To know how reset Dell XPS BIOS password read this blog post that covers several steps to deal with this issue and solve various other issues related with password of Dell XPS BIOS. Dell XPS laptop users need help to reset the BIOS password on their system can read this blog to find the right procedure of resetting the password with complete privacy.
Actually, there are several steps that can be followed to reset the password and if there is any problem online help service is also available here to help users and solve the problem remotely. To get online assistance call at Dell customer support number 1-844-395-2200 and allow the remote access of the system and solve such issues and Dell computer problem with assured results.
Step 1: First flip your Dell laptop over and remove the battery, as well as any cables connected to the laptop.
Step 2: Remove the screw latched into the memory panel on the bottom of the computer found near the rear.
Step 3: Lift the panel off and expose the memory modules.
Step 4: Seek for a small push-button and press it, holding it for about two seconds. Reassemble your Dell laptop and start it up.


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Mapping Out History
April 15, 2018 / admin / 1 Comment
It has always been important to us at UWG's Center for Public History to include and to highlight student work in our projects. In this blog post we feature maps of the Textile Heritage Trail created by students in Dr. Andy Walter's advanced economic geography seminar during Spring 2017. These maps reveal spatial dimensions of, and provide geographical context for, the history we are seeking to bring to life through the Textile Trail project.
Dr. Walter began collaborating with the Center's directors, Dr. Ann McCleary and Keri Adams, in 2016. For Dr. Walter, an economic geographer in the Department of Geosciences, the Textile Trail project was an opportunity to engage in mapping (did you know that geographers love maps?) while learning about an industry that has played a defining role in the West Georgia region's economy and urban system. Ultimately, the goal of this partnership is an interactive mapping component to the Textile Trail, perhaps built into a mobile app, as well as series of maps representing the historical geographical evolution of the textile industry in the region. Dr. Walter is also working on the Center's University History Project to create maps and a digital mapping platform to tell the stories of the people, programs, and spaces that make up our university. The heat map above was created by Dr. Walter to show the spatial density of mills across space, i.e. the number of mills in localities from place to place.
Heat Map displaying mill concentration in 1910 according to Davison Textile Blue Book. Created by Dr. Andy Walter.
During the Spring 2017 semester, Dr. Walter taught his advanced economic geography seminar under the topic "Mapping the Economy". With the Textile Trail serving as the semester-long object of analysis, the idea was for students to learn about economic spatial relations through a cartographic lens while developing capacities with mapping tools, such as Google Maps, CartoDB, and ArcMap. The students met with Dr. McCleary and Ms. Adams, who pointed them to the Davison Blue Book, an annual directory of establishments in the various sub-sectors of the textile industry by location. Using the 1901 and 1910 editions of the book, students extracted raw data for the towns on the Textile Trail (e.g. number of employees, number of boilers, capitalized value, etc.), created new variables (e.g. number of workers per boiler), organized datasets appropriate for use with the different mapping tools, and worked to create clear, effective maps.
Carrollton, Georgia, entry from the 1910 Davison Textile Blue Book.
The purpose of the West Georgia Textile Heritage Trail is to tell the history of the textile industry throughout the West Georgia region while focusing on the people who worked in the industry. The maps created by Dr. Walter's students offer a view of dimensions and changes in the West Georgia textile industry during the first decade of the 20th century. Some of the maps show the variation in the number of mills in localities, while others represent the geography of textile employment and capital investment in mills.
Map displaying Mills Capital Value 1901-1902. Created by Mark Hammond, 2017.
Map displaying the Change of Capital Value of the Mills. Created by Ryan Thornhill, 2017.
These two maps were created for an analysis of the capital value of textile production facilities in different towns. Each map overlays data from the Davison Blue Book on a georeferenced historic map (i.e. the students found the historic maps in digital library collections, downloaded them, and used a geospatial technique known as "georeferencing" to put the downloaded images in the same coordinate system as their other spatial data). The map above, created by Mark Hammond (Geography, 2017), reveals a stark contrast in capital concentration between large and small textile production centers in 1901, as well as the dominance of the northern region of the Textile Trail, with Columbus standing out as the giant of the southern region. Beyond what it suggests about the textile industry, this map also provides context for understanding differential patterns of urbanization occurring in the region during this era. The map below , created by Ryan Thornhill (Geography, 2017), shows the change in capital value between 1901 and 1910-11. The map shows an uneven pattern of growth and decline, with much of the decline concentrated in the smaller towns of the central region of the Textile Trail and significant growth in the relatively large towns and cities in the northern and southern regions.
Map of West Georgia Textile Mills 1900-1911. Created by Chih Hsiao, 2017.
This pair of maps, created by Chih Hsiao (Global Studies, 2017), compares the number of textile mills in towns on the Textile Trail in 1901 and 1910-11. They illustrate the industrialization of the region during the early twentieth century. As firms consolidated, technology improved, and the rail network connected industrial towns to the national and world economies, the number of mills grew and declined across the region. As these maps show, for example, in 1911, there were mills where there had never been before, and existing textile towns, including the city of Columbus, added even more mills. Of note, these two maps situate the Davison Blue Book data is on the 1883 Cram Railroad Map, providing a reminder of the increasingly vital importance of accessibility by rail to the textile industry at this time.
The maps below, created by Ellen Randolph (Geography, 2017), offer a comparison of the number of mills across a much larger time scale, from 1901 to 1980. While some of the 1901 pattern is evident in the 1980 map, a comparison reveals several dramatic differences, such as the relative disappearance of the textile industry (in terms of the number of mills) in Columbus and the appearance of Carrollton and Calhoun as major centers of textile production in 1980 due to technological innovations throughout the twentieth century.
Map displaying the Difference in Mill Number 1901-1980. Created by Ellen Randolph, 2017.
Map showing number of West Georgia Heritage Trail Employees per Boiler (1910). Created by Haley Patterson, 2018.
The map above, created by Haley Patterson (Geography, 2018), was produced for an exploration of variation in the relationship between capital and human labor inputs in textile production. Here the map shows the number of employees per boiler (boilers provided steam power). In this map, the geographic disparity between the towns in the central region's towns and Columbus in the southern region is much less dramatic than in the map of the number of mills per town. This map shows how GIS mapping can enhance the perspectives of the Textile Trail by making the map about the people who worked at the mills rather than about just the quantity of mills.
By incorporating GIS mapping systems into our textile trail history, we can provide a more engaging, more inclusive, and more multidisciplinary approach to educating about the textile industry in Georgia. Student work was the focus of this blog, as their creativity was evident in their interpretations of the mill industry onto maps. However, we also want to extend recognition to Dr. Walter for involving his students on this project, and for all of his assistance previously, currently, and into the future as we continue to tell the story of the Textile Trail in more innovative and interactive ways.
A special thanks goes out to all of the previous students who have worked on this project along with the Center faculty Drs. Julia Brock and Ann McCleary, and Keri Adams. This blog would not have been possible without Dr. Andy Walter and his collaborative efforts.
Examining the Trail's Sanborn Maps
Visualizing Data
Textile Talk
My Journey Into Mapping
Advanced Economic Geography coursecapitalDavisons Textile Blue Booksgeographic information systemGISGIS mappinglabormappingmapsmillsresearch assistantsstudentsundergraduate researchUniversity of West Georgia
Laurine Dixon Entrekin: Bremen-Bowdon Investment Employee
Ricky Hudgins
Great job on this. It was very good reading for an old man from LaGrange.

redpajama_set_name: RedPajamaCommonCrawl


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HomeNewsIndustry
Ducati Could Go Electric Much Sooner Than We Expect It
We are finally getting an e-Ducati, we just don't know when.
By: Sabrina Giacomini
At this point in time, most manufacturers have dabbled in the wonderful world of electric motorcycles whether by actually producing their own, introducing concepts, and/or by unveiling a 10-year plan with electrification listed in the near future. Not all, however, have left the stronghold of performance-oriented, petrol-powered bikes. Ducati is one of them, or at least was one them. In fact, the big boss has revealed that we could be getting an electric Ducati much sooner than we expect.
A BRIGHT (ORANGE) FUTURE?
Ducati's Future Could Be With… KTM?
Ducati Demonstrates How Cars And Bikes Can Talk At CES
"The future is electric and we're not far from beginning production of the series," Ducati CEO Claudio Domenicali commented during the podium celebrations of the Motostudent racing event last weekend at the Aragon racetrack in Spain. The University of Bologna's UniBo rookie team, supported by the Ducati Foundation, took the third place in the electric class competition.
Obviously, a "soon" is all we have to work with at the moment as Domenicali didn't expand on the subject of an upcoming e-Ducati. Ducati going electric, however, isn't a new concept. For a few years now, the terms "electric" and "Ducati" have been sharing the headlines. Whether it's Domenicali riding on a converted electric, Zero FX powertrain-propelled Hypermotard in Thailand, or the Ducati Zero concept designed by a student of the Milan Polytechnic School of Design in collaboration with the brand, Ducati has shown a marked but discreet interest in the technology.
What an electric Ducati could end up looking or being like, however, remains a mystery for now. Will the company opt for a performance-oriented sportbike or instead borrow the same path as Zero Motorcycles and tackle the trails first? Or, maybe, we'll be treated to a Ducati e-scooter—admit it, the thought makes you smile. Until the iconic Bologna company chooses to let us in on what its electric future holds, we'll get to play my favorite game of the guessing type. What would you like the first electric Ducati to look like?
Source: Corse di Moto
Industry News Racing Electric Motorcycles


