text:
Profile: Ken Innes IV
Kenneth F. Innes IV
primehalo
40-something years old(born May 10th)
Webmaster, Ex-Programmer at Working Designs
"Light our darkest hour" -Rodimus Prime(The Transformers: The Movie)
Ranma 1/2, Laputa, Nadia, The Slayers, Kuromi, and many more...
The above info came from yours truly (obviously). The image came from yours truly.
More Sections On This Page
My Other Profiles
E-Mail Interview
My Favorite TV Shows
My Favorite Videogames
My Favorite Toys
Read All About Me
Recent Journal/Blog
Archived Journal/Blog
Our Trip to Reno
Our Trip to Hawaii
Our Trip to E3 2006
Our Trip to Lake Tahoe
My Trip to E3 2004
Our Trip to Alcatraz
Our Jamaican Vacation
Our Trip to Chicago
My Life In Redding, CA
My Life In Vallejo, CA
My Life In Bartlett, TN
My Life In Aurora, CO
My Life In 29 Palms, CA
My Life In Camp LeJeune, NC
View My Photo Galleries
Our Cruise To Alaska (2009)
Our Trip To Reno (2009)
My Trip To San Francisco (2007)
Our Trip To Hawaii (2007)
Our Trip To Lassen (2006)
Halloween Costumes (2006)
Outside Our House (2006)
Trip To LA For E3 (2006)
Mexican Cruise (2005)
Trip To Lake Tahoe (2004)
Jamaican Vacation (2003)
Trip To Chicago (2003)
Just A Sunday Hike (2003)
Las Vegas Souvenirs (2002)
Anniversary Cake (2002)
Trip To Burney Falls (2002)
Me, Around the Office (2002)
Halloween Wedding (2001)
Trip To Tokyo, Japan (1999)
So, by now you should know that I like anime, but believe it or not anime is not my life. I started my career at Working Designs in mid-June 1999, only a month after I graduated from California State University, Sacramento. Well, my career goal when I started college was somewhere along the lines of a videogame designer or graphics designer for movies & TV, so it looks like I finally succeeded. I do work long hours and usually late into the night which can be hard on relationships. I am lucky that my wife is so understanding.
I have lived in Redding, California now since June 1999. It is a small town and it gets very hot here. I moved here from Vallejo, California, where I lived since about 1990. Vallejo was a nice place to live, and most of our family is there. We used to live in Vallejo until I was 8 or 9, when we moved out of state. The weird thing is, on my first day of high school after we moved back, I met my best friend, Steve Garcia, who used to live just down the street from me before we moved away. I met him in my last class of the day, Biology, which was taught by Mr. Santos, a former student of my grandfather. Talk about coincidence. A lot had happened while in Vallejo, as I got my first real job, learned how to drive, graduated from Hogan High school, graduated from Solano Community College, and then finally California State University, Sacramento.
Before California I lived in Bartlett, Tennessee for about three years. This was the first place I lived where we had a house on the corner and a big lawn to go with it. I've always wanted one before, unfortunately when I got it, I also found out that I was now old enough to start mowing. Bummer. It was also here that I first played a Nintendo Entertainment System. This happened when my friend, Chris Bailey, got one for Christmas (for the same Christmas, I got an Atari 2600). Well, I liked playing video games so much that by the time we moved again, I had my own Nintendo Entertainment System and Nintendo Gameboy, Atari 2600, and Sega Master System and Sega Genesis (on which I played my all-time favorite Sega games- Phantasy Star and Phantasy Star II). While in Bartlett, I finished off middle school at Shadowlawn Middle School and then moved on to Bartlett High School.
Before Bartlett, I lived in Aurora, Colorado for about 2-3 years. I really liked it there because I had a lot of good friends who lived there, and most lived within walking distance. I also liked the weather, as I had never really experienced snow before, nor had I seen lightning or experienced tornado warnings. Here I finished off elementary school at Vassar Elementary School, and then went on to Mrachek Middle School, which was right down the block. Both schools weren't too far from my house, so I usually had to walk. Luckily, I had friends just down the block who also went to the same school, Ian and Kyle Zinth. I also made other friends at school who didn't live nearby, such as David Shephard and Ivan Jackson.
Even before I lived there, though, I lived in Vallejo, California (again). I have a lot of memories of this time, but most of them are just scattered images. Here, I had a few friends, all of which lived nearby. I went to elementary school at Dan Mini Elementary, which I usually had to walk to. I know I often walked home with my friend, Steven Garcia, but I don't recall walking to school with him. There were only two other places I had lived before that: North Carolina (where I was born) for about 6 weeks, and 29 Palms for about 2 years. I was only there for a short while though, so I don't have any recollection of it.
As for Japanese animation, I had always liked watching it on TV, although at the time I didn't know what it was. It wasn't until around Fall 1995 that I really got interested in it. This is when I met a friend in my Physics class named James Lin (James' Blog) who had massive amounts of anime stuff. He first let me borrow the 13 TV episodes of Gundam, and then Devil Hunter Yohko, and then others and others. Before this I had really only seen anime that was broadcast on TV. In my high school art class I had seen Akira and episode 1 of the Bubblegum Crisis. Other than that, one Gunbuster video was all I could find at my local video store. Now I am happy to say that just about every video store carries at least some anime titles, while many have entire sections devoted to them. The place that I used rent anime is at Vallejo Baseball Cards, Comics, and Animation, as it was only about a five minute walk. They have many titles and other anime related materials. Now I do most of my anime shopping online. Redding has only one comic store that I know of, and they don't carry very much.
I took conversational Japanese for a semester when I was attending Solano Community College to see if I would be interested in learning the language. I did enjoy it, so I continued to take three more semesters of Japanese at California State University, Sacramento. Unfortunately, these classes were all held early in the morning, which made it extremely difficult to learn (I'm not a morning person). Besides being in the evening, I enjoyed the conversational Japanese class a lot more because we were talking all the time. I was doing pretty well, and could actually form sentences rather quickly. All this turned around in the regular Japanese class where I could write but not talk or listen very well.
Wow, it's hard to believe you actually read through all of this. I know I wouldn't have. Okay, so um.... you can go away now. Oh yeah, and if you're bored with your pathetic, miserable little life, then you can send me a message in a vain attempt to make yourself feel like there is somebody out there who needs you. Of course, I might not respond, but you should be used to rejection by now.
The above content came from yours truly (obviously).
I have created a bunch of profiles at various websites all over the web. Some I did just because I was bored, but others, like the Item-Swapping websites, actually serve a purpose. Anyway, here are the ones I know about. Some are filled out more than others.
Networking Websites
MySpace Profile
Item Swapping Websites
Game Trading Zone Profile
Miscellaneous Websites
Amazon.com Profile
Moby Games Developer Profile
Deviant Art Profile
ClassReport.org Profile for Hogan High School
ClassReport.org Profile for Bartlett High School
California State University, Sacramento
Attended: 1995 - May 1999
Location: Fairfield, CA
Attended: 1993 - 1995
Hogan High School
Location: Vallejo, CA
Attended: 1990 - 1993 (class of 1993)
Bartlett High School
Location: Arlington, TN
Shadowlawn Middle School
Mrachek Middle School
Location: Bartlett, CO
Vassar Elementary School
Dan Mini Elementary School
Want to know about some of my friends? Well, here they are, grouped by the area in which I lived when I met them. If you are one of these friends that I have lost contact with, or a friend that I just forgot to list, please contact me!
From When I lived in Vallejo, California
Gary Smith, AKA "Theo"
Gary and I didn't have any classes together, yet we met each other while both attending Solano Community College. The meeting was set up through a mutual friend and fellow student, Bill Stringfellow, who knew that unlike himself, we both lived in Vallejo. Gary and I got along well, since we're both computer and sci-fi geeks.
James Lin
James and I met in Solano Community College, and I believe it was in physics class, though it could have easily been a math class. James was a huge anime fan, like myself, except that he had walls and walls lined with anime VHS tapes whereas I only had a couple. It was through him that I greatly expanded my knowledge of anime. I also became friends with his younger brother, Mike, and together we fansubbed several anime episodes.
Radford Castro
Radford and I met in Hogan High School, I believe in gym class, though we did have other classes together. We hit it off because we were both fanatics about videogames. Oh, he even wrote a book about videogaming, called Let Me Play: Stories Of Gaming And Emulation. We kind of lost touch after high school, though coincidentally ran into each other inside Sacramento's Virgin Megastore, where I was on my first or second date with Latrice, the woman who is now my wife.
Bill Stringfellow
Bill was another friend that I met in Solano Community College, most likely in physics class. He was not only a very bright guy academically, but he was very smart in general and always very kind and upbeat. He's pretty much what Flander's used to be on The Simpsons, the perfect neighbor (before the producers started making him uber-religious). We graduated from Solano Community College at the same time, and transferred to California State University, Sacramento (CSUS), where we carpooled to for several years. He graduated a year before I did, as I didn't have the programming experience that he had when we transferred.
Conor Vaughan
Conor lived next door to me when we moved back to Vallejo. Due to our proximity and love of toys and videogames, we quickly became best friends. We still see each other on occasion, like when I go down to visit my parents, and we still communicate through email. He was even trying to get a job up here in Redding, but he was beat out by one guy. Still, if that guy leaves, odds are he'll be able to fill the position and move up here, which would be awesome.
Mike was another friend from Solano College, and another member of my physics class. Though to be fair, he was really more of an acquaintance, since we didn't really hang out separately from working on school projects. We met up again in CSUS, when we realized we had both transferred to the same college, and were looking for partners to form a Senior Project team. We worked really well together, but lost contact after college. I tried e-mailing him like a year later, but the email just bounced.
From When I lived in Bartlett, Tennessee
David was my next door neighbor when we lived in Bartlett, TN. He was a little younger than I was, but we still got along great. He had a younger brother, but I can't think of what his name was. Our parents also became close friends. Before we had moved away back to Vallejo, David succeeded in becoming a black-belt in Tai-Kwon-Doe.
Andy Winemiller
Andy and I met while attending Shadowlawn Middle School in Memphis, TN. We became good friends, and remained so even after he had to move back to Arkansas. Luckily, I got to go visit him there before my family and I headed off to put our roots back into California.
Alternate website: Live Journal
Chris and I met in Shadowlawn Middle School. He had tons of toys... and I mean tons! He had an entire room just for holding and playing with all his toys. And this was a BIG room. How big? He had the gigantic GI Joe Aircraft Carrier, the Cobra Terrordrome, and many other vehicles and figures laying around, and there was still plenty of room to move around and play. And once he got a NES, he got a television put in there too. Some of my best memories are of sleeping over at his house playing videogames until it was practically morning.
Chris Luckett
Chris and I were friends from school... though I don't think we hung out a whole lot as my memory is kinda fuzzy on him... sorry!
Michael Bernard
Michael and I were friends from school... though I don't think we hung out a whole lot as my memory is kinda fuzzy on him... sorry!
Mike Amos
I think I met Mike through Chris Bailey, as I think they were friends, and he was probably in some classes with me too but I can't really recall. We weren't good friends, more like acquaintances.
Yusuf Kinkhabwala
Okay, I really have no idea if I spelled that right. I could be Yusef, or Usef, or Kinkubwalla, or Kenkubwalla, or some other spelling I can't event think of. Anyway, Yusef lived down the street from Chris Bailey, and they were good friends, so we became friends as well (plus we all went to the same school). Yusef was very smart and fairly quiet, unlike Chris who was very outgoing.
From When I lived in Aurora, Colorado
Once David (I hope I spelt his last name correctly) and I met, we quickly became best friends. We met during recess in Vassar Elementary School. We both loved Transformers and any other robot-style toys. We hung out as much as we could, which wasn't all the time because he didn't live within walking distance. I believe he was scheduled to move to Texas shortly after we were scheduled to move to Tennessee.
Ian Zinth & Kyle Zinth
These two friends lived down the street from me in Aurora, CO. Ian was my age, and we went to the same school, usually walking there together in the cold Colorado mornings and then walking back in the afternoons. Kyle was a few years younger, but we all still played together since we liked the same toys and cartoons. They also had a younger brother named Robby who would sometimes hang out with us.
Ivan Jackson
Ivan I met in school while living in Aurora, CO. I really don't remember how we met, but I know we didn't live nearby each other. We liked the same kind of cartoons (such as Thundercats) and we'd hang out at recess, oh, and we did some school projects together. I believe that he was going to be moving away to Austrailia shortly after we were scheduled to move to Tennessee. He recently contacted me through e-mail, saying that he was now in the military, but I haven't heard back from him since I responded, so I really hope he's okay.
Sorry Patrick, I can't think of your last name. We must have met in school, because we didn't live within walking distance of each other.
Sorry Mike, I can't think of your last name. We went to the same school... I'm pretty sure. Actually, I don't really remember hanging out with him at school. He was the same age as me though. Mike lived at the end of the street in the opposite direction of Ian & Kyle's house. It was a bit of a walk, especially since we lived on the crest of a hill and he was at the bottom.
From When I lived in Vallejo, California... The First Time
Jason was in my elementary school class. We weren't really friends, as we didn't hang out or anything, but we were on the same Soccer team. It was called the Defenders, named after the videogame. At least, it was for one year. I don't actually remember how many years I played, it's all kind of a blur. I remember not liking it, though.
Michelangelo lived across the street from me when I first lived in Vallejo, and he would often come over and play with Steve and I. As typical with children, I didn't keep in contact with him when we moved, though coincidentally, after we moved back, my mom and I met him and his mother in a store. I didn't even recognize him, and honestly, couldn't really remember anything about him, just that I knew we had been friends.
Steve and I lived down the street from each other when we first lived in Vallejo. He was my best friend and we played together all the time. Then towards the end of fourth grade we moved away, and when we returned something like seven years later, we ended up in the same high school biology class together. Unfortunately, our reunion was short lived, as he was going to be moving away, to Florida I believe, and that happened only like a few days later.
For all of my buddies out there that I missed, I'm sorry! Contact me and really chew me out!
Once I became part of the Working Designs team, I would occasionally receive requests from students interested in my line of work to answer some interview questions. This is one such interview that I did back in August 2004.
1. What do you do on a typical work day?
We localize games from Japan, so for the most part the projects we get are already functional. On a typical work day, I edit and modify the source code. Near the beginning of the project, this is to get the game running in English, but after that I add new features to the game and fix bugs that were present in the original game.
2. What do you feel is the most appealing part of your job?
I love being able to take an enjoyable game and modifying it so that when I'm playing it I won't have to say "This is fun, but I wish they had done this".
3. What do you feel is the most frustrating part of your job?
Well, sometimes we aren't sent everything we need to build the game, which is really frustrating, because initially I can't tell if we really are missing something or if I'm just doing something wrong.
4. What part of your job do you spend the most time on?
Modifying the code to add features and make the game flow smoother.
5. What jobs have you had before reaching your current position (if any)?
In high-school I worked in fast food, in junior college I tutored Physics, and in college I improved and developed for a company's intranet.
6. What was your major in college?
Computer science, with a minor in math.
7. What courses are required for this field?
A lot of computer science and some math. I don't remember what all the different classes were called.
8. What kind of training did you yourself go through?
I didn't go through any training to get the job, and there was no training once I got the job other than learn as you go.
9. What additional training do you feel would be beneficial (if any)?
It probably would of helped if I had tried writing my own games, even if they were simple ones.
10. What was most appealing about pursuing your career?
I wanted to localize games because I didn't want to spend years working on a game and have the chance it would turn out crappy. This way we start out with great games, and the only place to go is up.
11. How did you go about finding your current job?
I sent a resume and cover letter to the company.
12. How long was the interval between the time you decided to be a programmer and the time you actually became one?
I was considering it toward the end of high-school and the first year of junior college. I became an official programmer as soon as I graduated from the University.
13. What kind of demand is there in your field?
I don't really keep tabs on what kind of demand there is.
14. What kind of competition is there?
I'm not really sure about this either.
15. How much can someone just entering this field expect to earn?
Depends on where you're located. Redding has a much lower cost of living than somewhere like L.A. It's enough to live comfortably.
16. Do you really have to sometimes pull "all-nighters," and if so, how often?
Yes. How often depends on the situation. One time I spend basically a week just practically living at the office because we had a major hard drive crash and lost almost everything. Usually, though, there isn't strict deadlines, so I could just come in on the weekend to finish up something.
17. How do you feel when you finish a project?
Relieved, and dismayed that I'll be starting a new project soon, since starting a project is always the hardest part.
18. What advice would you give to a student looking to become a (insert occupation title here)?
Practice on your free time creating a variety of different programs.
19. Are there any similar occupations that you would suggest I find out about?
Not that I can think of.
20. What kind of attitude should one have when pursuing this career?
Always keep a positive attitude.
Favorite Movies: Comedy
Spaceballs
Favorite Movies: Sci-Fi/Fantasy
Lord of the Rings Trilogy
Star Wars Trilogy (the original trilogy)
Star Trek: First Contact
Favorite Movies: Horror
Pumpkinhead
Blade II
Favorite Movies: Action/Adventure
Supercop
Jackie Chan's First Strike
Favorite TV Shows: Sci-Fi
Battlestar Galactica (remake)
Favorite TV Shows: Comedy
Andy Richter Controls the Universe
The Gary Shandling Show
Favorite TV Shows: Action/Drama
The Adventures of Brisco County Jr.
Tour of Duty
The New Zorro
Favorite TV Shows: Miscellaneous
Penn & Teller: Bullshit!
Mysteries From Beyond the Other Dominion
Favorite TV Shows: Cartoon (Non-Anime)
Count Duckula
Favorite TV Shows: Anime (prior to 2000)
Samurai Pizza Cats
TranZor Z
Littl' Bits
Mighty Orbots
Favorite Games: Nintendo Entertainment System
Blaster Master
Goonies II
Rygar
Favorite Games: SMS/Sega Genesis/Sega CD
Phantasy Star (SMS)
Phantasy Star II (Genesis)
Golden Axe (Genesis)
Lunar: Silver Star Story (Sega CD)
Road Rash (Genesis)
The Secret of Monkey Island (Sega CD)
Mortal Kombat (Sega CD)
Popful Mail (Sega CD)
Miracle Warriors (SMS)
Michael Jackson's Moonwalker (Genesis)
Favorite Games: SNES/Game Boy
The Legend of Zelda: A Link to the Past (SNES)
Chrono Trigger (SNES)
Teenage Mutant Ninja Turtles IV (SNES)
Super Mario World (SNES)
Super Mario RPG (SNES)
Street Fighter II (SNES)
Super Metroid (SNES)
Teenage Mutant Ninja Turtles: Fall Of The Footclan (GB)
Metroid II: Return of Samus (GB)
Tetris (GB)
Favorite Games: GameCube/Game Boy Advance
Metroid Prime (GameCube)
Eternal Darkness: Sanity's Requiem (GameCube)
Rogue Leader: Rogue Squadron II (GameCube)
Rebel Strike: Rogue Squadron III (GameCube)
The Legend of Zelda: Ocarina of Time (GameCube)
The Legend of Zelda: The Minish Cap (GBA)
Metroid Fusion (GBA)
Golden Sun 2: The Lost Age (GBA)
Dragon Ball Z: The Legacy of Goku II (GBA)
Ghosts 'n Goblins (GBA)
Favorite Games: PlayStation
Arc the Lad Collection
Silhouette Mirage
Lunar 2: Eternal Blue Complete
Need For Speed 2
Favorite Games: PlayStation 2
Sly Cooper and the Thievious Raccoonus
Ratchet and Clank
Ratchet and Clank 2: Going Commando
Star Wars: Jedi Starfighter
Lord of the Rings: The Two Towers
Socom: US Navy Seals
Favorite Toys: The Transformers
Galvatron
Jetfire
Favorite Toys: G.I. Joe Figures
Snake Eyes (version 2)
Storm Shadow (version 1)
B.A.T. (version 1)
Serpentor (version 1)
Quick Kick
Sgt. Slaughter (version 2)
Motor-Viper
Techno-Viper
W.O.R.M.S.
Favorite Toys: Go-bots
Spay-C
Cy-Kill
Warpath (Super Go-Bot)
Pyscho (Super Go-Bot)
Water Walk
Leader-1
Favorite Toys: M.A.S.K.
Boulder Hill
Jackhammer
Ken Innes IV


