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Black and White? Not so much...
I have been avoiding this commentary for the entire history of this blog. It's one of those topics I have continually wrestled with because I'm really not sure if I have anything constructive to add to the dialogue. By nature, this blog is pretty accusatory and at times vitriolic…so be it…it is the nature of the beast. So I've debated whether or not this platform is the proper place to broach this particular subject. After having reflected on some of my recent posts regarding E-maelstrom…particularly this one…..I've decided it would behoove me to pull the trigger on this taboo of taboo subjects.
I think I am obliged to start by explaining who I am and why I see things the way I do.
I am white, of Irish descent (dad's side…not sure what mom's side is)….hillbilly to be specific. I grew up in an incredibly racist environment. It was an interesting type of racism though…because it was kind of like an inactive virus. 98% of the people around me were white, we had almost no African-Americans, Asians, Latinos, or any other races around us for miles. The few folks of color who were around, were treated very poorly and looking back, it saddens me to think of the prejudices they must have endured.
I left that environment and went on to get a degree in anthropology. I was always a little different and much more tolerant than my peers, growing up in the hills....but I still had a lot of ingrained prejudices which I carried with me to college.
Having chosen to study social science, anthropology no less, I submerged myself in the study of human culture, religion, behavior, sexuality, ritual, etc. That path shifted my consciousness and global perspective in magnificent ways. I often look back on my youth and experience moments of compunction over the way I spoke and even treated people….but I suppose we all have those moments…with perhaps the exception of Dick Cheney and Karl Rove.
Ok, that's my background, just so you know who is preaching here.
After 20 full years of studying human behavior, human culture, and evolutionary disciplines, I have come to the conclusion that "race" as we define it….is actually a myth. I could take you down the physiological path of my theory (genotypes to phenotypes), but I don't think anyone really wants to hear that….suffice to say I would make the argument that speciation is actually a myth as well. Here I will stick to the cerebral interpretation of my theory.
I think more often than not, in the current psyche of the average American, we confuse the semantics of "race" with "culture".
A good example of this was sometime last year, when the chief of police in Chalmette said they were going to stop everyone with "Chee Wee hairstyles, white T shirts, and baggy pants." The racism charges immediately followed….even on a national level. I'm not saying the man is or is not a racist, I don't know….but if you actually look at what he said, he was identifying culture, not race. It was true, and still is, that a disproportionate number of people committing crimes in our city, fit that profile….and that profile did not denote race, it denoted style...which is a product of culture that transcends race. I think it was a very stupid thing for him to say politically, but regardless, what he was denoting was culture, not race. I bring this up because I think it is incredibly important to make a distinction between the two, and more often than not, that distinction isn't qualified.
I also think much of the current turmoil we are witnessing within City Hall is truly a debate of culture…not race. In some cases I think people are purposely using this confusion to their advantage, i.e. Ray Nagin.
Someone once said "Patriotism is the last refuge of a scoundrel." We witnessed that first hand on a national level for the past 8 years. In our case (New Orleans) I would say, quite often, racism could supplant patriotism in that adage.
If there is a cogent and constructive point to this diatribe I offer it here:
This city is crippled. It is not crippled by Katrina or the threat of hurricanes….we can overcome that. It is not crippled by a lack of economic development….we can overcome that too. It is not crippled by lawmakers in Baton Rouge….we generate more money than them and we can overcome that as well. It's not even crippled by it's education system…I believe we can overcome that and in fact have made great strides in recent years to do so.
This city is crippled by its bombastic perception of race. It's not actually racism….it's the perception of race and the confusion created by those invoking racial sentiments.
I think every city in this nation deals with issues of "race" in some form or another…it's just that in New Orleans….it tends to pervade and eventually destroy everything we do.
Why? Why is that?
Is there truly a grand conspiracy by some group of people, based on their skin color, to gain control of this city's political process by ousting any other group of people whose skin color doesn't match theirs? Black, White, Asian, etc?
I believe the answer is no….on all accounts. And I mean all accounts…so don't extract that question and comment that Tracie Washington and crew are in that number…because I don't believe that is their true motivation.
I do believe, however, that there are cultural cliques within this city that wield enormous power. I believe those cliques use the perception of race to manipulate, polarize, confuse, and inflame people to achieve their political, financial, and egomaniacal goals. I believe there is a CULTURE of corruption within this city which has rotted everything from our education system to our public transit system to our public housing to our technology systems…..essentially everything that has a public budget behind it. I believe that culture of corruption is race neutral, but the people ingrained in this culture want you to believe race is the motivating factor behind every action within our government.
The bigger the budget, the bigger the corruption….case in point, the pre-K, NOPS budget which topped out as the largest in the state at over 500 million. The layers of corruption within that school system were legendary, from the lowest levels to the highest. The result was the worst public school system in the nation. The number of public officials and private enterprises, white and black, who were grafting money out of that system, at the expense of the children within it, was truly sickening. To make matters worse, many of these thieves portrayed themselves as champions of the children while methodically siphoning off as much cash from the system as they could.
When faced with accusations of corruption and theft in the documentary, Left Behind, former school board member, Ellenese Brook Simms responded, "I've been black my whole life…these are my children…what is your motivation…what are you after?" She went on to accuse Jackie Clarkson of being "racist" because she was backing another candidate for her position on the school board.
Within a year of that interview, she was indicted for accepting kickbacks for favoring a specific software package being offered by Mos Jefferson, former congressman Bill Jefferson's brother. She plead guilty to the charges.
There were many other crimes she committed while seated on the board which will probably never come to light…when confronted with her transgressions, her immediate response was to portray her accusers of racism.
Was Clarkson's support of another candidate (Lourdes Moran of Honduran heritage) to run against Simms motivated simply because Simms was African-American? Or was it because she knew Ellenese was corrupt? I think, perhaps, the track record now speaks for itself.
But here we are again….and now the target is Stacy Head. Head has been instrumental in routing out issues regarding trash contracts, and in case you didn't know this…..MIS/Tech contracts including the crime cameras. Now that these issues are being brought to the public's attention, she's suddenly being charged with racist motivations.
Veronica White, Tracie Washington, and Ray Nagin would have us believe that Head has spent the last three years of her life digging into contracts and trying to get a hold on city expenditures for the sheer motivation of ill will towards black people. They would have us believe that Stacy Head's primary motivating factor in life is to expunge New Orleans' government of any black person which may hold office.
Never mind that White has been unable to produce any semblance of an audited report on our city sanitation contracts….which is her job. Never mind that our Inspector General's office released a painstaking report which detailed massive corruption within Nagin's technology office….with the lynchpin (Meffert) being as white as the driven snow. Never mind that the majority of the contract and financial benefactors of Nagin's 8 years were white republicans, i.e., HSOA/Benetech. Cipher, LSI, Imagine & shell companies.
No don't pay attention to the facts….this is a "racist" conspiracy.