I posted last month about CVS's new Abound line, which is a great new line of snack food from CVS's Gold Emblem that offers some healthy snacking options. Wanted to share specifics now that we've tried some of the different products in the line.
Hands down, my favorite is the Fruit & Nutty Probiotic Trail Mix. There are yogurt covered raisins in the mix and the yogurt is probiotic yogurt dipped – delish!
My boys have been loving the Heavenly Light Popcorn. The name describes it best – it's light and it's definitely heavenly.
And I mentioned this in my last post but we continue to snack on the Rice Pop Clusters and I can seriously go through a bag of these in one sitting. Don't forget to clip the $1 off coupon if you're going to check out the new line and let me know what you think!
Thanks for the Gold Emblem suggestions. I just bought the Gold Emblem Select colossal cashews(made with sea salt). Delicious! I will definiitely return to CVS to try more of the Gold Emblem products.


The McGhee family established a scholarship fund in memory of their son Paulie who died tragically in a car accident on Thanksgiving Day November 24, 2011 at the age of 16. Paulie's spirit lives on and the family felt that a memorial scholarship in the name of their son was an appropriate way to give back to others.
Proceeds from your participation allows for the Fund to continue giving to deserving high school students. Thank you!


The first leg of the test started really bad and already after that I have made 3 contacts on the night at 03 local time did I experiences a huge SWR with 100W in reflection from the antenna. It was only to shut down the station and try to sleep to the morning.
The next day did I take down the antenna array to examine what could be the big trouble. After some checking did I find what was the fault. TX/RX relay did not seems to be so good any more. Lucky as I was to have one Transco relay in spare. So after a hard work with the repairing could I set up the antenna again to be ready at lunch time and make some few contacts before my moon set this first moon pass.
In the evening this day was I ready to go for It.
The good thing with the first leg was that I worked China and Crete as new DXCC.
In the second leg seem the conditions to be worse. It was also not so good times for us in EU and Asia, I was really tired when contest started at 01 local time at Saturday but the result do not look so bad when I look at it with 102 QSO. Also happy to work several new squares.


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Gigola est un film français réalisé par Laure Charpentier, sorti le .

Synopsis
Dans les années 1960, Gigola est une jeune garçonne attirée par les femmes belles et riches.
Jeune femme révoltée, elle s'habille en homme et ne sort que la nuit pour fréquenter les bars et cabarets homosexuels de Pigalle et Montparnasse à Paris.
Elle vend ses charmes a des femmes âgées et très fortunées.
Elle évolue dans le milieu de la prostitution lesbienne et homosexuelle des années 60 à Pigalle.
Très autoritaire et violente, elle vit au jour le jour, ou plutôt de nuit en nuit, et provoque chez les gens une fascination endiablé, de par sa beauté irrésistible et sa personnalité perverse.

Fiche technique

Distribution

Bande originale

Créditées au générique, on entend parmi les musiques additionnelles les chansons Sag Warum de Camillo Felgen, Tombe la neige de Salvatore Adamo et Comme un garçon de Sylvie Vartan.

Liens externes

 Gigola sur ELMS

Film français sorti en 2011
Film dramatique français
LGBT au cinéma en France
Lesbianisme au cinéma
Film se déroulant dans les années 1960
Film se déroulant à Paris
Film sur le suicide
Travestissement au cinéma

redpajama_set_name: RedPajamaWikipedia


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Matt Garza is Becoming Tradeable
The veteran hurler is quietly enjoying a solid bounceback campaign.
By Kyle Lesniewski@Kyle_Lesniewski Sep 15, 2016, 8:03am CDT
Share All sharing options for: Matt Garza is Becoming Tradeable
Photo by Stacy Revere/Getty Images
Around Milwaukee, the general sense is that most of the folks that follow our local nine are not fans of Matt Garza. Outside of his solid first season with the Brewers after signing as a free agent in 2014, Garza exactly hasn't lived up to the expectations that came with his $50 mil contract, the richest free agent expenditure in Brewers' history. He didn't endear himself to many, either, when he opted to leave the team last September (to be with his wife while she was enduring a difficult pregnancy) rather than pitch out of the bullpen after 25 disastrous starts.
Garza's value was at an all-time low following the 2015 season, after he posted a 5.63 ERA in 148.2 innings pitched, and with another two years and $25 mil on his deal plus a vesting option for 2018, he was all but untradeable. It wouldn't have been that much of a surprise to see new GM David Stearns decide to simply cut bait with Garza at that time and eat the rest of the money owed to a free agent mistake made by the previous regime. Milwaukee stuck with the 32 year old righty, however, who has quietly re-established himself as a viable option in the rotation once again this season.
Matt missed the start of the year with an injury to his shoulder, but since returning to the rotation in June he's thrown 91.2 innings of 4.22 ERA baseball. Not quite front-of-line starter type run prevention there, obviously, but good enough for a better-than-league-average ERA- of 99.
Garza's still not striking batters out at the rate he did earlier in his career (15.4% K rate this season), but he has cut his walk rate from last year (8.7% to 7.9%) as well as his home run rate (1.39 HR/9 to 1.08 HR/9). To help mitigate some of those missing whiffs, Garza has become a ground ball pitcher, generating them at a 54.1% clip this year - 12 percent higher than his career rate in 11 big league seasons. He's also induced infield fly balls - which are considered about as effective as strikeouts inasmuch as being an "automatic out" - at a well-above average rate of 14.0%, which ranks him tied for 13th-best in baseball among starting pitchers with 90+ innings pitched. He's slashed his line drive against rate by more than four points from last year down to 17.9% and his soft contact rate of 19.6% is above Garza's career average.
A FIP of 4.48 and FIP- of 104 generally support that Garza has been about a league-average starter when healthy this season. Given his immense struggles last season, the production Garza has provided this year is about as useful as anyone could have hoped for, and it has come at a good time for Milwaukee. It's setting up to be a rather weak market for free agent pitching this winter, which puts Slingin' David Stearns in a good position to capitalize on Garza's rebound season.
With Matt's vesting option for 2018 no longer a concern due to the two months worth of starts he missed early in the year, he's owed only $12.5 mil next season along with a $5 mil club option for 2018. With the rising cost of doing business in the MLB, a potential two-year, $17.5 mil deal for a capable back end of the rotation starter shouldn't be a hard sell to other teams around the league. The top starting pitchers on the upcoming market - likely Rich Hill, Jeremy Hellickson, and Ivan Nova - should each have no trouble commanding two to three times higher of a guarantee despite question marks and limited track records of recent success for all three. Beyond that trio, it's slim pickings as far as free agent starters go.
The baseball season is a marathon of 162 games, and teams are willing to pay to find competent arms to fill all those innings. That's why there should be a good deal of interest in Garza this winter. That's not say Milwaukee will get a major haul for their veteran starter, but Stearns should be able to command a B-level prospect while being required to pay down little to none of the remaining money owed to him. Considering where his value was a year ago at this time, that's undoubtedly an encouraging development.
Statistics courtesy of Fangraphs


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Shackleton's Boat Journey – Fact and Fiction in the Visual Record of the Rescue
Senior Picture Editor Julie McMahon researched the photographs featured in our edition of Shackleton's Boat Journey. Here she tell the story behind one crucial and seemingly simple image. The Endurance expedition was famously documented by the Australian photographer Frank Hurley. One photograph from our selection stands out, and not just for its memorable imagery. Hurley captioned this photo 'The Rescue'. It appears to depict Shackleton's party returning to save the stranded crew on Elephant Island, who wait on the shore waving at the boat – the lifeboat from the rescue steamer Yelcho – as it comes into view. It is a picture full of emotion: they have been saved. Hurley's caption has accompanied this photograph since it was first shown to the public nearly 100 years ago. However, the caption in Frank Worsley's 1931 memoir, Endurance, contradicts this. It reads: 'The departure of the James Caird from Elephant Island', in which case the men are waving the boat off as it sails away to get help. And it's probably not relief they're feeling but dread, aware that their fate will be sealed by this journey. Hurley's photograph shows that part of the image has been scratched out. In fact, it originally showed two boats: the James Caird and Shackleton's supply boat, the Stancomb Wills. The former was erased, while the latter – small enough to pass for the Yelcho lifeboat – remained. Why would Hurley deliberately change this scene? It was usual for expedition members, on returning home, to give public lectures, and Hurley doctored this particular image in order to deliver a suitable visual climax to his talk. But the manipulation was also unfortunate in that it changed an authentic, emotionally charged scene into a contrived happy ending. JULIE MCMAHON Senior Picture Editor [caption id="attachment_2939" align="aligncenter" width="290"] The Folio edition of Shackleton's Boat Journey[/caption]


