set of word embeddings, {\it nurse} is the nearest neighbor to {\it doctor}; no amount of debiasing will change this.}.

\subsection{UCI Adult Dataset}\label{subsec:uci}

\begin{table}
    \centering
    \small
    \begin{tabular}{@{ }l@{ }|@{\hspace{.25em}}l@{\hspace{.25em}}|@{\hspace{.4em}}p{16em}}
    {\bf Feature} & {\bf Type} &  {\bf Description} \\\hline
age	& Cont & Age of the individual\\
capital\_gain& Cont& Capital gains recorded\\
capital\_loss& Cont& Capital losses recorded\\
education\_num& Cont& Highest education level (numerical form)\\
fnlwgt& Cont& \# of people census takers believe that observation represents\\
hours\_per\_week& Cont& Hours worked per week\\\hline
education& Cat& Highest level of education achieved\\
income& 	Cat& Whether individual makes $>$ \$50K annually   \\
marital\_status& Cat& Marital status\\
native\_country& Cat& Country of origin\\
occupation& Cat& Occupation \\
race& Cat& White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black\\
relationship& Cat& Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried\\
sex& Cat& Female, Male\\
workclass&  Cat& Employer type \\
 \end{tabular}
    \caption{Features in the UCI dataset per individual.  Features are either continuous (Cont) or Categorical (Cat).  Categorical features are converted to sparse tensors for the model.}
    \label{tab:uci}
\end{table}

To better align with the work in \incite{beutel2017data}, we attempt to enforce {\sc equality of odds} on a model for the task of predicting the income of a person -- in particular, predicting whether the income is $>\$50k$ -- given various attributes about the person, as made available in the UCI Adult dataset \cite{asuncion2007uci}.

Details on the features that the dataset provides are available in Table \ref{tab:uci}.  We use both categorical and continuous columns as given, with exception to the {\tt fnlwgt} feature, which we discard.  We convert the remaining columns into tensors where the categorical columns are sparse tensors, age is bucketized at boundaries $[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]$, and the rest of the continuous columns are real-valued.

As discussed before, to enforce equality of odds, we give the adversary access to the true label $y$. The adversary will learn the relationship between $y$ and $z$ regardless of what the predictor does; further, if the predictor's predictions $\hat{y}$ give more information about $z$ than is already contained in $y$, the adversary will be able to improve its loss. Thus, the predictor, in attempting to fool the adversary, will move toward making sure that $\hat{y}$ does not give such additional information; in other words, toward equality of odds.

Our protected variable $z$ is a binary-valued variable for the two sexes annotated, {\sc male} and {\sc female}.

Our predictor model is straightforward logistic regression: $\hat{y}=\sigma(w_{1}\cdot x+b)$, where $\sigma$ is the sigmoid function.  Our adversary model takes the form of the following logistic-regression-like model:
\begin{alignat*}{2}
s=\sigma\qty((1+|c|)\sigma^{-1}(\hat{y}))\quad \hat{z}=w_{2}\cdot[s,sy,s(1-y)]+b
\end{alignat*}
where $c$ and $b$ are learnable scalars,  $w_{2}$ is a learnable vector, and $\sigma^{-1}$ is the inverse of the sigmoid function (logit function) $\sigma^{-1}(t)=\log t-\log (1-t)$. Intuitively, we want our adversary to be able to learn functions of the form $\hat{z}=f(y,[\hat{y}>0.5])$ (i.e. dependent only on the boolean predicted value $[\hat{y}>0.5]$), and thus enforce equality of odds. Here, the adversary would learn such a function by making $c$ extremely large. We add 1 to $|c|$ to make sure the adversary never tries to ignore $\hat{y}$ by setting $c=0$, which could be a difficult local minimum for the adversary to escape\footnote{This value added to $|c|$ is an adjustable hyperparameter; we found reasonable results using the value 1 and thus not feel the need to experiment further.}. This adversary is both general enough to be used whenever $y$ and $z$ are both discrete\footnote{If $y$ and $z$ are multi-class, then the sigmoid becomes a softmax, but everything else remains the same.}, and powerful enough that deviation from true equality of odds should cause the adversary to be able to decrease its loss.

