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text:
Q: Disable all class member functions if the class is empty I have a diagnostic drawing class to graphically plot the status of objects in a multithreaded environment. But this is costing me CPU cycles when I don't run in the diagnostic mode. I don't want to check every line for diagnose mode too. Rather I like to a declare the diagnostic object as empty ( or through some other member functions/ extra variables) and all the diagnostic member functions will be rendered irrelevant. Is this possible? I dont't want to change the existing member functions
bool dmode = true; // want to avoid this
class plot{
     void draw();
};

class foo{
    void something(){
       plot p;
       if(dmode) p.draw(); // don't want the checking as plot objects are littered everywhere.
    }
};

Edited:
// dmode will be decided only at run time based on my settings configuration

A: Compile time discard
You may use constexpr:
constexpr bool dmode = true;

//...

  if constexpr (dmode) {

Using constexpr this way will make sure the branch is discarded at compile time

It should be noted that this is a c++17 feature, before that you can use macros to discard code at compile time.

A third option is to just rely on compiler optimizations(ie. constant folding), but it is much harder to give any guarentees and might not be feasible for a debug build if that is ever the case.
Runtime solution
Assuming your draw function is virtual - you then need to somehow make a non-virtual function to stick the if into. Then one way to accomplish this is by making the virtual function private.
class plot{
public:
     void draw() {
       if (!dmode) {
           return;
       }
       do_draw();
     }
private://<-- or protected
     virtual void do_draw() = 0;//rename in all subclasses
};

However, this is just one way to solve it - without knowing more of the actual case its hard to say which method is more appropriate.

A: You can do the check inside the draw() function:
void plot::draw() {
    if (!dmode) {
        return;
    }
    // Do the actual drawing.
}

If that's not an option for some reason, you could make plot a pure abstract class, and provide two implementations, RealPlot and NullPlot. Then create a function create_plot() that, depending on the value of dmode, returns either a RealPlot or a NullPlot instance.

A: You have options. Here are some of them:
Concepts - C++20
constexpr bool dmode = true;

class plot
{

public:
    void draw() requires (!dmode)
    {
        /* actual implementation */
    }

    void draw() requires dmode
    {
        // empty
    }
};

class foo
{
    void something()
    {
       plot p;
       p.draw();
    }
};

Constexpr if - C++17
constexpr bool dmode = true;

class plot
{

public:
    void draw()
    {
        if constexpr (!dmode)
        {
            /* actual implementation */
        }
    }
};

class foo
{
    void something()
    {
       plot p;
       p.draw();
    }
};

Templated class and an alias - C++11
constexpr bool dmode = true;

template <bool Dmode>
class plot_base
{
public:
    void draw()
    {
        /* actual implementation */
    }
};

template <>
class plot_base<true>
{
public:
    void draw()
    {
        // empty
    }
};

template <bool Dmode = dmode>
using plot = plot_base<Dmode>;

class foo
{
    void something()
    {
       plot<> p;
       p.draw();
    }
};

Mock (a.k.a. None of the above)
And ultimately (most likely) none of the solutions are appropriate. It seems to me like what you want is a mock. So look into that.

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text:
HHS.gov Civil Rights
Information for Individuals
Information for Providers
HHS > Civil Rights Home > For Individuals > Disability > Disability Laws, Regulations, & Guidance
Civil Rights For Consumers Menu
Civil Rights for Individuals and Advocateshas sub items, Civil Rights for Individuals and Advocates
Race, Color, National Origin
Hill-Burton
Civil Rights and Opioids
HIPAA and FTC Act
Civil Rights FAQs
Disability Laws, Regulations, and Guidance
OCR enforces the following laws and regulations that prohibit discrimination on the basis of disability in HHS-funded programs and activities.
Section 504 of the Rehabilitation act of 1973, as amended, (29 U.S.C. 794) prohibits discrimination against otherwise qualified individuals on the basis of disability:
Programs and activities that receive financial assistance from HHS; 45 C.F.R. Part 84 and
Programs or activities conducted by HHS. 45 C.F.R. Part 85
Section 542 of the Public Health Service Act, as amended, (42 U.S.C. 290dd-1) bars discrimination in admission or treatment against substance abusers suffering from medical conditions by Federally-assisted hospitals and outpatient facilities. 45 C.F.R. Section 84.53
Title II of the Americans with Disabilities Act (42 U.S.C. 12131et. seq.). OCR is the "designated agency" to investigate complaints of discrimination on the basis of disability by state and local government health and social service agencies. 28 C.F.R. Part 35
Section 508 of the Rehabilitation Act of 1973, as amended, (29 U.S.C. Section 794) prohibits discrimination on the basis of disability in electronic and information technology as they relate to programs and activities conducted by HHS. See the Accessibility Board 508 Homepage for more information.
Sections 794 and 855 of the Public Health Service Act, 42 U.S.C. 295m and 42 U.S.C. 296g, prohibits discrimination on the basis of sex (gender) in Federally-Assisted Health Training Programs (45 CFR 83).
Section 508 of the Social Security Act (42 USC § 708) prohibits discrimination on the basis of age, race, color, national origin, disability, sex (gender), or religion in the Maternal and Child Health Services Block Grant.
Section 533 of the Public Health Service Act (42 USC § 290cc-33) prohibits discrimination on the basis of age, race, color, national origin, disability, sex (gender), or religion in Projects for Assistance in Transition from Homelessness.
Section 1908 of the Public Health Service Act (42 USC § 300w-7) prohibits discrimination on the basis of age, race, color, national origin, disability, sex (gender), or religion in programs, services, and activities funded by Preventative Health and Health Services Block Grants.
Section 1947 of the Public Health Service Act (42 USC § 300x-57) prohibits discrimination on the basis of age, race, color, national origin, disability, sex (gender), or religion in programs and activities funded by Community Mental Health Services Block Grant and Substance Abuse Prevention and Treatment Block Grants.
The Family Violence Prevention and Services Act(42 USC § 10406) prohibits discrimination on the basis of age, race, color, national origin, disability, sex (gender), or religion in programs and activities funded under this Act.
The Low-Income Home Energy Assistance act of 1981 (42 USC § 8625) prohibits discrimination on the basis of race, color, national origin, or sex (gender) in programs and activities funded under this Act.
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View Laws and Regulations Enforced by OCR.
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text:
Alison Rakoto – full website makeover
by mchaloupecky | Nov 6, 2018 | Portfolio
Website makeover
Alison Rakoto
Over the years I'd worked with enough websites updating content that I thought I could build my own. With the help of WordPress & Elegant Themes, I almost did: I made an attractive enough site that sort of got my message across; unfortunately, my Google analytics told another story. People who landed on my site left almost immediately. And, when I shared my site with folks, it rarely made a sale, so I knew I needed help.
I met Marie-Anne virtually through the Digital Nomad Girls group, and after watching her interaction and discussions regarding user experience and content strategy, I realized that I'd possibly found my savior. Someone who could restructure my content to make it make it pertinent, user-friendly and user-focused, while also getting the tone and branding right, cutting down my content while keeping the spirit and essentials.
Marie-Anne did just this, she organized and streamlined my content so that people know exactly what I do. Now my site has both good UX and it's pretty! I've received dozens of compliments on my new design — even my mum finds it functional — a huge compliment and sign that Marie-Anne did a proper job! I am thrilled.
LinkedIn Strategist & Career Coach
Alison is a LinkedIn strategist and career coach. She contacted me through an online community we both belong to, and told me she wanted to give her website a makeover. Her main concern was that it wasn't easy enough to use, and she was aware that she needed work on her Services page.
In her words, "My dream would be someone who can look at the overall UX and see best how to present the info I have to my audience so the pages flow and people can actually figure out how to order a service or get in touch with me."
So we got to work.
Using the information that Alison gave me through my questionnaire, and the conversation we had following my UX audit of her website, I started by looking at what exactly she is offering her clients.
I sorted her services into 3 distinct categories, and created clear USPs (unique selling points). This way users on her homepage can immediately understand what she's selling and why it's great.
Finding the flow
We know that people are likely to have some idea of what service they want from Alison by the time they land on her website. For this reason, it's best for them to have easy access to her services from whichever page they land on.
There are CTAs (calls to action) strategically placed across each page so that the user has the chance to click through to where they are most likely to want to go after reading or scanning through each section.
Headings and subheadings that speak for themselves allow the user to scan the page rather than having to read each section to find the information that interests them most. This makes their journey across the website much faster and smoother.
HEADINGS AND SUBHEADINGS
CTAs ON THE HOMEPAGE
Consistency is key
Communication and writing are key to Alison's profession, so I wanted to make sure her voice still came through across her website. I gathered and re-used her own words (from her old website, from the questionnaire, and from our conversations) as much as possible.
Together with the key aspects of her new branding (compass and travel imagery), these contributed to the creation of a consistent voice that is distinctly Alison's.
At the same time, it was important to refine the copy according to the new structure, so as to have only the information that is needed by the user in each area of the website. Previously, there was a lot of interesting information repeated across different pages. With a clear new structure, it is immediately obvious to the user where they should click to find any particular piece of information that they might be looking for.
LinkedIn and beyond
I thoroughly enjoyed working with Alison on her website, and it opened my eyes to a whole new world regarding LinkedIn. You can imagine my delight when I heard back from her that she was getting great feedback about it through LinkedIn (of course) and that even her mum now finds it nice and functional. Alison now reports that she's getting requests from more diverse clients — which she was aiming for — and that she feels her website reflects her brand much better.
So here's to making more meaningful connections through online communities, and to finding work you love.
Go back to my portfolio
See my website makeover package
Van Dijk Accounting – full website makeover
Booking.com — the big picture
Payments for Booking.com partners: Landing page
Syntax adventures in 43 languages


text:
Q: How to cast object list into pojo class? Here is the problem I am currently having. My DAO class returns data in index but I want to return the selected data to client in JSON format. How can I do it?
The controller class which returns data in json format.
MyController.java
@RequestMapping(value = "/ohlc",method = RequestMethod.POST)
public @ResponseBody List<OhlcResponse> getOhlc(@RequestBody OhlcRequest ohlcRequest) {
    List<OhlcResponse> ohlc = ohlcService.getOhlc(ohlcRequest);
    return ohlc;
}