Told from the point of view of thirteen-year-old Anne Turner, the story begins on a beautiful, hope-filled day in the spring of 1931. But as the sun sinks toward the horizon, so do Anne's spirits. Her family is moving again. When they arrive in Minnesota in hope of finding work for Anne's father, there is none. Every new place the Turners stop, work is no where to be found. Readers travel with Anne and her family across the Midwest and West as Anne keeps a journal of the daily struggle to survive during the depression. This is a story of hardship and human compassion as people help the Turners along their way--town and county officials who give them gas, laborers and shop owners who share their meager supplies. This is a story of a young girl's strong hope in the face of overwhelming despair.


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Where's my explanation?
By wishyouweremeds - 30/10/2019 16:00 - Australia - Sydney
Today, after two and half years at my company and after recently being promoted, I was told by my boss the business doesn't feel it needs me, and invited me to find another job. FML
By drunk under 18 teenager - 19/08/2014 13:37 - Morocco - Marrakech
Today, I was hammered, and on my way home I walked into a policeman. My logic was: if I'm on the phone, he can't talk to me, so I pulled my phone out and started speaking. The officer then asked me why I was speaking to my wallet. FML
By Anonymous - 04/06/2014 14:34 - United Kingdom - Ormskirk
Today, my mother-in-law confessed that for the longest time, she had fantasies about killing me for "ruining" her daughter's life, basically because I'm not wealthy enough for my mother-in-law's standards. She made sure to let me know the situation hasn't changed at all. FML
By billjoebob424 - 10/03/2016 00:01 - Canada
Today, I've been so accustomed to kissing my girlfriend that when I gave my best bud a hug, I kissed him on the neck. FML
By dr mamour - 30/01/2013 21:57 - France - Paris
Today, during my first day as a medical intern in a new ward, I was performing a rectal exam. My supervisor thought it would be funny to burst into the room and scream, "Who are you?! You don't even work here, you pervert!" FML
By Anonymous - 23/06/2012 14:59 - Ireland - Cork
Today, as a romantic gesture, my boyfriend gifted me an origami vagina. FML
Anti Joke
By Anonymous - 14/02/2014 21:39 - United States - Daytona Beach
Today, a guy called customer support, claiming his internet wasn't working. I asked for his customer details, and he gave his name as Mike Hunt. I recognized the old joke, called him an idiot, and hung up on him. It turned out that was his real name. I'm now on suspension. FML
By depraved - 08/01/2009 11:21 - United States
Today, taking the train to work after the worst hangover ever, two immense fat women start talking about rim jobs. I got up to switch cabins just in time for their conversation to switch over to RECEIVING rim jobs. I sprayed puke all over myself and an innocent bystander. FML
By well great. - 17/05/2013 22:28 - United States - Pascagoula
Today, my friend told me she'd lost her notebook filled with crucial notes for our finals. She asked if she could borrow mine and copy my notes. She then lost my notebook too. FML
By Panek - 07/10/2021 23:01
Today, I had a panic attack because my wife yelped at me. What did I say that made her yell? I told her that I had just been diagnosed with a panic disorder that's triggered by people yelling at me. FML
By Jusawkward - 01/10/2009 07:07 - United States
Today, I went to my former high school's homecoming game. A classmate who I always had a crush on looks up and goes "Brian!", holding her arms out for a hug. I hug her and she seems a bit suprised but hugs back anyway. I get up and see another Brian from our class behind me. Shame. FML
By Detroitman125 - 10/11/2018 03:00
Today, my parents told me that they think I've gone off the deep end. I don't drink or take drugs. What did I do? I got McDonald's a couple times after school. FML
By M.L. - 06/09/2009 21:09 - United States
Today, I went on a busy train. I packed myself in close to a very good looking woman and, too scared to say anything, looked out the window. After feeling a hand on my ass, I assumed it was her. To not ruin the moment, I didn't look back. After a few stops, I looked back. It was an old man. FML
By What-_- - 25/01/2017 00:00
Today, my roommates keep opening my bedroom door to "let the WiFi out." FML
By stuff - 16/08/2009 04:36 - United States
Today, I was calling my cable company to tell me how to fix my internet. I stayed on hold for almost half an hour, and then when someone finally picked up, my phone died. FML
By tittyboomboom - 24/03/2016 13:16 - Australia
Today, I was taking the train home from another unsuccessful job interview. As I was sitting there, I felt the urge to yawn, but before I could raise my hand to cover my mouth some guy stuck his finger in it. FML
By JadedBaker - 08/07/2012 06:58 - United States
Today, I woke up early and spent hours baking and icing a three-tier cake for my friend's eighteenth birthday, which is this evening. I just found out my mother threw it in the compost bin because she's on a diet and it was "tempting" her. FML
By usmcpain - 23/06/2009 05:16 - United States
Today, I couldn't sleep and in the process of tossing and turning I did something very painful to my back. I spent 4 years in the marine corps infantry, but laying in a bed kicks my ass. FML
By ohemgee23 - 19/02/2010 05:31 - United States
Today, my little sister walked in on me and my boyfriend. I told her I would give her 10$ if she just pretended it never happened. She agreed, walked out and shut the door. Later, when my parents arrived, she yells: "Nicole and Joe were naked upstairs!" FML
Today, I found out my boyfriend is cheating on me with a woman twice my age. I'm 32. FML
By lanaoftherey - 02/06/2015 21:30 - United States
Today, I went to get my car washed at one of those places where you can watch through the glass. I watched in horror, face pressed against the glass, as my car went through with the window down. FML
By mmmmmm no - 24/06/2021 02:00
Today. I worked my 10-hour shift and drove over 3 hours to the hotel that I'd reserved a room at for 3 nights. The hotel required ID to get a room with a bank or credit card. I realized I'd lost my state ID, they wouldn't accept my work photo ID, they won't let me pay cash… No ID, no room. FML
By Stuckism - 15/09/2009 17:16 - Malaysia
Today, I got stuck in an elevator. The help-line tells me, "Don't touch the door. We'll be there soon". Two hours later, I've got a headache, my legs are stiff and my date must think I stood her up. The tech finally arrives, pries the door open, then rudely asks, "Why didn't you do that yourself?" FML
By Danger Dave - 21/12/2020 04:58
Today, it's been almost 24 hours since my wife bent down and couldn't get back up. After laying in bed all day in agony with a bad back, an ambulance was called. She still currently can't stand in the hospital. I have two kids to look after and am supposed to be in work tomorrow. I have no idea what to do. FML
By pop - 29/12/2008 01:41 - France
Today, my best friend invited me to dinner at his house. When I went to the toilet, I found my wife's wedding ring in a cup, which she'd lost a week ago. FML
By mlowy - 09/05/2014 05:35 - Azerbaijan - Baku
Today, my computer crashed and lost all of its data while I was making a back up. FML
Is it not your baby as well?
Today, I'm starting to realize my wife's baby loves me more than my wife. FML
By Undercooked - 24/09/2013 19:08 - Canada - Mississauga
Today, my girlfriend made bacon sandwiches for lunch. I didn't want to be rude, but I couldn't help but mention that the bacon smelled and tasted weird. I thought it may have expired. She said not to worry because she used the dry bacon under the counter. Those were dog treats. FML
By CJ - 12/08/2016 01:02 - Canada - Lethbridge
Today, I was looking for a moustache comb, couldn't find it anywhere. Then I noticed it in a basket by the toilet with scissors. My wife's been using it to trim her pubes. FML
By nooneinparticular - 21/05/2020 08:00
Today, I realized that I get turned on by my girlfriend's perfume. My mother started using the same one. FML
Stuck with you
Today, I sent my abusive girlfriend a text, explaining I was breaking up with her via text because I was afraid for my physical safety if I broke up with her in person. She video chatted me less than five seconds later from our doctor's office, where the doctor confirmed she is in fact pregnant. FML
By scribbles1475 - 15/12/2011 05:00 - United States
Today, after trying to find the perfect picture for the guy I have a huge crush on, I finally found one and sent it to him. His return picture? Himself in a Batman mask and sombrero. FML
By piper55 - 10/05/2019 14:00
Today, for the first time in a month, I talked to someone in person besides my grandmother and my husband. It was a store clerk calling me "Miss" and asking if I was finding everything okay. FML
By tycrist8 - 26/06/2015 23:32 - United States - Lawrence
Today, my family and I were driving and we passed a strip club called DB's Golden Banana. My 5-year-old sister asked what it was, so my dad said it was a place where people dance. Now my sister keeps telling people she wants to be a dancer at DB's Golden Banana. FML
By cjgreer70 - 18/01/2014 23:09 - United States - West Linn
Today, I was told I looked like Beaker from the Muppets. After doing a side-by-side comparison, I realized it's true. FML
By mraow123 - 30/05/2009 05:04 - United States
Today, I was walking my dog but every time he looked like he was about to "go", he became uninterested with the spot and kept walking. I, following behind, tripped over a bump in the sidewalk and face planted. My dog then finally urinated, all over my aching body. FML
By horriblejoke - 10/10/2012 15:05 - Australia - Perth
Today, I completely shaved my head as a gesture for my boyfriend's mother, who was suffering from cancer and having a terrible time undergoing chemotherapy. Turns out she doesn't even have cancer, and my boyfriend thought I wouldn't have the guts to do it. FML
Today, I learned I have over $10,000 in debt, despite never owning a credit card. Apparently, my ex-roommate had been replying to the credit card offers I was receiving in the mail. That also explains my missing driver's license a few months back. FML
By melonhead - 14/01/2010 08:46 - Australia
Today, my friends thought it would be funny to try and break a watermelon on my head while I was asleep on the couch. FML
By DeeDee - 04/02/2014 22:08 - Austria - Vienna
Today, my brother came to my first standup comedy act. He'd apparently read my material beforehand, and kept finishing my jokes for me. FML
By DD - 08/02/2010 14:18 - France
Today, my wife told me that she wants a divorce. Apparently, I'm taking too long to forgive her for having an affair. FML
Drai Gray 8
Wednesday 30 October 2019 6:08
DO NOT RESIGN, if he doesn't want you their, he has to fire you, don't give him a valid reason
Ideally, the OP can find another job and quit this one before getting fired.
kfchicken 31
Friday 1 November 2019 9:32
Promotions are a trap. Often you get a little more money, but you also get a lot more BS duties and you're subject to a lot more scrutiny. Hopefully, you'll find another anthropomorphized business that "feels" like it needs you.
Mungolikecandy 19
So they are trying to force you out of the door without getting redundancy? Maybe they have someone they want to put into your role but can only do that if you are gone first.
mfranks19891989 10
Wednesday 30 October 2019 15:39
Congratulations your unemployed what do you want a medal.
wishyouweremeds 11
update. still have my job and all efforts to get rid of me have failed xx
You're **
E2dav 5
Thursday 31 October 2019 2:48
Someone else is after our job
Love Parents Relatable LGBTQIA+ Money Kids Grandparents Awkward Death Noise Sleep Birthday Family Work Weird Intimacy Sex Capitalism Fight Sports Underwear Health Covid Mental health Gross Injury Poopoo peepee Friends Jealousy Tinder