Without tweaking, this algorithm ran into issues with local minima, and the resulting models were often closer to demographic parity than equality of odds.
We implemented a technique that helped: by increasing the hyperparameter $\alpha$ in Eqn. \ref{eq:grads} over time, the predictor had a much easier time learning to deceive the adversary and therefore more strictly enforce equality of odds. We set $\alpha=\sqrt{t}$ (where $t$ is the step counter), and to avoid divergence we set the predictor's step size to $\eta \propto 1/t$, so that $\alpha \eta \rightarrow 0$ as is preferred for stochastic gradient-based methods such as Adam.

We train the model twice, once with debiasing and once without, and present side-by-side confusion matrices on the test set for income bracket with respect to the protected variable values {\it Male} and {\it Female}, shown in Table \ref{table:adult}, and we present the false positive rates (FPR) and false negative rates (FNR) in Table \ref{tab:false_rates}.  Note that false negative rate is equal to $1 -{}$ true positive rate, so the trade-offs are directly comparable to the $(x,y)$ values of an ROC curve.

\begin{table}[h!tb]
    \begin{center}
    \begin{tabular}{|c|c|c|c|c|c|c|}
    \hline
    \multicolumn{3}{|c|}{Without Debiasing} & \multicolumn{3}{|c|}{With Debiasing}   \\
    \hline
    \it Female & Pred 0 & Pred 1 & \it Female & Pred 0 & Pred 1  \\
    \hline
    True 0 & 4711 & 120 & True 0 & 4518 & 313  \\
    \hline
    True 1 & 265 & 325 & True 1 & 263 & 327  \\
    \hline
    \it Male & Pred 0 & Pred 1 & \it Male & Pred 0 & Pred 1  \\
    \hline
    True 0 & 6907 & 697 & True 0 & 7071 & 533  \\
    \hline
    True 1 & 1194 & 2062 & True 1 & 1416 & 1840  \\
    \hline
    \end{tabular}
    \caption{Confusion matrices on the UCI Adult dataset, with and without equality  of odds enforcement.}
    \label{table:adult}
    \end{center}
\end{table}

\begin{table}
\resizebox{\columnwidth}{!}{
\begin{tabular}{l|l|ll|ll|}
& & \multicolumn{2}{c|}{Female} & \multicolumn{2}{|c|}{Male} \\
 & & Without & With  & Without & With \\\hline
\multirow{2}{*}{\incite{beutel2017data}} & FPR & 0.1875 & 0.0308 & 0.1200 & 0.1778 \\
& FNR & 0.0651 & 0.0822 & 0.1828 & 0.1520 \\\hline

\multirow{2}{*}{Current work} & FPR & 0.0248 & 0.0647 & 0.0917 & 0.0701 \\
& FNR & 0.4492 & 0.4458 & 0.3667 & 0.4349 \\\hline
\end{tabular}}
\caption{False Positive Rate (FPR) and False Negative Rate (FNR) for income bracket predictions for the two sex subgroups, with and without adversarial debiasing.}\label{tab:false_rates}
\end{table}

We notice that debiasing has only a small effect on overall accuracy ($86.0\%$ vs $84.5\%$), and that the debiased model indeed (nearly) obeys equality of odds: as shown in Table 4, with debiasing, the FNR and FPR values are approximately equal across sex subgroups: $0.0647 \approx 0.0701$ and $0.4458 \approx 0.4349$.

Although the values don't exactly reach equality, neither difference is statistically significant: a two-proportion two-tail large sample $z$-test yields $p$-values 0.25 for $y=0$ and 0.62 for $y=1$.

\section{Conclusion}

In this work, we demonstrate a general and powerful method for training unbiased machine learning models.
We state and prove theoretical guarantees for our method under reasonable assumptions, demonstrating in theory that the method can enforce the constraints that we claim, across multiple definitions of fairness, regardless of the complexity of the predictor's model, or the nature (discrete or continuous) of the predicted and protected variables in question. We apply the method in practice to two very different scenarios: a standard supervised learning task, and the task of debiasing word embeddings while still maintaining ability to perform a certain task (analogies).  We demonstrate in both cases the ability to train a model that is demonstrably less biased than the original one, and yet still performs extremely well on the task at hand. We discuss difficulties in getting these models to converge. We propose, in the common case of discrete output and protected variables, a simple adversary that is usable regardless of the complexity of the underlying model.