Dao class returns 4 datas (minprice, maxprice, closingprice and previousclosingprice)  by executing stored procedure.
OhlcDaoImpl.java
public List<OhlcResponse> getOhlc(OhlcRequest ohlcRequest) {
    session = sessionFactory.openSession();

    SQLQuery q = session.createSQLQuery("EXEC uspGetOhlc :StockCode, :fromDate, :toDate");
            q.setString("StockCode",ohlcRequest.getStockSymbol());
            q.setDate("fromDate",ohlcRequest.getFromDate());
            q.setDate("toDate", ohlcRequest.getToDate());
    List<OhlcResponse> l = q.list();
    return l;
}

My stored procedure
USE WealthFeedSrv
GO
CREATE PROCEDURE uspGetOhlc
@StockCode varchar(50),
@fromDate date,
@toDate date
AS
BEGIN

SELECT  spd.ClosingPrice, spd.PreviousClosingPrice, spd.MinPrice, spd.MaxPrice
FROM StockPriceDetl spd
inner join Stock stk on stk.Id = spd.StockId
inner join StockPriceMast spm on spm.Id = spd.MastId
WHERE stk.StockSymbol= @StockCode AND spm.TranDate Between @fromDate and @toDate
END
GO

The pojo class I want returned data to bind.
OhlcResponse.java
public class OhlcResponse {
private BigDecimal MaxPrice;
private BigDecimal MinPrice;
private BigDecimal PreviousClosingPrice;
private BigDecimal ClosingPrice;

public BigDecimal getMaxPrice() {
    return MaxPrice;
}

public void setMaxPrice(BigDecimal maxPrice) {
    MaxPrice = maxPrice;
}

public BigDecimal getMinPrice() {
    return MinPrice;
}

public void setMinPrice(BigDecimal minPrice) {
    MinPrice = minPrice;
}

public BigDecimal getPreviousClosingPrice() {
    return PreviousClosingPrice;
}

public void setPreviousClosingPrice(BigDecimal previousClosingPrice) {
    PreviousClosingPrice = previousClosingPrice;
}

public BigDecimal getClosingPrice() {
    return ClosingPrice;
}

public void setClosingPrice(BigDecimal closingPrice) {
    ClosingPrice = closingPrice;
}

public OhlcResponse(){

}
}

The result i want
"maxPrice":"200",
"minPrice":"300",
"ClosingPrice":"400",
"PreviousClosingPrice":"500"

The error says cannot cast to OhlcResponse. How can I bind the object returned by OhlcDao into OhlcResponse class and return JSON Format?


College football is just Clemson and Alabama, Alabama and Clemson. That's it.
There's a lot to recap when it comes to college football. A ton. And yet, absolutely nothing at all. Because really, college football is just Clemson and Alabama, Alabama and Clemson. That's it.
Now, Texas is back. I said it on Twitter last night and I mean it. A Sugar Bowl win in primetime? That's back. Coming in as a nearly two touchdown underdog to Georgia and absolutely stomping them 28-21 in a statement game? That's back. Your mascot, Bevo, busting through a barricade and nearly steamrolling the opposing mascot before the game? That's back.
A 10-win season with wins over two of the top five teams in the college football rankings? That's back. Tom Herman has that thing moving and you actually get the sense that they're just scratching the surface. They didn't just beat Georgia, they beat them the hell up. That's back. They're still not even firing on all cylinders and they're already coming up with wins like that? That's back.
Oh, and here's another thing about that game – remember when Georgia honks and Georgia players lost their collective bleep over being left out of the college football playoff. And how they droned on and on about how badly the committee jammed them. Then you go out and get knocked the hell out?? You probably want that back don't you Georgia honks? And don't tell me you had nothing to play for. Because you did. You had chance to make your point on a big stage. A chance to send a message. But the only message you delivered was that the committee got it right.
You can't complain that you're one of the four best teams in the country, spend a month talking about that, fire it back up on twitter while clowning the teams that did make it in, and then get hammered the way they did last night. It doesn't work that way. And it's embarrassing. What a horrible look. You think the committee screwed you? You think you deserve to be in the playoff? Then show the hell up, and show the hell out. Instead, you got knocked the hell out. Period.
But it wasn't just Georgia. Ohio State fans had a lot of say about getting left out of the college football playoff. But, unlike Georgia, they handled their stuff in a 28-23 win over Washington. Sort of; they did damn near blew that huge lead against Washington in the second half. And by the way… how did that back door cover taste? I'm guessing like a crap sando. The worst crap sando of the bowl season if you're into that sort of thing.
But the Buckeyes don't care how much money they cost you. They weren't' looking to cover, just to win. And they did. And if Urban Meyer was the person who Urban Meyer thinks he is, and tells everyone he is, that Rose Bowl win would've been a pretty special moment, not just for him, but for the whole sport. If he was the leader and molder of men that he claims to be, the fact that he's stepping down… in part or in whole due to health concerns, would be pretty freaking sad, right? A guy, in his prime, doing what he loves to do, and doing it at a very high level, but having to walk away because of physical issues. And because the stress of the game had taken its toll. If he was even half the dude he thinks he is, that would be your story line.
But he's not that person. Nowhere near it. And the fact that he wins a Rose Bowl in the same season that he was suspended for three games for failing to properly handle allegations of domestic violence by one of staff members is a part of his legacy. A huge part of his legacy. He may think it doesn't matter. Hell, this guy walks around like it never even happened. Just like those 30 plus arrests at Florida never happened. Urb walks around like HE'S somehow the victim. Wins cover and erase a lot of things. But not this. At least they shouldn't. Damn good coach. But a total fraud.
I don't know if he's done coaching forever and I don't care. Because he's an unrepentant, unremorseful fraud. And any school who hires him knows that. And by the way, does anyone, anywhere, really think this guy is done coaching??
Oh, and Buckeye fans who felt like last night and Saturday night justified your complaints about not making the playoff: you lost to Purdue by 29 and Purdue lost their bowl game to Auburn by a billion. So no one wants to hear it.
But as much as last night was about Texas and Ohio State, really all of college football is about one thing and one thing only: Alabama-Clemson IV.
Saturday night started with Clemson and Notre Dame feeling each other out. Each team trying to figure out the other. And at the end of the first quarter, it was tied at 3. And for a moment, it looked like we were going to have the rarest of things: a competitive college football semifinal. Then Notre Dame's All-American cornerback Julian Love gets injured in the second quarter, Clemson scores 20 points, ball game. Thanks for coming. Same as it ever was.
And then it was time for everyone to clown Notre Dame for not belonging in the college football playoff. Until Oklahoma promptly went out and fell behind 28-0 in the first 17 minutes of the second semifinal. The most exciting offense, led by a Heisman trophy winner, got absolutely hammered by Alabama. Kyler Murray was sacked twice before he was able to get off his first pass. And he didn't complete a pass until the Sooners were down 21-0.
The problem isn't that college football doesn't have an eight team playoff. The problem is that college football has a two team playoff. There's Alabama and Clemson, and then there's everyone else.
And while everyone else wants to claim that they have a shot, they don't. Ohio State forfeited its shot when it lost to Purdue. Georgia's best claim that they belonged was that they've nearly beaten Alabama twice, but have found a way to lose both times. And then they got destroyed by Texas.
People tried to talk themselves into believing Oklahoma had a shot with their offense. And they were crushed. Instead of turning it into a shootout, they got boatraced.
Notre Dame looked like they might have a chance when Dexter Lawrence was suspended, but Clemson just replaced one future NFL d-lineman with another and still dominated on defense.


