text:
Zimou Tan
View ARC Gallery
Zimou Tan, Artist
Zimou Tan was named "Chinese Master Artist" in December of 2007 by the Chinese government
Art and Culture department, also selected as "The 50 Artists impacted China". Mr. Tan graduated
from the Academy of Art College in San Francisco in 2000. There he earned degrees in both
Traditional Illustration and Fine Arts.
2003, won the Northern California Gold Metal portrait painting competition.
2005, Gold Medal Winner at the Hudson Valley Art Association's 74th Annual Exhibit.
2006, won Best of Show at the Morro Bay Art Association Group Exhibit.
Tan has exhibited with the California Art Club's Gold Medal and Group Exhibits, New York
Portrait Society Gallery in New York City; American Artists Professional League Grand National
Exhibition in the New York City; de Young Museum, San Francisco, California, and Asia Society
of Arts of America, San Francisco, California. 20th Annual Marin Open Studio as the main feature
artist at Corte Madera, California.
Tan is a member of the California Art Club, Oil Painters of America, American Society of
Portrait Artists. Zimou's work has been featured in "International Artist Magazine" "Art of the
West Magazine", "The artist magazine", "Stroke of Genius", "Chinese Master Artist", "The 50
Artists inputted China". Art Renew Center 2014, 2015, 2016 Finalist. First world watercolor
finalist 2014, Collection Beaux-Arts Réaliste's first prize. 2014. Grand prize winner for
International Artist Magazine portrait and figure challenge 2015.
2017, honored as ARC Associate Living Master.
Art is life, life is art, life without art is meaningless.


Tasting Profile / Serving Suggestions: From Jerez, Spain, and made with the traditional Solera method, our Sherry Reserva 25 year aged Wine Vinegar, has a true sherry flavor that can only come from cask-aging. This fine cask-aged Spanish Sherry wine vinegar is fabulous in reduction sauces, glazes, soups, stews, casseroles, and wherever strength and depth of flavor is desired. Blend with our fresh Extra Virgin Olive Oils for a stunning vinaigrette!


A classical women's rain coat made of 100% recycled Polyester with coating in 100% PVC free PU. Erna is as suitable for the countryside as it is for the city. With its heat-welded seams, it's completely water-resistant and does not compromise on either fashion or function. Erna Rain Coat has a comfortable inner lining, metallic press buttons and a two-way zip for improved movement. Practical pockets and an adjustable hood provide the jacket with good functionality and a timeless silhouette. A classic that one can return to season after season. Erna 2.0 Raincoat belongs to the ECO Essentials range.


The new Creamery District Art Market is seeking vendors. The Art Market is held at 1251 9th Street in Arcata, from 9 am to 1 pm, on the last Saturday of each month from May through November. The first Art Market will be held on May 31, 2014. This is an outdoor venue with a concrete or asphalt floor.
Coffee and food booths supply refreshments and a local band plays from 10 am to 1 pm.
The Market has a focus on "The Arts" in the broadest sense of the idea, with the sale of the following types of items: all types of artwork, crafts, culinary and homemaking arts, handmade items, jewelry, decorative art and furniture, art collectables, clothing, performance art items, music and musical instruments.
Any vendors or musicians are encouraged to contact the Creamery District Art Market Manager at CreamryInfo@gmail.com or use the online application process links below.


text:
Q: Excel bar chart with two y axis & split areas I have data like this.
source table
I would like to make a bar chart like below.
Chart I want
The problem is that since Dioxin ranges are way smaller than the others,
I can't look at the dioxin data properly.
In order to solve this problem, I would like to separate the chart areas into two and make a line between two areas with ranges of Dioxin shown in the right y axis.
How do I do this?

redpajama_set_name: RedPajamaStackExchange


Recent project completed by Quill Construction Ltd, 7 days start notice to complete within 17 weeks, completed within 12 weeks and within budget. Another very happy client.
Quill Construction Limited has a large client base, carrying out maintenance and refurbishment projects to commercial and private clients. During the last sixteen years Quill Construction Ltd have demonstrated a professional, reliable and friendly service to all our clients.
© 2017 Quill Construction. All Rights Reserved. Design by Safetech LTD.


text:
local MemoryStream = require("MemoryStream");
local NetStream = require("NetStream")
local HttpRequest = require ("HttpRequest");
local URL = require ("url");
local zlib = require "zlib"

local RemoteScreen = {}
setmetatable(RemoteScreen, {
	__call = function(self, ...)
		return self:create(...);
	end,
});

local RemoteScreen_mt = {
	__index = RemoteScreen,
}

RemoteScreen.init = function(self, url)
	local urlparts = URL.parse(url, {port="80", path="/", scheme="http"});

	local obj = {
		Url = urlparts;
		Path = urlparts.path;
		Port = urlparts.port;
		ScreenRequest = HttpRequest.new("GET", urlparts.path, {["Host"] = urlparts.host,}, nil),
		ZStream = MemoryStream.new(256*1024);
		ImageStream = MemoryStream.new(1024*1024*1024);
	}

	setmetatable(obj, RemoteScreen_mt);

	-- make connection
	obj.Connection = NetStream.Open(urlparts.host, urlparts.port);
	if not obj.Connection then
		return nil
	end

	return obj
end

local readImage = function(stream)
	local bs = BinaryStream.new(stream);

	-- Read File Header
	local bm1 = bs:ReadByte()
	local bm2 = bs:ReadByte()
	local filesize = bs:ReadInt32();
	bs:ReadInt16(0);
	bs:ReadInt16(0);
	local pixeloffset = bs:ReadInt32();

	-- Bitmap information header
	local bminfo = ffi.new("BITMAPINFO");
	bminfo.bmiHeader.biSize = bs:ReadInt32();
	bminfo.bmiHeader.biWidth = bs:ReadInt32();
	bminfo.bmiHeader.biHeight = bs:ReadInt32();
	bminfo.bmiHeader.biPlanes = bs:ReadInt16();
	bminfo.bmiHeader.biBitCount = bs:ReadInt16();
	bminfo.bmiHeader.biCompression = bs:ReadInt32();
	bminfo.bmiHeader.biSizeImage = bs:ReadInt32();
	bminfo.bmiHeader.biXPelsPerMeter = bs:ReadInt32();
	bminfo.bmiHeader.biYPelsPerMeter = bs:ReadInt32();
	bminfo.bmiHeader.biClrUsed = bs:ReadInt32();
	bminfo.bmiHeader.biClrImportant = bs:ReadInt32();

	-- Read the actual pixel data
	--local pixelarray = ffi.new("uint8_t[?]", bminfo.bmiHeader.biSizeImage);
	--stream:ReadBytes(pixelarray, bminfo.bmiHeader.biSizeImage, 0);

	return bminfo, pixeloffset;
end

RemoteScreen_t.GetCurrentImage = function(self)
	-- issue the request
	local success, err = self.ScreenRequest:Send(self.Connection);

	--print("GetCurrentImage: ", success, err);

	if not success then
		return false, err
	end

	-- get the response
	local response, err = HttpResponse.Parse(self.Connection);

	self.ZStream:Seek(0);
	local result

	if response then
		-- successful read of preamble
		-- so print it out, and keep out of queue
		--response:WritePreamble(sout);

		for chunk, err in chunkiter.ReadChunks(response) do
			-- read chunks stuffing into memory stream
			self.ZStream:WriteString(chunk);
		end
		result = "OK";
	else
		result = "FAILURE:"..tostring(err);
	end

--print("Bytes in ZStream: ", self.ZStream:GetPosition(), result);
	-- unzip it
	local destLen = ffi.new("unsigned long[1]", self.ImageStream.Length);
	local err = zlib.uncompress(self.ImageStream.Buffer, destLen, self.ZStream.Buffer, self.ZStream:GetPosition());

--print("UNCOMPRESS: ", err, destLen[0])

	if err ~= 0 then
		return nil, err
	end

	self.ImageStream.BytesWritten = destLen[0];
	self.ImageStream:Seek(0);

	local info, pixeloffset = readImage(self.ImageStream)
	if not info then
		return nil, pixeloffset
	end

	local pixels = self.ImageStream.Buffer + pixeloffset;

	return info, pixels
end

return RemoteScreen

redpajama_set_name: RedPajamaGithub


text:
McCain's Missing Middle
Posted: Aug 21, 2008 5:38 PM
Barack Obama has been flailing in response to the McCain campaign's new aggressiveness, consumed by crying foul. He's gone from hopemonger to whinemonger.
Hypersensitive and defensive, Obama seems obsessed with campaign tactics. This not only plays into the notion that he isn't tough enough, it keeps him from engaging forcefully on substance, especially on domestic issues where he has an advantage.
For the way ahead for Obama, don't look to his tetchy stump performances, but to his paid advertising. Obama's TV ads have taken clear aim at one of John McCain's vulnerabilities -- the Republican offers less direct tax relief to the middle class.
Obama's spots say he'll put "the middle class first," that he'll "cut taxes for working families," that he has "a plan that cuts taxes for middle-class families three times as much as John McCain would," and under McCain, "100 million Americans get no tax relief at all."
Even though their image is a shambles, Republicans are sending onto the field a presidential candidate who perhaps has the least to offer middle-class voters on taxes since the first George Bush in 1992. Of course, Bush lost that year to a Democrat promising only to raise taxes on "the rich" and to cut them for the middle class -- exactly Obama's position now.
In his primary campaign, McCain had to endorse the extension of the Bush tax cuts he voted against in the Senate. But they have turned into a trap. Because Obama also wants to extend the middle-class aspects of the Bush cuts, McCain is left alone plugging for extending the cuts on higher earners. The same McCain who "in good conscience" opposed the original Bush package because it was too skewed to upper-income groups now is supporting a package that, relative to Obama's plan, is even more skewed to the top.
Not to mention the Bush tax cuts have the obvious branding problem of the moniker "the Bush" tax cuts. McCain might be worse off if they were called "the Hoover" tax cuts, but not by much.
McCain has gamely tried to play offense on taxes, although the Obama plan is a diminishing target. Obama has steadily focused his tax increases on families making $250,000 a year or more. In one ad, McCain had to reach back to an Obama vote on a budget resolution to accuse him of wanting to increase the taxes of people making as little as $42,000 a year -- never mind that Obama plans to cut their taxes now.
Obama's middle-class tax cuts are a dog's breakfast of credits and giveaways. McCain's camp is right that the proposals are pandering, and that the Obama approach taken in its totality -- higher taxes on investment, health-care mandates, massive new spending, etc. -- will dampen growth.
But when it comes to the economy, it's a cost-of-living election, with health-care costs and energy prices eroding workers' wages. This is why McCain's new support for offshore drilling has had such resonance. On the tax front, he offers families a doubling of the dependent exemption from $3,500 to $7,000. That sounds like a lot, but people down the income scale don't pay enough in income taxes for the exemption to make much of a difference, and for everyone else, McCain fully phases it in only in 2016.
Unless McCain finds a way to get more relief to middle-class families, he has an enormous weakness. Policy doesn't win or lose elections alone, but it can interact powerfully with a candidate's narrative. One of Obama's best defenses against the notion that he's a celebrity-candidate aloof from average Americans is a tax plan that says he feels their pain. One of McCain's greatest risks of being branded "Bush III" and just a typical Republican is a tax plan crafted long ago by George W. Bush and tilted toward upper-income brackets.
McCain can still adjust, and he'd better. Obama can't be counted on to complain all the way to November.