I bet there is a group of old and young, white aristocrats who meet every full moon in the backyard of an uptown mansion on St. Charles street. There, they burn effigies of black politicians and dance naked around a fire, while chanting racial slurs and worshipping a 30-foot tall, ivory (of course) statue of Stacy Head.
No, not really....
That kind of stuff happens on the North Shore, because there are some true freaks up there.
That kind of stuff happens in Baton Rouge because they're so consumed with religiosity and moral confusion they have to act out their frustrations in deviant, orgiastic, rituals.
Maybe? Sounds plausible to me.
Sarcasm aside, my hope in this whole scandal….E-maelstrom, Crime Cameras, and everything else that is transpiring….is that justice is served blindly and the public sees clearly that this is truly about corruption. It's about a culture of corruption which transcends race or the notion of race. The only motivating color in this scandal is green. Oh how I hope, we as a city, come to Buddha on this…I believe our future is contingent upon this realization.
KamaAina has left a new comment on your post "Black and White? Not so much...":
Jack Strain, of "Chee Wee hairdo" fame (or at least notoriety), is the sheriff of St. Tammany Parish, not Chalmette.
Then again, half of St. Bernard Parish (literally) seems to have resettled over there...
Update: My somewhat irate mother informed me that I am German on her side. She also mentioned Bohemian, but I'm not sure I can claim that as a nationality...Oh what the hell, its sounds cool. I would say above all....I am hillbilly, which for all it's faults, I am rather proud of.
Posted by Jason Brad Berry at 3/23/2009 11:35:00 PM
I curtsey to you Darlin' for making these statements on the issues we currently face here in New Orleans and the Nation.
In my Blog I have tried to touch on the same concept, but you have given me hope that my thoughts about Culture and yours jive to a great extent. And maybe I'll go ahead and run with it. (so many drafts on this issue to finish)
Great piece. Namaste
March 24, 2009 at 12:41:00 AM CDT
It seems you both suffer from White Guilt. The fact that you wax on about your educational heritage announces loudly that you fear starting an honest conversation about race and it's related issues for fear that you may be labeled a racist.
Bill Cosby speaks honestly about the problem without fear of being called a racist. We need more like him.
March 24, 2009 at 7:33:00 AM CDT
Thanks for writing this Dambala. You've given me some inspiration to delve into the subject on my blog. I've been so exhausted from the soap opera drama that is local politics playing out in the daily news. But, as someone said in the early pre-k days, 'there's no more sitting on the fence here post-Katrina new orleans. You're either working for the recovery of the city or you're not.' something like that...
If we fail, I wouldn't want to look back on this and know there was more that I could have said or done to make a different outcome. In the midst of so much toxicity, its hard to remember the butterfly effect. But it's real.
Okay, now that I've used the words 'butterfly' and 'inspiration' in the same commentary, I'm out!
Bravo! Bravo! Bravo! I blogged on this issue, but not nearly so elegantly, and with not nearly as much aplomb.
Leigh C. said...
The Almighty Color Green - oh, how we all throw in every trick in the book and then some in order to get the stuff.
I second GG.
What a fine piece. It is so long overdue that someone in a position of responsivbility in this City say just exactly that. Nagin had his chance NLK Day `06 and said just the opposite. - On the issue of "race" Oscar Wild (or someone) once said something akin to the idea that 90% of all disputes result from miscommunication. Never has that been more evident than on the issue of "race" and never has *that* been anywhere more evident than in this town where "race" has such fluid meaning (see arguments in Plessy vs. Ferguson, which involved Homer Plessy, a man of diverse lineage, like so many of us). "Race" is not one of the biological classifications and inherent uselessness of it from a practical matter is evident every time someone tries to employ it. I'll leave it there. Good article.
ALM said...
Jason Brad Berry said...
- It seems you both suffer from White Guilt. The fact that you wax on about your educational heritage announces loudly that you fear starting an honest conversation about race and it's related issues for fear that you may be labeled a racist.
bullshit. i offered my background simply to provide perspective....you know....the thing were sorely lacking in this city.
And no...I can't live up to the prowess of Bill Cosby...what a loser I am.
Would you like to compare my writing skills to Faulkner or Nabokov as well?
March 24, 2009 at 1:22:00 PM CDT
Killer post, Dambala. I spoke to the IOP tonight and was really uplifted by the YOUNG African-American professionals who have just had it with Nagin and the politics of race. The next citywide elections will determine whether New Orleans survives as a city. I remain optimistic because of our people, all of them, not because of our politicians.
March 24, 2009 at 11:19:00 PM CDT
Jeffrey said...
That's some good stuff, Dambala.
Bravo, Dambala. I'm a white New Orleanian of Spanish, English, German, and who knows what else heritage--my father's family came to NOLA from the Balearic Islands via Havana in the 1850's (the original "immigrant" is in St. Roch cemetary) from a town where there've been found signs of the Phoenician culture, so maybe I'm one of those rare Phoenician-Americans. I grew up in the Ninth Ward playing a lot of playground basketball, and some of my black teammates were already cluing me in at 12 or 13 years of age that African-American New Orleanians were no more a homogenous group than were white New Orleanians. Many a fair-skinned 7th Ward Creole emphasized that they shared very little culturally with the guy at the side of the court trying to sell stereos out of the trunk of his car, for example.
I've long held pretty much the same opinion on race as you've laid out here, although I've never been able to explain myself quite so succintly.
As for the first Anonymous...obviously you don't need me to come to your defense, Dambala, but I will say this--the points he's put forward here are usually offered by those who find more of a need to fix blame rather than to address problems. Thrilled to quote Cosby when he emphasizes the self-inflicted ills in the black community. Probably not as thrilled to explore reasons behind some of those ills. The kind of guy who tells the fourth-generation alcoholic to "just stop drinking".
mominem said...
Great post. My thinking is along similar lines.
All fair enough, but it's also true that the priorities of city governance (appointments, appropriations, contracts, etc) are going to shift drastically as the post-K voting demographic churns out non-black elected officials. Will they consciously act against the interests of the black community? Perhaps not, but one certainly has the right to feel apprehensive about the very real power shift that has taken place since '06.
- Perhaps not, but one certainly has the right to feel apprehensive about the very real power shift that has taken place since '06.
I guess if you look at it purely in terms of race...yeah....you certainly have that right. But if you think Nagin has had the African-American community's best interest at heart, or for that matter Gill-Pratt....I think you should take a harder look. I think Mitch Landrieu would have served the N.O. African-American interests much more so than Nagin has.
I don't know...I still can't justify anyone voting simply along racial lines.
I have to concur with Mr Longo.
There is a systematic genocide going on in New Orleans that started long before Katrina, ie moving folks out to Nawlins East, that is now in full force once again.
The politicians are merely politicians, regardless of party, controlled by the power elite, who, like most other city in the United States,(Baton Rouge is a classic example) want their cities lived in by high tax paying individuals.
The race card just as the violence card serves all parties both black and white.
An extraordinary commentary on an exhausting subject.
It seems so hard to escape but you nailed it with culture. Bravo.
"Systematic genocide"????
Overly dramatic, much?? Yeah, I was at the Lowe's on Read Tuesday, and they were just saying how it's just like Darfur out there.....
Angelique said...
I'd like to add my words of appreciation for driving to the core of what I think, but could never articulate. Your words ring much truer and avoid many of the sour notes present in most discussions on the topic. Extremely worthwhile contribution.
I'd like to think there are plenty more in this city who think like you, me, and the many commenters on this blog. I hope our rejection of corruption wins favor over "racial" divisiveness (on both sides, unfortunately) at the end of the day.
- The race card just as the violence card serves all parties both black and white.
what exactly is the violence card? never heard of anyone hiding behind violence as an excuse.
Cait said...
::slow clap::
Well written.
May 14, 2009 at 12:51:00 PM CDT