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What Hedges doesn't seem to grasp, is over 40. % of American college graduates are functionally illiterate. He is really addressing probably 10 % of the population. So if the unwashed and illiterate people don't see images from the murder and starvation in Yemen, then it didn't happen. If you don't show images of Israel's psychopaths shooting unarmed children, then it didn't happen.
The Vietnam war ended because the "war" was on television every night and people could see what was wrong.
Since the MSM is totally propaganda we will never see the images that could wake people up.
Just because digital media and images are abused and used to infantilize and confuse doesn't mean they aren't powerful tools. When images are used responsibly to convey a coherent message, or complex situation, they can communicate in a succinct and powerful way. Money, energy, media are all tools that we must use to change the world. They are neither good nor bad in and of themselves. If we had a people's media, from grassroots, it would be wildly entertaining and informative.
Theoretically true–but it's no accident that the tools are in the hands of the wealthy sociopaths who have ruled in the past and still do. We DO have a people's media–Popular Resistance is one segment of it–but it's drowned our by the corporate majority, and even so is seen as a threat, hence the guns trained on net neutrality, and the efforts by the gatekeepers of today like Facebook and Google to set up censorship arrangements. So far, these have been only lightly used. Given evidence of a powerful movement becoming united, such efforts would step up rapidly. In a capitalist society, the odds that these "neutral tools" would be in the hands of those seeking liberation, justice, ecological responsibility, equality–are approximately zero.
"The unwashed and illiterate" 40%?? WTF? This is elitist, professional class rhetoric that justifies inequality. BTW, oral cultures are not the ones who have created the conditions for the eventual extinction of our species. In one of my university classes where one episode of On Contact was viewed, Hedges' arguments went right over the heads of the 20-something students because they thought that he had an arrogant, white male elitist attitude. I disagreed, but totally understood why they didn't hear the message because they were so angry at the delivery. I give young people more credit for thinking independently than Hedges does, apparently.
If there is one thing I have learned in graduate school, it is that the professional class are the brownshirts of the elite. Call me functionally illiterate for that, I don't care. You are on the other side of the class war than the side I am on.


The 2nd of this year's Career Fair is happened on 4th October 2018, from 12pm - 5pm!
It was held in our brand new PSB Academy STEM Campus!
As the campus name suggests, the fair revolved around our School of Engineering & Technology as well as the School of Life & Physical Sciences!
We also had 3 Career Coaches at our Hive Counters to give students a 15minutes 1-to-1 career counselling session from 12pm - 2pm!


text:
Гош-філ (Persian: گُوش فيل, ) — афганське та іранське печиво-пончик, зроблене у формі вуха (gosh) і підсмажене в олії. Перед подаванням на стіл кожен пончик посипають подрібненими фісташками і цукровою пудрою. Афганці зазвичай готують gosh-e fil до Eid ul-Fitr (Свято розговіння).

Назва печива перекладається із перської мови як "вухо слона".

Для приготування використовують такі інгредієнти:
 яйця;
 цукор;
 сіль;
 молоко;
 масло;
 борошно;
 кардамон;
 фісташки;
 олію (для смажіння).

Див. також

 Пончик
 Пампушка

Примітки

Посилання
 طرز تهیه شیرینی گوشفیل ترد و خوشمزه + تصویر

Пончики


text:
Posted on July 17, 2015 by Xenia Gazette
Column: Misery is about the only company for Tiger Woods
By Tim Dahlberg - AP Sports Columnist
ST. ANDREWS, Scotland (AP) — Tiger Woods already looked miserable when the sky briefly darkened and a cold mist forced him to pull out his rain suit on the 10th hole at St. Andrews.
The weather was something Woods could handle. What seemed to perplex him on this day were the basics of a game he once took for granted.
Woods put a shot into water near the first green on the Old Course that few others in the field even gave a second thought. He chunked wedges, and made a string of bad club choices that were mind boggling for a player once thought to be on his way to being the greatest ever.
On a front nine just begging to be attacked — where Sweden's David Lingmerth shot a 29 just a few groups in front of him — and on a course where he twice romped to wins, Woods carded a 40 on his way to a fat 4-over 76 that effectively put him out of the British Open almost before it began.
By the time he reached for his rain suit, Woods was already seven shots behind his playing partners and so far out of the lead that any thoughts of competing on Sunday had to be dismissed as sheer fantasy.
Not that Woods seemed to care all that much. The indifference with which he played bordered on shocking, with the fire that once burned fiercely inside of him seemingly extinguished, at least for now.
Woods would smile and laugh about it later, as if the whole thing was some kind of inside joke. If it is, he would be better served letting the fans who still gamely rooted him on in Thursday's first round to know they should stop bothering.
Because, for all of Woods' protestations, it appears he has already done that himself.
Earlier in the week, Woods had insisted he is still young, still loves to play and compete and still gets up for the biggest events. But if this day and the dismal U.S. Open he had last month are any indication, he may be just as delusional as the fans who still call out his name.
Jason Day could only help but notice as he played alongside the golfer he grew up idolizing.
"The good thing about it is I saw him struggle a little bit before and he came back and got to No. 1, as well, so I know that he can get back out of this," Day said. "It's just depending on how much he wants it."
Just how much Woods does want it is at the center of any discussion of his play. At the age of 39, helping raise two young children and with no worries about his finances, it might be too much to expect him to apply the same kind of energy to his latest swing change as he did the others before it.
If that's the case, he might be better served simply staying at home for the PGA Championship next month. With seemingly no clue what is going on in his game, there's no reason to further diminish his legacy — not to mention embarrass himself — by missing yet another cut in a major.
Who would have thought there would be a time we started feeling sorry for Tiger Woods?
"It's hard to watch the greatest player of this generation be a middle-of-the-pack hack," analyst and former player Paul Azinger said during the ESPN broadcast of the first round.
Woods talked about being angry at his play, though he didn't look at it when chatting with Day while waiting to hit on the 15th tee. He said he was frustrated, though that's an old story in a winless streak that will reach two years next month.
Actually, he didn't say anything he hasn't said in recent times, including his belief that he was actually in contention at the Masters when he started the final round 10 shots back and finished 13 behind.
Delusional? You decide.
Woods, of course, is an easy target for analysts and anyone else. He can never be as great as he once was, just as he can never become the warm and friendly kind of player Arnold Palmer was.
He's caused a lot of his own troubles, whether by his actions off the course or the disdain he treated both fans and media with for years. He also can't stop changing swings, even if his original swing was good enough to make him the best player on the planet the minute he turned pro.
It was inevitable that a new group of players would one day take over the game, and they have. Jordan Spieth may not make fans jump up and scream his name, but he shoots low scores almost every time he goes out and after an opening 67 he has a chance to win a third straight major and perhaps the Grand Slam that no player has ever won.
Meanwhile, the forecast for Friday includes heavy rain and gusts up to 50 mph, the kind of bad weather that Woods believes may help get him back into contention.
Not a chance of that happening. The only thing it will do is make him more miserable than ever.
By Tim Dahlberg
AP Sports Columnist
Hi! A visitor to our site felt the following article might be of interest to you: Column: Misery is about the only company for Tiger Woods. Here is a link to that story: http://www.xeniagazette.com/sports/1219/column-misery-is-about-the-only-company-for-tiger-woods