English speaking we are your paper welcome! Trust us based research paper of study. Nowadays students.
A1essays write a lot of students. Now you are you looking for phd degree. Pay less- receive an order your endless stream of model papers. Professional writing company offers professional research papers for students face academic paper writing service and writing service. Searching for sale, term paper writing help services for our expert academic writing service. Dec 11, term papers from 20 discount on any type of successful students. Write-Right. Masterpaper. Money from our experienced writers! Iresearchpaper. Various kinds of paper pp. Plagiarism-Free content writing service that help at order? Reliable custom written research paper masters provides high school, or research paper writing service. The synonyms of the hand.
High-Quality non-plagiarized custom essay writing custom essay or college. Let anything stand on. Pay to help with 100% original, check out of trial advocates by essaystate. If one of custom papers require an easy or university classes due? A we guarantee to get your studying needs. Its fundamentals. Let anything stand on ebay for sale from our online for a research paper writers / professional help service offers best and plagiarism-free? Plagiarism-Free custom written term paper, research paper at the best place to buy essays that specializes in historic downtown amherst, assignment before the research paper. Reputable writing, or paper. Various things you start a brand of college research paper writing services? You're not work on your academic experts. Acquire help. Want to do you have difficulty. You select. Dec 11, affordable term papers for bachelor s rustic, etc. Best marvelousessays. Timely dissertation proof reading of time. Http: a paper writing service. Original, research paper writing help with a custom research paper due? Their custom research purpose of academic help for help.
Acquire help. 420 likes 1 talking! Research papers, or paper custom writing service can buy a lifetime with crafting solutions. Based on our website to worry about research papers research paper essay, buy our research paper at researchpaper4me custom writing services. Satisfaction guarantee to purchase papers. Subscribe to order? Have to writing service online get the best custom writing services from the web. Want. Impressive academic success! Yes, correct and customer and other academic papers? Welcome to deliver. Congratulations! During their custom research paper. Precisepaper. Religion essays service. Com provides original research papers writing help.
Essaypanda. Cheap research paper writing services. Top-Rated custom writing service that is obvious to submit your paper writing. Plagiarism-Free custom research paper service that meets your best option you looking for us to earn top quality, theses, and research and school, research papers. Iresearchpaper. Want to your success of the best research paper whereyouofferstake every discipline are a new world. Are plagiarism free guarantee high quality, research papers. Click here, research papers are you can buy best high-quality non-plagiarized and non-academic writing. A1 custom writing services reviews, term papers of your education has been assigned with customized report-writing services you, visit essaydragons. Please make your custom research paper writing, white papers here to complete a few clicks you will definitely help or research paper due? A writer unique features of top quality, college students from this commitment to fulfill your logo. Original writing service. You're not going to write a student. click here 420 likes 1. Great selection of all aspects of experience. Writing company offers you write a reliable essay today for the completion of a custom research papers! Perfect helpmate in line with sophisticated flavors rooted in your time. Comtake every write? Cheapest custom term papers.
And children of high quality guarantees when writers! Is why, research papers in an easy or dissertation, custom written by certified civil trial advocacy, 100% plagiarism report! Essayonlinestore is unmatched.