text:
Two enormous black holes are on the verge of colliding. After they do, the explosion can be incalculable
Stars like our solar are violent creatures — consistently spewing out radiation, gamma rays and all types of nasty stuff (although fortunately Earth's ozone layers and ambiance shield us from the worst of it.) However when stars die, particularly large ones, their wrath turns into much more cruel. Stars on the finish of their life cycle which are sufficiently enormous will collapse in on themselves, forming a black gap. These singularities are outlined by their gravitational pull, which is so extremely sturdy that nothing — not even gentle — can escape. In different phrases, what occurs in a black gap stays in a black gap.
However black holes aren't too restricted by measurement or quantity. On common, a regular black gap is about three to 10 occasions the dimensions of our solar. As large as "common" black holes can get, supermassive black holes — which possible kind over billions of years as black holes merge — can attain a measurement thousands and thousands or billions that of our nearest star. And the universe might be crammed with billions of supermassive black holes. In reality, there's one on the heart of our galaxy, the Milky Means, known as Sagittarius A*, round which all issues within the galaxy rotate.
The closest two supermassive black holes ever recorded are every about 200 million and 125 million occasions the mass of our solar.
Supermassive black holes actually stay as much as their identify, but when two have been to bump up and mutually envelope one another, it might create some of the insanely massive explosions within the universe — sending out reverberations within the type of gravitational waves that may ripple all through the complete universe. Scientists not too long ago introduced the invention of simply such a scenario: the closest two supermassive black holes on a collision course, not less than that people have detected up to now. The invention additionally means that merging black holes could also be extra frequent than beforehand believed.
Regardless of their relative frequency and unfathomable measurement, discovering a supermassive black gap isn't any simple job. They do not generate gentle after all; Thus, scientists should deduce the dimensions and site of black holes utilizing oblique clues, similar to how they warp spacetime, their impact on close by stars, the pace of orbiting close by starsand the detection of super gravitational waves which are produced when black holes smack collectively. When this occurs, two turn out to be one much more large black gap.
To search out these explicit two supermassive black holes, a group of 29 scientists needed to crunch plenty of information. They analyzed information from a dozen devices throughout seven telescopes scattered around the globe and in orbit, together with the Hubble House Telescope, the Keck Observatory in Hawaii and the cluster of 66 radio telescopes in a desert in Chile often called the Atacama Giant Millimeter Array. They have been taking a look at UGC 4211, a galaxy tucked away within the constellation of Most cancers.
The hole between them "is pretty near the restrict of what we will detect, which is why that is so thrilling."
No single remark was sufficient to pinpoint these gigantic useless stars, however collectively the information paints a transparent image. On the heart of UGC 4211 — which is technically two galaxies that crashed into one another — is a particularly shiny blob of matter known as the lively galactic nuclei (AGN). Astronomers imagine that AGNs are attributable to supermassive black holes, however after they seemed nearer and nearer on the heart of UGC 4211, they discovered not one black gap, however two. Their analysis was printed this month in Astrophysical Journal Letters.
Each are roughly the identical measurement and they seem like tremendous shut collectively — once more, the closest two supermassive black holes ever recorded — and are every about 200 million and 125 million occasions the mass of our solar. Fortunately, these monstrosities are nowhere close to us, positioned some 480 million light-years away from the Milky Means.
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The hole between them "is pretty near the restrict of what we will detect, which is why that is so thrilling," Chiara Mingarelli, one of many research authors and an affiliate analysis scientist on the Flatiron Institute's Middle for Computational Astrophysics in New York Metropolis, stated in a statements.
"It is essential that with all these totally different photos, you get the identical story — that there are two black holes," Mingarelli added, evaluating this new multi-observation analysis with earlier efforts. "That is the place different research [of close-proximity supermassive black holes] have fallen down prior to now. When folks adopted them up, it turned out that there was only one black gap. [This time we] have many observations, all in settlement."
Ultimately, the 2 supermassive black holes will collide, however it will not occur any time quickly. Though these two big useless stars are nearer collectively than another supermassive black holes we have detected, they're nonetheless about 750 gentle years aside. This inevitable collision in all probability will not happen for one more hundred million years or so.
However when it does occur, the wave of vitality it'll blast out into area can be incomprehensible. First, the 2 former stars will swirl ever nearer, ultimately crashing and sending out gravitational waves bigger than something people have ever captured earlier than. Presently, the largest black gap merger ever detected by Earth's gravitational wave observatories resulted in a new black gap with a mass of 142 photo voltaic plenty (or 142 occasions our Solar's mass). Upon merging, eight photo voltaic plenty have been deleted from the universe and instantly transformed into vitality within the type of gravitational waves. When these two supermassive black holes merge, the vitality launch can be exponentially larger.
This analysis is about much more than simply discovering some cool area objects. It could assist us higher perceive the evolution and life cycles of stars and would possibly assist astronomers pinpoint extra black holes within the close by universe. In flip, this data would possibly assist researchers higher calibrate gravity wave detecting units. Black holes could also be mysterious and arduous to seek out, however it simply obtained a little bit simpler to understand how and the place to look.
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Final budget must empower businesses to continue economic growth
Condeco urges more support on property costs and access to skills to maintain the UK's momentum.
London, UK – March, 2015 – The final budget before the general election is expected to hinge on a radical shake up of business rates to support the UK's business and prevent excessive property costs from holding their ability to invest in growth and innovation. Mid-size businesses now contribute £165bn to the UK's GDP, up 33 per cent in five years.
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Alongside financial support, Paul believes the next government must also do everything it can to empower businesses with access to the skilled workforce they need to fuel expansion.
Paul adds: "Immigration is an increasingly hot-button political issue, but a knee jerk reaction towards closed borders would be very harmful to our economy. The UK is a magnet for the world's best professionals, and this influx of innovation and talent is a tremendous boon for businesses in all sectors. We have grown into a global operation, but the international team in our UK headquarters has always played an integral role in our identity and ability to innovate and grow."
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Opportunities such as apprenticeships are enormously beneficial for young people and businesses alike, and should be a central policy in both keeping youth unemployment down and ensuring future economic growth."
Official figures from the ONS revealed that GDP grew by 0.5 per cent in the final quarter of 2014, bringing the year's total growth to 2.6 per cent, the highest since 2007 . Leading the charge is the technology sector, which according to new research from Barclays is on track to grow four times faster than the national GDP this year.