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text:
\section{Introduction}
{
\renewcommand{\thefootnote}{\fnsymbol{footnote}}
\footnotetext[1]{equal contribution}
}
\vspace*{-2mm}
Like all deep learning applications that follow the supervised learning paradigm, the success of learning optical flow estimation stands and falls with the availability and quality of training data. In case of optical flow, the creation of ground-truth annotation on real images is extremely tedious and virtually impossible on large datasets.
For this reason, state-of-the-art networks for optical flow estimation, such as FlowNet 2.0~\cite{flownet2} and PWC-Net~\cite{PWCnet} have been trained on synthetically rendered images.
These networks tend to generalize comparatively well to real images -- in contrast to semantic tasks, such as object detection or semantic segmentation. This is because correspondence estimation is different from recognition and does not depend so much on the content of the images. In fact, optical flow estimation is possible without any learning, thus not requiring any training data. There is a long history of unsupervised optical flow methods that implement the concept of correspondence. These classical methods perform equally well as a state-of-the-art optical flow network, yet with significantly higher runtimes.

The advantage of learning comes in when correspondences cannot be established easily and priors are needed to make decisions. Typical examples are areas in the image that have homogeneous color (aperture problem) or areas that are occluded in the other image. Works from the pre-learning era used handcrafted regularizers~\cite{schunck,meminperez} and corresponding optimization heuristics to hallucinate optical flow in these areas. Learning such priors is much more elegant and also more successful: networks tend to outperform these classical techniques especially in occluded areas. However, such learning of priors is no longer independent of the image content: while basic hallucination strategies for occluded regions can be estimated from synthetic data, the hallucinated content should ideally depend on the objects in the scene. Thus, there is a domain gap between synthesized training images and real images, just like in semantic tasks. Real images are required for training.
Multiple strategies have been proposed to integrate real images into the training procedure. These span from using the same unsupervised training loss for the network as is used in variational methods~\cite{ahmadi,unflow}, over multi-task learning with an auxiliary task that allows learning from unlabelled images~\cite{SZB17}, to training on pseudo-ground-truth obtained from running an (unsupervised) variational method~\cite{guided_flow_17}.

This paper comprises two contributions. First, we present an assessment network that learns to predict the error for each of a set of flow fields generated with various optical flow estimation techniques. Then, a fused optical flow field can be trivially obtained by selecting for each pixel the flow vector with the smallest predicted error. We show that this assessment network, which we call FusionNet combines the advantages of a potentially large set of techniques and avoids their limitations. As a consequence, FusionNet yields results that exceed the performance of all methods that produced its input. Independent on how the state of the art will improve in the future, FusionNet can always benefit from these improvements. However, this comes at the cost of very large runtimes, since a whole set of partially slow methods must be run on the test image for the assessment network to assemble the final flow field. This is a show stopper for most optical flow applications.

Thus, as a second contribution, we augment a FlowNet by training it on the flow obtained with the assessment network, which now serves as proxy ground-truth. This shifts the large runtimes to the training phase, while the final network is as fast as a regular FlowNet at test time. Training data can be generated on all sorts of unlabeled videos, which allows the augmented FlowNet to learn priors from real images. This yields the currently best accuracy-runtime trade-off and enables the specialization to target domains directly on real images without tedious modeling of synthetic scenes in such domains. We show-case this with state-of-the-art results on the KITTI benchmarks.

\section{Related Work}

\textbf{Traditional optical flow estimation.}
Optical flow estimation goes back to the works of Lucas\&Kanade~\cite{lucaskanade} and Horn\&Schunck~\cite{schunck}. Both rely on a brightness constancy term combined with a
local or global smoothness assumption. Especially the variational approach of Horn\&Schunck was extended by many successive works~\cite{meminperez,Bro04a,pocktvl1}.
While variational methods are very precise in small displacement cases, they have deficits in case of large displacements. This was taken into account by Brox et al.~\cite{ldof}, who mixed the variational method with a simple nearest-neighbor matching of local descriptors. DeepMatching~\cite{deepmatching} elaborated on the matching, and EpicFlow~\cite{epicflow} improved the variational refinement.
FlowFields~\cite{flowfields} builds upon EpicFlow and elaborates on the matching using a random search strategy.
The present state of the art is defined by DCFlow~\cite{dcflow} and MRFlow~\cite{Wulff:CVPR:2017}.
The accuracy of these techniques is very high and on-par or even higher than with learning based techniques. However, the combinatorial search in state-of-the-art methods leads to quite large runtimes that do not allow for interactive frame rates.

\textbf{Optical flow with supervised learning.}
End-to-end learning of optical flow was pioneered by the work of Dosovitskiy et al.~\cite{flownet}, which presented the two network architectures FlowNetS and FlowNetC. The former is purely convolutional, while the latter includes an explicit correlation. The networks were trained on a simplistic dataset made from Flickr and chair images to which affine transformations were applied (FlyingChairs). Mayer et al.~\cite{dispnet} introduced a more sophisticated 3D dataset (FlyingThings3D).
Ilg et al.~\cite{flownet2} presented a stack of networks termed FlowNet 2.0 with high accuracy and fast runtime.
Ranjan et al.~\cite{spynet} presented a network architecture that contains a spatial pyramid and runs even faster than FlowNet 2.0, but at the cost of accuracy. Sun et al.~\cite{PWCnet} extended this idea by introducing correlations at the different pyramid levels. Their network termed PWC-Net currently achieves state-of-the-art results.

Other methods combine feature learning with traditional methods: FlowFieldsCNN~\cite{FlowFieldsCNN} uses an improved hinge embedding loss to train a Siamese architecture for feature extraction, which is then used in combination with FlowFields. PatchBatch~\cite{PatchBatch} shows that CNN features can even be improved to a level on which plain nearest-neighbor matching performs well. DeepDiscreteFlow~\cite{DeepDiscreteFlow} combines a local network with a context network and discrete optimization.

\textbf{Optical flow with unsupervised learning components.}
Ahmadi et al.~\cite{ahmadi} proposed an unsupervised learning approach by using the brightness constancy loss from variational approaches to train a CNN. In principle, their approach replaces the Gauss-Newton step in variational optimization with back-propagation on a network representation. While coming from a fully unsupervised approach, the resulting flow fields are inferior to those of unsupervised variational techniques. Meister et al.~\cite{unflow} proposed an additional unsupervised loss based on forward-backward consistency to train the network termed UnFlow in a completely unsupervised manner.

