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Nature & SceneryHistorical SitesHot SpringsOutdoor ActivitiesCultural ExperiencesLocal FoodsArts & CraftsMuseums & GalleriesShopping & Souvenirs
SpringSummerAutumnWinter
Aizu AreaCentral AreaCoastal Area
Itineraries Access Getting Around Where to Stay Safety in Fukushima Tours in Fukushima
Mt. Iwatsuno
Central Area
Mt. Iwatsuno is the name of a hill in Motomiya City which is populated with numerous temples, shrines, carvings, statues, caves, and other ancient things. Mt. Iwatsuno has long been known as a place for Shugendo and other religious training for Buddhist monks from the school of Tendai.
One of the most notable of Mt. Iwatsuno's temples is Gankakuji Temple, which was founded in 851. Other highlights include Okunoin, located at the top of Mt. Iwatsuno, which was built in the Kamakura Era, and Bisshamondo, which was rebuilt in the mid-19th century.
Mt. Iwatsuno can be explored on foot in around 1 hour, but visitors can easily spend longer if they want to explore all of the hidden treasures the hill has to offer.
It's possible for groups to do Zazen meditation on the hillside if visitors contact Mt. Iwatsuno in advance (bookings must be conducted in Japanese).
https://www.city.motomiya.lg.jp/site/kanko/miru.html(Automatic translation)
http://iwatsunosan.com/inquiry/
Estimated Visit Time
Free to visit
A festival is held at Mt. Iwatsuno during the new year holidays.
Higashiyaguchi 84, Wada, Motomiya City, Fukushima Pref. 969-1205
By Car: 15 min drive from the Motomiya I.C. exit off the Tohoku Expressway.
By Train: 15 min by taxi from Motomiya Sta. (JR Tohoku Main Line).
Kyu Horikiri-tei
Kyu Horikiri-tei is a property steeped in history. Built in 1775, the building has been preserved since the Edo Period thanks to wealthy farmers and merchants. The property contains a large kura (storehouse), called Jukken Kura, as well as a traditional Japanese manor house. There is a public footbath located onsite. Use of the public footbath - which gets its water from the nearby onsen hot spring source - is accessible for wheelchair users. Japanese-speaking volunteer guides, knowledgeable about the history of Kyu Horikiri-tei and the rest of Iizaka Onsen, are available upon request.
Futamata Onsen
Outdoor hot spring baths line the sides of the valley at the foot of Mt. Futamata. The open-air baths of Futamata Onsen's ryokan are situated in a very peaceful location, surrounded by ancient forests full of beech trees – all at an altitude of 800 m. Futamata Onsen's hot spring baths have been used for about 1200 years, and are particularly revered for the hot spring water's healing properties. What's more, being close to Ouch-juku, Futamata Onsen is conveniently located for a visit during your trip to Fukushima.
Nanko Park
In 1801, Matsudaira Sadanobu, the twelfth Lord of Shirakawa, constructed a recreational area which was to be opened to anybody - regardless of status or family background. This recreational area turned into Nanko Park, which is considered to be the Japan's oldest public park. There are Yoshino cherry blossoms (about 800 trees), azaleas, pine trees, and maple trees at the edge of lake. You can enjoy seasonal scenery such as cherry blossoms in spring, fresh green leaves in early summer, autumn colors, and winter scenery with the beautiful contrast of the Nasu Mountains. The park contains Nanko Shrine, where Sadanobu is enshrined as a deity. Next to Nanko Shrine stands the beautiful Japanese gardens Suirakuen. At Suirakuen, visitors can try traditional Japanese tea served in a tea room, which boasts a spectacular view of the gardens. There are a number of shops, cafés, and restaurants along the edge of Lake Nanko. One of the local specialities to look out for is nanko dango, which are sticky rice balls on a skewer, served with different toppings.
Nakano Fudoson Temple
Nakano Fudoson is a Zen Buddhist temple built around a waterfall. Nakano Fudoson Temple is dedicated to the Buddhist deity Acala (Fudo in Japanese), one of the Buddhist 'Kings of Knowledge'. Three forms of this deity can be praised at different areas within this temple. Those hoping to ward off evil & bad luck can worship the deity at the main temple. Those looking to protect their eyesight in the coming year can pray at the Kitoden. Those wanting to worship the Fudo deity even more intimately can do so at the Okunoin cave complex, which contains 36 Buddhist statues.
Discover Central Area
Korinji Temple (Hydrangea Temple)
Korinji Temple, known as Hydrangea Temple, is home to around 20 kinds of hydrangeas. Many tourists visit every year to see the beautiful blue, purple and pink flowers that bloom from late-June to mid-July.
Yamamoto Fudoson Temple
Yamamoto Fudoson Temple was built over 1000 years ago in a rocky cavern. The temple can be reached by taking paths lined with century-old Japanese cedar trees, and climbing a 130-step stone staircase. The cave that makes up part of the Yamamoto Fudoson temple grounds is where the Buddhist deity enshrined at this temple is worshipped. Yamamoto Fudoson Temple is located in Yamamoto Park. This park is centered in a valley – 5 km of which is designated as an Okukuji Prefectural Natural Park. A wonderful place for flower-viewing throughout the year, this area is also great for experiencing beautiful autumn leaves.
Komine Castle
Shirakawa Castle (Komine Castle) was heavily damaged during the Boshin War (also known as the Meiji Restoration), and was restored in the 1990s. Komine Castle's restoration marked the first time in over 120 years that a restoration had been attempted on a triple turret (yagura) structure. Blueprints from the late Edo Period were used as references for the repair of this structure. As a result of using these blueprints, it was possible to restore the castle almost exclusively using wood construction techniques. This amazing architecture, along with the extraordinary techniques used to make the stone wall around the castle, make this castle extremely special. There is also an exhibition hall on site.
Shopping & Souvenirs
Aizu Area
Coastal Area
Safety in Fukushima
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Official Fukushima Tourism Information
If you have any questions, suggestions, or tour enquiries, please contact us using this form.Replies within 2 business days.


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Q: How to efficiently generate only odd (or even) random numbers? From looking at other questions regarding the generation of only even or odd numbers, I was curious as to what method would be the most efficient (in terms of speed).
For example, let's say that I wanted to generate many odd numbers that range from 1 (inclusive) to 1000 (exclusive); for each iteration, the method I would normally use is the following:

*

*Generate a random number between 0 (inclusive) and 500 (exclusive)

*Multiply by 2

*Add 1
Is there any better method?

A: I happened to stumble upon this method today and I can't find any use of it on StackOverflow for generating only even or odd numbers.
This method takes advantage of the fact that, in binary, all even numbers (including 0) have their least-significant bit set to 0 whereas all odd numbers have their least-significant bit set to 1.
Therefore, to generate an odd number, we can simply generate a random number within the desired range and bitwise-or it with 1:
ThreadLocalRandom.current().nextInt(0, 1_000) | 1

With this method, if the generator selects an odd number, then it is left alone.  However, if an even number is selected, then bitwise-or'ing it with 1 essentially increments it.
Likewise, to generate even numbers between 2 (inclusive) and 1000 (exclusive), then we just need to clear the least-significant bit.  To do this, we can simply bitwise-and the value with -2:
ThreadLocalRandom.current().nextInt(2, 1_000) & -2

With this method, if the generator selects an even number, then it is left alone.  However, if an odd number is selected, then bitwise-and'ing it with -2 essentially decrements it.
These methods work perfectly fine with negative values, and long as well.
Here is a JMH benchmark comparing the two methods for generating an odd number between 1 (inclusive) and 1000 (exclusive):
@State(Scope.Benchmark)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
@Warmup(iterations = 10, time = 500, timeUnit = TimeUnit.MILLISECONDS)
@Measurement(iterations = 20, time = 500, timeUnit = TimeUnit.MILLISECONDS)
@Fork(5)
public class MyBenchmark {

    private static final ThreadLocalRandom RANDOM = ThreadLocalRandom.current();

    public static void main(String[] args) throws Exception {
        org.openjdk.jmh.Main.main(args);
    }

    @Benchmark
    public int oldMethod() {
        return RANDOM.nextInt(0, 500) * 2 + 1;
    }

    @Benchmark
    public int newMethod() {
        return RANDOM.nextInt(0, 1000) | 1;
    }
}

And the results:
Benchmark              Mode  Cnt  Score   Error  Units
MyBenchmark.newMethod  avgt  100  6.079 ± 0.137  ns/op
MyBenchmark.oldMethod  avgt  100  6.325 ± 0.009  ns/op

oldMethod can be improved slightly by using << 1 instead of * 2, but newMethod is still slightly faster.

A: Your 2n + 1 approach is good. You can use streams and wrap it up in a helper method for ease of use if you like:
import java.util.Random;

public static IntStream randomOdds(int from, int upTo) {
    return new Random().ints(from/2, upTo/2).map(n -> 2*n + 1);
}

Usage:
randomOdds(1, 1000).limit(20).forEach(System.out::println);

redpajama_set_name: RedPajamaStackExchange


Novelist David Gilmour: "I'm not interested in teaching books by women"
The University of Toronto professor only teaches his students books written by "serious, heterosexual guys"
There are a few things that University of Toronto professor David Gilmour does not like: all Canadian writers, Chinese writers and female writers.
What does he like? Himself, it seems.
Also, dudes. Dudes can write.
In a recent interview with Hazlitt, an online magazine by Random House Canada, Gilmour said, "I'm not interested in teaching books by women."
"What I teach is guys. Serious heterosexual guys."
But I can only teach stuff I love. I can't teach stuff that I don't, and I haven't encountered any Canadian writers yet that I love enough to teach.
I got this job six or seven years ago, usually the University of Toronto doesn't allow people to become professors without a doctorate. You have to have a doctorate to teach here, but they asked if I would teach a course, and I said I would. I'm a natural teacher, I was trained in television for many years. I know how to talk to a camera, therefore I know how to talk to a room of students. It's the same thing. And my book 'The Film Club' is about teaching my son about life and the world through film.
Gilmour's extreme point of view is a perfect example of the woman problem in literature. (Also, it's not just literature).