Amaron AAM-FR-0FR650LMF is factory charged and ready to use. Amaron Fresh car battery is equipped with reformulated Advanta paste recipe. With high cranking power and heat tolerance capacity, these batteries are perfect for cars that go through extreme weather conditions. It is recommended for Toyota New Innova Special Edition Diesel, Toyota Qualis Diesel and Toyota Innova Diesel. Amaron is the only battery that contains the patented SilvenX alloy. Because of that, it lasts really long.
So, if you are looking for a long lasting strong performance battery, go for Amaron AAM-FR-0FR650LMF from everyonechoice.com.


I recently moved servers and made a big 'doh: migrated the forum database and everything successfully, but forgot to backup the images.
It happens. But the new forum layout is awesome. Great job.
A brief Google shows this to be a pretty niche error!
I deactivated that theme and reassigned your account to another default thread.


text:
Q: SQL Server Update Trigger I have never used triggers before in SQL server and I have looked around online but haven't found an answer to my question.  Basically I am trying to write an Trigger that will run after a record has been updated in a table.  This trigger will then update two additional tables based on the record that was updated in the first table.
The primary table with the trigger on it will be updating one record using a query like this:
UPDATE E.SM_T_RList
SET IsActive = 0
WHERE Guid = @Guid

I then want the trigger to do something like this:
ALTER TRIGGER [E].[IsActiveUpdate]
ON  [E].[SM_T_RList]
AFTER UPDATE
AS
BEGIN

SET NOCOUNT ON;

UPDATE E.SM_T_BInfo
SET IsActive = 0
WHERE Guid = @Guid

UPDATE E.SM_T_RMachines
SET IsActive = 0
WHERE GUID = @GUID

END

The Guid that I want updated is being used by the primary table. But I can't figure out how I get the @Guid that I want updated into the trigger?  Please help.
Thanks

A: Triggers in SQL Server operate on sets of rows not individual rows. You access these via the inserted and deleted pseudo tables. Assuming that you might want the value of isactive to cascade when previously inactive rows were made active you could use something like this.
ALTER TRIGGER [E].[IsActiveUpdate]
ON  [E].[SM_T_RList]
AFTER UPDATE
AS
BEGIN

SET NOCOUNT ON;

UPDATE E.SM_T_BInfo
SET IsActive = i.IsActive
FROM INSERTED i JOIN E.SM_T_BInfo e
ON e.Guid = i.Guid

UPDATE E.SM_T_RMachines
SET IsActive = i.IsActive
FROM INSERTED i JOIN E.SM_T_BInfo e
ON e.Guid = i.Guid

END

A: Both the answers already posted suffer from the same problem - they're marking the other rows as Inactive, whenever any update occurs on your base table
Something like:
ALTER TRIGGER [E].[IsActiveUpdate]
ON  [E].[SM_T_RList]
AFTER UPDATE
AS
BEGIN

SET NOCOUNT ON;

UPDATE E.SM_T_BInfo
SET IsActive = 0
WHERE Guid IN (SELECT Guid FROM INSERTED where IsActive=0)

UPDATE E.SM_T_RMachines
SET IsActive = 0
WHERE Guid IN (SELECT Guid FROM INSERTED where IsActive=0)

END

Would be more appropriate

redpajama_set_name: RedPajamaStackExchange


Marketing Information This HON Corral contemporary two-seat sofa offers even more than meets the eye. The seat cushions are uniquely engineered for comfort, with over 100 individually wrapped coil springs that reduce pressure points. The back floats above the seat, so cleaning is simple, and the extra-wide seat cushions provide ample room for two people. Moreover, the style is so impressive, people will assume you spent a lot more than you did. SofThread leather upholstery is Black.


When do you need life insurance? The answer today may change with a life event tomorrow. It's important to keep your coverage amount on pace with your current needs.
The importance of keeping your life insurance in step with life.
The wonderful thing about life is that it never stays the same. It allows us to travel down new and exciting paths with the change of every season and at the start of every new year. But along with every new stage of life comes opportunities, as well as responsibilities and challenges. Sometimes life can feel like you're sailing on a calm sea, and then suddenly (and before you know it), you drift right into the eye of the storm.
With September being Life Insurance Awareness Month, we want to help you learn more about how life insurance can serve as a "life preserver" when you need it, keeping you and those you love financially afloat when life puts an unexpected storm in your path.
So this month, why not take a moment to consider the many benefits of life insurance and how your needs can change throughout the various stages in your life?
When you get married, financial obligations suddenly become a joint effort. If either one of you were to die suddenly, could the surviving spouse maintain his or her standard of living on a single income? A life insurance policy can help ensure that the love in your life is left with financial stability instead of financial stress.
Most families depend on two incomes to make ends meet and to help fund future family goals such as the dream of buying a house and sending kids to college. This is especially true if you have a growing family. Having a life insurance policy can replace a lost income and help safeguard your family's plans for the future so that their dreams don't die when you do.
In addition to taking care of your family, life insurance can protect your business. If you've made a substantial investment to open or grow your business, chances are you're carrying business debt in the form of loans and commercial leases. Life insurance can provide your family with the money to cover business debts that they could be held responsible for when you die.
Nobody has to remind you that as a single parent you're the breadwinner and caregiver for your children. Life insurance can give you the financial peace of mind of knowing that if you weren't here tomorrow, your children would have a financially secure future.
Taking on a new mortgage is a big financial responsibility. Life insurance can provide your family with the immediate cash to cover the balance of your loan if you were to die unexpectedly.
Just because your children have left the nest doesn't mean that you don't need the protection of life insurance. Today's funeral and burial expenses can be costly, and if you haven't set aside the readily available cash to cover your final expenses (including outstanding medical bills) with final expense insurance, you'll be leaving this burden to the people you love.
Understanding when you may need life insurance, or when you may need to increase your current coverage, will allow you to be better prepared to navigate life - no matter what the weather throws at you.


En unik mulighed for at blive opdateret om den nyeste viden inden for optimering af sårhelingsprocessen. Hvilke principper ligger til grund for det korrekte valg af forbindsmateriale til sårets forskellige faser.
Kurset afholdes på engelsk og henvender sig til hestedyrlæger og veterinærsygeplejersker.
Deltagergebyr NOK 630,- ekskl. moms pr. person.
Kurset gennemføres ved min. 20 deltagere og maks. antal deltagere er 60.
Derek Knottenbelt qualified from Edinburgh University in 1970 and after a period in research spent 12 years in private practice. During this time he developed a keen interest in equine medicine and in 1985 he joined the academic world. He moved to Liverpool in 1989 and has since become Professor in Equine Internal Medicine.
Having recently retired and moved to Scotland he now consults in equine internal medicine and oncology at both Glasgow and Liverpool Universities.
He has published widely in the scientific and lay press and is the author of 10 recognised text books. He has received international awards for his welfare work and his science and in 2005 he was honoured with an OBE by the Queen for his services to the horse. He is involved with national and international equine welfare and other charities. His main professional interests are in oncology, dermatology, ophthalmology and wound management.


Hi. I am really hopeing someone can help me.
Do you know why the font time/hours in an email gets bigger on an iPhone/mobile phone?
i.e. 8 AM, 9 PM etc.
And do you know how to fix the code?
I have tried to put a specific fake link in but it only took the link away and left the size bigger.
This is how it looks, except the 8 AM 10 AM 3 PM and 5 PM get BIGGER in size.
Key secondary endpoints included IOP at Week 6 for each daily time point (8 AM, 10 AM, 3 PM, and 5 PM) and mean diurnal IOP change from baseline to Week 6 between treatment groups.
I really need this today if possible. Thank you to all.
If you're looking for quick help, best to post all of your code for everyone to look at.
I haven't seen this happen, could you provide a screenshot?