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text:
Q: Есть ли возможность задать стиль для отдельного элемента Bottom Navigation View? Дизайнеры постарались и создали один из элементов меню отличным от других?
Сейчас использую обычную кнопку в дополнении к меню, но это создает много неудобств.
Есть ли возможность задать свой стиль для отдельного элемента BottomNavigationView?

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
\label{sec:intro}
Long form video understanding (LVU) is a sub-domain of video recognition concerned with understanding contextual information across contiguous shots which can contain multiple locations, scenes, interactions, and actions. While a blurb can give a snapshot of a book, stories are enriched by the development of characters and their interactions with objects, people, and locations. The same applies in video recognition where understanding the context of individual moments in relation to a whole video can provide valuable information for tasks such as classification, speaker recognition, character understanding, and video retrieval. However, current video recognition methods and datasets have tended to focus on short videos \cite{hollywood,vlog,3dconv,cascante2019moviescope,movienet,zhou2010movie,multimodalfusion-video-attention,x3d,pseudo3d}, effectively aggregating convolutional image features via late fusion and inflation \cite{x3d,largescale,3dconv,pseudo3d,channel-seperated}, or extending attention based image classification methods to the tasks of action recognition, object tracking, and segmentation \cite{videobert,vilbert,axial-attention,Anticipate-vid-transformer,TimesFormer,attention-nas}.

 \begin{figure}[htbp]
 \label{fig:overview}
  \centering
  \includegraphics[width=0.9\textwidth]{Figures/overview.png}

  \caption{An overview of our approach, STAN. We encode video scenes into two feature representations using a two-stream spatio-temporal convolutional network. A transformer encoder is then used to model temporal dependencies between the tokens via an additional classification token randomly initialised. The proposed method allows us to model long term dependencies between individual frames of long videos which feature multiple actions and environments.}
 \end{figure}

Intuitively, an image can provide a good summary of a moment in time, and so it is logical that these methods perform well at classifying short videos from only a few frames \cite{beyond_snippets}; however, many video classification tasks require, or can be improved with, long-term temporal reasoning \cite{wu2021towards}. The temporal fusion of features via recurrent networks \cite{rnn,lstm} provides one solution by aggregating frames over longer sequences of frames, but these architectures suffer from computational inefficiencies due to their recursive design while 3D convolutional approaches also do not scale effectively, primarily due to their high computational footprint and the linear growth of the receptive field in the temporal dimension.

More recently, transformers \cite{bert,16x16} have been adapted to the video domain \cite{vivit,videobert,Anticipate-vid-transformer}, achieving SOTA results on multiple short video tasks. But due to the lack of inductive bias in transformer architectures, it has been observed that video transformers are not as data efficient as their CNN counterparts \cite{DEITT}, requiring more data and thus more time to train. Furthermore, it has been shown that transformer image networks trained on natural videos work just as effectively when inputs are shuffled \cite{multiscale}, demonstrating weak temporal bias. This issue is compounded when addressing LVU tasks, as we encounter larger datasets with fewer opportunities for parameter updates during training caused by a small target to data ratio.

The following question then arises.\textbf{How can we leverage inductive bias from image CNN's to make video transformer networks more data and memory efficient for long form video understanding tasks?}

Our solution is a two-stream Spatio-Temporal Attention Network (STAN) with which we gain data and computational efficiency by introducing inductive bias via convolution. Existing methods for image classification with transformers such as \cite{16x16} split images into $16 \times 16$ pixel regions encoded with a positional embedding to introduce permutation and translation invariance. This method has been extended to video by expanding these regions temporally to create 3D tokens with 2D positional embeddings \cite{vivit}. Inspired by work in two-stream slow-fast convolutional video networks for short video classification \cite{slowfast}, our approach replaces this tokenisation method with both \textit{image} spatial and \textit{context} temporal scene features extracted from pre-trained convolutional neural networks. We then use a two-stream transformer architecture to model temporal dependencies between the scene features for classifying long videos of up to two minutes in length. Our method is data and memory efficient and achieves SOTA results on a range of long form video understanding tasks.