Posted at 1:08 pm on June 26, 2018 by Sarah D.
I believe that those opposed to the despicable Trump administration need to stand against it with civility.
It's easy to fight hate with hate, but that's no way to win the battle. We must, as a people, rise above and be the moral beacons of love and leadership we no longer have.
Parkland anti-gun crusader Cameron Kasky. The same kid who mocked Kyle Kashuv for his respect for shooting survivor Steve Scalise. The same kid who claimed that the NRA loves the mass murder of children. Leading the charge of love.
We'd like to think he deleted it because he realized he's a flaming hypocrite, but he doesn't seem self-aware enough for that. But hopefully his buddy David "the NRA are 'pathetic f**kers that want to keep killing our children'" Hogg will weigh in soon with his own call for civility.


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Statements of Common Ground detail protection of woodland and more
Eastleigh Borough Council has recently published a range of 'Statements of Common Ground' with key partner and statutory organisations that outline an agreed approach in relation to its Local Plan.
The Council has received a positive Statement of Common Ground that specifically relates to measures that will be included in the plan to ensure that Ancient Woodland is protected. This Statement, that has been agreed by Natural England, the Forestry Commission, Forestry England, Highwood Group Ltd, and Galliford Try Partnership/Drew Smith Ltd, will provide extra protection to ensure that the ancient woodland and the biodiversity they support are protected and enhanced..
The Council has always maintained a commitment to protecting the ancient woodland and associated ecological networks. During a consultation process last year widespread comments were received about concerns of the impact of new housing on a site close to ancient woodland. The resulting modifications to the local plan policy that the Council has proposed include:
A net gain in the biodiversity of the ancient woodland;
The provision of new green spaces, tree planting and habitat creation, including: open buffers of at least 50 metres
Measures to provide environmental connections across the link road (for example green bridges, animal tunnels, and overarching trees);
Retention of individual mature trees and hedgerows within the site wherever possible, or else ensure their replacement;
Restoration and enhancement of the existing woodland;
A visitor management plan and long-term funding to maintain the new habitats, nature park and visitor management measures.
Statements of Common Ground have also been agreed between The Council and Winchester City Council in terms of 'duty to cooperate' and a general policy approach to phasing and safeguarding the route of the link road. These Statements of Common Ground have been placed on our website (document reference ED30 & ED31) and other Statements of Common Ground will be published in due course.
Council Leader Cllr Keith House said, "These Statements of Common Ground represents a very important and positive step in demonstrating that the Council has a sound local plan. We look forward to receiving the dates for the public inquiry in due course."

redpajama_set_name: RedPajamaCommonCrawl


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Organic Olivia
Why Cauliflower Heals Your Brain + Boosts Memory
Published on: 19 March, 2016
Last update: 07 August, 2017
Oh cauliflower, how I love you (after years of resisting you as a kid)! This cruciferous vegetable is one of the healthiest foods on the planet, and I'll tell you why. First of all, it looks like a brain. And although something looking like a body part isn't very science-y, it tends to be true throughout all of nature.
Luckily, there's a ton of information to back that up. Cauliflower (and several other cruciferous veggies such as bok choy and broccoli) is rich in a compound I've been obsessed with learning about lately: lecithin. Lecithin, in my opinion, is the most important nutrient that your brain and nerve signals need to do their job.
What is lecithin?
Lecithin is a fat-like compound present in cell structures that participates in metabolism, and is the source of two highly essential nutrients: choline and inositol. (We'll talk about these in a moment!)
Lecithin is fundamental in breaking down fats in your body.
This means that it can prevent fat build ups in the cell walls of your heart, arteries and brain by breaking down the fatty deposits. This is very important, due to the recent discovery that people with Alzheimer's disease have fat deposits in their brains.
Researchers affiliated with the University of Montreal Hospital Research Centre (CRCHUM) were responsible for the discovery of these fat droplets. "We found fatty acid deposits in the brains of patients who died from the disease and in mice that were genetically modified to develop Alzheimer's disease. Our experiments suggest that these abnormal fat deposits could be a trigger for the disease", said Karl Fernandes, a researcher at the CRCHUM and a professor at University of Montreal.
"We discovered that these fatty acids are produced by the brain, and build up slowly with normal aging, but that the process is accelerated significantly in the presence of genes that predispose to Alzheimer's disease" explained Karl Fernandes. "In mice predisposed to the disease, we showed that these fatty acids accumulate very early on, at two months of age, which corresponds to the early twenties in humans. Therefore, we think that the build-up of fatty acids is not a consequence but rather a cause or accelerator of the disease."
This would be where lecithin comes in, with its unique ability to break down and emulsify fat accumulations all throughout the body. In fact, lecithin is one of the very few materials able to pass the blood/brain barrier. That means it gets to work its magic in our gray matter, too.
A breakthrough study done in 1975 at the Massachusetts Institute of Technology (MIT) found that a daily supply of lecithin (through food or dietary supplements) "improves brain chemical activity." In particular, they found that lecithin affected brain activities such as learning, memory, motor coordination, sensory feedback, and sleep patterns. In his book Total Concentration, Dr. Levinson, a neurologist from New York, states that he frequently uses lecithin to help both attention and learning in his young patients.
Lecithin's additional functions in the human body include:
normalizes reproductive health
enhances liver + heart health
lowers cholesterol
and increases physical performance.
Why so many benefits? What is lecithin made of?
Lecithin is made up of many components that are crucial to organ and brain health. I've already mentioned two of them (choline and inositol) but the other major stars are linoleic acid and phosphatidyl serine. Let's go through each of them.
• Linoleic Acid
Linoleic acid accounts for 60% of the fatty acids in lecithin. A deficiency in linoleic acid can cause changes to the skin, disturbed water balance, atrophy of the reproductive glands, and a decrease or cessation in lactation among nursing mothers. The rich linoleic acid content of lecithin is a part of the reason why lecithin granules are often eaten as a supplement to unblock milk ducts in those who are having trouble producing enough breastmilk.
• Phosphatidylserine
Phosphatidylserine (PS) makes up about 5.6% of lecithin. It is highly concentrated in the cell membranes of our nerve cells. It has been found to stimulate nerve growth factor synthesis involved in nerve tissue regeneration (great for healing and rebuilding the brain/neurons)! This study also found that PS reverse fading EEG signals that have been correlated with diminishing memory functions. It truly is a remarkable brain nutrient. There are currently about 64 human studies showing that dietary intake or supplementation with PS can alleviate and sometimes even reverse age-related decline of memory, learning, concentration, word skills, and mood. PS also may improve the body's capacities to cope with stress and maintain the internal circadian rhythms.
• Choline (also known as phosphatidylcholine)
Choline is sometimes classified as a B vitamin (B4). It makes up a large percentage of lecithin, and its function is to prevent the accumulation of fat in the liver and in the transition of nerve impulses across cholinergic synapses. Most importantly, choline is required by the brain for the production of acetylcholine, which is involved in muscle function, memory, and learning among other vital processes.
Research has shown that choline deficiency can result in Metabolic Syndrome, a condition involving insulin resistance, elevated serum triglycerides, increased serum cholesterol and obesity.
Deficiency of choline can also cause high homocysteine in the brain (leading to mood troubles and high cardiovascular risk), kidney damage, fatty liver, stunted growth, poor memory, Alzheimer's disease, Huntington's chorea, and more. Deficiency during pregnancy has been linked to long-term learning disability and memory problems in offspring.
Inositol, like choline, is often classified as a B vitamin (B8) although it's not really a vitamin at all. Inositol makes up ~7% of lecithin. It is present in green plants, organ meats, and grains such as brown rice. In our bodies, inositol is used to build our liver, kidneys, spleen, brain, and the lens of the eye.
A deficiency of inositol can cause stunted growth, hair loss, and fatty liver — very similar symptoms to a deficiency in choline, since they're both part of the lecithin we're discussing here.
Research has shown that those with low levels of inositol in their body have a higher chance of suffering from mood disorders such as depression, anxiety, and bipolar disorder. Inositol is known to participate in the action of serotonin production in the brain and works on nerve systems that respond to mood stabilizers.
Cauliflower as a source of lecithin
Lecithin is found in many animal and vegetable sources, including beef liver, steak, eggs, peanuts, cauliflower, and oranges. Although its concentration is highest in animal foods (a whopping 3,362 mg per egg yolk!), you can get a very healthy amount from cruciferous vegetables. Plus, you get all the other brain-boosting benefits that come with cauliflower!
1 cup of cauliflower will provide you with 214mg lecithin, and from that, 60mg of choline (which will allow your brain to make the neurotransmitter acetylcholine that I mentioned earlier – it's important for memory, learning, and even muscle function).
The benefits don't stop there!
On top of all this, cauliflower is a rich source of B5, which supports the adrenal glands, prevents chronic fatigue syndrome, and helps the body deal with tension. It also contains B1, B2, B3, and B6 (meaning cauliflower is a superfood for daily stress, since the B vitamins are responsible for protecting your brain and nerves).
And although the lecithin itself present in cauliflower is an excellent liver-booster, other compounds found in the cruciferous vegetable such as sulforaphane, glucobrassicin, glucoraphanin, and gluconasturtiian are just as helpful. These sulfur-based compounds support phase I and II of the liver's detoxification process. They help to trigger the liver to produce detoxifying enzymes that block free-radical damage.
And lastly, of course, cauliflower protects against cancer. Cauliflower has been shown to have chemo-preventive agents that stall early phases of cancer development to help shut off tumor growth. Studies demonstrate that cauliflower is especially useful for preventing breast cancer in addition to colon, liver, lung, and stomach cancers.
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Cassie Autumn Tran
I love cauliflower! It's such a beautiful and versatile vegetable. I try to eat it at least once every single week!
Amory
Olivia, how do you eat your cauliflower ? Raw or lightly steamed? Or another way? Thanks!! I love all that you do, you are AMAZING ❤