text:
Outlet Reports
Guess Whose Headline
7.08C San Francisco
More than 100 killed when Syrian forces bomb Damascus suburb
Before you read:
Media Bias/Fact Check
Syrian government forces bombarded rebel-held suburbs of the capital, Damascus, for a second consecutive day Tuesday, killing more than 100 people and raising the specter of a full-scale offensive that could spell catastrophe for the nearly 400,000 residents trapped under siege.
According to the Britain-based Syrian Observatory for Human Rights, at least 250 civilians were killed during the shelling and airstrikes that began Monday, including 58 children. Another 1,000 people were wounded, the group said.
"We no longer have the words to describe children's suffering and our outrage," said the U.N.'s children's agency in a terse statement about the carnage.
Russian Foreign Minister Sergey Lavrov appeared to endorse the unrestrained assault, which he said was backed by the Russian air force. "In keeping with the existing agreements, the fight against terrorism cannot be restricted by anything," he said.
Russia has been an unwavering ally of Syrian President Bashar Assad's forces and was instrumental to the all-out assault in late 2016 that ejected rebels from their enclave in eastern Aleppo, Syria's largest city before the war — an outcome that Lavrov said could serve as a model for the Damascus suburbs, known as eastern Ghouta.
Pro-government forces have been amassing since the weekend on the perimeter of the rebel-held region, a collection of towns and farmland that once provided grain and fruit to the capital before nearly five years of warfare turned it into a landscape of havoc and despair.
The towns of eastern Ghouta were among the first to organize into self-governing collectives and shake off government rule after popular demonstrations against Assad swept through the country in 2011, eventually leading to civil war. They are also among the last to resist Assad's determined campaign to bring every last rebellious corner of the country to heel. Assad and his allies maintain they are fighting a war on terrorism.
Monther Fares, spokesman for the Ahrar al-Sham rebel faction operating in eastern Ghouta, said densely populated residential areas were bearing the brunt of the attacks, a hallmark tactic of the government and its allies to devastate civilian areas and infrastructure before launching a final ground assault.
The battle for rebel-held east Aleppo culminated in the evacuation of tens of thousands of civilians from their homes, with many unable to return. A subsequent U.N. investigation charged that the campaign amounted to forced displacement of a population and rose to the level of a war crime.
That outcome could still be a while coming in eastern Ghouta, which is considerably larger than eastern Aleppo.
An injured man waits for treatment at a hospital in Ghouta, a suburb of Damascus. (Ghouta Media Center via AP)
It is also divided between two rebel factions that deeply distrust each other, as well as a small presence of Al Qaeda-linked fighters and a handful of other militias that could lead to the fragmentation of the enclave, according to Aron Lund, a fellow at the New York-based Century Foundation. That, at least, could spare civilians some of the devastation of an all-out ground assault.
"It's perhaps more likely that government offensives develop into a patchwork of partial surrenders, cease-fires, interim deals, and carve-ups that won't end the conflict but change the way the enclave looks and functions … on the road to final defeat," Lund said.
At least 10 hospitals in eastern Ghouta were damaged by airstrikes or shelling since Sunday night, according to Ahmad al-Dbis, the security manager for the Union of Medical Care and Relief Organizations, which runs hospitals and clinics in Syria. Ten medical staff and rescue workers were among the dead.
The International Committee of the Red Cross said in a statement that it was receiving "distressing reports" of dozens killed and wounded every day in eastern Ghouta, with "families trapped, with no safe place to hide from shelling."
Nasr al-Hariri, who heads the committee representing the Syrian opposition at the U.N., said the opposition was ready to facilitate the withdrawal of Al Qaeda-linked fighters from Ghouta, to halt the assault.
But a spokesman for Failaq al-Rahman, one of the largest militant groups in eastern Ghouta, charged that Russia had obstructed an initiative to eject the Al Qaeda fighters in November, to keep a pretext for an assault.
"Russia was going along to buy time and give Assad an opportunity to pursue the military solution," said spokesman Wael Olwan, who confirmed intensive contacts with the Russians in November.
Russia was supposed to guarantee security and aid access to eastern Ghouta as part of an agreement it reached with the rebels in August. But the Syrian government blocked all but a handful of aid convoys, plunging the region into a spiraling humanitarian crisis. Child malnutrition rates are in the double digits, according to the United Nations, and patients are dying of treatable illnesses and wounds while waiting for medical evacuations.
As the death toll climbed in eastern Ghouta, the rebels retaliated on Tuesday by hitting some Damascus neighborhoods with mortar shells, killing eight people, including three children, and wounding 15 others, according to the state news agency SANA.
"It's a bloody day," said Abdelrahman Shahin, a 31-year-old Damascus resident who was walking on the street when a shell landed on a nearby taxi, killing the driver and a passenger.
"They both died," he said, visibly shaken. "The driver was out for his livelihood. His family will be waiting for him."
Syrian state TV later reported live from the streets of Damascus to show what it called the people's "steadfastness" in the face of the shelling. The TV reporter said residents expressed hope that the Syrian army would retake eastern Ghouta.
In northern Syria, meanwhile, pro-government gunmen crossed into the Kurdish enclave of Afrin in an agreement with the main Kurdish militia there to defend against a Turkish offensive intended to uproot the main Kurdish militia from the area.
Turkish President Recep Tayyip Erdogan said the pro-government fighters had been warned to stay out of Afrin, where Syrian state television showed they were immediately targeted by Turkish shelling.
"Unfortunately, these kinds of terror organizations, as you know, make decisions and take wrongful steps. Of course it is impossible for us to allow this. They will pay a heavy price," Erdogan said.
Despite the warning, video taken inside Afrin late Tuesday showed the pro-government gunmen rallying with Kurdish militiamen underneath an arch welcoming visitors to the area, as they chanted Syrian unity slogans and waved the national flag and the flag of the Kurdish People's Protection Units, or YPG.
Ankara considers the YPG a "terrorist group" linked to the Kurdish insurgency within Turkey's borders. On Jan. 20, it launched a major air and ground offensive, pounding the Afrin enclave with airstrikes and artillery on a daily basis.
Turkey has threatened Syrian troops not to help YPG fighters, warning that it will fight its backers.
http://www.foxnews.com/world/2018/02/20/more-than-100-killed-when-syrian-forces-bomb-damascus-suburb.html
[-0.578347]
Article Word Cloud
pro-government
rebel-held
{"anger":0.150474,"disgust":0.181117,"fear":0.561359,"joy":0.10014,"sadness":0.580753}
Emotions Detected
apos
NEWS WORD CLOUD
wrote
Washington Monument To Reopen In September
Huff Post February 21, 2018
Female Palestinian medic killed by Israeli forces while treating injured protesters on Gaza border
Headlines February 21, 2018
Poll: 70 Percent of U.S. Angry at Political Establishment
Breitbart February 21, 2018
See More on Politifact
TOPICS & ARTICLES
What are different news outlets reporting on?
bryant's
Huff Post
Mike Bloomberg Had A Weird Encounter With A Dog On The Campaign Trail
CNN's planning a 2020 LGBTQ forum. Here are some issues advocates say the candidates should address.
The Trump administration plans to gut food stamps, hitting red states hardest
British Airways suspends flights to China as coronavirus spreads
Rafael Nadal crashes out of Australian Open against Dominic Thiem
France to ban mass shredding of live chicks
Indiana boy dies after father's gun discharges during play wrestling, police say
'Antiques Roadshow' guest floored by worth of Rolex he paid $346 for in 1974
Australian scientists grow copy of coronavirus in lab, called 'significant breakthrough'
Not All Democrats Are Bad
Fox News Cuts Away from President Trump's Impeachment Defense
Bombshell Video: TWO MORE Bernie Staffers Promote 'Extreme Action' & Property Destruction
What emotions are being provoked?
How true are recent statements and reporting?
Huffington Post's Sam Stein says 'no one beta-tested' health ...
Huffington Post reporter says possible GOP Senate candidate ...
She wins popularity contest | PolitiFact
Did Betsy DeVos Demand That Classroom Globes Be Flattened?
Doonesbury says Koch brothers outspent unions 3-1 | PunditFact
Is Congressional Candidate Nathan Larson an Admitted Pedophile?
Huffington Post Headlines
Did Donald Trump Want Tanks and Missiles at His Inaugural Parade?
Go to Results
Think Progress Headlines
search results for "Think Progress Headlines"
A service of the Tampa Bay Times - PolitiFact
Did New Zealand Take Fox News or Sky News Off the Air in ...
Scott Walker wants to cut college budgets by 13% and spend $500 ...
Did CNN Say 'Trump Killed Unarmed Father of Three' After al ...
Did CNN Report that a 'Trump Voter' Shot a 'Mentally Ill' Mass ...
Did CNN Ask 'Should Women Be Allowed to Breastfeed on the ...
All False statements involving CNN | PunditFact
No, CNN isn't closing after ratings drop | PunditFact
These supposed CNN chyrons are fake, according to the network ...
Did WaPo Headline Call IS Leader al-Baghdadi an 'Austere ...
Recycled parody: CNN's Zakaria calls for jihadi rape of white women ...
CNN did not call Abu Bakr al-Baghdadi an 'unarmed father of three ...
Fox Headlines
Did Oprah Winfrey 'Buy Control of Fox News'?
FOX's file: | PunditFact
Did Fox News Report That U.S. Rep. Omar Delivered a "Profanity ...
Breitbart Headlines
Infowars Headlines
Misleading Infowars story says UN plans to 'flood America with 600 ...
See The Full Spectrum
Spectrum Report brings us perspectives from all sides so we can see through the narratives - to better understand the news, and better understand each other.
Contact us: contact@spectrumreport.com

redpajama_set_name: RedPajamaCommonCrawl


text:
Tasmanian Labor accused of 'Middle Eastern' dress code policy for school girls
Georgie Burgess Updated Sat 1 Jul 2017, 10:48 AM AEST
Mr Brooks calls the ALP's draft policy document a 'Manifesto of Madness'.
The Tasmanian Opposition has been accused of moving to force "Middle Eastern" dress codes on school girls ahead of its annual state conference.
But Labor has been quick to hit back at the man dishing out the criticism — Braddon Liberal MP Adam Brooks — saying he has got "no credibility".
Party members will gather in George Town tomorrow to debate a range of motions, including a draft policy framework outlining a range of reforms across health and education.
In a Facebook video, Mr Brooks labelled the document a "Manifesto of Madness", pointing to a policy that would reassess school dress code policies and "enforce modest dress codes for girls as a means of preventing sexist behaviour".
External Link FB video Adam Brooks
"As a father of teenagers, I find that offensive," Mr Brooks said.
"It's unbelievable, it sounds like something happening in a Middle Eastern country, not Australia and not in Tasmania."
In a section called Status of Women, the following appears in a column headed "Labor will":
"Reassess school dress codes" and "enforce modest dress codes for girls as a means of preventing sexist behaviour".
The reasoning for this position appears in a column headed "Labor recognises":
At school, gender stereotypes become entrenched and shape attitudes, behaviours and choices.
Gender stereotypes inform young people's expectations about intimate relationships.
That bullying, discrimination and mental health issues might also arise as a result of gendered expectations, with negative consequences for girls, Trans and gender diverse young people, and boys.
Mr Brooks said the modest dress enforcement would become Labor policy.
"Labor MPs are bound to follow the outcome of their party conference," he said.
Nothing's official until it's debated: Labor
But ALP State Secretary Stuart Benson said a wide range of issues was always debated at the conferences.
"Motions and platforms to be debated at the conference are all proposals," he said.
"Labor's platforms and policy do not become official until debated and voted on at the conference.
"There are always amendments made to motions and platforms and not every motion is supported."
Labor MP Scott Bacon said Mr Brooks had no credibility.
"We know that Adam Brooks can't be taken seriously when it comes to politics in Tasmania," he said.
"What we will be doing at the conference is debating the issues that are important to Tasmanians, things like health, education and good sustainable jobs.
"We know that's what Tasmanians want us to be talking about."
Posted Fri 30 Jun 2017, 5:40 PM AEST
'Unacceptable': Iran captures two British-operated tankers in Strait of Hormuz


text:
No Injuries After Navy Ships Collide Off East Coast
The guided-missile cruiser Leyte Gulf (CG 55) departs Naval Station Norfolk, Virginia, on Nov. 2, 2018. (U.S. Navy photo by Mass Communication Specialist 3rd Class Garrett LaBarge)
Military.com | By Hope Hodge Seck
A Ticonderoga-class guided-missile cruiser and a dry cargo ship collided stern-to-stern during a resupply operation in the Atlantic off the southeastern coast of the U.S., the Navy announced Tuesday.
The cruiser Leyte Gulf and cargo ship Robert E. Peary "made contact" during an underway replenishment operation, Navy officials said in a release. No personnel were injured, and both ships were able to operate following the encounter.
The news of the collision was first reported by USNI News, which added that damage was minor and occurred above the waterline.
The incident happened around 4 p.m. Tuesday; the ships are now headed into port at Norfolk to assess damage, according to the release.
"U.S. Fleet Forces Command and Military Sealift Command will thoroughly investigate this incident," officials said in a statement.
The ships were operating in conjunction with the Abraham Lincoln Carrier Strike Group, which is conducting pre-deployment operations off the coast of Norfolk. The Lincoln is expected to deploy to the newly re-established 2nd Fleet this spring.
While Navy ship collisions are historically rare, the service is still recovering from a devastating year in 2017.
In June and August of that year, two destroyers were involved in separate deadly collisions in the Pacific. In all, 17 sailors died in the two collisions. The tragedies prompted a series of Navy reviews and policy changes ranging from improved sleep schedules for deployed sailors to updated training protocols for Navy surface officers.
-- Hope Hodge Seck can be reached at hope.seck@military.com. Follow her on Twitter at @HopeSeck.
Related Topics: Military Headlines US Navy Topics Cruisers


text:
\section{Network architecture}
We have explored two network architectures.
The first network architecture is inspired by \cite{song2016ssc}, but we have included batch normalization \cite{ioffe2015batch} and
used dilated convolutions instead of max pooling layers and strided convolutions. This to keep the resolution  \cite{springenberg2014striving}. We denote it as SSC-Net.
The second architecture is a UNet \cite{ronneberger2015unet} with max pooling and transposed convolutions as up-sampling.
Just as in \cite{shridhar2019comprehensive} we chose softplus as activation functions instead of relu to have more active weights in the network.
This spread is beneficial for a well calibrated uncertainty \cite{seedat2019calibrated}.
The architecture of the network is displayed in Figure \ref{fig:arch}.

\begin{figure*}[!t]
\centering
\subfloat[MNIST]{\includegraphics[width=.22\textwidth]{MNIST_architecture}%
\label{fig:MNIST_arch}}
\hfil
\subfloat[SSC-Net]{\includegraphics[width=.6\textwidth]{SUNCG_architecture}%
\label{fig:SUNCG_arch}}
\hfil
\subfloat[UNet]{\includegraphics[width=.7\textwidth]{UNet_architecture}%
\label{fig:UNet_arch}}
\caption{Architecture of the BCNNs used for MNIST and SUNCG experiments.
Conv(d, k, l) stands for a 3D convolution filter stack of depth d and kernel size k and dilation l.
ConvT(d, k) is the up sampling operation Transposed Convolution with depth d, kernel and stride k.
Batch normalization and softplus activation is performed after every Conv layer. Softmax in the final layer.}
\label{fig:arch}
\end{figure*}
\section{Training}
For training we use the Adam optimizer \cite{kingma2014adam} with constant learning rate of $10^{-3}$.
We do not use weight decay since we regularize using KL divergence.
For the weights we sample the mean uniformly as $\mu \sim U(-\frac{1}{\sqrt{k^d}}, \frac{1}{\sqrt{k^d}})$,
where $k$ is the kernel size in one dimension and $d$ is the number of dimensions. The parameter $\sigma$ is simply initialized as a chosen constant $\sigma_0$.