text:
Tag Archives: Road America
Sherman's M12 – McLaren M12 #60-14
Following the success of his '67 and '68 Can Am unlimited sports car programmes, which would net championship success from 1967 to 1971, Bruce McLaren turned his attention to building a GT Coupé version of his Can Am winning Chevrolet powered M6 roadster.
According to McLaren designer Gordon Coppuck the initial idea was for the M6GT to become a racer against the likes of the Ford GT40, later Ferrari 512 and Porsche 917 before McLaren realised they could not meet the minimum production requirements for the World Sports Car series.
Bruce and Gordon then continued working on the project as a road car project and the Prototype was finished in 1969 and received much adulation as Bruce proceeded to use it as his daily driver.
Unfortunately Bruce was killed testing his new 1970 Can Am Challenger at Goodwood and plans to put the M6GT into production also died with it until Trojan, who built the McLaren customer Can Am racers, had a go at test marketing the M6GT and built two further M6GT cars before abandoning the idea.
Since then any number of replica M6GT's have been built using everything from Volkswagen Beetle chassis to full race spec McLaren M6 and M12, Trojan production variant of the M6 chassis, with everything from flat four Beetle power to race spec 700hp plus Chevy aluminium big blocks.
The chassis of today's featured car is Trojan built McLaren M12, chassis 60-14 one of two that appear to have passed through Holman Moody to the Great Western Champagne team for Gary Wilson to use in the 1971 Can Am championship season.
It is not known for certain that Gary used chassis 60-14 in 1971 but the following year Pete Sherman acquired the car and raced it in the Road America Can Am race where he qualified 22nd and finished 17th.
Pete is known to have raced 60-14 on at least one further occasion in 1972 at Donnybrooke where he started 27th and finished 9th.
Lawrence Crossan is said to have bought 60-14 in 1977 and during his decade of ownership he had the race spec M12 and 8 700 hp 8 litre 488 cui Chevy fitted with an M6GT type body.
Note that the windscreen wiper appears far too small compared to that fitted to Bruce McLaren's original which also featured a pair of pop up headlights.
60-14 now has a Swiss owner and it is registered in France as what must be one of the wilder street legal machines with a genuine 1972 Can Am race history.
Thanks for joining me on this "Sherman's M12" edition of "Gettin' a li'l psycho on tyres" I hope you will join me again tomorrow. Don't forget to come back now !
This entry was posted in Uncategorized and tagged Can Am, Chevrolet, Colmar, Coppuck, Crossan, Donnybrooke, Festival of Speed, Goodwood, Great Western Champagne, Holman Moody, M12, M6, M6GT, McLaren, Psychoontyres, Ralph, Road America, Sherman, Trojan, Wilson on September 25, 2014 by Ralph.
The Surtees Hill Lola – Lola Chevrolet T70 Mk II Spyder SL71/43
Team Surtees started running Lola T70's in sports car races in 1965, for 1966 the teams proprietor, 1964 World Champion, John Surtees ran a Chevrolet Mk II spyder variant, chassis #SL71/17, in the Canadian American (Can Am) Challenge winning the inaugural Can Am race at St Jovite from pole with the car.
Surtees retired from the next couple of Can Am races with a broken oil pipe and then after a start line accident at Bridgehampton and Mosport respectively. At Laguna Seca the team had a new chassis #SL71/43, today's featured car, which he qualified 7th but retired for a third time after 92 laps with suspension damage.
John Surtees returned to chassis #SL71/17 at Riverside where he qualified second and won, 1962 World Champion and 1966 Indy 500 Winner Graham Hill was drafted into chassis #SL71/43 and came home third from 5th on the grid in the cars final appearance for Team Surtees.
Surtees went into the final round of the 1966 Can Am Challenge at Stardust International Raceway in Las Vegas sharing the series lead with 1961 World Champion Phil Hill who was driving a Chaparral 2E.
Despite qualifying 4th behind Jim Hall on pole with his Chaparral team mate Phil Hill beside him and Chris Amon in a McLaren Elva in third, John Surtees forced his way through to the lead on the opening lap. John did not relinquish that lead for the entire 70 lap race and so secured the inaugural Can Am Championship.
Chassis SL71/43 was acquired by George Ralph for 1968 his best results with the car were two 11th place finishes one in the USRRC Championnat Nord-Americain race held at Mont-Tremblant from 17th on the grid and the other in the Road America Can Am race from 21st on the grid.
Current owner Paul Knapfield is seen driving the car at the 2011 Goodwood revival in these photo's.
My thanks to Tom RA Announcer Schultz for kindly visiting his den to dig out the chassis details of today's featured car from his copy of Lola T70 – The Design, Development & Racing History Hardcover – December 1, 2012 by John Starkey and Franco Varani.
Thanks for joining me on this "The Surtees Hill Lola" edition of "Gettin' a li'l psycho on tyres" I hope you will join me again tomorrow. Don't for get to come back now !
This entry was posted in Uncategorized and tagged Can Am, Car, Challenge, Chevrolet, Colmar, GALPOT, George, Goodwood, Graham, Hill, John, Knapfield, Laguna Seca, Lola, Mk II, Mont-Tremblant, Paul, Racing, Ralph, Revival, Riverside, Road America, Schultz, Sports, Spyder, Surtees, T70, Team, Tom on August 12, 2013 by Ralph.


text:
Columbia Care Florida | Patient-Focused Medical Cannabis Dispensary
July 6, 2018 By Col Care Leave a Comment
Columbia Care Receives Authorization from the Florida Department of Health Office of Medical Marijuana Use to Begin Dispensing Its Cannabis-Based Products
The company is offering home delivery to patients registered with Florida's Office of Medical Marijuana Use
ARCADIA, FL – July 5, 2018 – Columbia Care LLC, the nation's leading medical cannabis company, announced that it has been authorized to begin dispensing the company's pharmaceutical-quality cannabis-based medicines in Florida. The company is now offering home delivery of its products to patients who are registered with the Florida Department of Health Office of Medical Marijuana Use.
"Florida has one of the largest medical cannabis programs in the country with over 100,000 registered patients and our authorization to begin dispensing in Florida represents a significant opportunity for Columbia Care to improve the lives of thousands of patients in need of high-quality cannabis-based medicines," said Nicholas Vita, chief executive officer of Columbia Care. "We are looking forward to offering our transformative pharmaceutical-quality products via home delivery to patients suffering from a range of debilitating disorders and continuing our nationwide commitment to providing the best patient experience. In addition to home delivery, we plan to open up to 30 dispensaries throughout the state, with the first expected in 2018."
All patients approved to use medical cannabis products by a certified physician and who are registered with the Florida Department of Health Office of Medical Marijuana Use are eligible to receive Columbia Care's medical cannabis products via home delivery. Initially, home delivery is available to patients within a 40-mile radius of Arcadia, FL. For more information on the Florida Medical Marijuana Program, including information on how to apply, please visit https://floridahealthstory.org/stories/ommu-patients/index.html.
About Columbia Care Florida
Columbia Care Florida will initially provide the company's high CBD sublingual tincture and will ultimately expand to include Columbia Care's core unique dose-metered pharmaceutical-quality offerings, including tinctures, vaporization oils and oral capsules, each in three CBD to THC ratios: high CBD, high THC and equal CBD to THC. Columbia Care products begin with a cultivation process that takes place in the Company's controlled-environment agricultural production facility in Arcadia, FL. The manufacturing and refinement processes take place in highly controlled labs with trained chemists who follow strict quality control guidelines to ensure product safety and consistency. All products undergo independent batch testing for a variety of measures to ensure the highest levels of purity. Columbia Care offers discounts to veterans, seniors and those on SSDI, Medicaid and Medicare. For more information on Columbia Care Florida and current product offerings, please visit www.col-carefl.com.
About Columbia Care
Columbia Care is the nation's largest and most experienced manufacturer and provider of medical cannabis products and services. The Company is licensed in highly selective and regulated jurisdictions and has completed more than 750,000 successful patient interactions since its inception. Working in collaboration with some of the most renowned and innovative teaching hospitals and medical centers in the world, Columbia Care is a patient-centered healthcare company setting the standard for compassion, professionalism, quality, caring and innovation for a rapidly expanding new industry. For more information on Columbia Care, please visit www.col-care.com.
Filed Under: In The News
Columbia Care
florida@col-care.com
© 2019 Columbia Care.


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text:
Entering Into the Perfection of Online Gaming Now
Paxton Ryan	October 30, 2018
Good numbers of games can be played today on the Internet, scrabble, poker, checkers, chess, racing games, monopoly. The online video game has become a major issue for industries. The global market for video games on the Internet reached nearly $ 1 billion in 2004. This market is expected to double by 2007 to 2.2 billion and the largest companies such as Electronics Arts or Microsoft are investing today considerably in this sector.
sssIn 2002, it was estimated that the number of players in the world, regardless of the platform used, around 430 million or 7% of the world's population, with online players accounting for a quarter of the total number of players. In France, a CREDOC study revealed that in 2003, 31% of the population aged 12 and over had a home Internet connection, with nearly one in five Internet users having played network games on the Internet while 7% planned to do so soon. On the basis of these figures, the potential market would amount to more than one million people willing to pay to play on the Internet.
Among this set of video games that can be practiced on the Internet, is what is called online role-playing games. This type of games is also known as MMORPG, or massively multiplayer games, or virtual worlds. In these games, it's about creating a character, what we call an avatar.
Further Development
This avatar is defined by encrypted features, an appearance that players can customize, a trade, and will have to evolve and grow in a simulated world. The incarnated character will earn during parts of the experience, equipment, new skills and promotions. Many of the characters that the player will interact with are the avatars of other connected players at the same time. These worlds, and the computer servers that support these virtual worlds, can accommodate several thousand players at the same time. From the Run 3 unblocked you will be able to discover the perfect games. We speak willingly to characterize these games of "persistent" worlds: thus is emphasized the continuity of the universe apart from the presence or not of the players. These universes are thus characterized by a simulation as close as possible to a reference universe, with proposals of cultural immersion more and more diverse. The proposed universes are essentially inspired by:
The laicized History: The universe of reference can be inspired by a precise period of the History. Thus, the game World War 2 Online offers as universe and as period World War II. It is in this game to choose a side (Axis or Allied Forces), a type of character (infantryman, armored driver, airplane pilot, etc.).
Science Fiction: the game Star Wars Galaxies offers to live in the universe of the Star Wars, as in the film saga of Georges Lucas. It is also about choosing a side ("the dark side of the force", or rebellion), a planet on which one lives, the type of alien that one wants to embody and the craft that the we wish to exercise (craftsman, soldier, mercenaries, "knight Jedi", etc.)
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text:
\section{Conclusions}
\vspace{-0.6em}
\label{sec:conclusion}
Our work introduces a novel architecture aiming at capturing shared appearance cues in images and videos, while enabling the separate modeling of image-specific properties and temporal information in video.
Our UniDual model accepts both images and video as input and activates different execution pathways depending on the input modality. We demonstrate that the strong performance of our model hinges on maintaining similar data statistics across all layers during optimization. We achieve this goal by means of auxiliary losses, one added to the image pathway and one added to the video pathway. Across all our experiments, our model achieves accuracy gains over 1) disjoint training, 2) pretraining followed by finetuning, and 3) traditional multi-task learning. In practical settings, our model enables the deployment of a single model to address different tasks over different modalities with the additional benefit of the improvements in accuracy. Future work will be focused on enhancing the scalability of the approach in order to enable the training of a unified model over a large number of different datasets and video/image tasks.