\section{Related Work}
\label{sec:literature-review}

Early video classification works focused on non-temporal aggregation, which included clustering \cite{vlad,fisher,learnablepooling} or fusing \cite{rethinking-genre} spatial features obtained from convolutional neural networks. Since a short video will share a similar distribution of pixels over concurrent frames, these networks perform well for video classification and object detection tasks. Naturally, fusing these output features using RNN's \cite{rnn,sequential,faster-rcnn,hybrid-network} such as an LSTM \cite{lstm,beyond_snippets} improves performance by introducing temporal information. However, as discussed in \cite{16x16}, these model architectures align with steps in computational time, and are inefficient at longer sequence lengths as memory constraints limit batching across sequences.

Later works explored extending convolution to video, inflating image CNN's via a temporal channel \cite{x3d,largescale,3dconv,pseudo3d,carreira2017quo_kinetics} and using two stream convolutional networks to aggregate spatial and temporal information introduced via optical flow \cite{two-stream}, or from various sampling and aggregation intervals \cite{r21d,actorcontextactor,spati-temp-speed-accuracy}. 3D Convolutional Neural Networks (CNN's) are highly effective at video classification, object detection, and action recognition tasks but are computationally intensive to train. For example, in \cite{threedCNN} the authors process just 16 frames at the cost of 40 GFLOPS per single pass, making the approach infeasible for sequences longer than a few seconds. To address computational overhead in training convolutional video networks, in \cite{slowfast} the authors present an efficient and high performance CNN for video classification, which utilises a fast and slow temporal sampling stream. The slow stream can process higher resolution images and extract key spatial information over a few key frames, while the fast stream maps low resolution frames to infer temporal information. This method is highly effective at video and action classification on short 10 second clips at a low computational cost.

Transformers \cite{16x16, bert,attention_all_you_need,gpt3} use self-attention to model dependencies between inputs and have recently been shown to work effectively for video classification tasks when implemented with temporal positional information \cite{videobert,vilbert,axial-attention,Anticipate-vid-transformer,TimesFormer,attention-nas}. Unlike convolutional methods, they lack inductive bias and, as such, take longer to model dependencies between neighbouring pixel regions which extends training time and harms data efficiency \cite{DEITT} thus making them innapropriate for LVU tasks. Introducing inductive bias via convolution, shifting windows, and gaussian bias to transformers has shown to be effective in the image domain \cite{token-to-token,cmt-convolutions-meet-transformers,residualattention,localness-for-self-attention,tsm}, and hybrid networks for video have also been proposed in \cite{video-transformer-network,convit,video-action-transformer}, but only on short segments of video. Finally, LVU networks have been explored in \cite{wu2021towards, zhao2019long} where the authors leverage short-term detection and tracking to form instance representations or use LSTM's for the task of long form video-question answering respectively.

\section{Method}
\label{method}
\textbf{Overview}
We aim to classify long videos by splitting them into discrete scenes and extracting spatial and temporal representations for temporal fusion via a two-stream transformer encoder. To do so, we first analyse the video for changes in average frame intensity/brightness using a running average over RGB video channels. We use these time stamps as scene segmentation points and uniformly sample 12 frames from each scene segment using a 3D CNN encoder to generate a low-resolution temporal feature tokens for each scene. We also extract the central frame of each scene at a higher resolution and use a 2D CNN to obtain a spatial feature token. The spatial and temporal scene tokens are encoded with a positional embedding and temporally aggregated using a two-stream transformer encoder. For classification, we randomly initialise an additional token prepended to the spatial and temporal sequence of embedding tokens, which learns to model the temporal inter-dependency of the individual scenes. An overview of the sampling methodology is shown in Fig \ref{fig:sampling}.

 \begin{figure}[htpb]
 \centering
  \includegraphics[width=0.8\textwidth]{Figures/sampling.png}

  \caption{For each scene $s$ in the video $v$ we obtain a temporal ($\hat{t}$) and spatial ($\hat{x}$) feature embedding using 2D and 3D convolutional neural networks denoted here as $h(.)$ and $g(.)$. A shared positional embedding, $p$, is added to every scene embedding to generate the spatial tokens, $z_s$, and the temporal tokens, $z_t$. A two stream spatio-temporal transformer with two layers ($l$), learns the dependency between the sequence of spatial tokens and the temporal tokens. Following normalisation and a linear projection, the features are fused for classification. In practice, we only fuse and classify the prepended CLS token as discussed in Section \ref{method}. When training the STAN-Small model, we do not back-propagate through the 3D CNN represented here by the stop-gradient line. For STAN-Large we continue to fine-tune both convolutional encoders.}
   \label{fig:sampling}
 \end{figure}

\textbf{Spatial and Temporal Tokens} Given a uniformly sampled set of 12 video frames from a scene defined here as $t \in \mathbb{R}^{12 \times H \times W \times C}$ we implement a R(2+1)d Video ResNet encoder \cite{r21d} pretrained on the Kinetics400 Dataset \cite{carreira2017quo_kinetics}, defined as $g(t)$, to encode each set of frames into a single feature embedding token $\hat{t}$ which represents the projected temporal features of the scene $s_i$ from a set of scenes $s$. We obtain an embedding for each scene in the video using the above method to generate a set ${z_t}$ which represents the set of all temporal embedding vectors for the scene $s_i \in s$.

\begin{equation}
z_t = [z_{cls}, \hat{t_0}, \hat{t_1}, ..., \hat{t_n}] + p
\label{eq:temporal_list}
\end{equation}

Where ${z}_{cls}$ is a randomly initialized vector in the same dimension as $\hat{t_i}$ and $p_i \in \mathcal{PE}$ is a positional embedding added to the sequence as described in \cite{attention_all_you_need} as,

\begin{equation}
\mathcal
{PE}_{pos,2_i} = sin(\frac{pos}{10000^{2i/d_{model}}})   \nonumber
\end{equation}

\begin{equation}
\mathcal
{PE}_{pos,2_{i+1}} = cos(\frac{pos}{10000^{2i/d_{model}}})
\end{equation}

Where $d_{model}$ is a common dimension for both the spatial and temporal tokens. In \cite{video-action-transformer,vidtr,16x16}, positional information is used to infer the relationship between cropped regions in an individual frame. As we have introduced inductive bias via convolution, we do not need to model the position of pixel regions and instead extend the positional embedding method to infer the position of each short temporal sequence. We argue that this is logical for longer videos that feature a narrative composed of multiple dynamic actions and environments such as those found in movies. In section \ref{sec:results} we show that this positional information benefits the model's performance on such a task.