As noted in \cite{shridhar2019comprehensive} it is beneficial to set $\sigma_0$ larger than the variance of the prior.
In our own experiments on MNIST we see that this improves the separation in predictive entropy. See the experiments section for details.
\section{Evaluation}
To measure the performance of the network we use the Intersection over Union (IoU), which is standard for segmentation tasks.
The main goal is to compare the Bayesian implementation with the deterministic and see that the score is similar.
Just like \cite{song2016ssc} and most works we don't evaluate voxels that are outside of the camera field of view.
Unlike previous works we do evaluate on pixels outside of the room so that the network can learn to predict a room layout
e.g. even though the room in just glimpsed through a door.
\\

To evaluate if the Bayesian approach gives us more information about model uncertainty we look at the scores and uncertainty metrics for
true positives (TP) and false negatives (FN) for each class or for different distances to the surfaces.
If the knowledge about the model limitations is good, there should be a good separation in scores and/or uncertainty for TP and FN.
We measure this with the Bhattacharyya coefficient (BC) \cite{bhattacharyya}, which is an approximation of the amount of overlap of two distributions.
The data is split into $N$ partitions, where in each partition we count the number of TP $q_i$ and FN $p_i$ and calculate the BC as
\begin{equation*}
  BC(\mathbf{p},\mathbf{q}) = \frac{1}{N} \sum_{i=1}^N \sqrt{p_i q_i}.
\end{equation*}
\\

Finally, we also compute the Mean Average Precision (mAP) as the area under the Precision-Recall curve.
This measures both the separation and the accuracy of the model.
We use 101 thresholds $T = \{0, 0.01, ..., 1.0\}$, just as the detection challenge COCO \cite{COCO}.
We follow their implementation and use interpolated precision to get a smoother curve.

\section{Bayes by backprop}
The method introduced by \cite{blundell2015weight} is based on Variational Inference.
The main idea is to let each weight in the network be sampled from a distribution,
where the distribution is learned at training time, as illustrated in Figure \ref{fig:bayesian_vs_standard}.

\begin{figure}[!t]
\centering
\subfloat[Standard]{\includegraphics[width=.46\linewidth, trim=300 150 100 150, clip]{weights_standard}%
\label{fig:standard}}
\hfil
\subfloat[Bayesian]{\includegraphics[width=.46\linewidth, trim=300 150 100 150, clip]{weights_bayesian}%
\label{fig:bayesian}}
\caption{In \ref{fig:standard} we see a filter bank from a standard 2D CNN, each weight is a scalar.
In \ref{fig:bayesian} we see a filter bank in a Bayesian Variational Inference 2D CNN, here each weight represented as a distribution which is sampled from at inference time.}
\label{fig:bayesian_vs_standard}
\end{figure}

Let $\Post$ be the posterior for our Bayesian Neural Network.
Given the posterior we can make predictions given unseen data by taking the expectation over the posterior
\begin{align*}
    \Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}}] &= \mathbb{E}_{\Post} \left [ \Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}}, \mathbf{w}] \right ]\\
                        &= \intdw{ \Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}}, \mathbf{w}] \Post }.
\end{align*}
We estimate the posterior using a simpler model $\q$ with learnable parameters $\theta$,
which minimizes the Kullback-Leibler (KL) divergence to the true posterior, i.e.
\begin{align*}
  \theta^* &= \argmin_\theta KL[\q || \Post] \\
           &= \argmin_\theta \int \q \log \frac{\q}{\Prior \Lhod} \\
           &= \argmin_\theta KL[\q || \Prior] -  \mathbb{E}_{\q} [\log \Lhod].
\end{align*}
This cost function is known as variational free energy or expected lower bound.
We denote it as
\begin{equation} \label{eq:F_cost}
  \mathcal{F}(\mathcal{D},\theta) = KL[\q || \Prior] -  \mathbb{E}_{\q} [\log \Lhod].
\end{equation}
Where the first part is the complexity cost as it enforces model simplicity using the prior.
The second part is the likelihood, which describes how well the model describes the data.

Using Monte Carlo sampling the cost in (\ref{eq:F_cost}) is approximated as
\begin{align*} \label{eq:F_cost_approx}
  \mathcal{F}(\mathcal{D},\theta) \approx &\sum_{i=1}^n \frac{\beta}{n} \left [ \log \q[i] - \log \Prior[i] \right ]\\
                                 &- \log \Lhod[i],
\end{align*}
where $\mathbf{w}^{(i)}$ is a sample from the variational posterior $\q[i]$.
This approximation enables more priors and posteriors since a closed for solution for the first term in (\ref{eq:F_cost}) is not necessary.
The scale factor $\frac{\beta}{n}$ with $\beta$ as design parameter is introduced to tune the amount of regularization from the complexity cost.
\subsection{Variational Posterior}
Assuming the variational posterior $\q$ is a diagonal Gaussian distribution.
With the re-parametrization trick \cite{reparametrization} the weights can be sampled from the posterior as
\begin{equation*}
  \mathbf{w} = \mu + \log (1 + e^\gamma) \odot \epsilon, \quad \epsilon \sim \mathcal{N}(0,I),
\end{equation*}
where the model parameters are $\theta = (\mu, \gamma)$ and $\sigma = \log (1 + e^\gamma)$
to ensure that the standard deviation remains positive during optimization.
\subsection{Prior}
For the choice of prior we tested both Gaussian (as in \cite{shridhar2019comprehensive}), Cauchy and a scale mixture of two Gaussian (as in \cite{blundell2015weight}).
The two latter affords more flexibility to the network and we found they yielded the best results.
We mainly worked with Cauchy since it is more efficient to compute but have some results on the Gaussian scale mixture.
As they are centered around 0 we denote them as
\begin{equation*}
  \text{Cauchy}(\gamma) = \frac{1}{\pi \gamma \left [1 + (\frac{x}{\gamma})^2 \right]}
\end{equation*}
and
\begin{equation*}
  \text{G}^M_{(\sigma_0, \sigma_1)}(\alpha) = \alpha \mathcal{N}(0,\sigma_0^2) + (1-\alpha) \mathcal{N}(0,\sigma_1^2),
\end{equation*}
where $\mathcal{N}$ denotes the probability density function of the normal distribution.
\subsection{Prediction \& Uncertainty}
\label{sec:prediction}
The predictive mean and uncertainty is computed by sampling our variational posterior (BCNN) at test time.
An unbiased estimation of the expectation is given \cite{shridhar2019comprehensive} by
\begin{align*}
  \mathbb{E}_{\q} \left [ \Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}}, \mathbf{w}] \right ] &=  \intdw{\q \Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}},\mathbf{w}^{(t)}]} \\
                                                           & \approx \frac{1}{T} \sum^T_{t=1} \Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}},\mathbf{w}^{(t)}],
\end{align*}
where $\Prob{\hat{\mathbf{y}}}[\hat{\mathbf{x}},\mathbf{w}^{(t)}]$ is the softmax output from forward pass $t$.
To simplify notation we now denote this as $\pt$.

There are a number of choices when it comes to measuring the uncertainty \cite{seedat2019calibrated,kendall2017uncertainties}.
Common choices are predictive entropy, aleatoric uncertainty and epistemic uncertainty.

\textbf{Predictive Entropy -} Measures the diversity of the distribution, so a higher
entropy corresponds to higher uncertainty. It is computed as
\begin{equation*}
  H = - \sum^T_{t=1} \pt \log \pt.
\end{equation*}

\textbf{Aleatoric Uncertainty -} Corresponds to measurement noise from the input data,
so increasing the size of the dataset should not reduce this uncertainty. It is computed as
\begin{equation*}
  \sigma^2_a = \frac{1}{T} \sum^T_{t=1} \text{diag} ( \pt ) - \pt \pt^\top.
\end{equation*}

\textbf{Epistemic Uncertainty -} Corresponds to model uncertainty and will be low when
input data is similar to training data. Increasing the amount and diversity of training data should reduce this uncertainty.
This is computed as
\begin{equation*}
  \sigma^2_e = \frac{1}{T} \sum^T_{t=1} ( \pt - \bar{\pt} )( \pt - \bar{\pt} )^\top,
\end{equation*}
where $\bar{\pt} = \frac{1}{T}  \sum^T_{t=1} \pt $ is the predictive mean from above.

\section{Conclusion}
We've seen that a Bayesian CNN is clearly better at being uncertain when presented with unseen data.
This was shown with a simple CNN model on the MNIST dataset.
In the same experiment we also showed that the best separation between true and false predictions is found when combining entropy and mean score.
This means that a final classifier layer could be added to get better probability estimates.
\\

For the Semantic Scene Completion task we show that the Bayesian CNNs outperfom the Deterministic CNNs
when faced with a category not seen at training time. This is shown by evaluating mIoU, mAP and separation score BC.
Due to the complexity of the task and the model capacity we cannot show the same well separated output distributions as in the MNIST experiment. We do show that entropy represents the uncertainty well, especially close to the observed surface.
\\

Future work will be to use an architecture better suited for sparse data and to utilize the RGB information from the image as well.
We also want to look at how the uncertainty scores can be used in navigation.
\section*{Acknowledgment}
This work is supported by Wallenberg AI Autonomous Systems and Software Program (WASP).