\section{Experiments}
\vspace{-0.8em}
We begin by presenting in subsection~\ref{sec:ablation} a comparative study to validate the advantages of the proposed unified model against the R2D and R(2+1)D architectures and several additional variants of these models to allow joint training over images and videos. In order to make this extensive comparison computationally feasible, we limit the depth of all models to 34 layers and we use short clips of 8 frames as input. We refer the reader to subsection~\ref{subsec:soa} for an evaluation using deeper models trained on longer clips, which will demonstrate the state-of-the-art accuracy of our model.

\vspace{-0.5em}
\subsection{Comparative Study}
\vspace{-0.5em}
\label{sec:ablation}

\paragraph{Datasets.}
For the image task, we use the ImageNet~\cite{imagenet} dataset for image classification. There are 1000 categories in this dataset. We use the full set of 1.2M images for training, and the validation set of 50K images for testing. For the video task, we use the Kinetics-400 dataset~\cite{kinetics} for action recognition. It contains 400 human action classes, with 240K training videos, and 20K validation videos. We use the full training set for training, and the validation set for testing.

\paragraph{Models.}
\vspace{-3mm}
\begin{table}[ht]
\vspace{-0.4em}
\caption{Models considered in our experimental comparison. For each model we list the input type (image and/or video) and the form of training (separate vs joint).}
   \centering
   \bgroup
   \scalebox{0.85}{
\def1.1{1.1}
\begin{tabular}{c|c|c|c}
\hline
\textbf{Training type }          & \textbf{Model}              & \textbf{Image pathway input}    & \textbf{Video pathway input}                  \\ \hline
\multirow{4}{*}{separate} & R2D                & image                  & N/A                                  \\
                          & R(2+1)D            & N/A                    & video clip                           \\
                          & R2D-finetuned      & image                  & N/A                                  \\
                          & R(2+1)D-finetuned  & N/A                    & video clip                           \\ \hline
\multirow{5}{*}{joint}    & R2D-multitask      & image inflated as clip & video clip                           \\
                          & R(2+1)D-multitask  & image inflated as clip & video clip                           \\
                          & R2D-LSTM-multitask & image inflated as clip & video clip                           \\
                          & UniDual            & image                  & video clip                           \\
                          & UniDual-Aux        & image OR video frame   & video clip OR image inflated as clip \\ \hline
\end{tabular}}
\egroup
\label{tbl:ablation_input}
\vspace{-0.3em}
\end{table}

Table~\ref{tbl:ablation_input} lists the models considered in our comparison, which we further discuss here:\vspace{-.1cm}
\begin{itemize}[leftmargin=*,noitemsep,topsep=0pt]
\item \textbf{R2D} and \textbf{R(2+1)D}. These are baseline models separately trained on images and videos, respectively. The R(2+1)D model is trained on Kinetics. The R2D model is built by replacing the $t \times 1 \times 1$ temporal convolution of R(2+1)D with a $1\times1$ convolution, and it is trained on ImageNet.
\item \textbf{R2D-finetuned} and \textbf{R(2+1)D-finetuned}. These are models pretrained on one modality and then finetuned on the other modality. Specifically, R2D-finetuned is obtained by {\em deflating} the R(2+1)D pretrained on Kinetics as a R2D model. This is done by converting each $t \times 1 \times 1$ filter into a $1 \times 1$ kernel by summing up the weights over the $t$ temporal channels. Then, the resulting R2D is finetuned on ImageNet. The model {R(2+1)D-finetuned} is instead obtained by {\em inflating} the R2D pretrained on ImageNet (as in~\cite{i3d}) and by then finetuning it on Kinetics.
\item \textbf{R2D-multitask} and \textbf{R(2+1)D-multitask}. Each of these models is jointly trained on ImageNet and Kinetics as in multi-task learning, with all layers shared except for the distinct final prediction layers. The input is either a single ImageNet image inflated as a clip or an actual Kinetics video clip. The learning objective is a weighted combination of the image classification loss on ImageNet input, and action recognition loss on Kinetics input. We set the loss weights to be equal for the two tasks. Each mini-batch is constructed by randomly sampling examples from both tasks. Since the size of the ImageNet dataset is larger than that of the Kinetics dataset, we use different sampling rates in order to produce mini-batches that contain an equal proportion of examples for both tasks on average. All layers in each of these models are shared across the two tasks, except for the final FC layers. The difference between R2D-multitask and R(2+1)D-multitask is that the former does not include temporal convolutions. Although R2D-multitask takes a clip as input, it separately processes the 8 frames in the clip via 2D convolutions and then performs temporal average pooling over the resulting 8 tensors before the final clip-classification by the FC layer. In~\cite{r2p1d} this model was shown to be superior to a R2D model using 2D filters spanning the entire temporal extent of the clip (as opposed to learning frame-based 2D filters, as we do here).
\item \textbf{R2D-LSTM}. The architecture of this model is identical to that of R2D-multitask, except that the temporal average pooling is replaced with a single-layer LSTM of 512 hidden units aggregating the information from the 8 tensors produced by the final 2D convolutional layer. This model is also trained in a multitask fashion over the two tasks. Two distinct prediction layers on top of the LSTM perform the classification for the two tasks.
\item \textbf{UniDual} and \textbf{UniDual-Aux}. These two models implement our proposed architecture. The former uses the conventional ImageNet loss and Kinetics loss on the image pathway and video pathway, respectively. The latter uses two auxiliary losses, one for image task and one for video task.
\end{itemize}

\vspace{-3mm}
\begin{table}[ht]
\caption{Accuracies achieved on the ImageNet and Kinetics benchmarks by different models, all based on the same R(2+1)D backbone of 34 layers (deflated to 2D for R2D). Our unified UniDual-Aux network gives the best performance on both tasks.}
   \centering
   \scalebox{0.85}{
\def1.1{1.1}
\begin{tabular}{c|c|cc|cc}
\hline
\multirow{2}{*}{\textbf{Use of other modality}} & \multirow{2}{*}{\textbf{Model}} & \multicolumn{2}{c|}{\textbf{Kinetics} }                                      & \multicolumn{2}{c}{\textbf{ImageNet}} \\ \cline{3-6}
                                       &                        & clip@1 (\%) & video@1  (\%)                                    & top 1 (\%)        & top 5 (\%)       \\ \hline \hline
N/A                                    & R2D                    & N/A           & N/A                                                 & 69.2        & 89.2       \\
N/A                                    & R(2+1)D                & 54.7        & 67.3                                              & N/A           & N/A          \\ \hline
\multirow{2}{*}{Pretrain}              & R2D-finetuned          & N/A           & N/A                                                 & 69.6        & 89.2       \\
                                       & R(2+1)D-finetuned      & 55.4        & 67.3                                              & N/A           & N/A          \\ \hline
\multirow{5}{*}{Joint training}        & R2D-multitask          & 56.0        & 67.9                                              & 70.5        & 90.1       \\
                                       & R(2+1)D-multitask      & 57.1        & 69.7                                              & N/A           & N/A          \\
                                       & R2D-LSTM-multitask     & 54.5        & 67.3                                              & 69.2        & 89.1       \\
                                       & UniDual                & 54.6        & 67.0                                              &         70.0      &       89.7      \\
                                       & UniDual-Aux            & \textbf{57.8}     &   \textbf{70.5} & \textbf{70.9}        & \textbf{90.3}       \\ \hline
\end{tabular}
}
\label{tbl:ablation}
\vspace{-0.8em}
\end{table}
\vspace{-0.8em}
\paragraph{Training and Inference.} All experiments in our comparative study are carried out using the same backbone model for fair comparison. We use a R(2+1)D model with 34 layers, taking as input a clip of 8 consecutive RGB frames.  During training, the 8-frame clips are randomly sampled from the original video. The video frames/images are resized to a spatial size of $128 \times 171$ pixels, and then the input to the model is formed by randomly cropping windows of size $112 \times 112$ .We train the models with synchronous distributed SGD on GPU clusters using caffe2 (\cite{caffe2}), with 16 4-GPU machines. We use mini-batches of size 16 per GPU. All the models are learned with a base learning rate of 0.01, and a total of 45 epochs. After the 10 initial warm-up epochs, the learning rate is reduced by a factor of 10 every 10 epochs. Epoch size is set to 1M for single modality, and 2M when training with both modalities. During inference, we resize the video spatially to size $128 \times 171$, and then use the center $112 \times 112$ window. For testing, we sample 10 uniformly spaced-out 8-frame clips from a full-length video, and compute the final prediction by averaging the prediction scores of individual clips.