\textbf{Spatial Encoding Token} To obtain a spatial encoding token, we perform a linear projection of the high resolution frame $x$, which is sampled from the center of $s_i$, to a spatial embedding token $\hat{x}$ using a ResNet18\cite{resnet} encoder model pretrained on the ImageNet Dataset \cite{imagenet_cvpr09} so that $\hat{x} = h(x)$.  As in the case of the temporal token, we form a sequence of spatial embedding tokens $z_x$ from spatial feature embeddings obtained via $h(.)$ so that $z_x = {\{\hat{x_0}, \hat{x_1}, ..., \hat{x_n}\}}$ where $n$ is the total number of scenes in the video $z$. In Eq \ref{eq:spatial-list} $z_x^{cls}$ is the randomly initialized spatial cls token used for classification.

\begin{equation}
{z_s} = [z_x^{cls}, \hat{x_{0}}, \hat{x_{1}}, ..., \hat{x_{n}}] + p
\label{eq:spatial-list}
\end{equation}

The positional embedding vector $p$ in Eq \ref{eq:spatial-list} is the same as defined in Eq \ref{eq:temporal_list} and as such the positional information is shared between the spatial and temporal tokens. This ensures that positional information is consistent between the spatial and temporal streams during fusion. We now define a spatial and temporal transformer for temporal aggregation of the input tokens and then discuss techniques to fuse the output classification from both streams.

\textbf{Spatial and Temporal Transformer Encoders} For the spatial and temporal transformer encoder architecture, we implement the transformer model introduced in \cite{attention_all_you_need} originally designed for natural language processing. As described in \cite{attention_all_you_need} we generate Query ($Q$), Key ($K$), and Value ($V$) matrices from both the spatial and temporal embedding representations where each row in the matrix represents a corresponding scene, with the first row representing our classification token. A matrix of outputs is computed as,

\begin{equation}
Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}V})
\label{eq:attention}
\end{equation}

The output matrix $A(Q,K,V)$ is summed with the input embedding matrix via a residual connection and normalised using Layer Normalisation. Finally, the output is summed with a second residual connection before a classification MLP head. In practice, we only apply the final classification MLP to the temporal and spatial transformer classification tokens which learn a representation of the input via self-attention. As such, we can discard the other rows of the output matrix and only backpropagate via the classification MLP. We use Multi-Headed-Self-Attention to model further representations in spatial and temporal domains by replicating the attention mechanism in Eq \ref{eq:attention} and concatenating the heads. This process is described in detail in \cite{attention_all_you_need}. In \cite{video-action-transformer,vidtr,convit} the authors use a linear layer for classification based on ${z}_{cls} \in \mathbb{R}^d$. In our work, we linearly project ${z}_{cls}$ to $\mathbb{R}^{d}_{model}$ for both the spatial and temporal features so we can experiment with several fusion methods, which we will describe next.

\textbf{Fusion and Classification} For fusion, we normalise the output embeddings and project to a common dimension with a linear layer before classification via a three-layer MLP separated by a gated non-linearity. The loss function can be defined as a binary cross entropy loss between the targets $y$ and the scaled sum of the feature embeddings,

\begin{equation}
   \mathcal {L}^{Fusion} = {L}_{BCE}(h(Norm([q(z_s^{cls}) + \lambda q(z_t^{cls})]), y)
\end{equation}

Where $\lambda < 1.0$ acts as a hyper-parameter to scale the influence of the temporal network in generating the output logits and set to 0.6. $q(.)$ Is an MLP with one hidden layer and weights shared for both the temporal and spatial streams, while the function $h(.)$ represents the final MLP used for classification after fusion. To improve training time we use transfer learning and pre-train the 2D CNN on ImageNet \cite{imagenet_cvpr09} and the 3D CNN on Kinetics400 \cite{carreira2017quo_kinetics}. We present both a large, and small version of the model. For the \textbf{STAN-Small}, we do not update the parameters in the temporal stream layers but back-propagate through the two-stream transformer to the 2D CNN. This reduces the number of trainable parameters by 48 Million, making the whole network trainable on a single Nvidia RTX5000 GPU with 16GB of memory. The larger model, \textbf{STAN-Large}, which back-propagates via both streams, improves performance by 11\% but requires 92.5 million trainable parameters compared with 45 million for STAN-Small. In Table \ref{tab:main-results} we experiment with a number of additional methods for fusion including collaborative gating \cite{liu2019CollabGate} and distillation \cite{hinton2015distilling}. A full experimental analysis follows in the next section.

\section{Results}
\label{sec:results}
We evaluate the proposed method on several long video classification tasks. As discussed in \cite{wu2021towards} video datasets have typically focused on short video tasks, therefore following~\cite{wu2021towards} we define long videos as videos which feature more than three shots or scenes, are longer than a minute in length, and in which classification relies on, or is improved by, contextual understanding of the relationship between the content of shots and their order. To evaluate our method, we use both the \textbf{MMX-Trailer-20 Dataset (MMX)} \cite{rethinking-genre}, and the \textbf{Long Video Understanding Benchmark (LVU)}~\cite{wu2021towards}.

\textbf{MMX Dataset} The MMX-Trailer-20 Dataset \cite{rethinking-genre} is a multi-modal movie trailer dataset with 7555 movie trailers spanning many scenes, locations, actions, and narratives. The MMX-Trailer dataset features movie trailers from 20 genres with six genre labels per video from 1900 $-$ 2021 with an average length of two minutes. The training set is 6047 videos, with the validation and test sets both at 754 samples. The dataset consists of ~37 million frames in total.

\textbf{LVU Benchmark} The Long Video Understanding Benchmark \cite{wu2021towards} contains ~30K videos with an average length of ~120 seconds. The benchmark comprises of content understanding, user engagement prediction, and movie metadata prediction tasks, demonstrating various requirements for long temporal modelling. We test our approach on the content understanding tasks including character relationship identification, speaking style, and scene recognition.