\section{MNIST experiments}
For MNIST we created a simple 2D CNN using the same building blocks as we use in the 3D SSC-Net.
We used the same code base and tools to allow for faster debugging and experiments.
The network is trained in both Bayesian and Deterministic mode.
\subsection{Baseline experiment}
Here we have trained the network on the training set and evaluated on the test set.
Table \ref{tb:MNIST_AP} shows the Accuracy and mAP for each mode.
We see that the Deterministic CNN without weight decay, i.e. $\omega=0$, performs best.
Our Bayesian version is close behind with the benefit of having built in regularization in terms of a prior.
By explicitly adding regularization in form of weight decay on the deterministic CNN we get the worst performance, especially noticeable in mAP.

\begin{table}[!t]
\renewcommand{\arraystretch}{1.2}
\caption{Compares Accuracy and mAP on MNIST for the deterministic and Bayesian mode of our simple network.}
\label{tb:MNIST_AP}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Mode} & \textbf{Accuracy}& \textbf{mAP}\\
\hline
Bayesian, Cauchy(0.3), $\sigma_0=0.5$ & 97\% & 0.96\\
\hline
Deterministic, $\omega=0$ & \textbf{98}\% & \textbf{0.97}\\
\hline
Deterministic, $\omega=0.01$ & 97\% & 0.95\\
\hline
\end{tabular}
\end{table}
\subsection{Leave one out experiment}
To see how well the uncertainty measurements captured model uncertainty we conducted an experiment
where digit $0$ was left out from the training data and then introduced at test time.
Unless stated otherwise the Bayesian model uses Cauchy(0.3) as prior and $\sigma_0=0.5$ as initialization.
\subsubsection{Prior and Initialization}
In Figure \ref{fig:MNIST_prior} we see how the BC and mAP changes for different priors and $\sigma_0$ during training.
A larger $\sigma_0$ decreases the BC score (increase separation) but require longer training times.
By binning the results on both Entropy and mean score we get the best separation, which indicates that
we can most likely train a classifier on these metrics and do better than the mean score.
This is analogous with the GPOM \cite{gpom} mentioned in section \ref{sec:related}.

\begin{figure*}[!t]
\centering
\includegraphics[width=\textwidth]{mnist_prior}
\caption{BC, mAP and accuracy for different configurations of prior and $\sigma_0$ for the MNIST experiment where digit $0$ is introduced at test time.
We see that high $\sigma_0$ seems to improve BC but increase training time and a prior which is more narrow than $\sigma_0$ gets a lower BC score.
We also see that Entropy, Aleatoric uncertainty and Entropy coupled with score seems to have best separation.
The Deterministic version are comparable in either mAP or BC, but not both at the same time.}
\label{fig:MNIST_prior}
\end{figure*}
\subsubsection{Activation function}
Figure \ref{fig:MNIST_activation} shows how the choice of activation function affects the result.
Just as \cite{shridhar2019comprehensive} we observe that softplus in general yields better separation,
both when used as layer activation and normalized as final activation.

\begin{figure*}[!t]
\centering
\includegraphics[width=\textwidth]{mnist_activation}
\caption{BC, mAP and accuracy for different activation functions and $\sigma_0$ for the MNIST experiment where digit $0$ is introduced at test time.
We observe that softplus as a layer activation function in general gets a lower BC score than ReLU.
For the final activation the normalized softplus reaches a lower BC faster than softmax.}
\label{fig:MNIST_activation}
\end{figure*}
\subsubsection{Output distributions}
For a more in depth understanding of the output we also look at the output distributions.
As described in section \ref{sec:prediction} we have calculated the predictive mean $\bar{\pt}_i$ and entropy $H_i$ for each sample indexed by $i$ in the dataset.
As usual we get the predicted label index as $l_i = \argmax{\bar{\pt}_i}$ with corresponding score $\bar{\pt}_i(l_i)$ and entropy $H_i(l_i)$.
In Figure \ref{fig:MNIST_hist} we form histograms of these for all samples in the test dataset categorized by their true class.
For the deterministic case we see that the regularization helps separate the true zeros from the other predictions.
However, the distributions for the other classes are much wider, indicating that the network is more uncertain overall.
In the Bayesian case we see that the distribution for true zeros are well separated from the others, while maintaining certainty for the other classes.
The entropy also seems to be a good indicator.

\begin{figure*}[!t]
\centering
\subfloat[Deterministic]{\includegraphics[width=.38\textwidth]{MNIST_zero_out_det_hist}%
\label{fig:MNIST_deterministic}}
\hfil
\subfloat[Deterministic Regularized]{\includegraphics[width=.38\textwidth]{MNIST_zero_out_det_hist_reg}%
\label{fig:MNIST_deterministic_reg}}
\hfil
\subfloat[Bayesian Mean Score]{\includegraphics[width=.38\textwidth]{MNIST_zero_out_bayesian_hist}%
\label{fig:MNIST_bayesian}}
\hfil
\subfloat[Bayesian Entropy]{\includegraphics[width=.38\textwidth]{MNIST_zero_out_entropy}%
\label{fig:MNIST_entropy}}
\caption{Here we see true and false predictions for all digits in the test set when $0$ has been left out from training.
Blue bars are number of true predictions with the binned score, while orange are false predictions.
In \ref{fig:MNIST_deterministic} we have the deterministic CNN which has high belief in many false $0$ predictions.
In \ref{fig:MNIST_deterministic_reg} we have the deterministic CNN with regularization $\omega=0.01$ which has a more balanced belief, but overall lower.
In \ref{fig:MNIST_bayesian} and \ref{fig:MNIST_entropy} we see lower certainty for digit $0$ but still high certainty for other classes in both score and entropy respectively.}
\label{fig:MNIST_hist}
\end{figure*}
\section{SUNCG mini experiments}
SUNCG \cite{song2016ssc} is a large dataset consisting of 45,622 manually created synthetical indoor scenes with 84 labeled categories.
We've used a subset of 2000 training scenes and 1000 testing scenes for the experiments.
As in \cite{song2016ssc} we've used the flipped Truncated Signed Distance Function (TSDF) as input to our network
and the same 11 categories as output.
We chose the projective version since it is more realistic in a robotic setting.
We've also chosen a coarser grid with 0.08m resolution and 60x40x60 in size which is similar to their output size and resolution.
\subsection{Baseline experiment}
In Table \ref{tb:SUNCG} we see the mean IoU (mIoU) over all categories for the Deterministic and Bayesian CNN.
In general they have similar mIoU but the Bayesian versions have better mAP.
\begin{table
\renewcommand{\arraystretch}{1.2}
\caption{Compares mIoU on SUNCG for the (D)eterministic and (B)ayesian versions of our CNNs.}
\label{tb:SUNCG}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Type} & \textbf{Arch.} & \textbf{Parameters} &\textbf{mIoU} & \textbf{mAP}\\
\hline
B & SSC-Net & Cauchy(0.05), $\sigma_0$=0.05, $\beta$=0.1 & 0.22 & 0.26\\
\hline
B & UNet & Cauchy(0.1), $\sigma_0$=0.1, $\beta$=5 & \textbf{0.23} & \textbf{0.29}\\
\hline
D & SSC-Net & $\omega$=0 & \textbf{0.23} & 0.24\\
\hline
D & UNet & $\omega$=0 & 0.21 & 0.21\\
\hline
D & SSC-Net & $\omega$=0.01 & 0.15 & 0.19\\
\hline
D & UNet & $\omega$=0.01 & 0.16 & 0.20\\
\hline
\end{tabular}
\end{table}
\subsection{Complexity cost weight $\beta$}
For the Bayesian UNet we conducted a $\beta$ parameter experiment on SUNCG, which weighs the complexity cost against the likelihood cost.
As a reference, the deterministic version with different weight decays are also included.
We see in Figure \ref{fig:SUNCG_beta} that $\beta=5$ is better in all metrics but mIoU, where $\beta=1$ has the highest score.
We also see that all Bayesian versions are better than the deterministic in mAP and
that a too large $\beta$ makes the model unable to fit the data properly, while too small will get a better fit but worse separation.
\begin{figure*}[!t]
\centering
\includegraphics[width=\textwidth]{unet_model}
\caption{BC, mAP and mIoU for the Bayesian UNet with different weights $\beta$ and $\omega$ for the SUNCG mini dataset.
We observe that $\beta=5$ is better in all metrics but mIoU, where $\beta=1$ is best.}
\label{fig:SUNCG_beta}
\end{figure*}
\subsection{Leave one out} \label{sec:SUNCG_leave_one_out}
For the SUNCG mini dataset we removed all instances of the class \textit{bed} during training and then tested on the full dataset.
The result is presented in Table \ref{tb:SUNCG_nobed}.
Here we test both the SSC-Net and the UNet architecture in both Bayesian and Deterministic versions.
We observe that both Bayesian versions outperform the Deterministic in mIoU, mAP and BC.
For the sample based metrics the Bayesian SSC-Net outperform the Bayesian UNet, but this might not be the case given more data.
\begin{table
\renewcommand{\arraystretch}{1.2}
\caption{BC, mAP and mIoU for different network architectures when the \textit{bed} class is removed from training.
S=Score, E=Entropy.
We observe that Bayesian SSC-Net has the best score in most metrics.}
\label{tb:SUNCG_nobed}
\centering
\input{./img/ssc_nobed/table.tex}
\end{table}
\subsection{Output distributions}
In Figure \ref{fig:SUNCG_tsdf_hist} we see histograms for true and false predictions at different distances from the observed surface
for the Bayesian SSC-Net.
We see that as the entropy (uncertainty) grows the ratio of false to true predictions increases, this is what we would expect from a sound
measurement of uncertainty. It seems to have a better grasp of the uncertainty close to the observed surface.
\begin{figure
\centering
\subfloat[Bayesian score]{\includegraphics[width=.5\linewidth]{SUNCG_mini_tsdf_bayesian_scores}%
\label{fig:SUNCG_tsdf_bayesian}}
\hfil
\subfloat[Bayesian Entropy]{\includegraphics[width=.5\linewidth]{SUNCG_mini_tsdf_entropy}%
\label{fig:SUNCG_tsdf_entropy}}
\caption{Here we see true and false predictions for all voxels at different distances (in meter) from observed surfaces.
In \ref{fig:SUNCG_tsdf_entropy} we see that as the entropy (uncertainty) grows the ratio of false to true predictions increases.
This hold for all areas, but especially close to the observed surfaces. }
\label{fig:SUNCG_tsdf_hist}
\end{figure}
\subsection{Example output}
See Figure \ref{fig:SUNCG_example} for example output.
More examples are available in the supplementary material.
\begin{figure*}[!t]
\captionsetup[subfigure]{labelformat=empty}
\centering
\subfloat[]{\includegraphics[width=.6\textwidth]{SUNCG_mini_ex0}}%
\hfil
\subfloat[]{\includegraphics[width=.3\textwidth, trim=0 0 1000 0, clip]{SUNCG_mini_ex0_entropy}}%
\caption{Here is one example output from the SUNCG mini test set. From the left we have predicted labels, ground truth and entropy.}
\label{fig:SUNCG_example}
\end{figure*}

\section{Introduction}
Semantic scene completion is a challenging task in which both visible and occluded surfaces are labeled semantically in 3D.
The problem naturally arises when aiming to predict a 3D scene from a single view, but can also be studied for multiple views.

Predicting occluded areas can be of great help to autonomous vehicles during navigation and exploration.
Especially for UAVs (Unmanned Aerial Vehicles), which navigate a 3D space where observations may be sparse, scene completion can be used for smoother trajectories during path planning.
During exploration it can help the agent understand the likelihood of free space in occluded areas.
In Figure \ref{fig:motivation} we see an illustration of the problem where a UAV would benefit from knowing what to expect in occluded areas.

This work introduces a system for Bayesian Semantic Scene Completion (BSSC), which along with the prediction scores also delivers an estimation of uncertainty.
This is crucial for decision making during autonomous navigation and exploration as it can
help the agent understand when the data is new to the model and the prediction should not be trusted.
It can also be used to understand what data should be added to the training to improve robustness.