redpajama_set_name: RedPajamaCommonCrawl


You are here: Home / Blog / Friends of i3 Detroit / i3's toys at Yelp's party: Friday!
i3's toys at Yelp's party: Friday!
When you're Yelp, and you're throwing a big party at the Fillmore, where do you look for entertainment? A bunch of local outfits, including i3 Detroit, of course! We're bringing the giant Connect Four and beanbag toss (I refuse to call it that other name), and Robot Fight Club, and the Squiggletrike, and maybe even the dangerously fast Power Wheels car #3.
It's more than just a party, of course: Yelp is raising funds to support a Detroit charity: "Real Life. My Music. is a nonprofit organization created to provide a musical and artistic outlet to inner city youth in Detroit". Considering all the education cuts, that sounds like it fills an important gap!
It's happening this Friday, 9/7. All the details are over at Yelp, and while the shindig is free, you have to RSVP to get in, so just go do that already. Oh, and bring a few bucks for the fundraiser. See you there!
https://www.i3detroit.org/wp-content/uploads/2014/03/Logo_large_png8-300x98.png 0 0 Nate Bezanson https://www.i3detroit.org/wp-content/uploads/2014/03/Logo_large_png8-300x98.png Nate Bezanson2012-09-06 16:16:462012-09-06 16:16:46i3's toys at Yelp's party: Friday!


The River XY is located in the south eastern part of Country XY. As a practical example on risk mapping the Town XY is chosen. Town XY was often affected by flood in the last years. Wide areas were inundated by the River XY and from groundwater.
As result from rainfall runoff modelling and hydraulic simulations inundation maps have been created. Furthermore information on the land use types of the flooded areas have been collected.
Supported by an inundation map the endangered areas can be located. The graphic below shows the extent of flooding caused by HQ 100. Considering more than one scenario allows to calculate different levels of risk.
The intersection of inundated areas and affected land use types can easily be realised using a geographical information system.
In Germany for example there are different sources where you can get survey data. Please use the link to get more information.


RE: The tariff classification of a cushion hairbrush, plastic jaw clips, and ponytail holders packed in a magazine rack caddy from China.
In your letter dated March 14, 2003, you requested a tariff classification ruling.
The submitted sample, Model 59219 22 Pc. Plastic PVC Magazine Rack with Hair Accessories consists of one cushion hairbrush valued at $0.55, one 9cm regular frosted plastic jaw clip, one 3 ½" x 18" interlock twister, two large terry ponytailers, fourteen thick no damage elastic ponytail holders, and two 6cm frosted plastic jaw clips packed in a plastic magazine rack shaped caddy.
Although sold as a set, the above items are not classifiable as a set because not all the items in the set are designed to meet a particular need or carry out a specific activity. The magazine rack shaped caddy, which is a separate article of commerce and more than just a packing container, has a different use than the other items in the set. Consequently, each item is classifiable separately under its appropriate heading.
The applicable subheading for the cushion hairbrush will be 9603.29.8010, Harmonized Tariff Schedule of the United States (HTS), which provides for "Hairbrushes valued over 40 cents each." The rate of duty will be 0.3 cents each plus 3.6% ad valorem.
The applicable subheading for the 9 cm and the 6 cm plastic jaw clips will be 9615.11.4000, HTS, which provides for "Combs, hair-slides and the like: Of hard rubber or plastics: Other: Not set with imitation pearls or imitation gemstones." The rate of duty will be 5.3% ad valorem.
The applicable subheading for the interlock twister and the large terry ponytailer will be 6117.80.8500, HTS, which provides for "Other made up clothing accessories, knitted or crocheted: knitted or crocheted parts of garments or of clothing accessories: Other accessories: Other: Headbands, ponytail holders and similar articles." The rate of duty will be 14.7% ad valorem.
The applicable subheading for the elastic ponytail holders will be 5609.00.4000, HTS, which provides for "Articles of yarn, strip or the like of heading 5404 or 5405, twine, cordage, rope or cables, not elsewhere specified or included: Other." The rate of duty will be 4.3% ad valorem.
The applicable subheading for the magazine rack shaped caddy case will be 3924.90.5500, HTS, which provides for "Other household articles of plastics, other, other." The rate of duty will be 3.4% ad valorem.


Give your best buddy a hearty meal with the Pedigree Chopped Ground Dinner With Chicken Canned Dog Food. Made with a ground texture that will make his mouth water, this meaty feast contains optimal levels of oils and minerals to promote healthy skin and a glistening coat. The balanced recipe is also highly digestible, which means your dog can easily absorb all those essential nutrients—so you can feel confident that he's getting the most out of every meal. But all he'll really care about is the irresistible flavor, which is sure to get a tail wag at every mealtime.
Features a hearty chopped texture with an irresistible taste of ground chicken.
Sufficient Water For Processing, Chicken By-Products, Chicken, Meat By-Products, Liver, Brewers Rice, Minerals Potassium Chloride, Magnesium Proteinate, Zinc Sulfate, Copper Lysine Complex, Copper Sulfate, Potassium Iodide, Vegetable Oil Source Of Linoleic Acid, Sodium Tripolyphosphate, Carrageenan, Dried Yam, Guar Gum, Xanthan Gum, Natural Flavor, Onion Powder, Bay Leaves, Vitamins Vitamin E Supplement, Vitamin A Supplement, Vitamin D3 Supplement, D-Calcium Pantothenate, Thiamine Mononitrate Vitamin B1, Biotin, Yellow #6, Garlic Powder, Yellow #5.
Feed 1/2 - 3/4 can daily per 10 lbs of Adult dog. Refrigerate after opening (up to 3 days). Serve at room temperature.
Introduce your dog to his new food gradually. For the first 2 days or so, his meals should be about 25% new food and 75% old food. Mix the 2 foods together, don't keep them separated.
My 5lb rescue chihuahua is super picky and this is his absolute favorite wet food. The vet has him on Royal Canin Satiety to keep his weight down because of patellar luxation - I mix the RC kibble with a spoonful of the Pedigree, and it takes him about 20 seconds to wolf down his whole meal I'm not joking. He just loves it to bits. He's looking really good right now with bright eyes, a lustrous coat, good energy levels and firm stool that's not smelly so I'm quite satisfied with the combo of Pedigree with the kibble.
Purchased to mix w/ dry food. Big cans/ plenty to mix. Great deal overall. This wet food mixed w/ high grade dry food makes for a big healthy meal for 2 German Shepherds and a Dutch Shepherd. Warm water dilutes gel for a flavorful meal that is devoured by these dogs !
Used to add flavor and wet texture to my dry dog food. The ground chicken made my dogs runny. Prefer beef and beef mixed flavors they do better on. May still be fine for other dogs, good luck!
My dogs love this, only wish there were smaller cans. Price is good, delivery to door keeps me from lugging it to the house from the store.
Dogs love this food. I only feed them chicken. One of my dogs is getting old. She has to eat chicken only.
I mix some with my dogs dry food and this seems to work well. He seems to like it and finishes it, rather than leaving bits in his dish. So I am happy he is happy!
chewy as always provides great service. I wish this variety came in the smaller 13.2 oz. cans. Other than that I always recommend Chewy.
I have tried over 50 different brands of canned dog food on my girls the past year and a half. From the most expensive to the least. They will eat this form and flavor of Pedigree food. Happy Human Here.
pixie my 4 year old Pomeranian just loves it . she inhales it in 3 seconds.


Have you tried space hopper races in the sand, tried to paddle on Childrens inflatable's?
If you are looking for a summer job in Benidorm then why not think about working for Club-it and joining our pr team. Club-it Benidorm has been promoting pubs and clubs since 1997 and this Benidorm job offers you the chance to have fun and earn money at the same time. It is the job of Club-it reps to work on a one to one level with holiday makers and most importantly sell tickets for Club-it events.
If you have never sold anything in your life don't worry as sales experience although a benefit for this job, it is not essential. What we are looking for is enthusiastic young people who are highly motivated, lively, willing to work on their own initiative and have good interpersonal skills. You will spend your day taking to people on holiday and bringing them on Club-it events so you will need to have an outgoing personality and be up for a laugh.
It is important to state that Club-it does not bring people over especially to work for us.
This job in Benidorm is advertised simply to let those know, who have decided to come to Benidorm looking for a job this year that we have positions available. Everyone who starts with Club-it starts on a trial basis. In our experience it is difficult to tell whether someone has what it takes to do this job until they try it out and so this job is available to anyone who is either in Benidorm or coming to Benidorm this summer. The job is very rewarding for the ideal candidates with many staff returning over the years to work for Club-it. You will have the freedom to work all around Benidorm and the hours are made to suit the party lifestyle.
Email: A value is required.Invalid format. Confirm Email: A value is required.The values don't match.
A value is required. Date Of Birth: A value is required.
Work From: A value is required. Work Until: A value is required.
Have you worked abroad before? A value is required. Do you have sales experience?
You will recieve an instant response so don't forget to check your JUNK MAIL if you don't receive a reply.
Once we have received your job application we will send you information on working in Benidorm and hopefully answer many of your questions.


text:
01 - Maggie's Song
02 - Only Dreaming
03 - Floating On The Water
04 - So Good Looking Man
05 - To Find A Better Life
07 - Mission To The Sun
08 - Take Me With You
09 - Cocoa Butter Ice Cream
10 - Wait for Your Peace
Birdsong was founded to commemorate the spirit and memory of musician David Martin, who died at the age of 30 from mental illness due to a lack of treatment options in Canada. David's love and passion were for songwriting and performance, and he was never far from his guitar throughout his short life.
David's songwriting and performance spans two decades. After his death, his computer and hard drives were taken to Vancouver by his mother to be re-recorded and mastered for release by Nygel Asselin of NYG Productions in Vancouver. The result is Waves of Existence — a beautiful, 9-song collection.
David wrote about his music in 2017; saying: "Recorded in my bedroom with the onboard mics of a ZOOM R16, I write simple songs about awakening loving awareness through higher levels of consciousness. Using earthly themes I like to paint subtle soundscapes that invoke the beauty of an ever-evolving Universe. The sound of the water, the call of the ocean, life in the waking dream — it's a musical journey through the Waves of Existence."
100% of the proceeds from the sale of the songs and album will be donated to the Charity Foundation.
A special thanks to singer Vanessa Peterson for her donation to Birdsong. Her voice is recorded on many of the songs on this album.
The cover art for Waves of Existence was painted by Dane Atkinson, who writes:
"I painted this portrait of David because I wanted to give something back. David was an inspiration to me and a great friend. I loved him with all my heart. He loved music more than anyone I have ever met."