\section{Future Work}

This process yields many questions that require further work to answer.
\begin{enumerate}
    \item The debiased word embeddings we have trained are still useful in analogies. Are they still useful in other, more complex tasks?
    \item The adversarial training method is hard to get right and often touchy, in that getting the hyperparameters wrong results in quick divergence of the algorithm. What ways can be used to stabilize training and ensure convergence, and thus ensure that the theoretical guarantees presented here can work?
    \item There is a body of existing work for image recognition using adversarial networks.  Image recognition in general can sometimes be subject to various biases such as being more or less successful at recognizing the faces of people of different races.  Can multiple adversaries be combined to create high accuracy image recognition systems which do not exhibit such biases?
    \item In general, do more complex predictors require more complex adversaries? It would appear that in the case of $y$ and $z$ discrete, a very simple adversary suffices no matter how complex the predictor. Does this also apply to continuous cases, or would a simple adversary be too easy to deceive for a complex predictor?
\end{enumerate}

\bibliographystyle{aaai}
\input{main-bibliography.bbl}

\end{document}

redpajama_set_name: RedPajamaArXiv


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Erratum to: Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at s = 13 TeV with the ATLAS detector (The European Physical Journal C, (2020), 80, 10, (957), 10.1140/epjc/s10052-020-8227-9)
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Atlas Collaboration (2021). Erratum to: Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at s = 13 TeV with the ATLAS detector (The European Physical Journal C, (2020), 80, 10, (957), 10.1140/epjc/s10052-020-8227-9). European Physical Journal C, 81(1), [29]. https://doi.org/10.1140/epjc/s10052-020-08644-x
Erratum to : Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at s = 13 TeV with the ATLAS detector (The European Physical Journal C, (2020), 80, 10, (957), 10.1140/epjc/s10052-020-8227-9). / Atlas Collaboration.
In: European Physical Journal C, Vol. 81, No. 1, 29, 01.2021.
Atlas Collaboration 2021, 'Erratum to: Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at s = 13 TeV with the ATLAS detector (The European Physical Journal C, (2020), 80, 10, (957), 10.1140/epjc/s10052-020-8227-9)', European Physical Journal C, vol. 81, no. 1, 29. https://doi.org/10.1140/epjc/s10052-020-08644-x
Atlas Collaboration. Erratum to: Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at s = 13 TeV with the ATLAS detector (The European Physical Journal C, (2020), 80, 10, (957), 10.1140/epjc/s10052-020-8227-9). European Physical Journal C. 2021 Jan;81(1):29. doi: 10.1140/epjc/s10052-020-08644-x
Atlas Collaboration. / Erratum to : Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at s = 13 TeV with the ATLAS detector (The European Physical Journal C, (2020), 80, 10, (957), 10.1140/epjc/s10052-020-8227-9). In: European Physical Journal C. 2021 ; Vol. 81, No. 1.
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abstract = "In the published HTML version of this article, the affiliations of the authors of the National Research Nuclear University MEPhI, Moscow, Russia were unfortunately marked incorrectly.",
author = "{Atlas Collaboration} and G. Aad and B. Abbott and Abbott, {D. C.} and Abud, {A. Abed} and K. Abeling and Abhayasinghe, {D. K.} and Abidi, {S. H.} and AbouZeid, {O. S.} and Abraham, {N. L.} and H. Abramowicz and H. Abreu and Y. Abulaiti and Acharya, {B. S.} and B. Achkar and L. Adam and Bourdarios, {C. Adam} and L. Adamczyk and L. Adamek and J. Adelman and M. Adersberger and A. Adiguzel and S. Adorni and T. Adye and Affolder, {A. A.} and Y. Afik and C. Agapopoulou and Agaras, {M. N.} and A. Aggarwal and C. Agheorghiesei and Aguilar-Saavedra, {J. A.} and A. Ahmad and F. Ahmadov and Ahmed, {W. S.} and X. Ai and G. Aielli and S. Akatsuka and {\AA}kesson, {T. P.A.} and E. Akilli and Akimov, {A. V.} and Khoury, {K. Al} and Alberghi, {G. L.} and J. Albert and Verzini, {M. J.Alconada} and S. Alderweireldt and M. Aleksa and Aleksandrov, {I. N.} and E. Cheu and Johns, {K. A.} and Rutherfoord, {J. P.} and Varnes, {E. W.}",
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N1 - Publisher Copyright: © 2021, CERN for the benefit of the ATLAS collaboration.
N2 - In the published HTML version of this article, the affiliations of the authors of the National Research Nuclear University MEPhI, Moscow, Russia were unfortunately marked incorrectly.