\vspace{-0.1em}
\paragraph{Results.}
Table~\ref{tbl:ablation} shows the accuracies achieved with different models on Kinetics and ImageNet (median of 3 runs). The first two numerical columns
report performance on Kinetics. From these numbers we can draw several interesting observations. First, we can notice that R(2+1)D, which is the only variant not using ImageNet as auxiliary training set, yields low accuracy. This underscores the importance of leveraging still-image information for video analysis. We can also observe that {R(2+1)D-multitask} performs better than {R(2+1)D-finetuned}, even though finetuning is more commonly adopted than multitask learning to optimize performance on a target task. A possible explanation is that finetuning causes the the model to ``forget'' the information originally learned from ImageNet, while multi-task learning helps retaining this information and leveraging it more effectively for action recognition. The accuracy gap between {R(2+1)D-multitask} and {R2D-multitask} stresses the importance of properly modeling the temporal information in a video for action recognition. Without auxiliary losses, {UniDual} performs similarly to vanilla R(2+1)D, which does not use ImageNet. However, the addition of auxiliary losses for our model gives a 3.5\% gain in video accuracy compared to {UniDual} and renders {UniDual-Aux} the best model in this comparison. This shows the importance of matching  data statistics for different layers through the network during the optimization. {UniDual-Aux} outperforms {R(2+1)D-multitask} by 0.8\% and {R(2+1)D-finetuned} by 3.2\% in video accuracy. This demonstrates the beneficial effect of the {\em UniDual} block, which enables the learning of shared appearance information from the image and video modalities but decouples the temporal and image-specific information through the dual convolutions.

The last two columns of Table~\ref{tbl:ablation} show top-1 and top-5 accuracy on ImageNet. The relative performance of the different models on this image task matches what seen on the video task. It is interesting to note that the use of Kinetics helps still-image recognition: the accuracy of R2D (without Kinetics) is considerably lower than that achieved by {R2D-finetuned} and especially by {R2D-multitask}. {UniDual-Aux} achieves again the highest accuracy among the models considered here, thus demonstrating the benefits of our unified architecture even for still-image analysis.

We note that, although the gap between UniDual-Aux and the multitask networks is not as large as for the other models, it is consistently present in all our experiments. For example, while Table~\ref{tbl:ablation} shows median accuracy over 3 runs for each model, the min/max clip@1 accuracy over the 3 runs is 57.0/57.1(\%) for R(2+1)D-multitask and 57.8/58.2 (\%) for UniDual-Aux, suggesting a stable gap. Furthermore, we note that R2D-multitask and R(2+1)D-multitask are also variants of our joint-training approach and they have not been studied in prior work.

\subsection{Learning a Unified Model from Several Sources}
\begin{table}[!t]
\vspace{-0.5em}
  \caption{UniDual-Aux enables joint learning of a unified model from several sources spanning several tasks. In this case the sources are: Kinetics, ImageNet and MiniSports. UniDual-Aux yields gains on all three tasks compared to training models separately on each dataset.\vspace{-.1cm}}
   \centering
   \scalebox{0.9}{
\def1.1{1.1}
\begin{tabular}{c|cc|cc|cc}
\hline
\multirow{2}{*}{\textbf{Model}} & \multicolumn{2}{c|}{\textbf{Kinetics}} & \multicolumn{2}{c|}{\textbf{ImageNet}} & \multicolumn{2}{c}{\textbf{MiniSports}} \\ \cline{2-7}
                       & clip@1 (\%)       & video@1 (\%)     & top 1  (\%)         & top 5   (\%)       & clip@1  (\%)        & video@1  (\%)       \\ \hline
R2D        & N/A           & N/A           & 69.2        & 89.2       & N/A            & N/A            \\
R(2+1)D            & 54.7        & 67.3        & N/A           & N/A          & 31.4         & 60.5         \\
UniDual-Aux            & 59.6        & 71.7        & 69.9        & 90.0       & 32.3         & 61.9        \\ \hline
\end{tabular}
}
\label{tbl:threedatasets}
\vspace{-0.4em}
\end{table}

Our approach can be extended to learn a single model from more than two datasets to address several visual tasks simultaneously. In this section, we show this potential feature using three datasets: ImageNet, Kinetics, and Sports1M. To make the joint training over these three datasets computationally feasible, we randomly subsample Sports1M to the size of Kinetics, but we still use its full validation set for evaluation. We refer to this reduced version of Sports1M as MiniSports.

We follow the training procedure in Section~\ref{sec:ablation}, but increase the epoch size to 3M examples for the joint training. During joint training, the model sees an equal number of examples from the three datasets. We compare UniDual with models trained separately on each dataset. Table~\ref{tbl:threedatasets} shows median results for 3 runs. Compared to the baselines, our unified model yield accuracy gains on all three datasets, with improvements of 0.8\%, 4.4\%, 1.4\% top 1 accuracy on ImageNet, Kinetics, and MiniSports, respectively. We see a relatively smaller gain on ImageNet compared to joint training on ImageNet and Kinetics only. This is due to the fact that we train with two datasets of video modality, and one dataset with image modality. There is opportunity for studying better balancing strategies over multiple sources in future work. At the same time, we can notice that the leveraging MiniSports as an additional source allows UniDual-Aux to further improve over the results for Kinetics in Table~\ref{tbl:ablation}.

\vspace{-0.2em}
\subsection{Evaluation on Kinetics and ImageNet Using UniDual-152}
\label{subsec:soa}
\vspace{-0.2em}

In this subsection we present results obtained by adopting a deeper architecture trained on longer clips in order to demonstrate the gains hold also when we apply our approach to higher-performing models. We also show that our UniDual network achieves state-of-the-art performance on Kinetics.
\vspace{-0.4em}
\paragraph {Training and inference.}
For these experiments, we adopt a R(2+1)D-152 (152 layers) as backbone for our model. We
 follow the video preprocessing steps described in~\cite{non-local}, but we us4 16-frame clips as input for the model. During training, each clip is formed by taking a random sequence and sampling 16 frames from it, by taking 1 frame every 4 frames. The input frames have a spatial size of $224 \times 224$ pixels, obtained by randomly cropping from a scaled frame whose shorter edge is randomly resized to either 256 or 320 pixels. For image input, the preprocessing is the same, and the image input is then inflated to a static 16-frame clip. Since the input spatial size is  twice that used by the original R(2+1)D model, we modify the model by adding a spatial max pooling layer after the first (2+1)D convolutional block. The max pooling layer uses pooling regions of size $1\times3\times3$, with a spatial stride of 2 and a temporal stride of 1. We use 16 4-GPU machines for training, with mini-batches of size 8 per GPU.
 We train the model with 45 epochs in total, with a 10 warm-up epochs, followed by a cosine learning rate schedule of 35 epochs. The base learning rate is set to 0.005. Epoch size is the same as in the previous experiments.
At inference time, for the Kinetics dataset, we follow the procedure described in~\cite{non-local}: for each 16-frame clip, we spatially resize the frames so that the shorter side is rescaled to 256 pixels, and then we perform fully-convolutional inference spatially on 3 clips of spatial size $256 \times 256$  cropped from the rescaled frames. Along the temporal dimension, we sample 10 clips uniformly over the original video, and we average the prediction scores from individual clips to get the final prediction for the video. For the ImageNet, we rescale the shorter edge to 256, and take $224 \times 224$ center crop as input.

\vspace{-3mm}
\begin{table}[ht!]
  \caption{Accuracies on Kinetics and ImageNet with models based on a R(2+1)D-152 backbone. On Kinetics, UniDual-Aux yields a sensible gain over the strong R(2+1)D-152, achieving accuracy comparable to the state-of-the-art. On ImageNet, the same UniDual-Aux outperforms R2D-152.\vspace{-.2cm}}
\centering
   \scalebox{0.9}{
\def1.1{1.1}
\begin{tabular}{c|cc|cc}
\hline
\multirow{2}{*}{\textbf{Model}  } & \multicolumn{2}{c|}{\textbf{Kinetics} }& \multicolumn{2}{c}{\textbf{ImageNet}} \\ \cline{2-5}
                         & video@1 (\%)      & top@5 (\%)      & top 1(\%)      & top 5(\%)     \\ \hline
R2D             & N/A           & N/A           & 74.2          & 92.0         \\
R(2+1)D         & 76.0          & 92.2          & N/A           & N/A          \\
UniDual-Aux              & 77.5          & 93.2          & 75.4          & 92.5         \\ \hline
Non-local network\cite{non-local} & 77.7          & 93.3          & N/A           & N/A          \\ \hline
\end{tabular}
}
\vspace{-0.8em}
\label{tbl:bestresult}
\end{table}

\paragraph {Results.} The results are shown in Table~\ref{tbl:bestresult} (mean of 2 runs).
On Kinetics, our proposed UniDual-Aux model yields a gain of 1.5\% over the already-strong performance achieved by R(2+1)D-152. Our model performs on-par with Non-Local Networks~\cite{non-local} which leverage a costly mechanism of nonlinear feature aggregation over the entire spatiotemporal volume. On the image task, the proposed model also outperforms the R2D-152 baseline model. We note that here we do not compare with state-of-the-art models on ImageNet since 1) our model uses a deflated R(2+1)D model, which has a smaller number of channels than the traditional ResNet model, and 2) we do not make use of data augmentation traditionally used to boost performance for image tasks. Our objective here is merely to show that our approach yields accuracy gains even when applied to high-capacity networks.