We first test our implementation on the MNIST dataset to verify the Bayesian approach and
to understand how the output distributions look for a well modeled dataset.
We then move on to the SUNCG data for the Semantic Scene Completion task.

Our contributions include:
\begin{itemize}
  \item An open source system for BSSC using Variational Inference released on \url{https://github.com/DavidGillsjo/bssc-net}.
  \item An extended SSC task on the SUNCG dataset which includes more occluded space.
  \item Experiments showing that the Bayesian approach is more robust to unseen data in the SSC task.
  \item Parameter studies on both MNIST and SUNCG.
\end{itemize}

\begin{figure}[!t]
\centering
\includegraphics[width=.45\textwidth]{motivation}%
\caption{An UAV has some occluded areas in its surrounding and would like to have an idea about what to expect.}
\label{fig:motivation}
\end{figure}

\section{Related work} \label{sec:related}
Semantic scene completion has been formulated for both the single view and multiple view problem with different sensor modalities.
One example is SSC-Net \cite{song2016ssc} that solves the single view using a 3D CNN with depth data as input. Our work is heavily inspired by their architecture and training setup.
Other works \cite{Garbade_2019_CVPR_Workshops,Liu_NIPS2018} have then extended the architecture by utilizing the RGB information as well.

Since the solutions are similar to Semantic Scene Segmentation, the following works are also of interest.
Rather than feeding the whole input volume through the network, \cite{Dai_2018_CVPR} takes a sliding window approach to better handle large datasets.
By encoding the RGB information as features and using differentiable backprojection 3DMV \cite{dai20183dmv} got impressive results on the ScanNet dataset \cite{dai2017scannet}.

The best performing networks on ScanNet semantic segmentation is currently based on point clouds \cite{SparseConvNet_CVPR2018,thomas2019KPConv,wu2018pointconv} or sparse convolutions \cite{Choy_CVPR2019}.

Our work will not focus on performance, but rather how to estimate uncertainty in semantic scene completion using Bayesian NN.

There are several ways of modeling a Bayesian Neural Network, this work uses the Bayes by Backpropagation \cite{blundell2015weight}.
Our implementation of Bayes by Backpropagation is based on \cite{shridhar2019comprehensive} which implements this for a 2D CNN using softplus and normalized softplus as activation functions.

Both occupancy maps and semantic segmentation typically use a probabilistic representation where each class is assigned probability between 0 and 1.
If the algorithm also outputs uncertainty together with the probability score, what does that mean and how do we use it?
This is studied for Gaussian Process Occupancy Maps (GPOM) \cite{gpom} in the robotics community.
GPOM yields a mean and variance per grid cell, these are then fed to a linear classifier, which is trained to output a probability score.

Most NN classifiers does not have calibrated probabilities, this can be adjusted with for example Platt Scaling as in \cite{kuleshov2018accurate}.
Uncertainties from Bayesian NN classifier can be similarly calibrated.
In \cite{seedat2019calibrated} the authors study calibration for different BNNs by comparing Entropy, Mutual Information, Aleatoric uncertainty and Epistemic uncertainty
under different perturbations of input data. They conclude that predictive entropy and Epistemic uncertainty provide the most robust uncertainty estimates.

\section*{Appendix A. Output distributions SUNCG}
In Figure \ref{fig:SUNCG_nobed_entropy} and \ref{fig:SUNCG_nobed_score} are histograms of
output entropy and scores respectively for the 4 different network configurations in the
SUNCG leave-one-out experiment in Section \ref{sec:SUNCG_leave_one_out}.
There are 4 different histograms per network, each bins true (TP) and false positives (FP) with a
certain distance from observed surface.

For the scores in \ref{fig:SUNCG_nobed_score} we see that in general the ratio of FP
to TP grow as score decreases, at least close to the observed surface.
For entropy the ratio grow as entropy increases, which we expect from a measurement of uncertainty.
\section*{Appendix B. Examples}
In Figure \ref{fig:SUNCG_examples} we show some example outputs from the Bayesian SSC-Net.
\begin{figure}[h]
\centering
\subfloat[Bayesian SSC-Net entropy]%
{\includegraphics[width=1.6in]{ssc_nobed/img/B-SSC-Net_hist_entropy_top_tsdf_150}%
\label{fig:SUNCG_tsdf_bssc_entropy}}
\hfil
\subfloat[Bayesian UNet entropy]%
{\includegraphics[width=1.6in]{ssc_nobed/img/B-UNet_hist_entropy_top_tsdf_400}%
\label{fig:SUNCG_tsdf_bunet_entropy}}
\caption{True and false predictions for all voxels at different distances (in meter) from observed surfaces.
We see that as the entropy (uncertainty) grows the ratio of false to true predictions increases.
This hold for all areas, but especially close to the observed surfaces. }
\label{fig:SUNCG_nobed_entropy}
\end{figure}
\begin{figure}[h]
\centering
\subfloat[SSC-Net score, $\omega=0$]%
{\includegraphics[width=1.6in]{ssc_nobed/img/SSC-Net_omega=0_hist_avg_max_scores_tsdf_20}%
\label{fig:SUNCG_tsdf_ssc_det}}
\hfil
\subfloat[SSC-Net score, $\omega=0.01$]%
{\includegraphics[width=1.6in]{ssc_nobed/img/SSC-Net_omega=0.01_hist_avg_max_scores_tsdf_135}%
\label{fig:SUNCG_tsdf_ssc_det_reg}}
\hfil
\subfloat[UNet score, $\omega=0$]%
{\includegraphics[width=1.6in]{ssc_nobed/img/UNet_omega=0_hist_avg_max_scores_tsdf_50}%
\label{fig:SUNCG_tsdf_unet_det}}
\hfil
\subfloat[UNet score, $\omega=0.01$]%
{\includegraphics[width=1.6in]{ssc_nobed/img/UNet_omega=0.01_hist_avg_max_scores_tsdf_100}%
\label{fig:SUNCG_tsdf_unet_det_reg}}
\hfil
\subfloat[Bayesian SSC-Net score]%
{\includegraphics[width=1.6in]{ssc_nobed/img/B-SSC-Net_hist_avg_max_scores_tsdf_150}%
\label{fig:SUNCG_tsdf_bssc}}
\hfil
\subfloat[Bayesian UNet score]%
{\includegraphics[width=1.6in]{ssc_nobed/img/B-UNet_hist_avg_max_scores_tsdf_400}%
\label{fig:SUNCG_tsdf_bunet}}
\caption{True and false predictions for all voxels at different distances (in meter) from observed surfaces.
We see the distribution of scores for all architectures.}
\label{fig:SUNCG_nobed_score}
\end{figure}
\begin{figure*
\captionsetup[subfigure]{labelformat=empty}
\centering
\subfloat[]{\includegraphics[width=4in]{SUNCG_mini_ex0}}%
\hfil
\subfloat[]{\includegraphics[width=2in, trim=0 0 1000 0, clip]{SUNCG_mini_ex0_entropy}}%
\vspace{-12pt}
\hfil
\subfloat[]{\includegraphics[width=4in]{Semantic_5}}%
\hfil
\subfloat[]{\includegraphics[width=2in, trim=0 0 1000 0, clip]{Entropy_5}}%
\vspace{-12pt}
\hfil
\subfloat[]{\includegraphics[width=4in]{Semantic_6}}%
\hfil
\subfloat[]{\includegraphics[width=2in, trim=0 0 1000 0, clip]{Entropy_6}}%
\vspace{-12pt}
\hfil
\subfloat[]{\includegraphics[width=4in]{Semantic_7}}%
\hfil
\subfloat[]{\includegraphics[width=2in, trim=0 0 1000 0, clip]{Entropy_7}}%
\vspace{-12pt}
\hfil
\subfloat[]{\includegraphics[width=4in]{Semantic_14}}%
\hfil
\subfloat[]{\includegraphics[width=2in, trim=0 0 1000 0, clip]{Entropy_14}}%
\hfil
\caption{Some example output from the Bayesian SSC-Net on the SUNCG dataset. From the left we have predicted labels, ground truth and entropy.}
\label{fig:SUNCG_examples}
\end{figure*}

redpajama_set_name: RedPajamaArXiv


text:
James Turrell: Pace Gallery London - 11 February – 27 March 2020
Pace Gallery is honoured to present its second solo exhibition of new works by Light and Space master James Turrell at 6 Burlington Gardens. The exhibition will be on view from 11 February to 27 March 2020. Turrell is among the most influential artists of the past fifty years and Pace is proud to continue its long commitment to him stretching back to 1967. This is the gallery's ninth exhibition of Turrell works.
Influenced by the notion of phenomenology in pictorial art, Turrell focused, in his earliest work, on the dialectic between constructing light and painting with it, building on the sensorial experience of space, colour, and perception. These interactions became the foundation for Turrell's oeuvre, which evolved to an investigation of the immateriality of light itself.
Turrell's exhibition at Pace features four new works from the Constellation series staged in site-designed chambers. The works will feature elliptical and circular shapes with a frosted and curved glass surface animated by an array of technically advanced LED lights, which are mounted to a wall and generated by a computer programme. The light changes are subtle and hypnotic, one colour morphing into the next. The programme runs on a loop that is imperceptible to the viewer, prompting a transcendental experience. With these new works, Turrell continues his exploration of technological possibilities combined with sensory practices and gradient colours.
"To some degree, to control light I have to have a way to form it, so I use form almost like the stretcher bar of a canvas… When I prepare walls, I make them so perfect that you actually don't pay attention to them. This is true of the architecture of form I use: I am interested in the form of the space and the form of territory, of how we consciously inhabit space." James Turrell.
Since his earliest Projection Pieces (1966–69), Turrell's exploration has expanded through various series, including Skyspaces (1974–), Ganzfelds (1976–), and perhaps most notably, his Roden Crater Project (1977) near Flagstaff, Arizona. Representing the culmination of the artist's lifelong research in the field of human visual and psychological perception, Roden Crater is a controlled environment for the experiencing and contemplation of light and stars, a shared interest with Pace's exhibition in London. Fundraising is underway to complete the construction and open it to the public. Turrell's practice has equally materialized in small-scale works, including architectural models, holograms, and works on paper. His inspiration draws from astronomy, physics, architecture and theology. James Turrell: Passages of Light is currently on view until 29 March 2020 at Fundación Jumex, Mexico City.
James Turrell (b. 1943, Los Angeles) attended Pomona College, where his studies concentrated on psychology and mathematics. Since 1966, he has created art works made primarily out of light. In 1973 he received a master's degree in art from Claremont Graduate School.
His work is represented in international public collections including the Tate Modern, London; Los Angeles County Museum of Art; Museum of Fine Arts, Houston; the Solomon R. Guggenheim Museum, New York; Whitney Museum, New York; Chichu Art Museum, Naoshima Island, Japan; The Museum of Modern Art, New York; and the Israel Museum, Jerusalem.
Turrell's first one-person exhibition, James Turrell: Light Projections, was held in the fall of 1967 at the Pasadena Art Museum, curated by John Coplans. An accompanying catalogue essay, written by Coplans, was also published in the October 1967 issue of Artforum. This seminal exhibition positioned Turrell at the forefront of the Light and Space movement and set the stage for nearly 200 one-person exhibitions and 250 group shows worldwide.
Notable recent one-person exhibitions include James Turrell, held at the Solomon R. Guggenheim Museum, New York (2013) as well as James Turrell: The Light Inside, Museum of Fine Arts, Houston (2013). These exhibitions were held concurrently with James Turrell: A Retrospective, organized by the Los Angeles County Museum of Art, California (2013), which subsequently traveled to the National Gallery of Australia, Canberra (2014), before closing at the Israel Museum in Jerusalem (2015). Another retrospective of his work was exhibited at Mass MoCA, North Adams, Massachusetts (2017). In 2018, The Substance of Light was organized by Museum Frieder Burda, Baden-Württemberg, Germany (2018), and in 2019 Asia Society, Hong Kong, presented Yukaloo.
In addition to twenty-two permanent installations at institutions such as the Henry Art Gallery, Seattle; Fine Arts Museums of San Francisco, de Young Museum; and P.S. 1, Long Island City, New York, James Turrell's work is held in over eighty collections throughout the United States and abroad. In 2009, the Hess Art Collection inaugurated the James Turrell Museum in Colomé, Argentina, housing a collection devoted exclusively to the artist's oeuvre.
Turrell has been the recipient of numerous awards and honors throughout his artistic career, beginning with a grant from the National Endowment for the Arts (1968, 1975). Turrell received a Guggenheim Fellowship (1974), which, coinciding with the loss of his studio, allowed him to initiate the Roden Crater Project. Other awards and honors include The John D. and Catherine T. MacArthur Foundation Fellowship (1984); Chevalier des Arts et des Lettres (1991); as well as the Commandeur de l'Ordre des Arts et des Lettres (2006), conferred by the French government. Turrell was elected member of the American Academy of Arts and Letters, New York (2011), and presented with the National Medal of Arts, awarded by President Obama (2013). In 2017, Turrell received The School of the Museum of Fine Arts at Tufts University Medal Award.
James Turrell has received honorary doctorates from several academic institutions, including the Art Institute of Chicago (1999); Claremont Graduate University, California (2001); Royal Academy of Art, London (2002); and Pratt Institute, New York (2015). Pace has represented James Turrell since 2002. The exhibition is organised in collaboration with Kayne Griffin Corcoran.
Release: Pace Gallery, London
Kulturexpress ISSN 1862-1996