Several other methods introduce unsupervised losses in addition to supervised training on synthetic data. Yu et al.~\cite{backtobasics} and Ren et al.~\cite{dstflow} use the loss from variational approaches to refine the decoder stages of a pre-trained FlowNet.
Lai et al.~\cite{NIPS2017_6639} use a GAN approach to distinguish the optical flow estimated by the generator network from ground-truth optical flow.
Sedaghat et al. \cite{SZB17} proposed the self-supervised auxiliary task of next frame prediction as additional loss. Like the above-mentioned works, this allows them to improve FlowNet on real-world data.
The guided optical flow proposed by Zhu et al.~\cite{guided_flow_17} uses the flow computed by a traditional, unsupervised method as proxy ground-truth to train a network in the usual supervised manner.
The final network is limited by the performance of the traditional method that provided the proxy ground-truth.
In contrast, we anticipate this drawback by training on flow fields produced by multiple different methods and locally selecting the best. This way, the final network can yield better results than any single method that produced the training data.

\textbf{Optical flow fusion.}
The principle to locally select the best flow vector from a set of flow fields has been implemented outside the scope of deep learning. Lempitsky et al.~\cite{lempitskyCVPR08} proposed a combinatorial optimization approach to combine flow fields from multiple methods based on a smoothness loss. Their approach was also used in MDPFlow~\cite{mdpflow}, which locally combined multiple hypotheses from coarser pyramid levels and nearest neighbor matching.
In contrast to these approaches, our selection among flow vectors is based on a deep network that learns to predict directly the optical flow error rather than just selecting based on smoothness priors.

\section{FusionNet}

\begin{figure}[t]
    \begin{center}
        \includegraphics[width=0.75\textwidth]{figures/FusionNet}
    \end{center}
    \caption{
        Overview of the FusionNet principle. Given the input images, the optical flow is estimated with various existing methods. Each method's optical flow estimate is used to warp the second image. The two input images, the warped image, and the flow are fed into the proposed assessment network, which is trained on predicting the error of each flow field. Finally the flow fields are merged by locally choosing the flow vector with the minimum predicted error.
        \label{fig:fusion_net}
    }
\end{figure}

We assume that various optical flow estimation methods have different strengths and weaknesses. This does not exclude that these methods may have also many difficulties in common. However, as long as there are differences, we want to exploit these differences to choose from the method that works best on a particular problem.

To this end, we propose an assessment network that predicts the errors of the optical flow estimated by a set of existing methods, as shown in Figure~\ref{fig:fusion_net} and is trained on synthetic data with available ground-truth optical flow. On first glance, this training on synthetic images looks like we are back at square one. However, the task of assessment is different from the task of flow estimation itself. First, we benefit from the information contained in the various input flow fields. Second, the assessment task may generalize more easily to other domains than the task of optical flow estimation, since it must only find ways to predict errors rather than predicting the flow field itself.

The assessment network uses a typical encoder-decoder architecture with skip-connections; the architecture details are as in FlowNetS~\cite{flownet2}. It takes the two input images into account together with the flow estimate and the second image warped by that flow.
This error map predicted by the assessment network is used to optimally combine the estimated flow fields. We refer to the complete setup, as shown in Figure~\ref{fig:fusion_net}, as FusionNet. We investigate two different loss functions: an L1 loss and a hinge loss.

\subsection{L1 Loss}
For training the assessment network with an L1 loss, we let the network directly estimate the pixel-wise endpoint error. Let the estimated optical flow at a certain pixel be denoted $w=(u,v)$ with the x- and the y-components $u$ and $v$. The ground-truth endpoint error $e_\mathrm{gt}$ for a pixel location is:
\begin{equation}
e_\mathrm{gt} = \sqrt{(u - u_{\mathrm{gt}})^2+(v - v_{\mathrm{gt}})^2}.
\end{equation}
Let $e$ be the error predicted by the assessment network. To improve on the predicted error, we apply back-propagation with the L1 loss:
\begin{equation}
\mathcal{L}_1(e) = |e_\mathrm{gt} - e|.
\end{equation}
In principle, one could train a separate, specialized network for each input method to be assessed. However, since we want to improve on the generalization of the assessment network, we use the same network for assessing all input flows, and rather sample the mini-batches during training from the different methods. More training details are provided in Section~\ref{training_details}.

\subsection{Hinge Loss}

Directly applying an L1 loss on the error makes the network estimate the error for each method. However, for the fusion we only need to know the input methods with the lowest error. That means, the L1 loss potentially solves a harder problem than necessary to reach the actual goal\footnote{That said, the prediction of the error could be valuable on its own right for a series of other purposes not discussed in this paper, for instance, uncertainty estimation.}.
A related problem to picking the input with the smallest error is the one of designing a distance metric to match patches.
This metric only needs to reflect the ranking, e.g. "A is closer to B than A is to C"~\cite{NIPS2003_2366,weinberger2009distance}.
Many feature learning algorithms use this as a triplet loss~\cite{dcflow,wang01,tripletnet,fastnet,wohlhart01}.
With the same motivation, we use the well-known multi-class hinge loss~\cite{multihinge,multisvm}
\begin{equation}
    \label{joint_ass_function}
         	\mathcal{L}_{\rm Margin}(e_1,...,e_N) = \sum_{i\neq j} \max(0,m+e_j-e_i),
\end{equation}

where $j$ is the index of the method with the lowest error according to the ground-truth,
and $m$ is the minimum margin between the best estimate and the other estimates. If the predicted best error corresponds to the true index $j$ and all other errors are at least $m$ larger than $e_j$, this loss will be zero. Otherwise, each error that is above the allowed margin will contribute to the loss.
Since the network is allowed to rescale the errors, we can set $m=1$ without loss of generality. Note that the errors predicted by the network with this loss do no longer correspond to the L1 error but may be rescaled. The rescaling factor may even be different for each pixel.
Obviously, the hinge loss implies joint training of the assessment network while giving all $N$ methods as input.

\begin{figure}[t]
    \begin{center}
        \includegraphics[width=0.35\textwidth]{figures/DataDomains}
    \end{center}
    \caption{
        Using our FusionNet to augment a FlowNet: FlowNet and FusionNet are trained on labeled data. Subsequently, FusionNet is used to augment FlowNet with large amounts of unlabeled data.
        \label{fig:domain_transfer}
	\vspace*{-3mm}
    }
\end{figure}

\section{Augmented FlowNet}

Given the FusionNet from the last section, we can apply it to any unlabelled data to estimate high-quality optical flow. However, running FusionNet is very costly, since it requires running the various, partially very slow optical flow estimation methods. In order to have fast optical flow estimation at test time, we use the optical flow fields estimated with FusionNet as proxy ground-truth in order to finetune a FlowNet, for instance, to optimize it for a specific domain or to make it run better on general real-world videos. The principle is quite straightforward  and illustrated in Figure~\ref{fig:domain_transfer}.

\section{Experiments}

We evaluated the concept on the common optical flow benchmarks, where we can quantify the improvements by the fusion and by the augmentation of FlowNet directly.
In addition, we demonstrate the effect of the augmentation in a motion segmentation context.

\subsection{Training Details} \label{training_details}
For training the assessment network, we followed the same training schedule as proposed in Ilg et al.~\cite{flownet2} for training FlowNet, i.e., we first train on FlyingChairs for 1.2m iterations and subsequently on FlyingThings3D for 500k iterations.
The augmented FlowNet is initialized with a FlowNet trained on the same schedule. We also applied the same data augmentation mechanism, i.e., a set of spatial and color transformations.
The networks were implemented using the Caffe framework. The code will be made publicly available upon publication.

\subsection{Datasets}

We used the two publicly available synthetic datasets FlyingChairs\cite{flownet} and FlyingThings3D\cite{flownet2} to train the assessment network and the initial FlowNet before augmentation. These are the two datasets for which labeled training data is available.
For the unsupervised fine-tuning, we use various unlabeled datasets that we grouped to two domains: animation movies and driving.

\textbf{Animation movies.}
We collected several animation movies from the Blender project\cite{blender} and used them for unsupervised training. For such animation movies there is the potential option to derive ground-truth optical flow, as shown in Butler et al.~\cite{Butler:ECCV:2012} and Mayer et al.~\cite{MIFDB16}, but we did not use this option here and rather used just the unlabeled videos for training.
For the evaluation in this domain we used the official Sintel benchmark dataset~\cite{Butler:ECCV:2012}.

\textbf{Driving.}
Driving scenes are a popular application domain for optical flow. Thus, we selected them to make a second evaluation domain. For unsupervised training, we took approximately 100k frames from the Frankfurt part of the publicly available Cityscapes dataset~\cite{Cordts2016Cityscapes}.
For the evaluation in this domain, we used the two publicly available KITTI2012~\cite{Geiger2012CVPR} and KITTI2015~\cite{Menze2015CVPR} benchmark datasets.

\textbf{Motion Segmentation.}
For indirect evaluation of the optical flow on a motion segmentation task, we used approximately 32k frames from the UdG-MS19 and UdG-MS20 datasets~\cite{udg} for unsupervised training.
We evaluated the motion segmentation on the FBMS benchmark dataset~\cite{OB14b}.