\paragraph{Visualization of UniDual.}

\begin{figure}[!htbp]
\vspace{-3mm}
\centering
     \begin{subfigure}[b]{0.95\textwidth}
         \centering
         \includegraphics[width=\textwidth]{visualization}
         \label{fig:visualization}
     \end{subfigure}
\vspace{-3mm}
     \caption{For a given video clip of a pull-up action, the video pathway (applied to the clip) and the image pathway (applied to the middle frame) capture different information in the dual point-wise layers. For each pathway we show the 3 most activated feature maps from the last residual block and the corresponding feature maps for the other pathway. The video pathway captures motion information (pull-up), while the image pathway focuses on appearance and object information (gym equipment). Grey numbers beside the feature maps indicate the channel index. }
   \label{fig:visualization}
\vspace{-2mm}
\end{figure}

In Figure~\ref{fig:visualization}, we show examples of feature maps computed from the dual point-wise layers in the last residual block, using UniDual-152-Aux. For the given input clip (representing a pull-up action), we visualize the 3 most activated feature maps by the video pathway (top, middle). We also show the activations computed on the same feature channels by the image pathway using just the middle frame as input  (bottom, middle). It can be seen that the most activated feature maps from the video pathway focus on motion of the upper body, while the activations on the corresponding feature maps from the image pathway are spatially diffused and less peaked. However, if we look at the 3 most activated feature maps by the image pathway (top, right) we see strong activations on the gym equipment. This example suggests that the dual point-wise filters capture different information, with the temporal filters activating on motion, while the $1\times 1$ filters focus on objects or appearance cues.

\section{Introduction}
\label{sec:intro}
\vspace{-0.4em}

Videos are commonly referred to as ``moving images'' as they differ from still-photos in their ability to capture and render dynamic aspects of the scene. Yet, each video frame is effectively an image. Thus, it is conceivable that computer vision systems may benefit from joint training over both modalities in order to take full advantage of their commonalities and of existing datasets in these two domains. Despite the intuition that image and videos share strong appearance cues and provide synergistic information, most existing machine vision models are designed to operate on only one of these two modalities and thus are typically trained on one form of data only. Indeed, the most prominent architectures in computer vision today are models that were specifically designed either for images (e.g., Inception~\cite{inception}, ResNet~\cite{resnet}, DenseNet~\cite{densenet}) or for videos (e.g., C3D~\cite{c3d}, Two-Stream Networks~\cite{SimonyanZ14}, I3D~\cite{i3d}).

We argue that there are two limitations in modeling images and videos independently. First, video models cannot fully benefit from the many existing large-scale and well-curated image datasets, such as ImageNet~\cite{imagenet}, Places~\cite{places}, or COCO~\cite{coco}, which are the precious result of multiple-year efforts in data collection and annotation by the research community. Similarly, image models cannot take advantage of a growing number of well-annotated video benchmarks~\cite{kinetics, something2, sigurdsson2016hollywood, zhao2019hacs, LiLV18}  due to model discrepancies between the two domains. Second, in many industry settings, image models and video models are separately developed, trained, and deployed even when they address related tasks that may be advantageous to tackle jointly. For example, the task of object classification in images may provide strong cues for action recognition in videos since actions (e.g., playing tennis) can  often be disambiguated on the basis of the objects appearing in the scene (e.g., a tennis racquet). Due to the model differences in the two domains, today such synergies are only suboptimally exploited (e.g., object recognition is typically optimized on images only rather than on images and videos) and often require costly duplication of functionalities and resources.

Rare exceptions to this separate handling of images and videos are represented by methods that pretrain a model on one modality and then adapt its weights and/or its architecture via fine-tuning on data of the other modality. For example, many early convolutional neural networks (CNNs) for action recognition leveraged CNNs pretrained on still-image datasets to compute frame-based descriptors from the video~\cite{tsn,SimonyanZ14,FeichtenhoferPZ16,FeichtenhoferNIPS16}. More recently, several authors have proposed strategies to take architectures pretrained on images and extend them in a second stage to model temporal information in video, e.g., by adding recurrent units~\cite{lrcn, prernn} by inflating 2D filters to 3D~\cite{i3d}, or by distillation~\cite{distinit}. Various attempts have also been made to transfer knowledge learned from videos to the image domain. Most of this prior work leverages the space-time consistency of moving objects in videos, and performs unsupervised or weakly supervised learning with videos to improve spatial feature representations or object detectors for still-images~\cite{Mobahi2009DeepLF,  Leistner11, Zou2012DeepLO, Alessandro12, Wang_UnsupICCV2015}. We note, however, that this entire genre of methods relies on a two-step training procedure that is aimed at optimizing performance on one modality only by pretraining on the other.

In this paper, we attempt to unify image and video understanding by proposing a {\em single} model that can be {\em jointly} trained on mixed collections of image and video examples in order to leverage the benefits of both types of data and to produce {\em strong performance on both domains}. Reaching this goal hinges on the following question: how can we model the spatial information shared by still and moving images, while also capturing the temporal information contained in video? We address this question by introducing a novel architecture that unifies the modeling of shared spatial appearance information in images and video, but it enables separate (dual) handling of temporal and image-specific information. Hence, we name our model {\em UniDual}.  The key ingredient in our architecture is a new network block that consists of a shared 2D spatial convolution followed by two parallel point-wise convolutional layers, one devoted to images and the other one used for videos. For video input, the point-wise filtering implements a temporal convolution. For image input, it performs a pixel-wise nonlinear transformation. Repeated stacking of such blocks gives rise to a network where images and videos undergo partially distinct execution pathways, unified by spatial convolutions (capturing commonalities in visual appearance) but separated by dual point-wise operations (modeling patterns specific to each modality). Our comparative experiments on Kinetics and ImageNet demonstrate that our proposed scheme yields accuracy gains on both tasks over 1) disjoint training, 2) traditional multi-task learning, and 3) finetuning after pretraining.

In summary, the contributions of this paper are as follows:\vspace{-.1cm}
\begin{itemize}[leftmargin=*,noitemsep,topsep=0pt]
	\item We introduce a unified model that jointly learns from both images and videos. Our {\em UniDual} building block includes a shared spatial 2D convolution learned from both modalities, and two parallel pointwise convolutions learned separately from videos and images.
	\item We investigate the effect of the joint learning on both modalities with an extensive comparative study. We show that the unified model outperforms various baselines, and boosts the performance on both the image task and the video task.
	\item We also demonstrate that the unified model can be learned from more than two datasets to address several visual tasks simultaneously.
\end{itemize}

\section{A Unified Model for Image and Video Understanding}
\vspace{-0.5em}
\subsection{Background}
\vspace{-0.4em}
In this subsection we review the Residual Network (ResNet)~\cite{resnet} and the R(2+1)D model~\cite{r2p1d}, which we use as inspiration for our network design.

\begin{figure}[htbp]
\vspace{-0.4em}
\centering
     \begin{subfigure}[t]{0.165\textwidth}
         \includegraphics[width=0.9\columnwidth]{r2d-eps-converted-to.pdf}
         \caption{R2D model for images.}
         \label{fig:r2d}
     \end{subfigure}   \hspace{2mm}
     \begin{subfigure}[t]{0.17\textwidth}
         \includegraphics[width=0.892\columnwidth]{r2p1d-eps-converted-to.pdf}
         \caption{R(2+1)D model for videos.}
         \label{fig:r2p1d}
     \end{subfigure}     \hspace{2mm}
          \begin{subfigure}[t]{0.29\textwidth}
         \centering
         \includegraphics[width=0.9\columnwidth]{unidual-eps-converted-to.pdf}
         \caption{Our {\em UniDual} network for images and videos.}
         \label{fig:unified model}
     \end{subfigure}     \hspace{2mm}
     \begin{subfigure}[t]{0.29\textwidth}
         \centering
         \includegraphics[width=0.9\columnwidth]{unidual-aux-eps-converted-to.pdf}
         \caption{Our {\em UniDual} for images and video with auxiliary losses.}
         \label{fig:unified model bothloss}
     \end{subfigure}
   \caption{Illustration of different image and video architectures. The blocks are stacked multiple times for all architectures. (a) The R2D model is designed for image task, where the input is a single image, and the residual blocks perform spatial convolutions with 2D or 1D filters. (b) The R(2+1)D model is designed for video tasks. The input is a video clip of $L$ frames, and each block consists of a 3D convolution with 2D spatial filters followed by a 3D convolution with 1D temporal filters. (c) The input to our {\em UniDual} model can be either an image or a video clip. The UniDual block includes a convolutional layer with 2D spatial filters followed by two distinct pointwise convolutional layers, one applied to video input (using $t\times 1 \times 1$ filters) and one applied to images (using $1 \times 1$ filters). (d) We propose to add an auxiliary loss to both the image pathway and the video pathway. This forces all filters to be updated with both modalities, which results in more similar data statistics in all layers and more stable optimization.}
   \label{fig:architecture}
\vspace{-1em}
\end{figure}