\textbf{Compared Methods} To demonstrate the effectiveness of the two-stream network, we implement several existing methods for video classification using the MMX-Trailer-20 dataset. They include extracting convolutional image features from a ResNet18 \cite{resnet} for each scene and simply using average pooling for classification. We also implement this same method with features obtained from a two-stream Inflated 3D Convolutional Neural Network \cite{3dconv} and a SENet \cite{squeeze}. We also show results for comparative temporal networks, including a Temporal Pyramid Network, which extracts multiple level features from a CNN to model dynamic temporal movements in videos, and a vanilla LSTM \cite{lstm} to model the temporal relationship between the feature representations from an 18 layer ResNet. More complex temporal aggregation strategies are also explored, such as the Fine-Grained Semantic \cite{rethinking-genre} architecture which uses concatenation to aggregate multi-modal features as well as the effectiveness of earlier audio-visual works such as audio-visual classification using support vector machines \cite{huang2012movie} and audio VGG network as described in \cite{youtube-8m}. We also explore distillation as described in \cite{DEITT}, using the temporal transformer encoder as a teacher network to the spatial transformer. For the LVU Benchmark task we compare our method with those presented in the paper including videoBERT \cite{videobert}, R101-SlowFast \cite{slowfast} and Object Transformer \cite{wu2021towards}.

\textbf{Metrics} As outlined in \cite{rethinking-genre} the MMX dataset is imbalanced and as such we follow other works \cite{dong2016word2visualvec,Miech2017,mithun2018learning} and use Mean Average Precision $mAP$ to evaluate the effectiveness of our classifier. To calculate the $mAP$, we average the area under the precision-recall curve per genre, weighting instances according to the class frequencies. We also show weighted Precision ($P_w$), weighted Recall ($R_w$), and weighted F1-Score ($F1_w$) in Table \ref{tab:genre-specific-results}. With all metrics, a higher value demonstrates improved accuracy. For LVU we compute the standard error averaged over five runs as proposed in the original paper \cite{wu2021towards} where higher values represent improved performance.

\textbf{Evaluation} First, in Table \ref{tab:main-results} we evaluate our approach against existing methods for video classification using the MMX-Trailer-20 Dataset. We demonstrate that our method outperforms pooling of convolutional spatial features by 10\%. Secondly, we show our method improves performance on scene level features extracted via an inflated 3D CNN \cite{x3d} by 11\%. Third, we compare our approach with other methods for combining convolutional features temporally, including an LSTM \cite{lstm}, S(TPN) \cite{s-tpn}, concatenation \cite{rethinking-genre}, and collaborative gating \cite{liu2019CollabGate}. Finally, we show results for an alternative method for two-stream aggregation using a distillation network as described in \cite{DEITT}. We outperform all existing methods for the genre classification task.

\begin{table}

\centering
\begin{adjustbox}{width=1\textwidth}
\begin{tabular}{l|ccc|c}
\toprule
Method                       &       CNN Feature Extractor & Frames per Scene  & Aggregation Method & $\overline{mAP}$   \\
\midrule
ResNet \cite{resnet} &  ResNet18 & 1 & Avg Pool & 0.434 \\
SqueezeExcite~\cite{squeeze} & SE-ResNet & 1 & Avg Pool &0.544 \\
\midrule
I3D~\cite{carreira2017quo_kinetics} & I3D & 12 & Avg Pool & 0.487 \\
\midrule
Collaborative Gating \cite{liu2019CollabGate} & ResNet18 & 12 & Gated Unit & 0.4723 \\
S(TPN) \cite{s-tpn}  & ResNet18 & 12 & Temporal Pyramid &0.492          \\
Fine-Grained Semantic \cite{rethinking-genre}  & Multi-Modal & 16 &  Concatenation &   0.583 \\
LSTM \cite{lstm} &  ResNet18 & 1 & LSTM & 0.596 \\
Distillation \cite{hinton2015distilling,DEITT}  & ResNet18 + R2+1D & 12 & Transformer &  0.601 \\
\midrule
\textbf{STAN-Small}  & ResNet18 + R2+1D & 12 &  Transformer & \textbf{0.640}     \\
\textbf{STAN-Large}  & ResNet18 + R2+1D & 12 &  Transformer & \textbf{0.750}     \\
\bottomrule
\end{tabular}

\end{adjustbox}
\bigskip
\caption{Comparison of our proposed approach with existing methods for video classification using CNN feature extractors and evaluated on the MMX-Trailer-20 Dataset. We implement several feature extraction and aggregation methods to evaluate their effectiveness for the long video classification task.}
\label{tab:main-results}

\end{table}

\begin{table}

\centering
\begin{tabular}{@{}l|lll@{}}
\toprule
Method & \multicolumn{1}{c}{Relation} & \multicolumn{1}{c}{Speaking} & \multicolumn{1}{c}{Scene} \\
\toprule
R101-SlowFast+NL \cite{slowfast}   & 52.4  & 35.8  & 54.7  \\
VideoBERT \cite{videobert}         & 52.8  & 37.9  & 54.9  \\
Object Transformer \cite{wu2021towards} & 53.1  & 39.4  & 56.9  \\
STAN-Large (ours)     & \textbf{56.25} & \textbf{41.41} & \textbf{58.33} \\
\bottomrule

\end{tabular}
\bigskip
\caption{Accuracy of our approach on long video understanding tasks using the Long Video Understanding Dataset. The reader can find references and further details in \cite{wu2021towards}. We outperform current approaches for classifying conversation (speaking), character relationships (relation), and locations of scenes (scene).}
\label{tab:lvu}

\end{table}
In Table 2 we show that we achieve SOTA results for three long-form video understanding tasks on the LVU Benchmark outperforming other methods which also utilise transformer architectures such as VideoBERT \cite{videobert} and \cite{wu2021towards} which utilises pre-trained CNN backbones plus self-supervised masked pre-training. Our architecture performs particularly well on the relationship identification task $(\bf{+3.1})$ despite having no prior knowledge of the domain in pre-training.