\subsection{FusionNet}

We evaluated the FusionNet with the following optical flow estimation techniques as input: LDOF~\cite{ldof}, DeepFlow~\cite{deepmatching}, EpicFlow~\cite{epicflow}, FlowFields~\cite{flowfields}, and FlowNet2~\cite{flownet2}. There are some very recent methods with even better performance, such as DCFlow~\cite{dcflow}, PWC-Net~\cite{PWCnet}, and MR-Flow\cite{Wulff:CVPR:2017}, but their code was not operational in time to include them for the experiments. A nice property of FusionNet is that new methods can be integrated trivially at any time to improve results further.

\begin{table}[t]
\begin{center}
\input{Tables/FusionNet_results}
\end{center}
\vspace*{3mm}
\caption{Comparison of FusionNet to the state-of-the-art. The upper section of the table corresponds to the input methods used for FusionNet. FusionNet performs better than any of the input methods. The Oracle Fusion refers to a fusion based on the ground-truth error.
\label{tab:fusiontable}
}
\end{table}
\setlength{\tabcolsep}{1.4pt}

Table \ref{tab:fusiontable} compares FusionNet to the state-of-the-art on the common benchmark datasets.
FusionNet consistently outperforms each of the techniques that have been provided as input, which demonstrates that the assessment network is able to locally select the best optical flow vectors.
As a consequence, this brings it close to the most recent state of the art and would most likely outperform it if these methods were also included for selection.
Table~\ref{tab:fusiontable} also reports the results when selecting the flow vectors based on the ground-truth error. This oracle fusion is the lower bound that FusionNet can achieve with the respective optical flow fields given as input.

\begin{figure}
  \resizebox{\linewidth}{!}{%
  \setlength{\tabcolsep}{0.7pt}%
  \begin{tabular}{cccc}
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-gt.jpg}  &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-occ_gt.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-img0.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-img1.jpg}
      \\
    Flow GT & Occlusion GT & Image 0 & Image 1 \\
    \hline \\ [-1.5ex]
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-FlowFields-edit.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_gt_FlowFields.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_FlowFields_L1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_FlowFields_Hinge.jpg} \\
    FlowFields~\cite{flowfields} flow & GT error & Predicted error (L1)  & Predicted error (Hinge) \\
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-FlowNet2-edit.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_gt_FlowNet2.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_FlowNet2_L1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_FlowNet2_Hinge.jpg} \\
    FlowNet2~\cite{flownet2} flow & GT error & Predicted error (L1) & Predicted error (Hinge) \\
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-EpicFlow.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_gt_EpicFlow.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_EpicFlow_L1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_EpicFlow_Hinge.jpg} \\
    EpicFlow~\cite{epicflow} flow  & GT error & Predicted error (L1) & Predicted error (Hinge) \\
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-DeepFlow.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_gt_DeepFlow.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_DeepFlow_L1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_DeepFlow_Hinge.jpg} \\
    DeepFlow~\cite{deepmatching} flow &  GT error & Predicted error (L1) & Predicted error (Hinge) \\
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-LDOF.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_gt_LDOF.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_LDOF_L1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-error_pred_LDOF_Hinge.jpg} \\
    LDOF~\cite{ldof} flow & GT error & Predicted error (L1) & Predicted error (Hinge) \\
        \hline \\ [-1.5ex]
      &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-Fused_Oracle.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-Fused_L1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/FusionNet_qualitative/0000633-Fused_Hinge.jpg} \\
    & Oracle & FusionNet (L1) & FusionNet (Hinge) \\
\end{tabular}%

     \caption{
        Qualitative example showing the error prediction of the assessment network and the corresponding fused optical flow field.
        \textbf{First column:} Different flows provided as input. \textbf{Second column:} Their ground truth error. \textbf{Third and fourth column:} Error prediction with the L1 loss and the hinge loss, respectively. The error predicted with the L1 loss is very close to the ground-truth error. The error predicted with the hinge loss is not interpretable, since it is a relative error and therefore is locally scaled. The bottom row shows the merged flow using the ground truth (Oracle) or the error predictions. The marked regions indicate how FusionNet picks the best motion vectors from the different input methods.
        \label{fig:qualitative_single_ass}
        }
\end{figure}

The predicted error when trained with an L1 loss is shown in
Figure~\ref{fig:qualitative_single_ass}. One can observe that it matches the ground-truth error quite well. Thus, in hard cases, if one of the input methods is able to estimate the motion successfully, FusionNet is able to select the best estimate. The predicted error for the margin loss is not directly interpretable due to the local scaling. However, the resulting final optical flow field is as good as the one obtained with the L1 loss. Also quantitatively comparing the L1 loss against the hinge loss, there is no significant difference.

\subsection{Augmented FlowNet}

While FusionNet yields excellent optical flow that combines the best from all available methods, it requires 84 seconds per frame. In contrast, FlowNet 2.0 runs at 8 frames per second \footnote{FlowNet2 runtime is reported on an Nvidia GTX1080 GPU, while the classical methods run on the CPU.}. In this section we test how far we can transfer the good results from FusionNet to a FlowNet, thus inheriting also the runtimes of the latter.

Table~\ref{tab:AugmentedFlowNetC} first shows the influence of the choice of the proxy ground-truth when fine-tuning a basic FlowNetC.
Augmenting the FlowNet with an optical flow field that is superior to the baseline improves results, whereas inferior flow fields can decrease the performance. When using just a single proxy method, there is the dilemma of which method to choose.
Feeding a random mixture of samples from various methods during fine-tuning (Rand. Mix) does not yield the best of all involved methods, but approximately the average of those. In contrast, the use of FusionNet resolves the dilemma.

\setlength{\tabcolsep}{4pt}
\begin{table}[t]
\begin{center}
\input{Tables/AugmentedFlowNetC_results}
\end{center}
\vspace*{3mm}
\caption{Influence of the proxy ground truth on the augmented FlowNetC. Average endpoint errors on the training set of Sintel and KITTI are reported. Augmentation with a single proxy can improve results, but it is not obvious which method to choose. Using multiple methods to generate the proxy ground-truth (FusionNet) yields consistent improvements across all benchmarks. The benefits come largely from combining FlowNet2 and FlowFields. The upper part of the table shows experiments which are trained on domain-specific data (denoted AugmentedFlowNetD), while the bottom part shows experiments where the training data came from multiple domains to yield a generic network (denoted AugmentedFlowNetG).
\label{tab:AugmentedFlowNetC}
}
\end{table}
\setlength{\tabcolsep}{1.4pt}

We also distinguish between augmentation for a specific domain and generic augmentation. In the first case, we augment the FlowNet only on data from the respective domain, i.e., animation movies in case of Sintel and driving videos in case of KITTI; in the second case, data from both domains is used for finetuning. Specialization to a certain domain is one of the big advantages of learning-based optical flow methods, and a particular advantage for those methods that do not require supervision in that domain, as in our case.

Table~\ref{tab:AugmentedFlowNetC} shows that domain-specific augmentation improves results considerably on KITTI, which is a very special scenario. The error is almost cut into half. However, also the generic augmentation is not much worse, as it also benefits from the training data from the special domain, even though it is now mixed with data from another domain. Obviously, the network can figure out automatically at test time from which domain the input is from, and applies the appropriate priors from that domain.

Table~\ref{tab:AugmentedFlowNetStacks} extends the augmentation to a stacked FlowNet and compares it to UnFlow~\cite{unflow}. UnFlow uses an unsupervised loss, thus it can be specialized conveniently to any domain. The table shows results for UnFlow trained on CityScapes or the unlabeled data from KITTI, which outperform the supervised baseline, which was trained on synthetic data outside this domain. For better comparison to our strategy, we also report results for a semi-supervised version of UnFlow, i.e., it is initialized with a FlowNet trained on synthetic data before the unsupervised training starts.

\definecolor{LightGray}{rgb}{0.9,0.9,0.9}
\setlength{\tabcolsep}{4pt}
\begin{table}[t]
\begin{center}
\input{Tables/AugmentedFlowNetStacks_results}
\end{center}
\vspace*{3mm}
\caption{Comparison of augmented FlowNet stacks to UnFlow~\cite{unflow}. AugmentedFlowNetD-C and UnFlow-C-ours are trained on the same domain-specific data and are initialized with the same model (Baseline). The results show that the purpose of domain adaptation is better achieved with the augmentation based on FusionNet  than with the unsupervised loss of UnFlow. The results for the FlowNet augmented with data from both domains even show that it is not necessary to train separate networks for each domain, but that a generic network augmented on both domains is equally good.
\label{tab:AugmentedFlowNetStacks}
}
\end{table}
\setlength{\tabcolsep}{1.4pt}

Results show that the domain adaptation with the augmented FlowNet is clearly superior to the one of UnFlow\footnote{UnFlow does not require any supervision, which makes it biologically more plausible. From the engineering perspective, however, this is irrelevant.}. As we already observed in Table~\ref{tab:AugmentedFlowNetC}, there is no significant difference between domain-specific training and training on a joint set of domains. This is also true for the stacked network.