We may issue a notice to property owners to remove rubbish and cut long grass, which could represent a potential fire hazard or is detrimental to the general amenity of the neighbourhood.
If you receive a notice from us there will be a timeframe defined — generally 14 days is provided to allow you to comply.
If the property is still not complying, we may arrange to have the property cleared and all expenses incurred by us will be borne by the owners of the property. Infringement notices may also be issued to property owners who have not complied with notices issued.


ALL ABOUT THEM. It was always about them, not about us. It was about bailing out Wall Street fat cats who play fast and loose with other people's money. What American citizen wants to bail out Wall Street? Not many, you can be sure. But that's how our nationally elected and appointed government officials framed the issue. It's no wonder the bill failed to pass in the U. S. House of Representatives Monday afternoon.
A PROBLEM MISCAST. Better leaders would have recognized immediately that many people despise Wall Street and the most mildly endure it's wranglings and excesses. More authentic leaders would have cast the problem and their solution in a very different scenario, in very different terms. They would have started and looked at the issues from a very different place.
MAIN STREET & ELM STREET. They would have started with ordinary citizens. They would have looked at problem from our perspective. They would have talked more, as Barack Obama has, about Main Street. Or, as I heard one Representative describe, "Elm Street." They would have started with the challenges every household faces and drawn out the implications of credit there. They would have taken us to the very roots of the problems in graphic terms. They would have spoken firmly and specifically about reforms intended to curb greed and abuse in the future.
TOO MUCH PRESSURE. But, in the 10 days since President Bush made his $700 billion request, no leader ever got around to doing that. Not once did I hear anyone come to the mainstream media with a clear, household-based explanation or appeal. No one tried to convince me--and I was very interested in finding out about it in a way I could get on board. Why didn't they talk straight to us? Because they wouldn't? Or because they couldn't? They didn't, and House Representatives got their ears--and e-mails--full of Americans who told them 100 to 1 that a "Yea" for this bill would mean a "Nay" at the ballot box in November. So, we're back to square one.
Thanks for saying it so well, again! Another example of why U.S. Americans feel distant from and deserted by those in Washington. Who knows the final result of the failure to pass this measure. The possibilities are intimidating. For better or worse, it seems THE PEOPLE were heard and represented, while the street and the government assumed their ineptitude.
David, this morning (Tuesday, Sept. 30) I heard a better perspective than Main Street from which the financial challenges facing our nation might be articulated: "Elm Street" or "Oak Street." These are the streets where we reside, where we live. Cast the scenario of challenge from this perspective.


text:
Community Programs & Events
Get Together at Old Town School!
Whether you have two hours or two months to spend with us, you're invited to be part of a vibrant community filled with people from all walks of life to make music, dance and the arts part of their lives every day. You belong here no matter what you like, what you already know or what you want to do! Many of our jams and showcases are free of charge and open to the public.
Class Session Dates
Session 1 2021
JAN 4 - FEB 28, 2021 · 8-week classes
Low-commitment and hands-on!
Browse Workshops
We're all over Chicagoland performing near you!
View Community Events
Free Jams
Join the circle with your favorite instrument or your voice
About Weekly Jams
Martin Luther King, Jr. Day Celebration - Monday, January 18
Join us for songs of peace and justice in a special Wiggleworms Mixtape and singalongs for all ages. Be inspired by the words of Dr. Martin Luther King, Jr., and gather in community as we celebrate the life and philosophy of this great American leader.
Don't miss out on some exciting online workshops with Badi Assad, Over the Rhine, Michael J. Miles & more.
See all upcoming online workshops
Old Town School Student Advisory Council
Learn about the Old Town School Student Advisory Council – a newly formed student group that serves to support and extend the school's mission.
Eric Johnson Electric Guitar Workshop: Top 10 Tips for Getting The Best Tone
Surrendering Into Song: A Joe Henry Songwriting Workshop Discussion
Surrendering Into Song: A Joe Henry Songwriting Workshop Series
Dylan Songbag: You Don't Need a Weatherman
Songwriter's Circle ONLINE with Daniel Connolly
British Rock Songbag w/ Tommi Zender
Our free daily jam. A tradition from the early days of Old Town School, gather together with other core guitar students after class for a jam led by teachers featuring songs from the songbook and pop classics. Location and times change depending on the day of the week and is free of charge.
Second Half Schedule
Most classes in our catalog end the session with a performance onsite at the school or offsite at some of Chicago's finest venues, bars, restaurants and community centers. It's a chance for students and ensembles of all skills and types to show off what they've been working on.
This Session's Showcase Schedule
Bringing the joy and power of the arts to over 2,500 school children each year through programs like Artists-In-Residence, Wiggleworms-In-Residence and Guitars For Growth. Your support through classes and concerts help provide quality arts education for Chicagoland students of all ages.
About Our School Partnerships & Sponsors
Music Moves Chicago
Old Town School's Arts & Community Wellness Initiative. This youth-focused, intergenerational program aims to provide individuals and organizations with creative resources necessary to build healthy, intentional arts-based communities across Chicago's 77 neighborhoods.
Teen Open Mic
An open mic just for teenagers, Ages 13-19. Join a supportive and fun atmosphere to celebrate everyone's contributions and welcome new performers to the community. All are welcome, whether you're a singer-songwriter, spoken work artist, vocalist or a full-fledged band, we'd love to have you on stage!
Teen Open Mic Schedule & Info
A great way to get more involved with Old Town School, supporting our mission providing arts education for everyone. Member benefits include class discounts, gifts and early access for concert tickets.
Hire Our Artists
Our artist engagement services offers a wide array of entertainment for any occasion by talented, professional artists. Everything from soloists to 11-piece bands, we can help take your event to the next level and help support the school at the same time!
Housed in the basement of our Lincoln Avenue campus, the Resource Center holds a collection of over 25,000 commercial and archival recordings, videos, books, photos, documents and periodicals.
Directions & Campuses
You have to get here to be here! Easy access to our campuses via public transportation in both Lincoln Square and Lincoln Park.
Songs Of The Session
A tradition since 1957, each session two songs are chosen, one from the songbook and one from the general repertoire. All classes are encouraged to learn these tunes and apply the skills they are working on at their individual levels.
Meet Our Teaching Artists
Over 250 of the most talented artists in Chicago ready to inspire you in classes and lessons!


I love this ring so much! Always getting complements and sits beautifully! So glad my partner got it. We did get it at $2500. They do go up in price nearly every six months which is annoying so get in asap!


text:
\section{Conclusions}

We have proposed a method which combines CNNs and CRFs to exploit complex
contextual information for semantic image segmentation.
We formulate CNN based pairwise potentials for modeling
semantic relations between image regions.
Our method shows best performance on several popular datasets including the PASCAL VOC 2012 dataset.
The proposed method is potentially widely applicable to other vision tasks.

\paragraph{Acknowledgments}
This research was supported by the Data to Decisions
Cooperative Research Centre and by the Australian Research Council
through the Australian Centre for Robotic Vision (CE140100016).
C. Shen's participation was supported by  an ARC Future Fellowship (FT120100969).
I. Reid's participation was supported by an ARC Laureate Fellowship (FL130100102).

C. Shen is the corresponding author (e-mail: chunhua.shen@adelaide.edu.au).

{\small
\bibliographystyle{ieee}

\section{Experiments}

We evaluate our method on $4$ popular semantic segmentation datasets: PASCAL VOC 2012, NYUDv2, PASCAL-Context and SIFT-flow.
The segmentation performance is measured by the intersection-over-union (IoU) score \cite{everingham2010pascal}, the pixel accuracy and the mean accuracy \cite{LongSD14}.

The first $5$ convolution blocks and the first convolution layer in the $6$th convolution block are initialized from the VGG-16 network \cite{simonyan2014very}.
All remaining layers are randomly initialized. All layers are trained using back-propagation/SGD.
As illustrated in Fig.~\ref{fig:pairwise},
we use $2$ types of pairwise potential functions.
In total, we have 1 type of unary potential function and 2 types of pairwise potential functions.
We formulate one specific FeatMap-Net and potential network (Unary-Net or Pairwise-Net) for one type of potential
function.
We apply simple data augmentation in the training stage;
specifically, we perform random scaling (from $0.7$ to $1.2$) and flipping of the images for training.
Our system is built on MatConvNet \cite{matconvnet}.

\subsection{Results on NYUDv2}

We first evaluate our method on the dataset NYUDv2 \cite{silberman2012indoor}.
NYUDv2 dataset has 1449 RGB-D images. We use the segmentation labels provided in \cite{gupta2013perceptual} in which labels are processed into $40$ classes.
We use the standard training set which contains $795$ images and the test set which contains $654$ images.
We train our models only on RGB images without using the depth information.

Results are shown in Table \ref{tab:nyud}.
Unless otherwise specified, our models are initialized using the VGG-16 network.
VGG-16 is also used in the competing method FCN \cite{LongSD14}.
Our contextual model with CNN pairwise potentials achieves the best performance, which sets a new state-of-the-art result on the NYUDv2 dataset.
Note that we do not use any depth information in our model.

\paragraph{Component Evaluation}
We evaluate the performance contribution of different components of the FeatMap-Net for capturing patch-background context on the NYUDv2 dataset.
We present the results of adding different components of FeatMap-Net in Table \ref{tab:featmapnet}.
We start from a baseline setting of our FeatMap-Net (``FullyConvNet Baseline" in the result table), for which multi-scale and sliding pooling is removed.
This baseline setting is the conventional fully convolution network
for segmentation, which can be considered as our implementation of the FCN method in \cite{LongSD14}.
The result shows that our CNN baseline implementation (``FullyConvNet") achieves very similar performance (slightly better) than the FCN method.
Applying multi-scale network design and sliding pyramid pooling significantly improve the performance,
which clearly shows the benefits of encoding rich background context in our approach.
Applying the dense CRF method \cite{krahenbuhl2012efficient} for boundary refinement gains further improvement.
Finally, adding our contextual CNN pairwise potentials brings significant further improvement, for which we achieve the best performance in this dataset.