(CBS13/CBS Local) – Why settle for a Yule Log when you can burn a KFC 11 Herbs and Spices Firelog this Christmas? And yes, it does smell like fried chicken.
The Firelog is now for sale on KFC's website. It's described as being: "Imbued with the unmistakable, mouth-watering aroma of Colonel Sanders' secret recipe." The Firelog is manufactured by Enviro-Log and made of 100% recycled materials.
Customers can buy the Firelog online, but are limited to one 5 pound Fried Chicken-scented log. The Firelog will burn for approximately 2.5-3 hours and can be stored since there isn't a shelf life.
Store in a safe, dry area away from pets.
The KFC 11 Herbs and Spices Firelog costs $18.99 and that includes tax, shipping & handling.


South Carolina children in need will receive funding for new books for their school libraries thanks to a gift from the South Carolina Conference of The United Methodist Church and its Million Book Effort.
The South Carolina Conference of the UMC donated $22,000 to three Title I schools in the state: West Lee Elementary School, Rembert; Main Street Elementary School, Lake City; and Greeleyville Elementary School, Greeleyville.
The schools received a check for $7,665.28 apiece.
"For me, it was a tremendous feeling of joy to see the smiles on the faces of the children and teachers and know that United Methodists in South Carolina were making a difference in their lives," Bishop Jonathan Holston said.
The funding came about thanks to the Million Book Effort the South Carolina Conference of the UMC initiated in 2014. United Methodists across the state were asked to donate funds and books to the initiative. The purpose was to raise awareness about the needs of public education by collecting books for preschool and elementary-aged children. Within nine months, 500,000 books had been collected and distributed to local schools, churches, community libraries, childcare centers and nonprofits that work directly with children. At the same time, many churches in the conference gave monetary gifts for the purpose of supporting underfunded public school libraries.
"There was over $22,000 in the Million Book Effort account that churches had given to our book drive last year," said Martha Thompson, chair of the Children in Poverty Task Force for the South Carolina UMC.
The Children in Poverty Task Force asked Thompson to work with the State Department of Education to help identify schools who could benefit from a gift for the library.
"All three schools are Title I schools with 94 percent receiving free or reduced lunches," Thompson said.
On Nov. 18, Thompson, along with other UMC leaders—including South Carolina Resident Bishop Jonathan Holston, Director of Connectional Ministries the Rev. Kathy James, Greenwood District Superintendent (and task force member) the Rev. James Friday and Director of Communications Matt Brodie—made a road trip around the state to personally present the schools' principals with their check.
Dr. Marilyn Bennett, principal of West Lee Elementary School in the Hartsville District, was speechless when told about the gift. She had retired from teaching but returned to West Lee Elementary as principal to make a difference to the children she loved. Dr. Robin Dease, Hartsville District superintendent, and other United Methodists in the area made a special trip to be part of the presentation.
Dr. Shemmica Moore, principal of Main Street Elementary in Lake City, not only needed books, but there were no tables in the library nor soft areas to enhance the environment. The Rev. John Hipp, Florence District superintendent, along with the Florence School District #3 Board of Trustees, gathered for the check presentation to Moore.
As the team drove to present the check to Greeleyville Elementary in Williamsburg County, the ravages of the state's recent floods were evident. At the school, Berlinda Mack was waiting with open arms of welcome. As students and faculty gathered in the small library for Bishop Holston's presentation, shouts of joy and disbelief rang through the halls.
Thompson chairs the South Carolina Conference's Children in Poverty Task Force. To learn more about its work, visit www.umcsc.org/home/ministries/children-poverty-initiative.


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Brooklyn Bangers & Dogs
Main Concourse,Upper Pavilion 8 & 225
Saul Bolton opened The Vanderbilt with a mission in mind: to make great sausages.
"We're all about sausages," said Bolton. "So we started in the facility making hot dogs, weisswurst, knockwurst, cheddar brats, across the board, everything we could get our little hands on.
"You can get the books, get all the stuff together, but you don't make a great sausage without good meat, a lot of hard work and attention to detail."
From there, Brooklyn Bangers was born. Bolton and his team brought their sausages out of the restaurant to flea markets — "It was like a guerilla affair," said Bolton — and now they're available in other restaurants, supermarkets, and at Barclays Center, where his Smoked White Cheddar Brat is one of the arena's most popular items.
"We're able to bring our sausages — delicious sausages I must say — the cheddar brat's there, the weisswurst is there, our hot dogs are also there," said Bolton. "We have kosher hot dogs by the way, which are made in Williamsburg. The rest is history. Now we're just super happy to be there."
After working with premier chefs Gordon Hamersley, David Bouley and Eric Ripert, Bolton opened Saul Restaurant on Smith Street in Boerum Hill in 1999. The restaurant earned a Michelin star for the first time in 2005 and maintained it for eight years.
In 2010, Bolton and partners decided to open a gastropub in a sunny corner spot just a few blocks from where Barclays Center would soon open. Sausages had always had a place at Saul Restaurant, but at The Vanderbilt they became the star of the show.
"To be able to take the food at Saul, which was catering to a specific demographic, maybe a little more expensive, and bring it to Prospect Heights on Vanderbilt Avenue in a more relaxed setting with good beer, it's a dream," said Bolton. "We can crank good music and really have a casual good time. That's what I came for."
The Vanderbilt is located at 570 Vanderbilt Avenue in Prospect Heights at the corner of Bergen Street. Saul Bolton is also the Culinary Director of The Norm at Brooklyn Museum.
Signature Item: Cheddar Brat


Pilkington Automotive provides glazing solutions for one in three vehicles in the world, as well as supplying many of the major automotive groups including Toyota, Ford, Volkswagen, Renault, Nissan, PSA Peugeot Citroen, Honda and BMW.
Its European business alone comprises of a complex network of large and small retail fitters across wholesale and distribution markets encompassing a large European Distribution Centre, five European hubs, as well as over 60 smaller warehouses.
The quality of the NSG Groups products has ensured a thriving integration into the manufacturing of vehicles. Most recently, The NSG Automotive plant at Collingwood, Ontario, Canada, has launched a production of windscreens for the Opel Astra in November 2011.
Collingwood's contract is concentrated on meeting Europe's demand for gravity bend windscreens. The gravity bending process is a complex process that involves heat being applied to large sheets of glass, and bent. The process is the most common production method for creating windscreens for large buses and specialised motor vehicles.
Other involvement, which marks two 'firsts' for Pilkington Automotive, includes the manufacture of a complete glazing unit for the new sleek Vauxhall Zafira Tourer – the first, manufacturing the tallest car windscreen, at 1.5m – and the second, the creation of the largest laminated roof light that any automotive manufacturer has ever produced.
Together, NSG and Vauxhall have created a 2.8m stretch of glass that extends from the dashboard to the rear seats. This massive stretch of glass has been intelligently manufactured – offering the driver a wide range of benefits.
The NSG group invests over £29 million a year in research for automotive glazing solutions – being able to provide manufacturers and drivers alike with the very best in windscreen technology.
Pilkington Automotive's Hotscreen™, a heatable glass solution, provides a technology that helps de-ice and de-mist a vehicles windscreen. The innovative incorporation of fine heatable wire gives the glass the capability to complete the task in temperatures of around -5°C, within two minutes.
Hydrophobic glazing, another solution that looks to improve a driver's visibility, tackles the issue of driving in rainy conditions. The glazing solution controls the contact angle that the raindrops land on the windscreen, ensuring that any water is dispersed quickly, improving the view through the car windows.
Solar Control Glazing is also available to reduce the effects of the heat build-up inside a car. Manufactured containing a special resin (Poly Vinyl Butyral, PVB), this glazing reflects the suns heat, improving the drivers comfort as well as minimising the sun's radiation on interior trims and fabric.
Pilkington's Automotive glazing solutions adds aesthetic, functionality as well as structural properties to a vehicle design, so next time you get in your car – take a moment to look at your windscreen, not just through it.
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\section{Introduction}
Deep Neural Network (DNN) has achieved great success in performing classification \cite{andor2016globally}, image detection \cite{he2015delving} and speech recognition \cite{xiong2016achieving}. All these successes lead to another interesting topic about how well DNN will perform practically depends heavily on how well we address the issues concerning the security of these complex models. It was observed by  Szegedy et al. (2014) for computer vision \cite{goodfellow2014explaining} and Biggio et al. (2013) for malware detection \cite{biggio2013evasion} that DNNs are vulnerable to adversarial examples which can be generated just by slightly changing inputs or by introducing random noise to the inputs. Not only image classification but also other popular fields of DNN are facing vulnerability to adversarial examples \cite{kos2017adversarial,kos2017delving}. Similarly, The behaviour of popular Convolutional Neural Network (CNN) models is no exception as it shows no resistance to adversarial examples.

\begin{figure}[h]
\centering
\scalebox{1}{
\includegraphics[width=8cm,height=3cm]{images/mnist.png}
}
\caption{Adversarial attack causing miss classification in MNIST data set}
\end{figure}

There are now various works on generating adversarial attacks and developing corresponding defense methods. Attacking CNN can be classified into two major categories. In one case the attacker has full access to the network model and parameter which is known as white box attack. On the other hand, for black box attack the attacker sees the model as a black box and can only play with the data. Fast Gradient sign method is one of the popular white box attack method which uses the sign of the gradient of loss function \cite{goodfellow2014explaining}. Different authors have proposed different attack models to adapt the attacking techniques beating the improving defenses \cite{szegedy2013intriguing,kurakin2016adversarial,kos2017delving,papernot2016limitations,moosavi2016deepfool}. Some of the recent attack methods achieved even better success causing CNN to classify whole MNIST data set to wrong classes. For example, projected gradient descent is one of the most powerful white box attack till date \cite{mkadry2017towards}.