\vspace{-0.8em}
\paragraph {Residual networks.}
ResNet models for image analysis are built by stacking residual blocks:
\begin{equation}
z_i=z_{i-1}+\mathcal{F}(z_{i-1}; {W_i})
\end{equation}
where $z_i$ denotes the activations of the $i$th residual block, and $\mathcal{F}(; W_i)$ is the residual mapping to be learned between block $(i-1)$ and block $i$. The residual mapping typically consists of two convolutional layers with $3\times3$ filters parameterized by $W_i$ with ReLU after each convolution layer. In deep ResNets, the block often has a bottleneck design consisting of three convolutional layers with filters of size $1\times1$, $3\times3$, and $1\times1$, respectively. The purpose of the first and the third $1\times1$ pointwise convolutions is to decrease and then restore the dimensionality in order to reduce the computational cost of the $3\times3$ convolution. In this paper we denote ResNet models for image understanding as R2D. Figure~\ref{fig:r2d} shows an illustration of a modified R2D block, with a $1\times1$ point-wise convolution after the $d\times d$ convolution. We explain this R2D variant in Section~\ref{sec:ablation}

\vspace{-0.8em}
\paragraph {R(2+1)D models.} R(2+1)D are ResNet models designed for video tasks. With the input being a video clip, the activations $z_i$ are 4D tensor $C_i\times L_{i}\times H_{i}\times W_i$, where $C_i$ is the number of channels,  and $L_i$ is the number of frames in the temporal dimension.  The building block of R(2+1)D can be viewed as replacing the traditional 3D convolution used in early video CNNs~\cite{c3d,i3d} with two separate layers, one performing 3D convolution with 2D spatial filters, the second applying a 3D convolution with 1D temporal filters. Specifically, the first layer in the block applies $M_i$ 2D spatial filters of size $C_i\times 1 \times d \times d$, and the second layer employs $C_{i+1}$ 1D point-wise temporal filters of size $M_i \times t \times 1 \times 1$. Typically, $t=3$ is used for temporal dimension, and $d=3$ or $d=1$ is used for the spatial dimension. $M_i$ is the dimension of the intermediate activation after spatial convolution. Note that because both the spatial and the temporal filters are applied via 3D convolutions, the activations remain 4D tensors preserving temporal ordering information. The factorization of temporal and spatial filtering was shown in~\cite{r2p1d} to be advantageous over traditional 3D convolution as  it eases the optimization by forcing the learning of separate spatial and temporal information. The R(2+1)D model is illustrated in Figure~\ref{fig:r2p1d}.

\vspace{-0.4em}
\subsection{Unified Models for Images and Video}
\vspace{-0.4em}
The R(2+1)D model separates the spatial convolutions and temporal convolutions, which complies with our motivation of learning 1) spatial appearance information jointly for both images and videos and 2) temporal information for videos only. Therefore, we naturally develop our unified model on top of a R(2+1)D backbone. Next, we first introduce a na\"{\i}ve application of R(2+1)D to jointly model images and video and we then present a more sophisticated solution using our UniDual block.

\vspace{-0.4em}
\paragraph{R(2+1)D jointly trained on videos and inflated images.}
The R(2+1)D model takes video clips of length $L$ as input. Therefore one simple way to jointly model videos and images with a vanilla R(2+1)D model is by inflating images as static videos. This entails taking an input image $I$ and copying it $L$ times to create a static video clip $I_{inflate} = [I, I, ...I]$ that can be fed into the R(2+1)D model. To handle simultaneous joint training with two distinct tasks, separate prediction layers for the image task and the video task are added, as in multi-task learning. Because both videos and images are represented in the form of clips, it is possible to train this model with mini-batches consisting of a mix of both images and videos. The gradient of each input will be computed with respect to the appropriate loss (either the image loss or the video loss) but all layers (except the prediction layers) will be updated with gradients computed for both types of input.

\vspace{-0.8em}
\paragraph{The UniDual block.}
The approach outlined above views images as static videos, and trains a shared R(2+1)D model on mixed collections of images and videos. This strategy neglects the essential difference between images and videos: videos include temporal information that is not present in images. The temporal convolutions in the R(2+1)D model is redundant for images

To give the model the power to learn temporal information for real videos while maintaining shared spatial convolutions between the two modalities, we propose a unified model with building block {\em UniDual}. The block consists of a shared spatial 2D convolution between images and videos, followed by two distinct but parallel point-wise convolutions, one for images and one for videos. The dual pointwise convolutions implement a $t \times 1 \times 1$ temporal convolution for videos and a $1 \times 1$  pixel-wise convolution for images. Under this model, images and videos undergo partially distinct execution pathways, unified by spatial convolutions but separated by point-wise operations that are specific to each modality. The temporal pointwise convolution are updated with gradients computed from video examples, while the $1 \times 1$ filters are updated with gradients computed from image examples only. The 2D convolutional layer is updated with both image and video information. The {\em UniDual} architecture resulting from stacking multiple such blocks is illustrated in Figure~\ref{fig:unified model}. For the final prediction, a pooling layer and a fully connected layer are stacked on top of the residual blocks for both pathways. The pooling for the video pathway is over the spatiotemporal volume, while that for the image pathway is only over the spatial volume. Note that with this model, there is no longer need to artificially inflate images in the form of video clips.

The $1 \times 1$  convolutions may appear superfluous as the image pathway could in principle be defined by the stacking of the 2D convolutions shared with the video pathway. However, by including $1 \times 1$ convolutions, the image pathway has the same total number of nonlinear activations as the video pathway, rendering the model equally deep for both modalities. Furthermore, the $1 \times 1$  convolution in each block can be viewed as ``aligning'' the output of the image pathway to that of the video pathway before the next shared 2D convolution in the subsequent block.

\vspace{-0.8em}
\paragraph{Auxiliary Losses.}
The unified model architecture still suffers from one problem caused by the fact that the dual point-wise convolutional layers are trained on single modalities. Specifically, the $t \times 1 \times 1$ filters are trained only with video data, the $1 \times 1 \times 1$  filters are trained only with image data while the 2D filters in the network are trained with data from both sources. This results in different data distributions for different layers through the network.
Empirically, we found this statistical data differences in the layers to render the optimization very  challenging.

To address this problem, we add an auxiliary loss to each pathway. As shown in Figure~\ref{fig:unified model bothloss}, the layers in the video pathway are trained with clips corresponding to both real video and inflated images by leveraging an auxiliary loss defined for inflated images with respect to the image task (say, classification on ImageNet). Similarly, an auxiliary loss over individual video frames with respect to the video task (say, action recognition on Kinetics) is added to the image pathway. With the addition of these auxiliary losses, all layers in the {\em UniDual} blocks are updated from both sources consistently, while maintaining the ability to learn image-specific features and temporal information for video.

\vspace{-0.8em}
\paragraph{Inference.}
After training, the auxiliary FC layers are removed. At inference time, only one pathway is activated for each input. When the input is an image, the unified model acts as a ResNet 2D model, while for the video input, the unified model turns into a R(2+1)D model. This allows our unified model to operate simultaneously on two different forms of input sources.

\section{More Visualization of {\em UniDual} Point-wise Filters}

In Figure~\ref{fig:viz}, we provide examples of feature maps computed from the dual point-wise layers in the third-to-last residual block, using UniDual-152-Aux. We choose the third-to-last residual block to show that in the middle of the network, the distinction between two pathways already emerges. Note that the receptive field is smaller in the feature maps we show here than that we show in the main paper (where we show the feature maps computed from point-wise layers in the last residual block).

We show 4 examples from 4 different actions on Kinetics in fig~\ref{fig:viz} (a)-(d). In each example, from left to right: we first show 3 images from the input clip (first, middle, and last), the top-3 most activated feature maps by the video pathway, and then the top-3 most activated feature maps by the image pathway. In video and image feature activation maps, we also visualize the corresponding feature maps for its dual path (bottom row).

As shown, the video pathway and the image pathway capture different information in the dual point-wise layers. For example, in Figure~\ref{fig:swing} with action of swinging, the video pathway focuses on the motion of the boy on the swing, while the activations on the corresponding feature maps from the image pathway are spatially diffused and less peaked. On the other hand, the image pathway focuses on the objects: the boy, the grass, and the other swing. In Figure~\ref{fig:shoot} with action of shooting basketball, video pathway focuses on the upper body of the moving person with the basketball, while image pathway focuses on both the basket and the person with the basketball. In Figure~\ref{fig:throw} with action of throwing a ball, the video pathway focuses on the action of ball thrown to the ground, while the image pathway focuses on the persons in the background.