\begin{table}[t]
\caption{Segmentation results on NYUDv2 dataset (40 classes).
We compare to a number of recent methods.
Our method significantly outperforms the existing methods.}
\centering
\resizebox{1\linewidth}{!}
  {
  \begin{tabular}{ r | c | c c c }
method  &training data  &pixel accuracy &mean accuracy  &IoU\\ \hline \hline
Gupta et al. \cite{gupta2014learning}   &RGB-D  &60.3 &-  &28.6\\
FCN-32s \cite{LongSD14} &RGB  &60.0 &42.2 &29.2\\
FCN-HHA \cite{LongSD14} &RGB-D  &65.4 &46.1 &34.0\\ \hline
ours  &RGB  &\bf 70.0 &\bf 53.6 &\bf 40.6\\
 \end{tabular}
  }
\label{tab:nyud}
\end{table}

\begin{table}[t]
\caption{Ablation Experiments. The table shows the value added
by the different system components of our method on the NYUDv2 dataset (40 classes).
}
\centering
\resizebox{1\linewidth}{!}
  {
  \begin{tabular}{ r | c c c }
method  &pixel accuracy &mean accuracy  &IoU\\ \hline \hline
FCN-32s \cite{LongSD14} &60.0 &42.2 &29.2\\ \hline
FullyConvNet Baseline  &61.5 &43.2 &30.5\\
$+$ sliding pyramid pooling  &63.5 &45.3 &32.4\\
$+$ multi-scales  &67.0 &50.1 &37.0\\
$+$ boundary refinement &68.5 &50.9 &38.3\\
$+$ CNN contextual pairwise &70.0 &53.6 &40.6\\
 \end{tabular}
  }
\label{tab:featmapnet}
\end{table}

\begin{table*}[t]
\caption{Individual category results on the PASCAL VOC 2012 test set (IoU scores). Our method performs the best}
\centering
\resizebox{1\linewidth}{!}
  {
  \begin{tabular}{ r | c c c c c c c c c c c c c c c c c c c c | c }

method & \rot{aero}  &\rot{bike} &\rot{bird} &\rot{boat} &\rot{bottle}   &\rot{bus}  &\rot{car}  &\rot{cat}  &\rot{chair}    &\rot{cow}  &\rot{table}    &\rot{dog}  &\rot{horse}    &\rot{mbike}    &\rot{person}   &\rot{potted}   &\rot{sheep}    &\rot{sofa} &\rot{train}    &\rot{tv}  & mean \\ \hline \hline
\multicolumn{22}{c}{\bf Only using VOC training data} \\ \hline
FCN-8s \cite{LongSD14}    &76.8   &34.2   &68.9   &49.4   &60.3   &75.3   &74.7   &77.6   &21.4   &62.5   &46.8   &71.8   &63.9   &76.5   &73.9   &45.2   &72.4   &37.4   &70.9 & 55.1 & 62.2 \\
Zoom-out \cite{MostajabiYS14}  &85.6 &37.3 &\bf 83.2 &62.5 &66.0 &85.1 &80.7 &84.9 &27.2 &73.2 &57.5 &78.1 &79.2 &81.1 &77.1 &53.6 &74.0 &49.2 &71.7 &63.3 &69.6\\
DeepLab \cite{ChenPKMY14} &84.4 &54.5 &81.5 &63.6 &65.9 &85.1 &79.1 &83.4 &30.7 &74.1 &59.8 &79.0 &76.1 &83.2 &80.8 &59.7 &82.2 &50.4 &73.1 &63.7 &71.6 \\
CRF-RNN \cite{zheng2015conditional} &87.5 &39.0 &79.7 &64.2 &68.3 &87.6 &80.8 &84.4 &30.4 &78.2 &60.4 &80.5 &77.8 &83.1 &80.6 &59.5 &82.8 &47.8 &78.3 &67.1 &72.0 \\
DeconvNet \cite{noh2015learning} &89.9 &39.3 &79.7 &63.9 &68.2 &87.4 &81.2 &86.1 &28.5 &77.0 &62.0 &79.0 &80.3 &83.6 &80.2 &58.8 &\bf 83.4 &54.3 &80.7 &65.0 &72.5\\
DPN \cite{LiuDPN}  &87.7  &\bf 59.4 &78.4 &64.9 &70.3 &89.3 &83.5 &86.1 &31.7 &79.9 &\bf 62.6 &81.9 &80.0 &83.5 &82.3 &60.5 &83.2 &53.4 &77.9 &65.0 &74.1\\
ours &\bf 90.6  &37.6 & 80.0 &\bf 67.8 &\bf 74.4 &\bf 92.0 &\bf 85.2 &\bf 86.2 &\bf 39.1 &\bf 81.2 & 58.9 &\bf 83.8 &\bf 83.9 &\bf 84.3 &\bf 84.8 &\bf 62.1 & 83.2 &\bf 58.2 &\bf 80.8 &\bf 72.3 & \bf \cellcolor[gray]{0.85} 75.3 \\ \hline \hline
\multicolumn{22}{c}{\bf Using VOC+COCO training data} \\ \hline
DeepLab \cite{ChenPKMY14} &89.1 &38.3 &88.1 &63.3 &69.7 &87.1 &83.1 &85.0 &29.3 &76.5 &56.5 &79.8 &77.9 &85.8 &82.4 &57.4 &84.3 &54.9 &80.5 &64.1 &72.7 \\
CRF-RNN \cite{zheng2015conditional} &90.4 &55.3 &88.7 &68.4 &69.8 &88.3 &82.4 &85.1 &32.6 &78.5 &64.4 &79.6 &81.9 &\bf 86.4 &81.8 &58.6 &82.4 &53.5 &77.4 &70.1 &74.7\\
BoxSup \cite{Dai2015arXiv} &89.8  &38.0 &\bf 89.2 &\bf 68.9 &68.0 &89.6 &83.0 &87.7 &34.4 &83.6 &\bf 67.1 &81.5 &83.7 &85.2 &83.5 &58.6 &84.9 &55.8 &\bf 81.2 &70.7 &75.2\\
DPN \cite{LiuDPN} &89.0 & \bf 61.6 &87.7 &66.8 &74.7 &91.2 &\bf 84.3 &87.6 &36.5 & 86.3 &66.1 &84.4 &87.8 &85.6 &85.4 &63.6 &87.3 &61.3 &79.4 &66.4 &77.5 \\
ours+ &\bf 94.1	&40.7	&84.1	&67.8	&\bf 75.9	&\bf 93.4	&\bf 84.3	&\bf 88.4	&\bf 42.5	&\bf 86.4	&64.7	&\bf 85.4	&\bf 89.0	& 85.8	&\bf 86.0	&\bf 67.5	&\bf 90.2	&\bf 63.8	&80.9	&\bf 73.0	&\bf \cellcolor[gray]{0.85} 78.0 \\

\end{tabular}
  }
\label{tab:voc12_test_details}
\end{table*}

\input{seg_example.tex}

\subsection{Results on PASCAL VOC 2012}

PASCAL VOC 2012 \cite{everingham2010pascal} is a well-known segmentation evaluation dataset which consists of 20 object categories and one background category.
This dataset is split into a training set, a validation set and a test set,
which respectively contain $1464$, $1449$ and $1456$ images.
Following a conventional setting in \cite{BharathECCV2014,ChenPKMY14}, the training set is augmented by extra annotated VOC images provided in \cite{HariharanABMM11}, which results in $10582$ training images.
We verify our performance on the PASCAL VOC 2012 test set.
We compare with a number of recent methods with competitive performance.
Since the ground truth labels are not available for the test set,
we report the result through the VOC evaluation server.

The results of IoU scores are shown in the last column of Table \ref{tab:voc12_test_details}.
We first train our model only using the VOC images.
We achieve $75.3$
IoU score which is the best result amongst methods that only use the VOC training data.

To improve the performance, following the setting in recent work \cite{ChenPKMY14,Dai2015arXiv},
we train our model with the extra images from the COCO dataset \cite{lin2014microsoft}.
With these extra training images, we achieve an IoU score of $77.2$.

For further improvement, we also exploit the the middle-layer features
as in the recent methods \cite{ChenPKMY14,LongSD14,hariharan2014hypercolumns}.
We learn extra refinement layers on the feature maps from middle layers to refine the coarse prediction.
The feature maps from the middle layers encode lower level visual information which
helps to predict details in the object boundaries.
Specifically, we add $3$ refinement convolution layers
on top of the feature maps from the first $5$ max-pooling layers and the input image.
The resulting feature maps and the coarse prediction score map
 are then concatenated and go through another $3$ refinement convolution layers to output the refined prediction.
The resolution of the prediction is increased from $1/16$ (coarse prediction) to $1/4$ of the input image.
With this refined prediction, we further perform boundary refinement \cite{krahenbuhl2012efficient} to generate the final prediction.
Finally,  we achieve an IoU score of $78.0$, {\em which is best reported result on this challenging dataset.}
\footnote{The result link at the VOC evaluation server: \url{http://host.robots.ox.ac.uk:8080/anonymous/XTTRFF.html}}

The results for each category are shown in Table \ref{tab:voc12_test_details}.
We outperform competing methods in most categories.
For only using the VOC training set, our method outperforms the second best method, DPN \cite{LiuDPN},
on $18$ categories out of $20$.
Using VOC+COCO training set, our method outperforms DPN \cite{LiuDPN}
on $15$ categories out of $20$.
Some prediction examples of our method are shown in Fig. \ref{fig:example_voc}.

\begin{table}[t]
\caption{Segmentation results on PASCAL-Context dataset (60 classes).
Our method performs the best.}
\centering
\resizebox{.8\linewidth}{!}
  {
  \begin{tabular}{ r | c c c}
  method	&pixel accuracy	&mean accuracy	&IoU\\ \hline \hline
O2P \cite{carreira2012semantic}	&-	&-	&18.1\\
CFM \cite{dai2014convolutional}	&-	&-	&34.4\\
FCN-8s \cite{LongSD14}	&65.9	&46.5	&35.1\\
BoxSup \cite{Dai2015arXiv}	&-	&-	&40.5\\ \hline
ours	&\bf 71.5	&\bf 53.9	&\bf 43.3\\
 \end{tabular}
  }
\label{tab:pascalcontext}
\end{table}

\begin{table}[t]
\caption{Segmentation results on SIFT-flow dataset (33 classes).
Our method performs the best.}
\centering
\resizebox{1\linewidth}{!}
  {
  \begin{tabular}{ r | c c c}
  method	&pixel accuracy	&mean accuracy	&IoU\\ \hline \hline
Liu et al. \cite{liu2011sift}	&76.7	&-	&-\\
Tighe et al. \cite{tighe2013finding} 	&75.6	&41.1	&-\\
Tighe et al. (MRF) \cite{tighe2013finding}	&78.6	&39.2	&-\\
Farabet et al. (balance) \cite{farabet2013learning}	&72.3	&50.8	&-\\
Farabet et al. \cite{farabet2013learning} 	&78.5	&29.6	&-\\
Pinheiro et al. \cite{pinheiro2013recurrent} &77.7 &29.8 &-\\
FCN-16s \cite{LongSD14}	&85.2	&51.7	&39.5\\ \hline
ours	&\bf 88.1	&\bf 53.4	&\bf 44.9\\
 \end{tabular}
  }
\label{tab:siftflow}
\end{table}

\subsection{Results on PASCAL-Context}
The
PASCAL-Context \cite{mottaghi2014role} dataset provides the segmentation labels of the whole scene (including the ``stuff" labels) for the PASCAL VOC images.
We use the segmentation labels which contain $60$ classes ($59$ classes plus the `` background" class ) for evaluation.
We use the provided training/test splits.
The training set contains $4998$ images and the test set has $5105$ images.

Results are shown in Table \ref{tab:pascalcontext}. Our method significantly outperforms the competing methods. To our knowledge, {\em ours is the best reported result on this dataset}.

\subsection{Results on SIFT-flow}

We further evaluate our method on the SIFT-flow dataset.
This dataset contains $2688$ images and provide the segmentation labels for $33$ classes.
We use the standard split for training and evaluation.
The training set has $2488$ images and the rest $200$ images are for testing.
Since images are in small sizes, we upscale the image by a factor of $2$ for training.
Results are shown in Table \ref{tab:siftflow}. We achieve the best performance for this dataset.