Many defense method have been proposed to beat adversarial attacks. Some of them achieved good successes mainly against weak attacks, such a Fast Gradient Sign Method(FGSM) and Jacobian Slaiency Map attack (JSMA) \cite{gu2014towards,papernot2016distillation,papernot2016towards,xu2017feature}. However, None of them could recover current state of the art accuracy against powerful attacks. One major improvement to the those defenses came when defending model started using adversarial examples while training \cite{tramer2017ensemble,mkadry2017towards}. But this defense has to two major drawbacks. First, as a defender we do not know the attack model. So it is difficult to choose which adversarial attack method should be used to generate adversary for training. Second, it would double training cost. Still most recent defenses use this technique to strengthen their defense. In this work we look to eliminate the need for using adversarial training by processing the input with the objective to make the neural network more robust. Moreover, we observed all these defenses mentioned here loses its strength when the attack strength is increased. So by implementing this proposed defense we look to make the defense more robust against a wide variety of attacks.

Inspired by recent One hot coding and thermometer encoding of input data \cite{buckman2018thermometer}, in this work, we propose blind pre-processing (BP) of input data. We process the input data using Tanh function, batch normalization, thermometer encoding and one hot encoding. These techniques work in combination with discrete data to defend CNN against adversarial attacks. However, we change the attack set up by introducing an engineering solution to the gradient problem suggested by recent Obfuscated Gradients paper \cite{obfuscated-gradients}. From the work of obfuscating gradient it is obvious that no white box defense model would work in the presence of powerful attack model. Specifically, none of them would work if it has gradient shattering, stochastic gradient and vanishing gradient as their defense weapon. They also suggested thermometer encoding is using stochastic gradient and they could fool this defense by their proposed attacking model. However, we introduce a more practical white box attack where the attacker will have access to the model, weights, input, gradient and training algorithm but not to the pre-processing layer. we will use obfuscated logic design by proposing a hardware assisted pre-processing layer to counter the obfuscated gradient's false sense of security \cite{obfuscated-gradients}. We strongly believe their attack model has too many freedom and restricting the defense with a lot of constraints. As a result, we believe future attack model should evaluate their models strength by considering this blind processing layer in front of the model. This defense is more realistic for practical application such as autonomous vehicles.

Our proposed defense method makes the CNN more robust to defend the strongest attack, Logit Space Gradient Ascent(LS-PGA) attack and also the general strong attack suggested by Anish et. al \cite{obfuscated-gradients}. It is a modified version of PGD attack on discrete data. The main contribution of this work is that we could achieve very high accuracy on MNIST without adversary training for the first time. If we include adversary training, our defense methodology provides the best accuracy against MNIST, Cifar 10 and SVHN till date. Not only that with increasing attack strength when all the defenses completely fails, blind pre-processing still defends the CNN causing minimal accuracy loss.  Contributions of this work are summarized as follows:

\begin{itemize}
    \item We derived from the previous analysis of adversarial examples that introducing non linearity would increase the defense to any adversarial attack. As a result, We introduced a series of functions performing non linear transformation of input data to build a robust defense.
    \item After making the model robust using pre-processing we propose obfuscated logic based implementation of the pre-processing layer so that this network do not fail due to its obfuscated gradients. This modification will also make all the defense that use obfuscated gradient to succeed again.
    \item This proposed defense resulted in increasing the accuracy for MNIST data set from 94.02\% to ~98.66\% with adversarial training and from 0\% to 98.29\% without adversarial training. This is the best so far achieved on LS-PGA attack without adversarial examples. Even with adversarial training, previous attempts could only reach as far as ~95\%.
    \item We showed that with increasing attack strength our defense remains one of the strongest. Our analysis provides a more detailed understanding of why blind pre-processing stands out in the advent of adversary attack while others fail.
    \item Additionally by using Adversary training we could achieve 95.06\% accuracy on SVHN data set and 86.66\% accuracy on Cifar 10 data set.

\end{itemize}

In the next two sections we summarize the attack model and blind processing scheme to defend those attack models. In section IV our proposed defense technique is described and the following two sections represents the results and analysis of the proposed BP method.

\section{Attack Algorithm}

In this work, we assume that the adversary has complete access to the network which is popularly known as the white box attack. It was observed from previous works that if a defense model performs well for white box attack it should naturally perform better against black box attack \cite{mkadry2017towards}. Since black box attack becomes really weak due to the unavailability of network parameters. That is why in general any defense that wants to prove its effectiveness must perform well against white box attack. In order to formulate the attack method for this work, we first investigate some of the popular white box attack models:

Madry et al. introduced Projected gradient descent which achieved 100\% success in fooling CNN to miss classify MNIST dataset  \cite{mkadry2017towards}. In their work they show that PGD will generate universal adversary among the first order approaches. They suggested that training network with this kind of adversary would make the neural network more robust. Taking their work forward this algorithm was modified for discrete input \cite{buckman2018thermometer} known as discrete gradient ascent and logit spcae projected gradient ascent.

\begin{algorithm}
   \caption{LS-PGA Attack}
    \begin{algorithmic}
        \STATE Input: Image(x),Label(l), attack steps(n), $\epsilon,\delta$,k(quantized level),Loss function L(w,f(x),l)
        \STATE mask = 0
        \STATE $low =max[0,x-\epsilon]$
        \STATE $high=min[0,x+\epsilon]$
        \FOR{$i = 1$ to ${k+1}$}
            \STATE $mask=mask+f_{sdi}(\alpha*low+(1-\alpha*high)$
        \ENDFOR
        \STATE $u^0\ Initialization\ using\ mask$
        \STATE $T=1$
        \STATE $z^0=F(\sigma(u^0/T))$
        \FOR{$j = 1$ to ${n}$}
            \STATE $grad=\nabla_{z^{j-1}}.L(w,z^{j-1},y)$
            \STATE $u^j=u^{j-1}+\epsilon.grad$
            \STATE $z^{j}=\sigma(z^{j-1}/T)$
            \STATE $T=T.\delta$
        \ENDFOR
        \STATE {\bfseries output:}\ z \ after\ n\ iteration
    \end{algorithmic}
\end{algorithm}

Since this method is more  suitable for discrete inputs we choose to evaluate our model against this attack. Additionally the success of this white box attack tempted us to evaluate our defense model against it. Finally, based on the success of these attacks against varying defenses we summarize their strength in table 1.

\begin{table}[]
\centering
\caption{Classification of the strength of the attacks}
\scalebox{0.85}{
\begin{tabular}{|l|l|l|l|l|}
\hline
Attack                                                                      & FGSM & JSMA & C \& W                                                                           & PGD                                                                                \\ \hline
\begin{tabular}[c]{@{}l@{}}List of \\ defense\\ it can \\ beat\end{tabular} & x    & x    & \begin{tabular}[c]{@{}l@{}}1.Distillation\\ 2. Feature \\ Squeezing\end{tabular} & \begin{tabular}[c]{@{}l@{}}1.Distillation\\ 2. Feature \\ squeezing\\ 3. PGD \\ training\\ 4.Thermometer \\ (Without adv)\end{tabular} \\ \hline
Strength                                                                    & Weak & Weak & Medium                                                                           & Strong                                                                                \\ \hline
\end{tabular}}
\end{table}

The vast majority of machine learning problem is solved with first order  methods like gradient descent or other closely modified versions. That is why those attacks that rely on only first order information could be labeled as universal attack. Since LS-PGA/PGD attack model depends only on the first order information, defending against LS-PGA attack would certainly make the model robust against a wide variety of attack models. This attack takes place by placing each pixel in a random bucket within $\epsilon$.
At each step of the attack it will look at that bucket to find values within $\epsilon$ of the true value and select value that will do the most harm. The outcome of this attack will vary depending on the initialization. So the attack needs to be run several times to get the desired result. In case of Logit space projected gradient ascent first discrete encoding was relaxed to continuous space to perform projected gradient ascent. By changing the values of $\epsilon$  and attack step we can certainly change the strength of the attack. We report the performance of our defense against varying attack strength in section VI.

Recent attack developed by \cite{obfuscated-gradients} is reported to be the more generalized and powerful till now. They not only fool the thermometer encoding but also all the recent defenses. In the next section based on their analysis we introduce the concept of blind processing and also present why obfuscated gradient can actually work as a defense under our defense model. Moreover, we present a attacking scheme for future attacking models to test and evaluate their attack models.