AB - In the published HTML version of this article, the affiliations of the authors of the National Research Nuclear University MEPhI, Moscow, Russia were unfortunately marked incorrectly.
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If you had told me that taking my six year old on a trip to show my work for four days would be fun.. I would have laughed at you. But still, that's what I did. Along with my husband William , we got on the road for our daughter's school break and drove up to San Francisco.
The first challenge was seeing if we could or should show our work together, that seemed to be better for art buyers and photo editors in a way, two for one. So that was good, but how about the kid? Well, hopefully it wasn't the stupidest thing I've done this year, but frankly I think it was good too. Everyone seemed pretty happy for a diversion in from the normal portfolio show, she is pretty charming ( generally speaking, depending on the time of day ), and it seemed to be a really great way for people who could potentially hire us to get to know us as real people, not just photographers. Now I don't bring my daughter to shoots, but still it was fun, and lots of people have a kid or two or maybe even three at home, so it's not the weirdest thing in the world to see one at work. That being said, we had a busy week, with lots of meetings, so we had to build in some time for fun every day. The first day we were in town, that meant spending the morning at the Ferry building ( AKA Disneyland for Glutons ) my absolute favorite place. We had some morning treats ( Blue Bottle Coffee ) honestly we ate everywhere in that place, and then went straight over to the ferry for a ride to Larksper. Now this was really about the trip on a boat, her first one so far, and it was FUN! We were all tired and cranky from the drive the day before, so being on a boat with the air in your face and the wind shaking you up a little was absolutely fantastic! I highly recommend it.
And another little note on kids. At Goodby Silverstein, we had a chance to meet the daughter of an old friend I worked with many many years ago, in fact the first time I worked with Dan Southwick, his daughter Isabella was about 6 months old. We've worked together and kept in touch over the years since, so it was great that we finally get to meet her on this trip. William did this sweet portrait of them at the office. Sometimes things come full circle.
Which was true for visiting my favorite all time cafe with William and Vivienne on this trip too. I LOVE, Cafe Trieste. It's a spot I used to visit weekly if not daily when I lived in the bay area and went to school at the San Francicsco Art Institute. This place has such an incredible history and atmosphere. Maybe the coffee isn't as sublime as some of the other hipster joints which I also appreciate, but it is straight up cafe culture, pure and simple. I got a quick image of Paul with my iphone, he's been pulling shots there since I was a student at SFAI….
Beyond the ferry, we went to the new Coret Children's playground in Golden Gate Park, which a friend in the area recommended. It is amazing, and slightly horrifying for any mother or parent with a penchant for slight anxiety. The kids can climb this rope structure up to 25 feet in the air… I'm sure it's safe, but I couldn't really stand to look.
On our last day in town we had lunch with one of William's collaborator's and friends Kevin. Kevin Roberson is an incredible designer and has worked for many of the best shops in town doing design and art direction over the years. These days, he has a design studio in Hayes Valley with a partner, they design packaging for all manor of adult and child beverages.
I loved Kevin's look, and fortunately, William did this great portrait of him. The white pants and black slip ons are the best part!
The last stop out of town was breakfast with friends Jessie and Mark at Sam's Log Cabin in Albany. It was delicious, and very child friendly. There were quite literally children lying on the floor in this place at any given time and the servers were so funny, they'd just step right over them. Oh boy, but still it was great.
Here's to taking your kid, to work once and a while. I highly recommend it in San Francisco for this or any other kind of trip, the playgrounds are well designed and plentiful, the food is beyond your expectations if you do a little research ahead of time, and you can get a great view or cup of coffee just about anywhere.