\section{Introduction}

Semantic image segmentation aims to predict a category label for every image pixel,
which is an important yet challenging task for image understanding.
Recent approaches have applied convolutional neural network (CNNs) \cite{farabet2013learning,LongSD14,ChenPKMY14}
to this pixel-level labeling task and achieved remarkable success.
Among these CNN-based methods, fully convolutional neural networks (FCNNs)~\cite{LongSD14,ChenPKMY14}
have become a popular choice, because of their computational efficiency for dense prediction and end-to-end style learning.

Contextual relationships are ubiquitous and provide important cues for scene understanding tasks.
Spatial context can be formulated in terms of  semantic compatibility relations between one object and its neighboring objects or image patches (stuff), in which a compatibility relation is an indication of the co-occurrence of visual patterns.
For example, a car is likely to appear over a road, and a glass is likely to appear over a table.
Context can also encode incompatibility relations.
For example, a car is not likely to be surrounded by sky.  These relations also exist
at finer scales, for example, in
object part-to-part relations, and part-to-object relations.
In some cases, contextual information is the most important cue, particularly when a single object shows significant visual ambiguities.
A more detailed discussion of the value of spatial context can be found in \cite{heitz2008learning}.

\begin{figure}[t]
	\centering
	\includegraphics[width=.95\linewidth]{./figs/general_graph}
\caption{An illustration of the prediction process of our method.
Both our unary and pairwise potentials are formulated as multi-scale CNNs for capturing semantic relations between image regions.
Our method outputs low-resolution prediction after CRF inference, then the prediction is up-sampled and refined in a standard post-processing stage to output the final prediction.}
\label{fig:general_graph}
\end{figure}

We explore two types of spatial context to improve the segmentation performance: patch-patch context and patch-background context.
The patch-patch context is the semantic relation between the visual patterns of two image patches. Likewise,
patch-background context is the semantic relation between a patch and a large background region.

Explicitly modeling the patch-patch contextual relations has not been well studied in recent CNN-based segmentation methods.
In this work, we propose to explicitly model the contextual relations using conditional random fields (CRFs).
We formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
Some recent methods  combine CNNs and CRFs for semantic segmentation,
e.g., the dense CRFs applied in \cite{ChenPKMY14,schwing2015fully,zheng2015conditional,Dai2015arXiv}.
The purpose of applying the dense CRFs in these methods is to refine the upsampled low-resolution prediction to sharpen object/region boundaries.
These methods consider Potts-model-based pairwise potentials for enforcing local smoothness.
There the pairwise potentials are conventional log-linear functions.
In contrast, we
learn more general pairwise potentials using CNNs to model the semantic compatibility between image regions.
Our CNN pairwise potentials aim to improve the coarse-level prediction rather than doing local smoothness,
and thus have a different purpose compared to Potts-model-based pairwise potentials.
Since these two types of potentials have different effects, they can be combined to improve the segmentation system.
Fig.~\ref{fig:general_graph} illustrates our prediction process.

In contrast to patch-patch context,
patch-background context is widely explored in the literature.
For CNN-based methods,
background information can be effectively captured
by combining features from a multi-scale image network input, and has shown good performance
in some recent segmentation methods \cite{farabet2013learning,MostajabiYS14}.
A special case of capturing patch-background context is considering the whole image as the background region and incorporating the image-level label information into learning.
In our approach, to encode rich background information, we construct multi-scale networks and apply sliding pyramid pooling on feature maps.
The traditional pyramid pooling (in a sliding manner) on the feature map is able to capture information from background regions of different sizes.

Incorporating general pairwise (or high-order) potentials usually involves expensive inference, which brings challenges for CRF learning.
To facilitate efficient learning we apply piecewise training of the CRF \cite{SuttonM05} to avoid repeated inference
 during back propagation training.

Thus our main \emph{contributions} are as follows.

  { 1.}~We formulate CNN-based general pairwise potential functions in CRFs to explicitly model patch-patch semantic relations.

  { 2.}~Deep CNN-based general pairwise potentials are challenging for efficient CNN-CRF joint learning.
  We perform approximate training, using piecewise training of CRFs \cite{SuttonM05},
  to avoid the repeated inference at every stochastic gradient descent iteration and thus achieve efficient learning.

  {3.}~We explore background context by applying a network architecture with traditional multi-scale image input \cite{farabet2013learning}
  and sliding pyramid pooling \cite{lazebnik2006beyond}.
   We empirically demonstrate the effectiveness of this network architecture for semantic segmentation.

  {4.}~We set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow.
	In particular, we achieve an intersection-over-union score of $78.0$ on the PASCAL VOC 2012 dataset, which is {\em  the  best reported result} to date.

\subsection{Related work}

Exploiting contextual information
has been widely studied in the literature (e.g., \cite{rabinovich2007objects,heitz2008learning,doersch2014context}).
For example, the early work ``TAS'' \cite{heitz2008learning} models different types of spatial context between {\em Things} and {\em Stuff}
using a generative probabilistic graphical model.

The most successful recent methods for semantic image segmentation are based on CNNs.
A number of these CNN-based methods for segmentation are region-proposal-based methods \cite{GirshickDDM13,BharathECCV2014}, which first generate region proposals and then assign category labels to each.  Very recently,  FCNNs
 \cite{LongSD14,ChenPKMY14,Dai2015arXiv} have become a popular choice for semantic segmentation, because of their effective feature generation and end-to-end training.
FCNNs
have also been applied to a range of other dense-prediction tasks recently,
 such as image restoration \cite{Eigen_iccv13}, image super-resolution \cite{Dong_eccv14}
 and depth estimation \cite{dcnn_nips14,liu2014deep}.
The method we propose here is similarly
built upon fully convolution-style networks.

The direct prediction of FCNN based methods usually are in low-resolution.
To obtain high-resolution predictions, a number of recent methods focus on refining the low-resolution prediction to obtain high resolution prediction.
DeepLab-CRF \cite{ChenPKMY14} performs bilinear upsampling of the prediction score map to the input image size and apply the dense CRF method \cite{krahenbuhl2012efficient} to refine the object boundary by leveraging the color contrast information.
CRF-RNN \cite{zheng2015conditional} extends this approach by implementing recurrent layers for end-to-end learning of the dense CRF and the FCNN network.
The work in \cite{noh2015learning} learns deconvolution layers to upsample the low-resolution predictions.
The depth estimation method \cite{liu2015learning} explores super-pixel pooling
for building the gap between low-resolution feature map and high-resolution final prediction.
Eigen \textit{et~al.} \cite{eigen2015predicting} perform coarse-to-fine learning of multiple networks with different resolution outputs for refining the coarse prediction.
The methods in \cite{hariharan2014hypercolumns,LongSD14} explore middle layer features (skip connections) for high-resolution prediction.
Unlike these methods, our method focuses on improving the coarse (low-resolution) prediction
by learning general CNN pairwise potentials to capture semantic relations between patches.
These refinement methods are complementary to our method.

Combining the strengths of CNNs and CRFs for segmentation
has been the focus of several recently developed approaches.
DeepLab-CRF in \cite{ChenPKMY14} trains FCNNs and applies a dense CRF~\cite{krahenbuhl2012efficient} method
as a post-processing step.
CRF-RNN \cite{zheng2015conditional} and the method in \cite{schwing2015fully} extend DeepLab and \cite{krahenbuhl2013parameter} by jointly learning the dense CRFs and CNNs.  They consider Potts-model based  pairwise potential functions which  enforce smoothness only.
The CRF model in these methods is for refining the up-sampled prediction.
Unlike  these methods, our approach learns CNN-based pairwise potential functions for modeling semantic relations between patches.

Jointly learning CNNs and CRFs has also been explored in other applications apart from segmentation.
The recent work in \cite{liu2014deep,liu2015learning} proposes to jointly learn {\em continuous} CRFs and CNNs for depth estimation
from  single monocular images.
The work in \cite{Lecun_nips14} combines CRFs and CNNs for human pose estimation.
The authors of \cite{chen2014learning} explore joint training of Markov random fields and deep neural networks for predicting words from noisy images and image s classification. Different from these methods, we explore efficient piecewise training of CRFs with CNN pairwise potentials.

\section{Modeling semantic pairwise relations}

Fig.~\ref{fig:potential_net} conceptualizes our architecture at a high level.
Given an image, we first apply a convolutional network to generate a feature map.
We refer to this network as `FeatMap-Net'.
The resulting feature map is at a lower resolution than the original image because of the down-sampling operations in the pooling layers.

We then create the CRF graph as follows:
for each location in the feature map (which corresponds to a rectangular region in the input image) we create one node in the CRF graph.
Pairwise connections in the CRF graph are constructed by connecting one node to all other nodes
which lie within a spatial range box (the dashed box in Fig.~\ref{fig:pairwise}).
We consider different spatial relations by defining different types of range box, and each type of spatial relation is modeled by a specific pairwise potential function.
As shown in Fig.~\ref{fig:pairwise},  our method models the ``surrounding" and ``above/below" spatial relations.
In our experiments, the size of the range box (dash box in the figure) size is $0.4a \times 0.4a$.
Here we denote by $a$ the length of the short edge of the feature map.

Note that although `FeatMap-Net' defines a common architecture, in fact we train three such networks: one for the unary potential and one each for the two types of pairwise potential.