\section{Concept of Blind Pre-Processing}
Obfuscated gradient occur due to gradient shattering, stochastic gradient and gradient vanishing \cite{obfuscated-gradients}. From the analysis presented by \cite{obfuscated-gradients}, it is evident any defense that uses one of these as their weapon to defend the adversary that will eventually fail. As reported this obfuscated gradient gives a false sense of security. Their generalized attack have caused all of the recent defense models to fail. Thermometer encoding is no exception as it has both gradient shattering and stochastic gradient issue. For our defense to work we need to make this obfuscated gradient based defense to function properly in the advent of powerful attack. So the final conclusion is in a typical white box attack set up it is impossible to defend white box adversarial attack. Even though \cite{mkadry2017towards} defense works but the success is very small. We conclude that the attacker has too many freedom and defense is constrained by different optimization problems. As a result, we propose a more practical way of dealing with the problem on hand. We propose blind processing where this processing layer is absolutely unknown to the attacker.

In this defense model, the attacker can not access the function of the pre-processing layer. We use obfuscated logic to hide the design of our pre-processing layer. The attacker can access the input and output of the process but can never predict the functionality of the model. Previous work have been done in this field using hybrid polymorphic logic gate to obscure the functionality of a logic gate. We propose using such logic gate to pre-process the data in a practical application such as autonomous vehicle. In this way the attacker can never replicate the module for two reason. Firstly, the layout using polymorphic logic looks same for all kinds of logic. In this way the hacker can never predict the functionality by hacking the module and analyzing layout. Secondly, the attacker can never guess the functionality using the pattern of input and output of the pre-processing layer because this logic is key assisted. In this way whoever does not have the key can never access the pre-processing module to generate input output data. The user have always the power to use this particular key to train the model and can switch off this particular functionality using another key. Additionally, the user can also use different key for different pre-processing functions. As a result it would become impossible for the attacker to guess the functionality of the defense model in the inference mode as well. In this way the whole pre-processing layer becomes a blind pre-processing layer for the attacker. Additionally, the key of this kind of logic is also secured. If the pre-processing layer has an input of M and logic gate of N then total number of key would be $2^{M+3N}$. So for a typical MNIST classifier the input to that function would be 256 and even if we assume the pre-processing layer is a simple and operation it still would have close to $9X10^{77}$ possible key combination. practically this is impossible for the attacker to hack in a limited amount of time. From this proposal we assure the security of the pre-processing layer which we define as blind pre-processing. As suggested by \cite{obfuscated-gradients} that a defence must define the attackers accessibility to make the defenses more practical. In our case, blind pre-processing keeps all the other attribute of the white box attack in tact. The attacker still has access to the following information:
\begin{enumerate}
    \item Model architecture
    \item weights and gradients
    \item Training algorithm
    \item Training data before and after pre-processing
\end{enumerate}

However, we are denying two accessibility to the attacker. Firstly, the attacker can not predict the pre-processing layer nor access the layer for gradients. Secondly access query is not allowed without the key in the pre-processing layer. This two attributes constitute the blind pre-processing.

\begin{figure}[h]
\centering
\scalebox{1}{
\includegraphics[width=8cm]{images/bloc.pdf}
}
\caption{Attacker access zone under Blind Pre-Processing defense}
\end{figure}

From the analysis of \cite{obfuscated-gradients} we concluded that it is impossible for traditional defenses to defend under the previous attacking scheme. As a consequence we introduce the concept of blind pre-processing to encounter the obfuscated gradient issue. As a result we set a new attacking scheme to evaluate their attack model against our defense model. We strongly claim that with blind pre-processing the generalized attack method suggested by \cite{obfuscated-gradients} becomes a failure. We suggest future attack model to generate adversary without back propagating through the pre-processing layer or generate adversary just by using the data after pre-processing layer. Since this new scheme for white box attack is more practical and gives the attacker some form of challenge. Thus any attack that want to succeed must prove its worth by beating these obfuscated gradient based defenses without approximating their gradient. Under this blind pre-processing defense model approximating the pre-processing function is close to impossible let alone the gradient.

\section{Proposed Defense Method}
In the analysis of existence of adversary in deep neural network, goodfellow et al. \cite{goodfellow2014explaining} concluded that deep neural networks exhibit vulnerability to adversarial examples due to their extreme linearity. Linearity of these models is the reason why they can not resist adversary. For example MNIST data set has an input precision of 8 bit. As a result, a common notion would be why a network would respond differently when the input is x+ $\epsilon$ instead of x where $\epsilon$ is really small. However, as practical experiments show that they do behave differently to these two inputs. To understand this we have to look into the linearity theory of deep models. Output of neural network y could be represented as y= $\sigma(W^TX')$.

\begin{equation}
{W^TX'=W^TX+W^T\epsilon}
\end{equation}

Even if $\epsilon$ is really small, if a weight matrix has n dimension and an average value of m, then this perturbation would result in $\epsilon$*m*n increase of activation. Thus with increased dimension, this noise keeps increasing linearly. It suggests that, with sufficient large input dimension, a network will always be vulnerable to adversary. It would then provide two possible directions to solve this issue. One is to introduce non-linearity to the network and the other is to eliminate unnecessary information from input data. First one is a bit tricky as introducing non-linearity inside the neural network creates problem for calculating gradient. Previously, Different non-linear activation functions were investigated but none of them worked due to the difficulty of computing gradients and poor accuracy on real test data set. So in this work, we choose the second direction by adding a pre-processing layer in front of neural network model which will not have any gradient issue. Additionally, blind processing makes the obfuscated gradient issue suggested by \cite{obfuscated-gradients} nullified. Moreover, adding pre-processing layer in front of the CNN model does not hamper the accuracy on clean data.

Our proposed blind pre-processing (BP) has tanh and Batch Normalization (BN) to process the input data in an attempt to make the CNN more robust. As shown in fig. 2 The input data goes through a non linear transformation first which is tanh function. A batch normalization layer is also added which is similar to the BN layer in CNN but it does not have any learnable parameters. After blind pre-processing input data were quantized to 15 different levels. They were presented in vectorized form of one hot coded value. Finally, they were encoded using thermometer encoding which basically inverts all the digits after first one appears. We observe that adding blind pre-processing before qunatization made the defense model more robust. So during all our experiment we quantize the input data after blind pre-processing. In our experiment it was found that adversarial training was no longer necessary for MNIST.

We also believe those function that uses local information works well as pre-processing layer. Moreover, this pre-processing can not sacrifice much accuracy on the clean data at the same time. Previously, thermometer encoding and quantization were suggested as some form of pre-processing. Our proposed blind pre-processing layer consists of tanh and batch normalization layer. But there could be other possible function that can be used as a blind pre-processing layer. The description of the functionality of those layers are presented here:
\begin{enumerate}
    \item Tanh and Sigmoid Function: The most popular activation functions are Sigmoid and Tanh functions. Both functions are used in typical deep neural networks. Mathematically they can be expressed as:
    \begin{equation}
    F_{tanh}(x)=\frac{e^x-e^{-x}}{e^x+e^{-x}}
    \end{equation}
    \begin{equation} F_{sigmoid}(x)=\frac{1}{1+e^{-x}}
    \end{equation}

Again during experiments it was observed that tanh outperforms sigmoid function. So we choose tanh to do the non linear transformation in the first layer.
    \item Batch Normalization (BN) function: Our normalization layer is similar to the batch normalization layer in a typical Convolutional Neural Network. However, it does not have any learnable parameters. Rather, this is just a pre-processing layer in front of the original neural network model.The function can be written as this:
    \begin{equation}
\small Y=\frac{X-\mu}{{\sigma}}
\end{equation}
where $Y$ and $X$ denote the input and output tensor respectively. $\sigma$, $\mu$ represents the standard deviation and mean of the input data for that particular batch. This is a linear layer which basically makes the mean of the input data to be zero and standard deviation to one.
\end{enumerate}

We used tanh in place of sigmoid in this work. As these set of combinations were found to perform better than the other. Each input image is passed through a tanh layer which works as a filter. Followed by a 3 by 3 filter of Maximum smoothing. Then the data goes through a Batch Normalization layer followed by a 15 level quantization. Finally encoded values were feed into the model for training.

\section{Experimental Results}
\subsection{Performance in MNIST Data set}

Our proposed defense method was first tested on a simple LeNet architecture for MNIST data set.MNISt is a set of hand written digit with 55000 training data and 10000 testing data. We used LS-PGA attack model to evaluate our model as described in section 2. In LS-PGA, we set $\epsilon$ =0.3, $\delta=1.2$, xi=1 and step-of-attack = 7 for the following experiments. Here, $\epsilon$ indicates how much the input pixels can be changes and $\delta$ is the attack step as described in section II.  In the later section, we also varied such parameters to see the effects on the performance of our proposed defense method.