\setlength{\tabcolsep}{4pt}
\begin{table}[t]
\begin{center}
\input{Tables/Benchmark_results}
\end{center}
\vspace*{3mm}
\caption{Comparison to the state-of-the-art. Numbers marked with $^\dagger$ have been obtained after fine-tuning on the training set of the respective benchmark. On the KITTI benchmarks, we clearly extend the state of the art. Thanks to additional fine-tuning with ground truth data, the augmented network even performs better than the FusionNet proxy, but also the generic version, which has only been finetuned on the FusionNet proxy, gets close to FusionNet and sometimes even outperforms it.
\label{tab:Benchmark_results}
}
\end{table}
\setlength{\tabcolsep}{1.4pt}

Table~\ref{tab:Benchmark_results} compares the stacked augmented FlowNet to the state of the art.
On the KITTI benchmarks, the augmented FlowNet sets the new state of the art after being finetuned also with the ground truth from the KITTI training set. But also the generic version, which has not been finetuned with ground truth data yields very good results. The direct comparison to FlowNet2 quantifies the improvement on stacked networks due to the augmentation.

Interestingly, the stacked augmented FlowNet often even outperforms the FusionNet proxy. This is due to the finetuning with ground truth in case of the domain-specific network. Sometimes, also the generic network is better than FusionNet, but not consistently.

Fig.~\ref{fig:qualitative_augmented_FlowNet_Sintel} shows some qualitative examples of the augmentation by the proxy, yet with the smaller, non-stacked network.

\begin{figure}
 \resizebox{\linewidth}{!}{%
  \setlength{\tabcolsep}{0.7pt}%
  \begin{tabular}{ccc}%
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case1/0000285-img0.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case1/0000285-flow_fwd_gt.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case1/0000285-pred_flow_fwd.jpg}
      \\
      {\tiny Image 1} & {\tiny Flow GT} & {\tiny Baseline Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case1/0000285-img1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case1/0000285-merged_flow.jpg} & \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case1/0000285-pred_flow_fwd-augmented.jpg}
      \\
      \vspace{5mm}%
      {\tiny Image 2} & {\tiny FusionNet Proxy} & {\tiny Augmented Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case2/0000610-img0.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case2/0000610-flow_fwd_gt.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case2/0000610-pred_flow_fwd.jpg}
      \\
      {\tiny Image 1} & {\tiny Flow GT} & {\tiny Baseline Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case2/0000610-img1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case2/0000610-merged_flow.jpg} &  \includegraphics[width=0.3\textwidth]{figures/augmented_sintel/case2/0000610-pred_flow_fwd-augmented.jpg}
      \\
      \vspace{5mm}%
      {\tiny Image 2} & {\tiny FusionNet Proxy} & {\tiny Augmented Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case1/0000044-img0.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case1/0000044-flow_fwd_gt.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case1/0000044-pred_flow_fwd_base.jpg}
      \\
      {\tiny Image 1} & {\tiny Flow GT} & {\tiny Baseline Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case1/0000044-img1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case1/0000044-merged_flow.jpg}  &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case1/0000044-pred_flow_fwd_augmented.jpg}
      \\
      \vspace{5mm}%
      {\tiny Image 2} & {\tiny FusionNet Proxy} & {\tiny Augmented Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case2/0000075-img0.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case2/0000075-flow_fwd_gt.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case2/0000075-pred_flow_fwd_base.jpg}
      \\
      {\tiny Image 1} & {\tiny Flow GT} & {\tiny Baseline Flow}\\
      \vspace{-1mm}%
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case2/0000075-img1.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case2/0000075-merged_flow.jpg} &
      \includegraphics[width=0.3\textwidth]{figures/augmented_kitti/case2/0000075-pred_flow_fwd_augmented.jpg}
      \\
      {\tiny Image 2} & {\tiny FusionNet Proxy} & {\tiny Augmented Flow}\\
\end{tabular}%

     \caption{Examples from Sintel Clean and KITTI2015. Using the proxy ground truth obtained with FusionNet for finetuning improves the network's performance. Note that the ground truth on KITTI is sparse and lacks some moving objects.
      \label{fig:qualitative_augmented_FlowNet_Sintel}
     }
    \end{figure}

Finally, we also evaluated the augmented FlowNet in an application scenario. We augmented the FlowNet on data from UdG-MS19 and UdG-MS20~\cite{udg} and fed its optical flow into the motion segmentation approach from Keuper et al.~\cite{KB15b}. The motion segmentation performance was evaluated on the FBMS benchmark~\cite{OB14b}.
Table~\ref{tab:augmentedFlowNet_motion_seg} shows that the adaptation to real images clearly helps a small FlowNetC to improve motion segmentation results. For the larger, stacked network, we do not see a significant improvement due to the augmentation. We attribute this to some saturation effect: the optical flow of FlowNet2 is already very good, such that other parts of the motion segmentation pipeline dominate the final result.

\setlength{\tabcolsep}{4pt}
\begin{table}[t]
\begin{center}
\input{Tables/MotionSeg_results}
\end{center}
\vspace*{3mm}
\caption{Motion segmentation results on the FBMS test set~\cite{OB14b}. We fed the optical flow from the listed methods to the motion segmentation approach from Keuper et al.~\cite{KB15b}. The augmentation on real data clearly improved the FlowNetC baseline. For the stacked network, we do not see a significant improvement. This is mainly because the optical flow is already very good in this case and other effects in the motion segmentation pipeline dominate the final result.
\label{tab:augmentedFlowNet_motion_seg}
}
\end{table}
\setlength{\tabcolsep}{1.4pt}

\section{Conclusion}

In this paper, we have presented two contributions: (1) We have presented a way to assemble a high-quality flow field from a set of input flow fields being computed in an unsupervised manner using existing optical flow estimation methods. This has been achieved by training an assessment network that learns to predict the errors of the input techniques. (2) We have shown that finetuning a FlowNet on such high-quality flow fields allows for unsupervised adaptation of the network to a specific domain. With this strategy, we obtained state-of-the-art results on the KITTI benchmarks. Moreover, we have shown that this strategy is more successful on domain adaptation than a fully unsupervised approach that does not make use of any synthetic data.

\section*{Acknowledgements}

We acknowledge funding by the German Research Foundation (grant \mbox{BR 3815/10-1}) and the EU Horizon 2020 project Trimbot2020.

\bibliographystyle{splncs}

redpajama_set_name: RedPajamaArXiv


text:
Q: SharePoint 2013 REST query not filtering properly I have a query that I want to filter on by AuthorID
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but this does not work.
But this does (if I remove the filter for author)
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Also this works too
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Does anyone know what's wrong? Why can I not combine the Author filter?
Thanks

A: /_api/web/lists/GetByTitle('News Item Preferences')/items?filter=(Author eq 3417)

It works but it's doing nothing in your query (It should return all items). You need to write $filter not filter.  Another thing it should be AuthorId instead of Author. So try following
/_api/web/lists/GetByTitle('News Item Preferences')/items?$select=Id,PreferredChannels/Title,MaxItems&$filter=(AppID eq '29235c13-0c36-472d-a5c6-ce4a584089db') and (AuthorId eq 3417)&$expand=PreferredChannels

Or use
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PS: I am assuming that you mean Created By column by Author

redpajama_set_name: RedPajamaStackExchange


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Well according to IEA Greenhouse Gas R&D Programme CO2 Capture and Storage Database; Carbon Capture and Sequestration Technologies Program at MIT, CO2 Capture and Storage Project Database, "There are no homes in America powered by 'clean' coal."
Clean coal technology is a marketing term used to describe technologies being developed that aim to reduce the environmental impact of coal energy generation.
If thats not enough, according to the article "Big Coal Campaigning to Keep Its Industry on Candidates' Minds," Wall Street Journal, Oct. 20, 2008 (link); IEA Greenhouse Gas R&D Programme CO2 Capture and Storage Database (link); Carbon Capture and Sequestration Technologies Program at MIT, CO2 Capture and Storage Project Database (link): "The coal industry is spending millions advertising 'clean' coal, but not a single 'clean' coal power plant exists in the U.S. today."
Decide for yourself: what is the future of coal?
My opinion: Coal is not going anywhere, anytime soon. But, we can change this!
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