\section{Contextual Deep CRFs}
\label{sec:crf_details}

Here we describe the details of our deep CRF model.
We denote by ${\boldsymbol x} \in \mathcal{X}$ one input image and ${\boldsymbol y} \in \mathcal{Y}$ the labeling mask which describes the label configuration of each node in the CRF graph.
The energy function is denoted by $E({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})$ which models the compatibility of the input-output pair, with a small output value indicating high confidence in the prediction ${\boldsymbol y}$.
All network parameters are denoted by ${\boldsymbol \theta}$ which we need to learn.
The conditional likelihood for one image is formulated as follows:
\begin{equation}\label{eq:prob}
\begin{aligned}
\small
{\displaystyle P}({\boldsymbol y}|{\boldsymbol x}) = \frac{1}{Z({\boldsymbol x})} \operatorname*{exp\,} [- E({\boldsymbol y}, {\boldsymbol x})].
\end{aligned}
\end{equation}
Here $Z({\boldsymbol x}) = \sum_{{\boldsymbol y}} \operatorname*{exp\,} [ -E({\boldsymbol y}, {\boldsymbol x}) ]$ is the partition function.
The energy function is typically formulated by a set of unary and pairwise potentials:
\begin{align}
\label{eq:energy}
\small
	E({\boldsymbol y}, {\boldsymbol x}) = &  \sum_{U \in \mathcal{U}} \sum_{p \in \mathcal{N}_U } U(y_{p}, {\boldsymbol x}_p) %
  + \sum_{V \in \mathcal{V}} \sum_{(p,q) \in \mathcal{S}_V} V(y_{p}, y_{q}, {\boldsymbol x}_{pq}). \notag
\end{align}
Here $U$ is a unary potential function,
and to make the exposition more general, we consider multiple types of unary potentials with
$\mathcal{U}$ the set of all such unary potentials.
$\mathcal{N}_U$ is a set of nodes for the potential $U$.
Likewise, $V$ is a pairwise potential function
 with $\mathcal{V}$ the set of all types of pairwise potential. $\mathcal{S}_V$ is the set of edges for the potential $V$.
${\boldsymbol x}_{p}$ and ${\boldsymbol x}_{pq}$  indicates the corresponding image regions which associate to the specified node and edge.

\begin{figure}[t]
	\centering
	\includegraphics[width=1\linewidth]{./figs/potential_net}
\caption{An illustration of generating unary or pairwise potential function outputs.
First a feature map is generated by a FeatMap-Net, and a CRF graph is constructed based on the spatial resolution of the feature map.
Finally the Unary-Net (or Pairwise-Net) produces potential function outputs.}
\label{fig:potential_net}
\end{figure}

\begin{figure*}[t]
	\center
	\includegraphics[width=.9\linewidth]{./figs/dense_cnn2}
\caption{The details of our FeatMap-Net.
An input image is first resized into $3$ scales,
then each resized image goes through 6 convolution blocks to output one feature map.
Top $5$ convolution blocks are shared for all scales. Every scale has a specific convolution block (Conv Block 6).
We perform $2$-level sliding pyramid pooling (see Fig.~\ref{fig:pooling} for details).
$d$ indicates the feature dimension.
}
\label{fig:featmapnet}
\end{figure*}

\subsection{Unary potential functions}
We formulate the unary potential function by stacking the FeatMap-Net for generating feature maps and
a shallow fully connected network (referred to as Unary-Net) to generate the final output of the unary potential function.
The unary potential function is written as follows:
\begin{equation}
	U(y_{p}, {\boldsymbol x}_p; {\boldsymbol \theta}_U) = - z_{p,y_p} ({\boldsymbol x}; {\boldsymbol \theta}_U).
\end{equation}
Here $z_{p, y_p }$ is the output value of Unary-Net, which corresponds to the $p$-th node and the $y_p$-th class.

Fig.~\ref{fig:potential_net} includes an illustration of the Unary-Net and how it integrates with FeatMap-Net.
The unary potential at each CRF node is simply the $K$-dimensional output (where $K$ is the number of classes) of Unary-Net applied to the node feature vector from the correpsonding location in the feature map (i.e. the output of FeatMap-Net).

\subsection{Pairwise potential functions}
Fig. \ref{fig:potential_net} likewise illustrates how the pairwise potentials are generated.  The edge features are formed by concatenating
the corresponding feature vectors of two connected nodes (similar to~\cite{kolesnikov2014closed}).  The feature vector for each node in the pair is from the feature map output by FeatMap-Net. The edge features of one pair  are then fed to a shallow fully connected network (referred to as Pairwise-Net) to generate the final output that is the pairwise potential.  The size of this is $K \times K$ to match the number of possible label combinations for a pair of nodes.
The pairwise potential function is written as follows:
\begin{align}
	V & (y_{p}, y_{q},  {\boldsymbol x}_{pq}; {\boldsymbol \theta}_V) = - z_{p, q, y_p, y_q} ({\boldsymbol x}; {\boldsymbol \theta}_V).
\end{align}
Here $z_{p, q,  y_p,  y_q }$ is the output value of Pairwise-Net.
It is the confidence value for the node pair $(p, q)$ when they are labeled with the class value $( y_p,  y_q )$, which measures the compatibility of the label pair $(y_{p}, y_{q}$) given the input image ${\boldsymbol x}$.
${\boldsymbol \theta}_V$ is the corresponding set of CNN parameters for the potential $V$, which we need to learn.

Our formulation of pairwise potentials is different from the Potts-model-based formulation in the existing methods of \cite{ChenPKMY14,zheng2015conditional}.
The Potts-model-based pairwise potentials are a log-linear functions and
employ a special formulation for enforcing neighborhood smoothness.
In contrast, our pairwise potentials model the semantic compatibility between two nodes
 with the output for every possible value of the label pair $(y_{p}, y_{q}$) individually parameterized by CNNs.

In our system, after obtaining the coarse level prediction, we still need to perform a refinement step to obtain the final high-resolution prediction
(as shown in Fig.~\ref{fig:general_graph}).
Hence we also apply the dense CRF method \cite{krahenbuhl2012efficient}, as in many other recent methods, in the prediction refinement step.
Therefore, our system takes advantage of both contextual CNN potentials and the traditional smoothness potentials to improve the final system.
More details are described in Sec.~\ref{sec:prediction}.

As in \cite{winn2006layout,heesch2010markov},
modeling asymmetric relations requires the potential function is capable of modeling input orders,
since we have: $ V  (y_{p}, y_{q},  {\boldsymbol x}_{pq}) \neq V (y_{q}, y_{p},  {\boldsymbol x}_{qp})$.
Take the asymmetric relation ``above/below" as an example;
we take advantage of the input pair order to indicate the spatial configuration of two nodes,
thus the input $(y_p, y_q, {\boldsymbol x}_{pq})$
indicates the configuration that the node $p$ is spatially lies above the node $q$.

The asymmetric property is readily achieved with our general formulation of pairwise potentials.
The potential output for every possible pairwise label combination for $(p,q)$ is individually parameterized by the pairwise CNNs.

\section{Exploiting background context}
\label{sec:network_details}

To encode rich background information,
we use multi-scale CNNs and  sliding pyramid pooling \cite{lazebnik2006beyond} for our FeatMap-Net.
Fig.~\ref{fig:featmapnet} shows the details of the FeatMap-Net.

CNNs with multi-scale image network inputs have shown good performance in some recent segmentation methods \cite{farabet2013learning,MostajabiYS14}.
The traditional pyramid pooling (in a sliding manner) on the feature map is able to capture information from background regions of different sizes.
We observe that these two techniques (multi-scale network design and pyramid pooling)
for encoding background information are very effective for improving performance.

Applying CNNs on multi-scale images has shown good performance in some recent segmentation methods \cite{farabet2013learning,MostajabiYS14}.
In our multi-scale network, an input image is first resized into $3$ scales,
then each resized image goes through 6 convolution blocks to output one feature map.
In our experiment, the $3$ scales for the input image are set to $1.2$, $0.8$ and $0.4$.
All scales share the same top $5$ convolution blocks.
In addition, each scale has an exclusive convolution block (``Conv Block 6" in the figure)
which captures scale-dependent information.
The resulting $3$ feature maps (corresponding to $3$ scales) are of different resolutions, therefore we  upscale the two smaller ones to the size of the largest feature map using bilinear interpolation. These feature maps are then concatenated to form one feature map.

We perform spatial pyramid pooling \cite{lazebnik2006beyond} (a modified version using sliding windows)
on the feature map to capture information from background regions in multiple sizes.
This increases the field-of-view for the feature map and thus it is able to capture the information from a large image region.
Increasing the field-of-view generally helps to improve performance \cite{ChenPKMY14}.

The details of spatial pyramid pooling are illustrated in Fig. \ref{fig:pooling}.
In our experiment, we perform $2$-level pooling for each image scale.
We define $5 \times 5$ and $9 \times 9$ sliding pooling windows (max-pooling) to generate $2$ sets of pooled feature maps,
which are then concatenated to the original feature map to construct the final feature map.

The detailed network layer configuration for all networks are described in Fig.~\ref{fig:network_conf}.

\begin{figure}[t]
	\center
	\includegraphics[width=1\linewidth]{./figs/pooling}
\caption{
Details for sliding pyramid pooling.
We perform $2$-level sliding pyramid pooling on the feature map for capturing patch-background context,
which encode rich background information and increase the field-of-view for the feature map.
}
\label{fig:pooling}
\end{figure}

\begin{figure}[t]
	\center
	\includegraphics[width=.75\linewidth]{./figs/network_layers}
\caption{The detailed configuration of the networks: FeatMap-Net, Unary-Net and Pairwise-Net.
  $K$ is the number of classes.
  For FeatMap-Net, the top 5 convolution blocks share the same configuration
as the convolution blocks in the VGG-16 network. The stride of the last max pooling layer is 1, and
for the other max pooling layers we use the same stride setting as VGG-16.
}
\label{fig:network_conf}
\end{figure}

\section{Prediction}
\label{sec:prediction}

In the prediction stage,
our deep structured model will generate low-resolution prediction (as shown in Fig.~\ref{fig:general_graph}),
which is $1/16$ of the input image size.
This is due to the stride setting of pooling or convolution layers for sub-sampling.
Therefore, we apply two prediction stages for obtaining the final high-resolution prediction:
the coarse-level prediction stage and the prediction refinement stage.

\subsection{Coarse-level prediction stage}
We perform CRF inference on our contextual structured model to obtain the coarse prediction of a test image.
We consider the marginal inference over nodes for prediction:
\begin{align} \label{eq:inference}
\forall p \in \mathcal{N}: \;\; {\displaystyle P}(y_p|{\boldsymbol x})&= {\textstyle \sum_{{\boldsymbol y} \backslash y_p } {\displaystyle P}({\boldsymbol y}|{\boldsymbol x})}.
\end{align}
The obtained marginal distribution can be further applied in the next prediction stage for boundary refinement.

Our CRF graph does not form a tree structure,
nor are the potentials submodular,
hence we need to an apply approximate inference.
To address this we
apply an efficient  message passing algorithm which is based on the mean field approximation \cite{Nowozinstruct}.
The mean field algorithm constructs a simpler distribution $Q({\boldsymbol y})$, e.g.,
a product of independent marginals:
$Q({\boldsymbol y})=\prod_{p \in \mathcal{N}} Q_p (y_p)$,
which minimizes the KL-divergence between the distribution $Q({\boldsymbol y})$ and ${\displaystyle P}({\boldsymbol y})$.
In our experiments, we perform $3$ mean field iterations.

\subsection{Prediction refinement stage}
We generate the score map for the coarse prediction from the marginal distribution which we obtain from the mean-field inference.
We first bilinearly up-sample the score map of the coarse prediction to the size of the input image.
Then we apply a common post-processing method \cite{krahenbuhl2012efficient} (dense CRF)
to sharpen the object boundary for generating the final high-resolution prediction.
This post-processing method leverages low-level pixel intensity information (color contrast) for boundary refinement.
Note that most recent work on image segmentation similarly produces low-resolution prediction
 and have a upsampling and refinement process/model for the final prediction, e.g.,
\cite{ChenPKMY14,zheng2015conditional,Dai2015arXiv}.

In summary, we simply perform bilinear upsampling of the coarse score map and apply the boundary refinement post-processing.
We argue that this stage can be further improved by applying more sophisticated refinement methods, e.g., training deconvolution networks \cite{noh2015learning},
training multiple coarse to fine learning networks \cite{eigen2015predicting},
and exploring middle layer features for high-resolution prediction \cite{hariharan2014hypercolumns,LongSD14}.
It is expected that applying better refinement approaches will gain further performance improvement.