Our defense model uses various processing layers to defend against adversary. We here investigate the efficacy of individual and combination of different pre-processing layers. Tanh function was chosen to be the first layer of  blind pre-processing. It was found that the performance is better if we put tanh function before batch normalization layer.
\begin{table}[h]
\centering
\caption{Result of Sigmoid and Tanh Functions}
\label{coding}
\scalebox{0.8}{
\begin{tabular}{@{}cccc@{}}
\toprule
\textbf{Function}  & \textbf{Clean training} & \textbf{ADV training} \\ \midrule
\textbf{Sigmoid} & \textbf{0} & \textbf{8} \\ \midrule
\textbf{tanh} & \textbf{69.55} & \textbf{94.42} \\
\bottomrule

\end{tabular}}
\end{table}

Table 2 indicates that the performance of tanh was far better than sigmoid as a filtering function. As a result, for our experiment we choose tanh instead of sigmoid. Meanwhile, thermometer encoding fails completely when adversarial training was removed. Whereas tanh function improved the accuracy even without adversary training. After tanh, we add a smoothing layer to investigate the effect of this layer. Based on the experiment, we observe maximum smoothing performs better than average smoothing.

\begin{figure}[h]
\centering
\scalebox{1}{
\includegraphics[width=8cm]{images/batch.pdf}
}
\caption{Effect of changing the batch size both with and without adversarial training}
\end{figure}

One of the key function here is the batch normalization layer. As a result, the batch size becomes an important tuning parameter for this defense to optimize. We also investigated the defense efficacy using varying batch size on blind pre-processing. Increasing the batch size would increase the accuracy of the overall data set. Because if the batch size is one, batch normalization is the same as normalizing the whole image. In experiments, we found that there is an optimal batch size to achieve highest accuracy without adversary training as shown in fig. 3. When adversarial training is included, in general, the accuracy increases when increasing batch size. For the case of no adversary training, we found that an optimum batch size of 50, which leads to the best accuracy. However, looking at the clean training we decided to choose an optimum batch size of 100 for our experiments. Because in figure 4 the red line goes up a little bit while blue line goes down from 50 to 100 batch size. It means we could optimize the accuracy between with adversary training and without adversary training using a batch size of 100 in place of 50.

\begin{table}[]
\centering
\caption{Accuracy on MNIST dataset using different defense combination}
\label{my-label}
\begin{tabular}{|l|l|l|l|l|}
\hline
\begin{tabular}[c]{@{}l@{}}Train\\ Type\end{tabular}                                 & \begin{tabular}[c]{@{}l@{}}Work\\ Type\end{tabular}                      & Defense                                                          & Clean & Attack \\ \hline
\multicolumn{2}{|l|}{Original CNN}                                                                                & No defense                                                       & 99.45 & 0      \\ \hline
\multirow{5}{*}{\begin{tabular}[c]{@{}l@{}}Without \\ Adv. \\ Training\end{tabular}} & \multirow{3}{*}{\begin{tabular}[c]{@{}l@{}}Proposed\\ BP\end{tabular}}   & BN                                                               & 99.23 & 98.05  \\ \cline{3-5}
                                                                                &                                                                          & Tanh                                                             & 99.56 & 69.55  \\ \cline{3-5}
                                                                                &                                                                          & Tanh \& BN                                                       & 99.2  & 98.3   \\ \cline{2-5}
                                                                                & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Previous\\ Work\end{tabular}} & \begin{tabular}[c]{@{}l@{}}Thermometer\\ Encoding\end{tabular}   & 99.2  & 0      \\ \cline{3-5}
                                                                                &                                                                          & \begin{tabular}[c]{@{}l@{}}Madry et. al\\ PGD Train\end{tabular} & 99.4  & 0      \\ \hline
\multirow{5}{*}{\begin{tabular}[c]{@{}l@{}}With\\ Adv.\\ Training\end{tabular}}      & \multirow{3}{*}{\begin{tabular}[c]{@{}l@{}}Proposed\\ BP\end{tabular}}   & BN                                                               & 99.4  & 98.64  \\ \cline{3-5}
                                                                                &                                                                          & Tanh                                                             & 99.5  & 94.42  \\ \cline{3-5}
                                                                                &                                                                          & Tanh \& BN                                                       & 99.43 & 98.65  \\ \cline{2-5}
                                                                                & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Previous\\ Work\end{tabular}} & \begin{tabular}[c]{@{}l@{}}Thermometer\\ Encoding\end{tabular}   & 99.03 & 94.02  \\ \cline{3-5}
                                                                                &                                                                          & \begin{tabular}[c]{@{}l@{}}Madry et al.\\ PGD train\end{tabular} & 99.2  & 95.7   \\ \hline
\end{tabular}
\end{table}

Table 3 tabulates the accuracy in MNIST with different combination of blind pre-processing layers as discussed above.It gives more insight about their performance against the attack model. The experiment were performed using two different types of training. Clean training  is performed on original training data of MNIST. While adversarial training includes adversarial examples inside the training data. Both training result was evaluated using clean data and attack data. Clean data test basically tells how well the network performs the classification task when there is no attack. One of the key point here is to find which combination works as a better defense method, while causing clean data accuracy to go down marginally. However, by including adversarial training this loss of accuracy could be recovered. This is quite obvious as adversarial training increases the overall robustness of the network. Batch normalization turns out to be the most powerful tool in this defense when we do not include adversarial training. As a result tanh and smoothing filter fails without the support of batch normalization. The best result without adversary training could be obtained is 98.36 \% which is one of the major contribution of this defense. Previously, both thermometer encoding and Madry et al. found that, under PGD/LS-PGA attack, CNN accuracy drops to zero on MNIST data set without adverarial training.Moreover, the combinations that include BN \& Tanh and Smoothing \& BN also performed really well to defend against adversary without adversary training. Based on the experimental results, it could be concluded that adverarial training is not necessary using our proposed defense method for MNIST dataset, which reduces training overhead and keeps the accuracy on the clean data to 99.24 \%.

Additionally if adversarial training is included in our proposed method, the accuracy could further be improved. In this case, the combination of tanh function and batch normalization provides the best performance up to 98.65 \% result. Smoothing only helps to defend the attack when adversary training is not included. Next, we will conduct similar analysis in another two famous dataset, i.e. Cifar 10 and SVHN.

\subsection{Performance in SVHN dataset}
The Street View House Numbers (SVHN) Dataset is a real world image for testing machine learning algorithms. For SVHN data set, we used Resnet-18 architecture \cite{he2016deep} in experiment and achieved state-of-the-art accuracy. Instead we only used tanh function for blind pre-processing on SVHN dataset. For this data set we used $\epsilon$ =0.047, $\delta=1.2$ xi=1 and step of attack = 10 as the the attack model parameter. We choose different attack model parameter for different data sets as reported by thermometer encoding defense. \cite{buckman2018thermometer} just to make the comparison fair.

\begin{table}[]
\centering
\caption{Result in SVHN dataset}
\label{my-label}
\begin{tabular}{|l|l|l|l|l|}
\hline
\begin{tabular}[c]{@{}l@{}}Train\\ Type\end{tabular}                                 & \begin{tabular}[c]{@{}l@{}}Work\\ Type\end{tabular}                    & Defense                                                 & Clean & Attack \\ \hline
\multicolumn{2}{|l|}{Original CNN}                                                                                & No defense                                              & 95.1  & 6.99   \\ \hline
\multirow{4}{*}{\begin{tabular}[c]{@{}l@{}}Without \\ Adv. \\ Training\end{tabular}} & \multirow{3}{*}{\begin{tabular}[c]{@{}l@{}}Proposed\\ BP\end{tabular}} & BN                                                      & 81.16 & 47.71  \\ \cline{3-5}
                                                                                &                                                                        & Tanh                                                    & 96.56 & 49.39  \\ \cline{3-5}
                                                                                &                                                                        & Tanh \& BN                                              & 84.19 & 47.86  \\ \cline{2-5}
                                                                                & \begin{tabular}[c]{@{}l@{}}Previous\\ Work\end{tabular}                & \begin{tabular}[c]{@{}l@{}}Thermometer\\ /\end{tabular} & 96.96 & 48.02  \\ \hline
\multirow{4}{*}{\begin{tabular}[c]{@{}l@{}}With \\ Adv. \\ Training\end{tabular}}    & \multirow{3}{*}{\begin{tabular}[c]{@{}l@{}}Proposed\\ BP\end{tabular}} & BN                                                      & 45.50 & 93.48  \\ \cline{3-5}
                                                                                &                                                                        & Tanh                                                    & 96.11 & 95.06  \\ \cline{3-5}
                                                                                &                                                                        & Tanh \& BN                                              & 59.84 & 94.55  \\ \cline{2-5}
                                                                                & \begin{tabular}[c]{@{}l@{}}Previous\\ Work\end{tabular}                & \begin{tabular}[c]{@{}l@{}}Thermometer\\ /\end{tabular} & 97.18 & 95.02  \\ \hline
\end{tabular}
\end{table}

From table-4, we could easily observe the effect of this defense changes for SVHN dataset. Firstly, batch normalization still works as a strong defense but it causes the clean data accuracy to go down a lot. Tanh and Smoothing performs better than thermometer encoding without adversarial training. Looking at the clean data accuracy we propose to keep only the tanh function for defending against adversary against SVHN dataset. Our proposed defense method combined with adversarial training could achieve 95.06 \% accuracy. But adversarial training had to be used since we removed BN for SVHN data to encounter for the loss of accuracy on clean data. Batch normalization layer worked as the key component in the defense of MNIST without adversary training. However, after including the adversary training and blind pre-processing we could recover state of the art accuracy on SVHN data set as well.

So here one of the key problem of using batch normalization and smoohting is that they do sacrifice a lot of clean data accuracy. After using BN and smoohting in the pre-processing layer we see that clean data accuracy drops down to 45.50 \% and 69.12 \% respectively. As a result even though they provide some form of non-linearity but fail as a defense. Thus one of the key conclusion from this work is that if we find more non-linear function that do not sacrifice clean data accuracy then they might be a potential candidate as a robust defense.