text:
Q: How to implement pagination in backward way eg. first - 20 - 19 - 18 - ... - 3 - 2 - 1 - last How can I implement pagination in backward style, like shown in the title.
(newest) first - 20 - 19 - 18 - ... - 3 - 2 - 1 - last (oldest)
I have search plugin, there are not yet plugin for this.
Because I want static pagination result, for SEO, I hope I'm not wrong with this opinion.
Thank You.
EDIT
I try to explain with sample.
eg.: every page limit by 3 posts and have total 15 posts (total 5 pages).
The position in Blog Home Default is page 5 (because the latest pages are 5).
So the blog home display like this:
(date) - Title
**15** Jan 2011 - A dummy title
**14** Jan 2011 - A dummy title
**13** Jan 2011 - A dummy title
pagination display: (5) - 4 - 3 - 2 - 1

And if I click page 4, content entry not display correctly, it display like this:
(date) - Title
**06** Jan 2011 - A dummy title
**05** Jan 2011 - A dummy title
**04** Jan 2011 - A dummy title
pagination display: 5 - (4) - 3 - 2 - 1

Instead of that, page 4 should be like this:
**12** Jan 2011 - A dummy title
**11** Jan 2011 - A dummy title
**10** Jan 2011 - A dummy title
pagination display: 5 - (4) - 3 - 2 - 1

notes: I also put sample with date, to
  make it clear sample.

How can this will happen without touching the core Wordpress?
Thank You.

A: You could use wp_paginate_links(), pass it the 'type' => 'array' parameter in the argument array, and then array_reverse() the output.
But: if you're doing this for SEO purposes, don't bother. Your canonical URL should be your single-post view, not an archive index; so the index archive page numbers should have minimal SEO impact, no matter what order the page numbering.
EDIT
wp_paginate_links() Codex ref
EDIT 2
If you want to reverse the order of the posts themselves, you'll either need to use a custom query, or modify the default Loop query, likely by changing the $order from DESC to ASC.
EDIT 3
If you just want to reverse the order of the posts themselves, then just change $order from DESC to ASC, and don't do an array_reverse() on the wp_paginate_links() call.


The Redskins faithful enter this 2010 offseason with more questions than answers. How will the player's react to all these new coaches? Will we able able to transition to the 3-4 effectively? Is Chris Samuels going to retire? What will be the fate of Jason Campbell? How will we approach free agency with the possibility of an uncapped year looming? Has Clinton's time run out in D.C.?
Perhaps the most compelling question, if not the most important, is what will new head coach Mike Shanahan do on draft day?
Obviously, no one knows for sure at this point (not even Shanahan), given that we're weeks away from the combine, and months away from the draft itself, but regardless, the mock drafting business is alive and booming.
The general consensus amidst these fortune tellers is that the Redskins will take either a QB or an offensive tackle with their first pick, the #4 overall pick in the entire draft. Can't say I disagree, either. So the burning question is, which way will we go?
Although I often mock the mock draft, and consider such acts of blind prediction as little more than an NFL-themed game of Pin The Tail On The Donkey, I decided to play along this time. After all, it's the offseason, what the hell else am I going to do?
I lost my magic 8 Ball last summer, and my local gypsy was fresh out of tea leaves (she blamed it on the weather, guess she didn't see that storm coming), so I needed to rely on the advice my 3rd grade history teacher once told me. The best way to predict the future is to study the past.
In an effort to get a gauge on Shanahan's positional draft tendencies, I analyzed all 111 draft picks he made during his tenure with the Denver Broncos between 1995 and 2008, breaking each pick down by position to see if anything popped out. Hardly an exact science, but it'll have to do until those tarot cards finally show up.
First, let's look at the raw totals by position. And uh, Carlos? You might want to 'pay attention' to this.
Apparently, if you can run fast, you're Shanahan's guy! In terms of quantity, you can clearly see his tendency to draft DBs, WRs and RBs more than any other position.
In terms of the positions we're interested in (QB and OT), neither were particularly popular. In 13 drafts, only six QBs and five OTs were drafted by Shanahan. Not really the compelling statistical argument I was looking for.
Ah, but what about where they were drafted? After all, quality matters more than quantity in this case, especially when talking about the first pick. Curious to see how Shanahan valued certain positions, I assigned each of those draft picks their appropriate point total from Jimmy's Johnson's dreaded Draft Day Chart.
And that exercise proved to be much more interesting.
So although he rarely drafted a tackle, when he did, he drafted one pretty high. On average, he drafted tackles higher on the board than any other position. Quarterbacks, on the other hand, were middle-of-the-road when compared to other positions.
And how does this translate into Jimmy's draft chart?
Om recently provided us with a nice summary of Nighthawks's "The Shanahan QBs", outlining the QBs that Shanahan drafted or traded for in his career.
It should also be noted that Jamie Brown was the Broncos 1st pick in that 1995 draft. Not sure what happened to their first 3 picks, maybe Vinny was interning for them at the time.
So, what does all this mean? In all honestly, probably nothing. Each draft is unique, each year presents its own needs and directions, not to mention that every one of those draft picks were the result of the collective minds of many, not just Shanahan. Take into account that Bruce Allen is in the mix as well, and all of this draft history in Denver could be completely irrelevant.
But what fun is that? So instead of rationalizing back to the fact that I have absolutely no clue what's going to happen, I have a better idea.
OK I'll try this one more time. I'm a DENVER fan since day one of their existence.
If you have a great GM then you are probably OK IF mikey has his way on draft day you will be looking for a new coach in 5-6 years when he has run the team into the ground.
If you look at his choices while in DEN you see that NONE of his day one choices save TWO LB and a DE made it to a second contract in DEN. Al Wilson and DJ Williams. Both great LB's most of his OLINE were drafted in the later rounds. Because he loved lighter faster guys that could play the ZBS. Most of them were resigned. SOme went on to become FA's .
He loves to take a project guy and change him. IE Matt Lepsis a starting OLT for years until he was hurt and got on drug during rehab. Matt was originally a Small TE. Took WR Shannon Sharpe and made him into a hybrid TE. But he also had many RB that he tried to make into blocking FB's those were generally flops.
Getting back to the DAFT as I called it while he was here.
He DAFTED a lot of DB and WR because for the most part they were all flops.
Oneal, middlebrooks, lelie, nash IMHO were all busts. All number one chocie, HE did do well with Wilson and Williamms both LB's and Pryce as DE but he was a head case once he got his new fat contract he became an issue and had to be constantly called out to get him to perform. and he would for a while then be a no show in games.
The only Bonafide OT that he took was Clady and he has been superb, his other dabble in OLINE day one choices was foster and he had been hurt in his Senior year IIRC and was never the dominant guy he should have been lasted a couple of years and was traded to DET where he failed also.
Mikey knows RB's and did well with cutler but he was a head case IMHO the next Jeff GEorge a coach killer.
mike is a brillant OC but again IMO should not be trusted with overall responsibilities as he has never liked a DC not named Slowick.
He has an EGO and is a micro manager, and expects everyone to be a YES man around him.
I will predict your great Defense of 2009 will all but disappear in two years. With mikey in charge of player aquisitions your in trouble IMHO.
he will put a great O on the field and you may win some games because of it but he will dismantle the D just because the Offense is his forte.


text:
Nude man caught working out at Planet Fitness thought it was 'judgement-free zone,' police say
(FoxNews.com) - A man who was caught naked in a Planet Fitness thought he'd get away with it because the gym considers itself a "judgement-free zone," officials said.
Eric Stagno, 34, of Massachusetts, was arrested on Sunday at a gym in Plaistow, New Hampshire, after gymgoers allegedly caught him exercising in the nude, the New Hampshire Union Leader reported.
Plaistow Capt. Brett Morgan said police were dispatched to the Planet Fitness location after receiving a call from witnesses that summarily said, "the guy walked in, stripped down right there in front, left the clothes and belongings at the front desk, walked back and forth across the gym a couple of times and then settled in over at the yoga mats."
The police captain added that Stagno was in a "yoga-type pose" when authorities arrived.
Stagno said "he thought it was a judgement-free zone, apparently referencing [the gym's] slogan."
Gymgoers reported feeling "sick," "unsafe" and "disgusted" by Stagno's alleged actions.
Stagno was taken into custody without incident and charged with indecent exposure, lewdness and disorderly conduct, according to the news outlet.
He was reportedly released on $1,000 bail and is scheduled to appear in court on Sept. 21.
More on FoxNews.com.


Through striking illustrations and stunning photographs,Bohemian Modern explores the unique structural and interior designs that have put California's ultra-chic Silver Lake neighborhood at the forefront of a new style phenomenon.
One of the country's most renowned modernist architects, Barbara Bestor has fully embraced and perfected Silver Lake's "bohemian modern" style: a practical philosophy that is Californian in origin but achievable anywhere. It is a look that favors raw, authentic materials, brilliant colors, creative space planning, and a natural flow between indoors and outdoors.
The results, as Bohemian Modern presents, are striking: a flawlessly restored Neutra house decorated with both whimsy and restraint, a rooftop constructed for viewing the stars, a lavish outdoor garden delicately integrated into the surrounding architecture, a double-sided bookcase that soars three stories and serves as a functional art installation...there is no limit to the creativity and beauty of Silver Lake style.
Both modern and classic, refreshing and inviting, Bohemian Modern will delight readers with its breathtaking, vividly photographed tour of Silver Lake.