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HomeRoderick Strong
E&C's POD OF AWESOMENESS – KYLE O'REILLY Release date: 3/15/19 Recap by: Jeff Rush DIRECT LINK TO LISTEN/DOWNLOAD Pre-interview discussion -Harlem Heat in the HOF. Edge says Doom were the barrier breaking precursor, but then it was Harlem Heat. -Brie Bella is retiring. Edge speculates it has to do with having a child. -Kurt Angle having his last match at […]
RECAP AND REVIEW: Notsam Wrestling with The Undisputed Era on having Shawn Michaels as a coach, why it's almost a "crime" to call NXT developmental, the NXT stars they want to face, State of Wrestling, Sam on why the Universal Title is cursed (Ep. 211)
Notsam Wrestling Podcast Episode 211: The Undisputed Era Release Date: November 8, 2018 Recap by: James Hayes DIRECT LINK TO LISTEN/DOWNLOAD From New York, Sam welcomes you to the show! It's like a dream, a hoop dream for Sam to interview all four members of The Undisputed. Well, kids 'dreams come true. The Undisputed Era Interview Roderick Strong, Adam Cole, […]
Colt Cabana's Art of Wrestling
Art of Wrestling Episode 370 Guest: Frankie The Mobster Release Date: October 12, 2017 Recap by: Tony Flores Direct Link to Listen/Download Show Highlights/Rundown Frankie explaining the importance of standing out in wrestling Working in an asylum Dealing with mental health and substance abuse His tryout with WWE and their audition process for highly regarded independent wrestlers A story with […]
QUICK QUOTES: SoCal Val on TNA's Hulk Hogan/Eric Bischoff era, early days of the company
SoCal Val was recently interviewed by Wrestle: List and talked about a few topics from her time in TNA. Here are some of the highlights: The early days of TNA: "I was there for nine years overall. I did my first show for them in January of 2003 when they were in Nashville. I was with a group travelling to […]
QUICK QUOTES: Daniel Bryan "working on" getting back into the ring, his relationship with C.M. Punk, NXT wrestlers he wants on the main roster
Daniel Bryan was recently interviewed by Sports Illustrated and talked about his desire to return to the ring, his relationship with C.M. Punk, and more. Here are the highlights: If he's going to wrestle again: "I'm working on it. Wrestling is more of a creative outlet, and especially for somebody like me, I view it as my creative outlet. Not […]
WRITTEN PODCAST RECAP: X-Pac 1,2,360 w/ Bill Hanstock, Justin Barrasso on Nakamura/Ziggler not delivering, chemistry in stables, negativity in the business (Ep. 38)
X-Pac 1,2,360 (Episode 38) Guest: Bill Hanstock, Justin Barrasso Date: May 24, 2017 Recap by: Christopher Gaspare DIRECT LINK TO LISTEN/DOWNLOAD Newsworthy Items -Test almost joined DX. -X-Pac thought Roode/Itami lacked intensity. -Nakamura/Ziggler failed to deliver. Timestamps 00:00: X-Pac and guests talk the importance of chemistry in wrestling. 28:38: The Bachelor, New Japan's U.S. plans, and Anthem releasing Hardy contracts. 56:58: Sponsor […]


University of Michigan School of Dentistry: To develop a tool for faculty to assess dental students' readiness for practice in the areas of critical thinking, professionalism, communication and interpersonal skills.
Medical University of South Carolina College of Dental Medicine Study: In support of a collaborative pilot study to determine the impact of the use of EZCodes dental diagnostic terminology in treatment planning and critical thinking skills.
The University of Tennessee Health Science Center Hinman Student Research Symposium: In support of a national meeting that features oral and poster presentations of research studies by dental students and graduate trainees from dental schools across North America.


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Bloodborne: The Board Game
"The city of Yharnam had been known for its healing industry, where many weary travelers would come to rest and recover. However, now, a disease is spreading among the populace. A mysterious curse has been placed on the city, its people are being twisted and mutated, forces from outside the city are creeping into its alleyways. Only the Hunters can uncover the source and have any chance of undoing it. But can they complete the task before the night's over?
In Bloodborne: The Board Game, players take on the role of Hunters in the city of Yharnam. Together, they must uncover what is happening in the city. They must defeat monsters and complete quests, making many meaningful decisions about their progress as they go. The game uses an innovative card-based combat system, with players able to customize their combat deck as the game progresses. In the end, they will either save the city, or it will forever be doomed. "


How can I undo this? I saw too late that it was a bug in this plugin in scm-manager 1.45. But scm-manager 1.46 is not a released version, it is a snapshot.
Information: It is installed as a service on a Windows Server 2012 R2 machine.
You could delete the plugins/classpath.xml file in the home folder and restart scm-manager. This will deactivate all plugins and you have to reinstall every plugin.
Thank you! phew Thanks for the quick answer!