\subsection{Performance in Cifar 10 dataset}

For Cifar 10 data set, we used Resnet-50 architecture with a little bit of modification on the attack strength. Even though thermometer encoding reported that using wider network helps in increasing the accuracy against adversarial examples and wide resnet is found to perform really well in defending adversary. However, in this paper we wanted to show the performance of functional filter in improving the accuracy from the previous defense model and we leave the analysis on the choice of architecture for future investigation. We choose the attack parameter for cifar 10 with $\epsilon$ equal to 0.031 and number of step of attack equal to 7.

\begin{table}[]
\centering
\caption{Result on CIFAR 10 dataset}
\label{my-label}
\begin{tabular}{|l|l|l|l|l|}
\hline
\begin{tabular}[c]{@{}l@{}}Train\\ Type\end{tabular}                                 & \begin{tabular}[c]{@{}l@{}}Work\\ Type\end{tabular}                    & Defense                                                        & Clean & Attack \\ \hline
\multicolumn{2}{|l|}{Original CNN}                                                                                & No defense                                                     & 93.72 & 4.10   \\ \hline
\multirow{4}{*}{\begin{tabular}[c]{@{}l@{}}Without \\ Adv. \\ Training\end{tabular}} & \multirow{3}{*}{\begin{tabular}[c]{@{}l@{}}Proposed\\ BP\end{tabular}} & BN                                                             & 79.27 & 10.69  \\ \cline{3-5}
                                                                                &                                                                        & Tanh                                                           & 90.36 & 46.14  \\ \cline{3-5}
                                                                                &                                                                        & Tanh \& BN                                                     & 79.67 & 15.45  \\ \cline{2-5}
                                                                                & \begin{tabular}[c]{@{}l@{}}Previous\\ Work\end{tabular}                & \begin{tabular}[c]{@{}l@{}}Thermometer\\ Encoding\end{tabular} & 90.42 & 43.5   \\ \hline
\multirow{4}{*}{\begin{tabular}[c]{@{}l@{}}With \\ Adv. \\ Training\end{tabular}}    & \multirow{3}{*}{\begin{tabular}[c]{@{}l@{}}Proposed\\ BP\end{tabular}} & BN                                                             & 53.61 & 75     \\ \cline{3-5}
                                                                                &                                                                        & Tanh                                                           & 89.52 & 86.61  \\ \cline{3-5}
                                                                                &                                                                        & Tanh \& BN                                                     & 63.71 & 77.46  \\ \cline{2-5}
                                                                                & \begin{tabular}[c]{@{}l@{}}Previous\\ Work\end{tabular}                & \begin{tabular}[c]{@{}l@{}}Thermometer\\ Encoding\end{tabular} & 90.18 & 83.6   \\ \hline
\end{tabular}
\end{table}

Similar to SVHN we found good result using tanh as a pre-processing filter for cifar 10 data.  Additionally, it is observed that thermometer encoding produced a lower accuracy even without adversarial training. We could improve the accuracy without adversary training by 3\% while maintaining close to 90\% accuracy on clean data. After using adversarial training we could get close to 86.66\% accuracy on CIFAR 10. So regardless of data set, our proposed defense method helps in improving accuracy without adversary training. However, to achieve better accuracy, we need to include adversarial training. The accuracy reported in this paper is higher than the original thermometer encoding paper because we used a deeper architecture which makes the thermometer defense more robust.

After observing result for SVHN and CIFAR 10 it is clear that BN and smoothing causes a lot of sacrifice in clean data accuracy. This results in the model not learning properly during the training. As a result when we combine BN and Smoothing we get the worst result for this two datasets. THus we chose to keep only the tanh function in our proposed defense for CIFAR 10 and SVHN dataset. In the next section we analyze the expreimental result and try to explain their relative phenomenon.

\section{Analysis}

Based on the experimental results discussed in previous section, it shows that blind pre-processing can defend only MNIST data set without adversarial training. blind pre-processing can also defend against SVHN and CIFAR10 with adversarial training. We thus claim that the need for adversarial training for MNIST data set was eliminated. In this section we present some more in-depth analysis on MNIST data set to establish our claim. We also report black box attack accuracy in this section to prove the attacks strength even further.

In order to demonstrate the strength of our defense we show how far our defense can go in terms of defending in the advent of very strong attacks \cite{mkadry2017towards,buckman2018thermometer}. By changing the value of $\epsilon$ the strength of the attack can be modified. A larger value of $\epsilon$ indicates stronger attack. It was observed that the performance of previous defenses decreases exponentially with increasing attack strength as shown in table 5. However, BP defense strength decreases only a small amount with increasing attack strength is another proof of the success of BP as a universal robust defense.

\begin{table}[h]
\centering
\caption{Result of defense after increasing the attack strength-$\epsilon$ on MNIST}
\scalebox{0.8}{
\begin{tabular}{ |c|c|c|c| }
 \hline
 Value of $\epsilon$ & BP WithoutAdv & BP WithAdv & Thermo WithAdv \\
 0.1 & 98.99 & 99.07 & 98.52 \\
 0.2 & 98.68 & 98.88 & 96.85 \\
 0.3 & 98.36 & 98.61 & 94.02 \\
 0.4 & 97.78 & 98.22 & 82.64 \\
 0.5 & 95.52 & 97.88 & 38.08 \\
 \hline
\end{tabular}}
\end{table}

As described earlier changing the value of $\epsilon$ would change the accuracy a lot since it decides how much the input image would be changed to cause the miss classifications. Our defense can withstand this power full attack even if the value of $\epsilon$ is increased to 0.5. No previous defense method, even under weaker attacks like FGSM, can withstand such large level of change against MNIST data set. What makes it more interesting is that this result can be achieved even without adversarial training. If this defense model combined with adversarial training then this would become the strongest defense ever reported against MNIST data set. On the other hand the accuracy thermometer encoding drops down to 38.08\% when $\epsilon$=0.5, even using adversarial training. We analyse this robustness of our defense against Carlini Wagner method now. The mean distance change to cause the miscalssification against carlini wagner attack is a metrice to evaluate e defenses robustness.

\begin{table}[h]
\centering
\caption{Value of $\alpha$ on Different combination}
\begin{tabular}{ |c|c|c| }
 \hline
 Defense & $\alpha$ & accuracy \\
 Tanh+BN & 49.8 & 98.65 \\
 Tanh+ Filter & 45.98 & 83.54 \\
 Filter+BN & 49.78 & 98.63 \\
 All three & 49.78 & 98.61 \\
 \hline
\end{tabular}
\end{table}

We also found that overall strength of the defense depends on the accuracy of clean data as well. In order to analyze it, we first define an important parameter called attacked data accuracy ratio \textit{($\alpha$)}. If we assume the accuracy on clean data is x\% and on attacked data is y\% then,
\begin{equation}
    \alpha= \frac{y}{(x+y)}*100\%
\end{equation}

We observed that defense combination that performed well in table 3 and 4 do have a higher value of $\alpha$. Lets look back at the same combination of table 3 for using adversarial training in table 7.

\begin{figure}[h]
\centering
\scalebox{1}{
\includegraphics[width=8cm]{images/dis.pdf}
}
\caption{CIFAR 10 dataset pixel frequency distribution by using different BP}
\end{figure}

The higher the accuracy, the higher the value of $\alpha$ in table 7 . It is also the same for svhn and cifar 10. The significance of increasing $\alpha$ is that, with increasing performance of the defense, the clean data accuracy actually goes down a little bit. Better defense performance usually comes with a little sacrifice of clean data accuracy. Since BN performed the best for MNIST it also maintained a greater value of $\alpha$ indicating it will hamper the clean data accuracy most. As MNIST is a small dataset the effect was negligible. However, for SVHN and CIFAR 10, this effect on accuracy degradation on clean data was severe. That's why we choose to drop BN from the model. As we found tanh optimizes between clean data accuracy and attacked data accuracy best for those two. We could also explain this phenomenon of BN not working against cifar by analysing the pixel intensity distribution of the dataset.

In order to understand the robustness of our defense more intuitively it is necessary to understand the frequency distribution of dataset pixels. As shown in fig. 4, the whole dataset's pixel distribution completely changes after applying blind pre-processing. We understand that this redistribution in the image pixels plays an important role in the robustness of the defense. In fig. 4 typical CIFAR 10 dataset frequency distribution is displayed with pixel values on the x axis and their appearing frequency in the y axis. This distribution's standard deviation changes completely after applying Tanh processing. The key point is before applying blind pre-processing the pixel values are clustered around a certain pixel range. After processing with tanh their pixel standard deviation increases and becomes more scattered. As a result the pixel difference between the edges of the digit or object becomes larger. Thus fooling the network becomes harder as introducing noises will have less impact on the images due to larger transition near the edges. However, for CIFAR 10 BN does not change the distribution much like MNIST. The inability to change this distribution is one of the reason for the failure of BN in CIFAR 10 dataset.

\section{Summary}
We presented a robust defense method that uses a combination of pre-processing layers to defend against adversarial attacks. We choose tanh and batch normalization as our blind pre-processing layer. This processing layer is completely unknown and not accessible to the attacker. We could defend MNIST data set for the first time without using adversarial training. That reduces training time and complexity in the defense model. A combination of tanh and batch normalization worked in improving MNIST accuracy to close to 98\% from zero without adversarial examples. Moreoever, we showed that increasing attack defense where other defenses fail, our defense remains one of the strongest ever reported. Additionally the proposed BP model worked better than recent defenses against SVHN and CIFAR 10 as well. But, it required adversarial training to recover the accuracy upto 95\% for SVHN and 86\% for Cifar 10. We also showed under varying attack strength our defense would outperform its recent counter part.