\section{CRF training}

A common approach for CRF learning is to maximize the likelihood,
or equivalently minimize the negative log-likelihood, which can be written
for one image as:
\begin{align}
 - \operatorname*{log\,} {\displaystyle P}({\boldsymbol y} | {\boldsymbol x}; {\boldsymbol \theta}) = E({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta}) + \operatorname*{log\,} Z({\boldsymbol x}; {\boldsymbol \theta}).
\label{eq:Prr}
\end{align}
Adding regularization to the CNN parameter ${\boldsymbol \theta}$, the optimization problem for CRF learning is:
{\small
\begin{align}
\label{eq:crf_learning_org}
\min_{{\boldsymbol \theta}} \frac{\lambda}{2} \fnorm {\boldsymbol \theta} + \sum_{i=1}^N \biggr[ E({\boldsymbol y}^{(i)}, {\boldsymbol x}^{(i)}; {\boldsymbol \theta}) + \operatorname*{log\,} Z({\boldsymbol x}^{(i)}; {\boldsymbol \theta}) \biggr].
\end{align}
}
Here ${\boldsymbol x}^{(i)}$, ${\boldsymbol y}^{(i)}$ denote the $i$-th training image and its segmentation mask; $N$ is the number of training images; $\lambda$ is the weight decay parameter.
We can apply stochastic gradient (SGD) based methods to optimize the above problem for learning ${\boldsymbol \theta}$.
The energy function $E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})$ is constructed from CNNs, and its
gradient ${\nabla}_{\boldsymbol \theta} E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})$ easily computed
by applying the chain rule as in conventional CNNs.
However, the partition function $Z$ brings difficulties for optimization.  Its gradient is:
\begin{align}
{\nabla}_{\boldsymbol \theta}  \operatorname*{log\,} &  Z({\boldsymbol x} ; {\boldsymbol \theta})  \notag  \\
	 = &  \sum_{\boldsymbol y} \frac{\operatorname*{exp\,}[-E({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})]} {\sum_{{\boldsymbol y}'} \operatorname*{exp\,}[-E({\boldsymbol y}', {\boldsymbol x}; {\boldsymbol \theta})]} {\nabla}_{\boldsymbol \theta} [ - E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})] \notag \\
	 = &  - {\mathbb E}_{{\boldsymbol y} \sim {\displaystyle P}({\boldsymbol y} | {\boldsymbol x} ; {\boldsymbol \theta})} {\nabla}_{\boldsymbol \theta} E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})
\end{align}
Generally the size of the output space $\mathcal{Y}$
is exponential in
the number of nodes, which prohibits the direct calculation of $Z$ and its gradient.
The CRF graph we considered for segmentation here is a loopy graph (not tree-structured),
for which the inference is generally computationally expensive.
More importantly, usually a large number of SGD iterations (tens or hundreds of thousands) are required for
training CNNs. Thus performing inference at each SGD iteration is very computationally expensive.

\subsection{Piecewise training of CRFs}

Instead of directly solving the optimization in \eqref{eq:crf_learning_org},
we propose to apply an approximate CRF learning method.
In the literature,
there are two popular types of learning methods which approximate the CRF objective
: pseudo-likelihood learning \cite{besag1977efficiency} and piecewise learning \cite{SuttonM05}.
The main advantage of these methods  in term of training deep CRF is that they  do not involve marginal inference for gradient calculation,
which significantly improves the efficiency of training.
Decision tree fields \cite{NowozinRBSYK11} and regression tree fields \cite{jancsary2012regression}
are based on pseudo-likelihood learning, while
piecewise learning has been applied in the work \cite{SuttonM05,kolesnikov2014closed}.

Here we develop this idea for the case of training the CRF with the CNN potentials.
In piecewise training,
the conditional likelihood
is formulated as a number of independent likelihoods defined on potentials, written as:
\begin{align}
{\displaystyle P} ({\boldsymbol y} | {\boldsymbol x}) = \prod_{U \in \mathcal{U}} \prod_{ p \in \mathcal{N}_{U}} {\displaystyle P}_{U} ( y_p | {\boldsymbol x}) \prod_{V \in \mathcal{V}}
\prod_{ (p,q) \in \mathcal{S}_{V}} {\displaystyle P}_{V} ( y_p, y_q | {\boldsymbol x}). \notag
\end{align}
The likelihood ${\displaystyle P}_{U} ( y_p | {\boldsymbol x})$ is constructed from the unary potential $U$.
Likewise, ${\displaystyle P}_{V} ( y_p, y_q | {\boldsymbol x})$ is constructed from the pairwise potential $V$.
${\displaystyle P}_{U}$ and ${\displaystyle P}_{V}$ are written as:
\begin{align}
{\displaystyle P}_{U}(y_p|{\boldsymbol x}) &  = \frac{\operatorname*{exp\,} [- U(y_p, {\boldsymbol x}_p)]}{\sum_{y'_p} \operatorname*{exp\,} [ -U(y'_p, {\boldsymbol x}_p) ]}, \label{eq:pu} \\
{\displaystyle P}_{V}(y_p, y_q|{\boldsymbol x}) &  = \frac{\operatorname*{exp\,} [- V(y_p, y_q, {\boldsymbol x}_{pq})]}{\sum_{y'_p, y'_q} \operatorname*{exp\,} [ -V(y'_p, y'_q, {\boldsymbol x}_{pq}) ]}
\label{eq:pv}.
\end{align}
 Thus the optimization for piecewise training is to minimize
 the negative log likelihood with regularization:
\begin{align}
	\min_{{\boldsymbol \theta}} \frac{\lambda}{2} & \fnorm {\boldsymbol \theta} -
	 \sum_{i=1}^N \biggr[ \sum_{U \in \mathcal{U}}
		\sum_{ p \in \mathcal{N}_{U}^{(i)}} \operatorname*{log\,} {\displaystyle P}_{U} ( y_p | {\boldsymbol x}^{(i)}; {\boldsymbol \theta}_U) \notag \\
	& + \sum_{V \in \mathcal{V}} \sum_{ (p,q) \in \mathcal{S}_{V}^{(i)}} \operatorname*{log\,} {\displaystyle P}_{V} ( y_p, y_q | {\boldsymbol x}^{(i)}; {\boldsymbol \theta}_V) \biggr].
	\label{eq:partitioned}
\end{align}
Compared to the objective in \eqref{eq:crf_learning_org} for direct maximum likelihood learning,
the above objective does not involve the global partition function $Z({\boldsymbol x} ; {\boldsymbol \theta})$.
To calculate the gradient of the above objective,
we only need to calculate the gradient
${\nabla}_{{\boldsymbol \theta}_U} \operatorname*{log\,} {\displaystyle P}_{U}$
and
${\nabla}_{{\boldsymbol \theta}_V} \operatorname*{log\,} {\displaystyle P}_{V}$.
With the definition in \eqref{eq:pu}, ${\displaystyle P}_{U}$ is a conventional Softmax normalization function over only $K$ (the number of classes) elements.
Similar analysis can also be applied to ${\displaystyle P}_{V}$.
Hence, we can easily calculate the gradient without involving expensive inference.
Moreover, we are able to perform parallel training of potential functions, since the above objective is formulated as a summation of independent log-likelihoods.

 As previously discussed, CNN training usually involves a large number of gradient update iterations.  However this means that expensive inference during every gradient iteration becomes impractical.  Our piecewise approach here provides a practical solution for learning CRFs with CNN potentials on large-scale data.

redpajama_set_name: RedPajamaArXiv


text:
Internal Documents Show Sexual Harassment And Misconduct is Rampant at California Women's Prisons
Sexual Harassment November 22, 2019
Nearly 26,000 correctional officers work in the California prison system. According to inmate advocates, incidents of sexual harassment and sexual abuse by staff are more rampant than what the official records reveal because very few officers get investigated or reported.
In the state's prison system, about four percent of the 124,000 inmates (nearly 5,000 inmates) are women.
Poor Record of Sexual Harassment Investigations
The state prison system is empowered to track and investigate all allegations of sexual harassment and misconduct by the staff in order to comply with the federal Prison Rape Elimination Act. However, in 2018, only 337 incidents involving staff-on-inmate misconduct were reported in California's prisons.
Worse still, just three of those allegations could be substantiated through investigations. Independent third-party probes into the prevailing culture in women's prisons in the state have found that formally reported allegations only represent a small fraction of the prevalent sexual harassment and abuse of incarcerated females by the prison staff.
Report Reveals Widespread Incidence of Sexual Harassment and Abuse
The Prison Law Office, an organization that monitors the condition of prison inmates, has interviewed hundreds of female inmates at the Central Valley prison and documented their complaints to present before the court.
According to the organization's report, out of a random sample of 80 women inmates, almost all of them had experienced sexual harassment or abuse while they were in prison.
A second round of interviews again revealed a similar trend, with women inmates often complaining of sexual misconduct by the same officers. The organization concluded that a culture of sexism and bigotry was prevalent at the prison.
According to a representative of Prison Law Office, "It was a constant stream of verbal sexual harassment or misogynist statements." Many women inmates complained that prison officers regularly addressed them as "hos" or "bitches". They would even make announcements on the P.A. system, such as: 'Hey bitc-es, time to go to lunch'.
Sexual Abuse Across Different Prison Facilities
Sexual harassment and abuse of female prisoners in California is not limited to just one facility, according to the records now available in public. In recent years, four correctional officers at the state's other major women's prison in Southern California also were fired for sexual misconduct.
Under the federal and state laws, prison inmates cannot consent to sex with prison guards. According to prison policies in California, sexual misconduct by a correctional officer can lead to their dismissal, and can also be grounds for criminal prosecution.
Speak to a Skilled and Knowledgeable California Sexual Harassment Attorney
If you have been a victim of sexual harassment in California, get ready to speak up and fight for your legal rights. Makarem & Associates has some of the best sexual harassment attorneys in California who will provide you legal counsel and support. Call us at 310-312-0299 or email at info@makaremlaw.com.
Legal Malpractice Statute of Limitations
If you have been wronged by an attorney or lawyer or any other legal professional, and that wrong has led…
California Legal Malpractice Statute of Limitations
If you believe that you have been wronged by an attorney, and have experienced damages as a result, then you…
What Does Emotional Nursing Home Abuse Look Like?
For some people, looking out for the signs of nursing home abuse is all about checking your loved one frequently…
Legal Malpractice and Attorney-Client Privilege
The issue of legal malpractice takes place when an attorney either negligently, or intentionally mishandles a legal case to such…
Nursing Home Law: Abuse of the Elderly
When an individual places their loved one within the care of a nursing home, they do so expecting a certain…
California Laws, Title 22 Licensing, and Elder Abuse
Today is World Elder Abuse Awareness Day, a day to recognize the elders who raised us. When someone chooses to…
Legal Malpractice and the Standard of Care for Professionals
Most often, people associate the term "malpractice" with negligence or carelessness shown by a member of a professional group –…
Overtime, Wage, and Hour Class Litigation
Statistics have regularly indicated that California, in particular, is a central hub for wage and hour class action litigation. It…
Standards for Personal Care in Nursing Homes
Nursing homes in California – just like care facilities in any other state, have a number of laws and standards…
Nursing Home Negligence: Medication Errors
Nursing home negligence comes in many different forms, from failing to help an individual live at a certain level of…
Nursing Home Litigation and Abuse
Generally, assisted living facilities such as nursing homes and care centers are a place intended to provide a safe and…
Nursing Home Litigation: Abuse of the Elderly
Forms of Legal Malpractice
First and foremost, one needs to understand what legal malpractice is before considering a lawsuit. Legal malpractice is when a…
California Employment Law and Paid Sick Leave
As of the first of July, 2015, the Healthy Families Act that was introduced last year in 2015 will begin…
What Lessons Can We Learn From The USC Scandal?
The sexual abuse scandal at the University of Southern California has reignited the debate on women's safety and sexual assault…