However, Figure~\ref{fig:swim} with the action of swimming, we can see that the most activated channels for video pathway and image pathway are both Channel \#118. This shows that when the appearance information is strong, the video pathway still captures the same appearance information with the image pathway.

\begin{figure}[!htbp]
\centering
     \begin{subfigure}[b]{1\textwidth}
         \centering
         \includegraphics[width=\textwidth]{swing}
         \caption{Swinging on something}
         \label{fig:swing}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\textwidth}
         \centering
         \includegraphics[width=\textwidth]{shoot}
         \caption{Shooting basketball}
         \label{fig:shoot}
     \end{subfigure}
     \hfill

     \begin{subfigure}[b]{1\textwidth}
         \centering
         \includegraphics[width=\textwidth]{throw}
         \caption{Catching or throwing baseball}
         \label{fig:throw}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{1\textwidth}
         \centering
         \includegraphics[width=\textwidth]{swim}
         \caption{Swimming butterfly stroke}
         \label{fig:swim}
     \end{subfigure}
   \caption{For a given input clip, the video pathway (applied to a 16-frame clip) and the image pathway (applied to the middle frame) capture different information in the dual point-wise layers.}
   \label{fig:viz}
\end{figure}

\clearpage

redpajama_set_name: RedPajamaArXiv


I'm planning to bring some big news to my upcoming All Hands staff meeting. As President and CEO of The Newberry Group – an IT firm based in Columbia, MD & St. Louis, MO – I typically present on past performance, our forecast, and our plans for achieving those forecasted results. But this time my plans are far from business as usual. I intend to propose that The Newberry Group align its vision more closely to that of a true social enterprise.
I was inspired to make this pitch after attending the Center for Social Value Creation's Social Enterprise Symposium (SES). Honestly, I didn't attend SES with the hope of being inspired. I showed up mainly out of personal interest, given the strong encouragement from my son, a staff member at the Smith School of Business. Though it didn't take long to discover just how relevant much of the content was to strategic questions I faced within my own company.
In a panel titled "Models for Impact: Reforming Capitalism" I listened to Jeff Cherry, Executive Director of Conscious Venture Lab, discuss the importance of having a clear company mission – both as an internal guidepost and as an external indicator of company value. I considered how a stronger mission could be a rallying point for all of The Newberry Group stakeholders; this was thought provoking.
After the first round of sessions, I connected with Tom Decker, a leader in the cooperative business movement, and discussed the advantages of more democratic corporate structures. I realized that, as an employee-owned company, The Newberry Group was well positioned to embrace many of these advantages. That was disruptive.
Finally, I attended a boardroom discussion led by Cheryl Kiser, Executive Director of the Babson Social Innovation Lab, on "Managing Human Capital in Mission Driven Organizations". I found my own management and development philosophies so aligned with Cheryl's that I wanted to finish her sentences. I began to see the dots connecting between my own practices at The Newberry Group and the abstract definition of a mission driven organization. That was motivating.
Over the weeks that have followed SES, I've done a lot of thinking. I've begun to understand that much of the value The Newberry Group provides for its employees remains currently untapped and if captured, would have a profound collateral impact on Newberry's clients. I foresee making greater investments in internal community building, representative of the employee's interests of the company. I also plan to implement a more democratic management strategy. My new working mission for The Newberry Group is – to improve the quality of life of its employees every year. And the business case for all of this? Lower turnover; greater diversity of ideas; improved talent and client acquisition.
I have a long way to go and still a great deal to explore, but I'm more committed than ever to having the interests of my entire company at heart in whatever direction I go. In this way, the Social Enterprise Symposium has inspired me to develop a truly mission driven organization.
Chris Steinbach, Chairman & CEO, provides overall direction for The Newberry Group and presides over the implementation of the corporate strategies, the implementation of best practices, and the profitable growth of the company. Prior to The Newberry Group, Mr. Steinbach worked at CSC where he started as a Senior Human Factors Engineer and eventually became the first Vice President of Operations for CSC's $1.3 billion Enforcement, Security, and Intelligence Division. Successfully serving in this capacity, until he took the role of CSC's Corporate Security Operations Executive wherein he served until his acceptance of The Newberry Group's President & CEO position.


I didn't fall off the face of the Earth. It just seems that way. The Belly is still in business; thanks for your concern.
I had every intention of sitting down for a nice, rousing blog post over the last week, but life got in the way. It tends to do that. While the days were whizzing by and I remained blog-less, several ideas for posts meandered through my brain, but because I didn't write any of them down, they're now a swirling mish-mash of disjointed words and unattached ideas. Probably best to just start fresh. That's what the New Year is for, right — starting fresh?
Being an overachieving busybody, I have tons of plans rattling around in my head. Yucky but necessary plans, like cleaning out my study and purging the stuff I don't use anymore. Like touching up my kitchen paint, which has been nicked in many places as this busy little family has gone about its business over the last few years. Like repairing the wallpaper in the powder bathroom, which has started to peel in a few spots. It's the only wallpaper in my house and I love it: big, lush banana tree leaves. Those giant leaves wrapping around that small space makes me smile, 8 years later. Lots of projects on the to-do list in addition to the everyday hub-bub of keeping my family up and running. After a long, leisurely winter break, my kids went back to school today (a somber affair that remained heavy and dreary despite the oh-so-rare appearance of a warm Pop Tart on each kid's plate. Yes, I am that mom — the one who deprives her kids of sugary processed pastries for breakfast. Poor babies).
Besides a fresh start and umpteen projects, the New Year brings me perspective. Before looking forward and lurching headlong into 2013, I want to take a mo to reflect back on 2012. While 2012 had its challenges, it was a far cry better than 2011; that year and the one that preceded it were pretty stinky, with a cancer diagnosis followed briskly and cruelly by a nosocomial infection that would. not. go. away. Looking back on those dark days makes me shudder.
If you ask me how it felt to have a wound vac attached to me 24-7 for weeks on end, my answer would be that I can't really say. I remember how loud that damn thing was, and how cumbersome it was to lug it around. I remember my home-health nurse coming four times a week to clean the wound; I know she measured the depth, length, and width of that wound every visit, and compiled the stats in a handy chart that we used to hang on to a semblance of hope that progress was being made, that the damn thing was healing. I know she used an oversized, medical-grade Q-Tip to clean the wound, and that it was horrific enough that she recommended a xanax before each visit (for me, not her). I remember her using that giant Q-Tip to gauge how deep the wound still was, yet when I try to recall how it actually felt, I can't. I guess that's a good thing.
This time last year, I was trying to regain my footing as I negotiated life after cancer and that awful infection. I didn't make any New Year's resolutions that year, or this year. I've never been one to inscribe grandiose plans upon my new calendar. Perhaps it's an effort to fly under the radar. To live a normal life. To step lightly around the sleeping beast that is cancer recurrence. To put as much time and distance as possible between then and now.
HAPPY New Year– those words have new meaning now, don't they? When I wish you that, I mean it in a way I never know before all these dark days (good description by the way; my few friends who know the truth of how bad it was for me have heard me call it the black hole of despair or the blackest night– I hope it will never get any darker). Maybe even though we don't remember some particulars of those times, we remember the important parts– that life can be very unhappy, so we truly appreciate happy times now when our days are filled with more light.
Isn't it wonderful how our minds protect us? Here's to a much easier year, filled with blessings!


The LDF government has instructed the Kerala State Electricity Regulatory Commission (KSERC) to simplify the procedure for releasing electricity connections.
In a rare move, the government has employed Section 108 of the Electricity Act to issue the instructions, amply indicating its decision to end its silence on the bitter stand-off between the commission and the Kerala State Electricity Board (KSEB) over multiple issues, including long-term power purchase deals and power tariff.
In a September 20 letter to the commission, the Additional Chief Secretary (Power) has asked the quasi-judicial body to "re-work" its Kerala Electricity Supply Code in the light of a Union Power Ministry directive seeking a much simpler, three-step process to release industrial and commercial connections. Besides, simpler procedures are also key to the success of the state government goal to provide electricity for all by March 2017, the letter said. Existing supply code guidelines specify "time-consuming manual procedures and entails multiple visits" to KSEB offices, the letter said.
Among other things, the government wants the commission to incorporate simpler application formats, create an online facility for filing applications and making payments, and to reduce the number of documents to two: 'proof of identity and proof of ownership/legal occupancy' instead of the seven required at present.
Only rarely has the state government used Section 108 in this manner, power department sources said. The last time it was done was by the previous LDF government when it directed the commission to accommodate the cross-subsidy component to keep power tariffs low. While the government has cited 'public interest' in issuing the instructions, top power sector officials said that it in reality shows the government's resolve to use Section 108 if needed. This section makes it clear that the commission would be guided by government directives in matters of policy involving public interest. In the event of a dispute, the government decision will prevail, it says.The KSEB and the commission headed by T M Manoharan have been at loggerheads over a host of issues in recent months. Blaming the KSEB for not filing proposals on time, the commission had launched its own measures to fix power tariff for the current year. The KSEB has contested the move. A commission directive to release open access facility to industrial consumers had not gone down down well with the KSEB. In the latest instance, the commission had set aside a major chunk of various power purchase deals which the KSEB had entered into, saying that they had not conformed to central guidelines.


Making a large investment in equipment for your processing or storage facility is a big decision. Selecting the company to purchase the equipment from, specifying the brands you prefer and designing your new facility or retrofit application are all important factors to consider. How much thought should you give to how you finance your purchase? Well, if it's a large investment you may want to consider looking in to the following government tax deductions.
$1,000,000 limitation and cannot create a net loss. Any unused loss is carried forward to offset future taxable income.
No limit on what can be depreciated and can create a net loss to reduce taxable income to a taxpayer.
e. bonus is being used on a piece of equipment under the 5 year class. This means all 5 year property placed into service must utilize bonus.
However, it is expected for Congress to address and pass this provision prior to year-end.
Navigating taxes and deductions can be confusing to those of us who don't read finance jargon on a regular basis. If you have questions about the information above, or would like to see if your company could benefit from it please feel free to contact us!