\section{Discussion}
Currently, we are conducting experiments on a variety of attack. We got more than good result in defending FGSM, Carlini Wagner attack and also on black box box. We are also the first to defend against FGSM attack without adversarial training reaching close to 96 \% accuracy. Again for Carlini Wagner attack our defense model requires highest amount input perturbation to miss-classify the input for all three distance metrices. One of the strongest attack recently released succeeded in breaking all the ICLR 2018 defend models. Even though we tested against obfuscated gradient attack \cite{obfuscated-gradients} and got reasonable amount of success but our concept of blind pre-processing actually makes their attack void.
\section{Acknowledgements}
We would like to thank Jifeng Yi for insightful discussions.

redpajama_set_name: RedPajamaArXiv


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A really nice reflection from a proud PhD supervisor!
Yesterday Adis Maksic defended his Ph D dissertation "Mobilizing for Ethnic Violence? Ethno-National Political Parties and the Dynamics of Ethno-Politicization." Adis is a Sarajevo native who was fortunately able to come to the United States with his family after his family suffered a tragedy at the outset of the Bosnian War in 1992. He completed a Masters in our program in 2009 on referendum discourse in Republika Srpska, and returned to our program for a Ph D in 2011 with funding from the US National Science Foundation (our program, unfortunately, provides no independent Ph D student funding so most of our Ph D students are part-time students and full time working professionals in the DC area). Through his diligent research and translation work, we were able to write together "Serbs, You are Allowed to be Serbs": Radovan Karadžić and the 1990 Election Campaign in Bosnia-Herzegovina" which was published in Ethnopolitics 13, 3 (2014).
December 1, 2014 in Uncategorized.


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text:
Özel Türkbaş (September 1, 1938 – July 22, 2012) was a Turkish-born actress, model, singer and belly dancer, who helped popularize belly dancing in the US and recorded traditional music aimed at a western audience.
She was born in Turkey, and lived in Ankara as a child and then moved to Istanbul. She was a member of the State Children's Theatre in Ankara, before starting a singing career with her sister in Istanbul. Her sister, Tancan Ferguson and herself were known as the Ozel Kardesler (meaning the Ozel Sisters [Ozel also meaning "special" in Turkish]). During the 1950s, she worked as a model, and also starred in films in Turkey including Gunah Koprusu in 1955, where she met Ayhan Türkbaş (film alias Batu Turkbas) who became her husband. She also represented Turkey at international film festivals, including Cannes Film Festival and Venice Film Festival.
Ozel came to the United States in 1959. After several years of touring the major cities of the United States and dancing in the top night clubs and venues, she was selected by Franco Zeffirelli to play the role of La Orientale in his production of the opera Thais, in Dallas, Texas, with the premier dancer Jacques D'Amboise of the New York City Ballet.
She settled in New York with her husband, and continued to dance throughout the country, including the Catskills and Mexico City. Türkbaş regularly worked with leading musicians, and in 1969 released the album How to Make Your Husband a Sultan, on which she was accompanied by the leading Turkish clarinet player Mustafa Kandıralı, together with Cevdet Çağla (violinist), Ahmet Yatman (kanun), Tarik Bulut (piano), Gerhard Rudolph (bass guitar), and Leszlo Kubinyi (drum). The record contained a complete 17-minute dance routine, together with other traditional songs. How To Make Your Husband A Sultan was included in the book 1,000 Recordings To Hear Before You Die. She followed it up with several further albums including How to Belly Dance for Your Sultan, Dance into Your Sultan's Heart, Alla Turca, and Kismet. She also wrote two books, The Turkish Cookbook by Ozel and The BellyDancer in You.
Ozel appeared on the Mike Douglas Show, Joe Franklin Show several times and the Dinah Shore Show. She modeled for PeReJa and appeared on the cover of major Turkish magazines, including Hayat Magazine, in the 60's and 70's.
After she retired from performing, she and her husband owned restaurants and invested in real estate, owning properties in Manhattan and Long Island. Her and her husband opened the one of the first Turkish restaurants in New York City named Ozel Restaurant. She continued to make appearances and speak about her passion for belly dancing. She occasionally appeared to sing as well. She passed away in New York in 2012. She is survived by her husband, Ayhan Turkbas and her two children, daughter Deniz Turkbas and son John Halil Turkbas.
Gilded Serpent presents… Özel Türkbas, Turkish Bellydance Trailblazer


text:
Health distributed more than 3.5 million flu vaccines
(Photo Pepe Delloro)
A total of 3,685,840 doses of the flu vaccine distributed by the Ministry of Health of the Nation to all the provinces, health institutions and the PAMI, one month after the launch of the 2022 flu vaccination campaign and before the advancement of the circulation of the influenza virus, the ministry reported.
"This year the circulation of the influenza virus came early, which is why we substantially accelerated the distribution of flu vaccines, to progress with vaccination before the arrival of the most intense cold weather and protect the most vulnerable from serious complications," he stressed. the Minister of Health, Carla Vizzotti.
He also explained that "the flu vaccine can be applied together with any other vaccine on the National Vaccination Calendar, as well as with those of Covid-19".
The flu or influenza is a highly contagious respiratory viral disease and usually most of those affected recover in one or two weeks without the need for medical treatment, but it can cause serious complicationsincluding death, to people aged 65 and over, the ministry said.
"The flu vaccine can be applied together with any other vaccine on the National Vaccination Calendar, as well as with those of Covid-19" Carla Vizzetti
It can also cause serious harm to young children, pregnant women and people with chronic diseases or conditions, such as heart, respiratory, kidney, immunosuppression, obesity and diabetes.
The national portfolio acquired 9,600,000 doses, of which 1,500,000 are pediatric, with the aim of reducing complications, hospitalizations, and deaths related to the influenza virus in the population at risk in Argentina.
The annual dose of the flu vaccine is available free of charge for health personnel, as well as for people aged 2 to 64 with risk conditions such as obesity, diabetes, respiratory diseases and chronic heart diseases, congenital or acquired immunodeficiencies, onco-haematological patients, transplant patients or people with chronic kidney failure on dialysis.
Likewise, for people 65 years of age or older, for pregnant women in any trimester of pregnancy, and puerperal women, up to 10 days after delivery.
Meanwhile, girls and boys from 6 to 24 months, if it is the first time they are vaccinated, they should receive two doses.
Rains have already caused the death of seven people in São Paulo
The heavy rains that hit the state of São Paulo
Doomsday Clock is expectant before the third week of January 2023
ERROR: The request could not be satisfied The request could
The ICAIC will digitize the work of Juan Padrón and Santiago Álvarez
The Cuban Institute of Cinematographic Art and Industry (Icaic) plans
Díaz-Canel counts on "the infinite creativity of the people" to save Cuba
2022 ends, a year that "has been very hard," according
Bolívar defeated Independiente (3-1) and secures the ticket to the quarterfinals
Michael Labarca absorbs Argentine cinema with his sights set on filming in Venezuela
Latest from Argentina
The Government accepted the resignation of the head of the Anti-Corruption Office
Photo Laura Lescano. The national government accepted the resignation presented by the head of the Anti-Corruption
Victoria Donda will not continue in charge of Inadi
Victoria Donda will no longer lead Inadi. Photo Osvaldo Fanton. Victoria Donda reported that she will
Banks: what is the schedule for this December 30
Days ago, the national government decreed a holiday for public administration workers on December 30, with
Hot Friday and with a maximum of 37 degrees in the AMBA
Photo: Victoria Egurza. This friday It occurs in the Metropolitan Area of Buenos Aires (AMBA) hot
Vehicle registrations increased 53% in May compared to 2021
Of the total sales, the participation of nationally manufactured models stands out. The number of vehicles


text:
News / Pablo Picasso / Announcements
Gagosian App for iPad
Gagosian announces the release of issue 2 of the Gagosian App for iPad on September 22, 2011. Artists featured in this issue include Cecily Brown, John Chamberlain, Douglas Gordon, Arshile Gorky, Joel Morrison, Takashi Murakami, Elizabeth Peyton, Pablo Picasso, Ed Ruscha, Mark Tansey, Robert Therrien, and Andy Warhol.
In issue 2 experience Douglas Gordon's film k.364 (2010)through a dual-channel 3-D room, explore the world of Robert Therrien as he transforms elements from everyday life into works of art that evoke mythic archetypes, and trace the evolution of economics over time through key figures identified in Mark Tansey's EC 101 (2009), viewing fine-grained detail in high resolution with gigapixel zoom and artwork rotator. We also introduce the issue manager, which allows users to store and browse multiple issues at once.
Gagosian announces the launch of a free iPad app, designed by award-winning firm RadicalMedia, which offers unprecedented access and takes users on an in-depth journey with Gagosian's artists and exhibitions, presented through visually stunning, richly informative and innovative features on June 12, 2011.
Artists featured in this issue include Richard Avedon, Cecily Brown, John Chamberlain, John Currin, Vera Lutter, Kazimir Malevich, Elizabeth Peyton, Pablo Picasso, Richard Prince, Robert Rauschenberg, and Rudolf Stingel.
Harold Ancart
Ashley Bickerton
Jadé Fadojutimi
Deana Lawson
Jordan Wolfson


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