Melissa Hoover graduated with a Bachelor's Degree in Communications from Southeastern Louisiana University and earned her real estate license in 2015. She began working at Elifin on Day 1 when we opened for business in November of 2016. She works behind the scenes at Elifin to ensure all client's needs are being met and the business continues to flow smoothly for our team.


text:
Opportunity for all
By MARIE WALDRON, 75th Assembly District
Access to a quality education is the best way to ensure our students a bright, successful future. But one size does not fit all, and not every student wants to go to a traditional college. Even so, California schools often focus on sending kids to colleges and universities, with less emphasis on trades.
We have a skilled worker shortage in California, and we need to make sure that high school students have the opportunity to learn technical skills that can lead to well-paying jobs after they graduate. That's why Career Technical Education is so important.
To help meet this need, last session I was an author of AB 1111, which will help ensure students have access to quality technical courses that provide the training to work for companies requiring highly-skilled workers and $15 million in the budget to pay for it. And last month, I was pleased to recognize Dennis Johnson, a Fallbrook High School transportation technology teacher, who received the second-place Harbor Freight Tools for Schools Prize for Teaching Excellence. We need more programs like Fallbrook High's skilled trades program, and more teachers to give young people the opportunity to learn skills that lead to good-paying careers in the trades. The award is a testament to Mr. Johnson's dedication and his skill in creating innovative programs that prepare students for their future automotive careers. The $50,000 prize will be split between Mr. Johnson, who receives $15,000, and Fallbrook High School's skilled trades program, which gets $35,000.
With almost 20% of California's population living below the poverty level, we should do all we can to expand the pool of skilled, well-paid workers. And thank you to Harbor Freight Tools for recognizing the importance of Career Tech Education.
Assembly Republican Leader Marie Waldron, R-Escondido, represents the 75th Assembly District in the California Legislature, which includes the communities of Bonsall, Escondido, Fallbrook, Hidden Meadows, Pala, Palomar Mountain, Pauma Valley, Rainbow, San Marcos, Temecula, Valley Center and Vista.
*Note: Opinions expressed by columnists and letter writers are those of the writers and not necessarily those of the newspaper.
More From Opinion Go To The Opinion Section
Good-bye to 2020 . . . but not to the indelible memories
The difficult goodbye
Kids need to get back to school, sports, as soon as possible


Swaminathan S. Anklesaria Aiyar, who writes articles for the Economic Times and The Times of India under the name "Swaminomics" (which is just a great name), has an interesting suggestion. He calls it community-led land acquisition. He means it to be applied to India, which has its own set of land acquisition problems, but it would work for our eminent domain ( Kelo v. City of New London (04-0108)) problem as well.
Here is the gist of his proposal as it applies to us: Governments should negotiate acquisition proposals with landowners, and then let the landowners vote on the deal. If a large majority--it could be two-thirds or three-quarters--vote in favour of selling, this should be binding on the minority.
The biggest advantage of this rule is that it prevents under-valuation of the property. The Constitution states "nor shall private property be taken for public use, without just compensation." Right now, the government decides what is just compensation, and then the government pays the price. Clearly there is vast potential for injustice there, and when you look at the history of eminent domain acquisitions, you will find plenty of charges of unjust forced sales. But by ensuring that a supermajority of landowners agrees that the price is "just compensation", that will ensure that the price really is fair.
UPDATE 3/1: Marvin writes in to point me to an article published in the Utah Daily Herald. Seems that the Utah Legislature had completely revoked the power of eminent domain. They have now replaced it with a scheme very much like the Swami describes above.


A dynamic, technically brilliant, Jamaican-born jazz pianist in the mold of Oscar Peterson who often explores his Caribbean roots, thrilled the Lesher audiences with his trio in 2017. With this band, Alexander spontaneously orchestrates, switching off from straight-ahead to two worlds meet. He also sings and plays the melodica. "There's always some kind of jet taking off when I transfer the music to one rhythm or the other. Whether it's 4/4 straight-ahead acoustic or a rhythm from Jamaica, it's cathartic. It's a bring-people-together thing, and the musicians enjoy each other. You can see the camaraderie... It never gets old." Over 70 recordings as leader. Harlem Kingston Express featuring Hassan Shakur, acoustic bass, Obed Calvaire, drums, Andy Bassford, guitar, Joshua Thomas, electric bass, Karl Wright, percussion/drums.


text:
Q: Why is router IP not listed in ifconfig? I am looking for the address that I type into my browser to get to the router configuration file. It ended up being 192.168.1.1, as usual. But when I enter ifconfig into the command line, this IP address is not listed anywhere.
Why is this? Shouldn't ifconfig give me the IP address of the router?
If the router IP address were something else instead of 192.168.1.1, how would I find out what it is?

A:
If the router IP address were something else instead of 192.168.1.1, how would I find out what it is?

Assuming some flavour of Linux:
$ route -n
Kernel IP routing table
Destination     Gateway         Genmask         Flags Metric Ref    Use Iface
0.0.0.0         192.168.2.1     0.0.0.0         UG    0      0        0 eth0
169.254.0.0     0.0.0.0         255.255.0.0     U     1000   0        0 eth0
192.168.1.0     0.0.0.0         255.255.255.0   U     0      0        0 eth0

(note on some flavours like Debian, route is not in $PATH for a non-root user, and you may have to use /sbin/route, or you can invoke netstat -rn for the same info)
In most common home cases, the router will be listed as the gateway for 0.0.0.0- in this made-up example 192.168.2.1.
If you want to find out more about the meaning of the output of route, see the question over on unix.se:
Understanding the output of route -n

Shouldn't ifconfig give me the IP address of the router?

ifconfig does not show gateway information - use 'route' to view or manipulate routing info.

A: Issue the command: ip route to find the address of the router(s) configured on your system.
To learn how to use it, simply do ip route help to see all the options.
The command ip can give you a lot of information about the IP configuration. Keep in mind that ifconfigis being deprecated on Linux.

A: netstat -nr

It will display directly the gateway address, which in the case of connections via router is the router IP address (example: 192.168.1.1).


With wide-ranging experience working on national security issues in the White House, the State Department, and the U.S. Senate and House of Representatives, Stephen Rademaker helps clients navigate international policy, sanctions, and CFIUS challenges.
Among his accomplishments in public service, Mr. Rademaker had lead responsibility, as a U.S. House staffer, for drafting the legislation that created the U.S. Department of Homeland Security. Serving as an Assistant Secretary of State from 2002 through 2006, he headed at various times three bureaus of the State Department, including the Bureau of Arms Control and the Bureau of International Security and Nonproliferation. He directed the Proliferation Security Initiative, as well as nonproliferation policy toward Iran and North Korea, and led strategic dialogues with Russia, China, India, and Pakistan. He also headed U.S. delegations to numerous international conferences, including the 2005 Review Conference of the Parties to the Treaty on the Nonproliferation of Nuclear Weapons.
Mr. Rademaker concluded his government career on Capitol Hill in 2007, serving as Senior Counsel and Policy Director for National Security Affairs for then-Senate Majority Leader Bill Frist (R-TN). In this role, he helped manage all aspects of the legislative process relating to foreign policy, defense, intelligence and national security. He earlier served as Chief Counsel for the House Select Committee on Homeland Security of the U.S. House of Representatives and as Deputy Staff Director and Chief Counsel of the House Committee on International Relations.
During President George H. W. Bush's administration, Mr. Rademaker served as General Counsel of the Peace Corps, Associate Counsel to the President in the Office of White House Counsel, and as Deputy Legal Adviser to the National Security Council. After leaving government in 2007, he continued to serve as the U.S. representative on the United Nations Secretary-General's Advisory Board on Disarmament Matters, and he was subsequently appointed by House Republican Leader John Boehner (R-OH) to the U.S. Commission on the Prevention of Weapons of Mass Destruction Proliferation and Terrorism.
Published by the American Bar Association's International Criminal Court Project and the Stanford Law School Project on International and Comparative Law.
Nonproliferation and Disarmament: What's the connection and what does that mean for U.S. security and Obama Administration policy?
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text:
платежных
карт National
Национальная платежная карта National payment card
National Payment Card System
Customer Support Phone
About NSPK
NSPK Corporate Governance Structure
Committees of the Supervisory Board
Disclosure Documents
MIR Payment System
MIR Terms and Fees
Russian National Payment Card System
For all Russian citizens and for the State
we create and develop payment services convenient,
beneficial and available to all,
whereby supporting the country's sovereignty
and setting up the industry standards.
Anna Goldstein: Instant Payment System Secured at Different Levels
The Instant Payment System (IPS) has an entire toolkit to ensure security of all its participants. The service is secured at different levels. This topic was covered by Anna Goldstein, a repre...
Turkey Starts Accepting Mir Cards
İşbank has become the first bank in Turkey to accept Mir cards at its ATMs and Point-of-Sale Terminals.
Acceptance of Mir Cards Launched in Kyrgyzstan
The national payment systems of Kyrgyzstan and Russia - Elcard and Mir - provide an opportunity to use Mir cards in the Kyrgyz Republic as part of the cross-system integration project.
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Welcome to the official website of the Russian National Payment System. National Payment Card System Joint Stock Company (NSPK JSC) was established on 23 July 2014. Its core business is cashless money transfers with the use of MIR bank cards in Russia.
National Payment Card System deals with all forms of interaction of the financial system's elements related to the transfer of money from the payer to the payee. On 1 April 2015, NSPK Operational and Payment Clearing Center started its work to ensure processing of major international payment systems' card transactions.
NSPK JSC's activities are regulated by Federal Law No. 161 On the National Payment System dated 27 June 2011. 100 percent of NSPK JSC's shares are held by the Central Bank of the Russian Federation.
NSPK JSC's objectives are to ensure uninterrupted processing of transactions performed with the international payment systems' cards in Russia, to establish and develop the National Payment System.
This Web resource Nspk.com uses cookies and tracks Web Visitors to make the Website better, personalize services and be Web Visitor friendly. Web Visitors' data is processed in accordance with the NSPK JSC Policy of Personal Data Processing and Protection. If you do not want to use cookies, change your browser settings.
Copyright 2019 NSPK. All rights reserved.


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