\textbf{Ablation Experiments} In Table~\ref{tab:ablation} we perform several ablation studies to assess the impact of the spatial and temporal features and the data efficiency of the model. To measure the data efficiency, we randomly sampled 2000 trailers from the MMX-Trailer-20 training partition, with the entire test partition of 754. Table~\ref{tab:ablation} shows that the model still achieves SOTA performance on the MMX-Trailer-Dataset despite being trained on only a quarter of the samples demonstrating high data efficiency. We infer that data efficiency is improved by using convolutional encoding as the transformer network only needs to map self attention between the scene feature tokens rather than pixel localities. Furthermore, the proposed network architecture can infer translation in-variance within the convolutional encoding.

In Table \ref{tab:ablation} we also provide further results for models that only use either the spatial or temporal convolutional encoder to assess the impact of propagating gradients through the convolutional encoders.  We observe that back-propagating through the spatial convolutional encoder provides the most significant performance gain with the most negligible effect on the number of trainable parameters. Training both the CNN encoders end to end (STAN-Large) is the most effective method for achieving high accuracy. Still, it comes at a cost, increasing the number of trainable parameters by 48 million. We find that using just the temporal transformer encoder and introducing a stop gradient before the convolutional feature extractor performs well but is improved with spatial features for 28 million additional trainable parameters. We also show genre specific results in Table \ref{tab:genre-specific-results}.

\begin{table*}[htbp]

\centering
\begin{adjustbox}{width=1\textwidth}
\begin{tabular}{l|cccccccccccccccccccc|cccc}
Model                                 &Actn &Advnt&Animtn&Bio &Cmdy&Crme&Doc &Drma&Famly&Fntsy&Hstry&Hrror&Mystry&Music&SciFi&Sprt &Shrt&Thrll&War&$F1_w$  &$mAP$&$P_w$     & $R_w$\\
\midrule
Support                               & 130 &197  &46    &13  &224 &102 &87  &267 &117  &115  &44   & 104 &41   &86   &107   &30   &45  &12   &21  & -     & -                & -        & -\\
\midrule
Random                                &0.29 &0.41 &0.11  &0.03&0.46&0.24&0.21&0.52&0.27 &0.26 &0.11 & 0.24&0.1   &0.2  &0.25  &0.08 &0.11&0.03 &0.05&0.318  &    	0.134	  &0.19	     &1   \\
ResNet~\cite{resnet}               &0.43&0.55  &0.74  &0   &0.49&0.38&0.63&0.55&0.51 &0.28 &0.24 &0.42 &0.3	 &0.28&0.41  &0.22 &0.19&0.11 &0.33&0.434  &	0.489	         &0.437     &	0.48    \\
VGG-Audio~\cite{youtube-8m}         &0.47&0.51  &0.40  &0.10&0.61&0.38&0.58&0.55&0.51&0.37 &0.11  &0.34 &0.39  &0.30&0.35   &0.16 &0.15&0.13 &0.12&0.454  &	0.449	         &  0.400   &0.537    \\
I3D~\cite{carreira2017quo_kinetics}&0.5 &0.59  &0.74	 &0   &0.62&0.33&0.63&0.56&0.55 &0.36 &0.2  &0.38&0.45  &0.24&0.37  &0.23 &0.14&0.10 &0.13&0.463  &	0.487	         &0.448	    &0.494   \\
SqueezeExcite~\cite{squeeze}            &0.48&0.63  &0.79  &0.12&0.65&0.41&0.60&0.59&0.55&0.42 &0.25  &0.47&0.42  &0.29&0.50  &0.34 &0.19&0.12 &0.31&0.516  &	0.554            &0.493     &0.572    \\
Naive Concat \cite{resnet}                             &0.56&0.61  &0.64  &0.09&0.64&0.35&0.69&0.60&0.58&0.39&0.19&0.49 &0.45 &0.21    &0.48 &0.39 &\textbf{0.28} &0.27&	0.41&0.525 &0.497             &0.522    &0.551\\
Fine-Grained Semantic \cite{rethinking-genre} &{0.62}&\textbf{0.69}&{0.71}&0.11&\textbf{0.71}&{0.53}&\textbf{0.73}&{0.62}&{0.51}&{0.34}&{0.56}     &\textbf{0.60}&\textbf{0.45} &0.50&\textbf{0.64}&\textbf{0.30}&0.11&{0.13}&\textbf{0.55}&{0.597}&{0.583}&{0.554}&{0.697} \\
\midrule
\textbf{STAN-Small}  &\textbf{0.71}&{0.68}&\textbf{0.92}&\textbf{0.21}&{0.61}&\textbf{0.65}&{0.62}&\textbf{0.69}&\textbf{0.86}&{0.49}&\textbf{0.46}&\textbf{0.58}&{0.43}&{0.39}&\textbf{0.53}&{0.13}&{0.20}&\textbf{0.85}&{0.50} & \textbf{0.65} & \textbf{0.64} & \textbf{0.62} & \textbf{0.73} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\bigskip
\caption{
Genre classification performance for each genre on the MMX-Trailer-20 dataset. We observe high performance gains on genres in which temporal information can be considered an important classifier such as Action $+9$ and Animation $+11$. We also observe that other network architectures perform very poorly on classification of the genre thriller while we improve accuracy by $+58$. We conclude that long-term temporal modelling performs well on this task as the content is difficult to classify when features are presented in isolation.}
\label{tab:genre-specific-results}

\end{table*}

\begin{table}[]
\centering

\begin{tabular}{l|ccc}
\toprule
Method                           & Samples & Parameters (Millions) &mAP\\
\midrule
Spatial with backprop     & 2000 &28 & 0.5903        \\
Temporal with backprop     &     2000  &48 &  0.6221         \\
\midrule
Spatial  no backprop            &2000  &16.5 & 0.4024 \\
Temporal no backprop        &2000  &16.5 & 0.59 \\
\midrule
Distillation Network         &2000 &44.5 &  0.6005           \\
Gated Fusion            &2000    &45 & 0.4728             \\
\midrule
STAN-Small          &2000 &45 &   0.6151          \\
STAN-Small       &6047 &45 &  0.6401 \\
STAN-Large       &6047 &92.5 &  0.7506 \\
\bottomrule
\end{tabular}
\bigskip
\caption{Ablation experiments assess the network quality under a constrained data training protocol. Each model is trained using only 2000 samples, while the test length remained consistent at 754 samples. The network continues to outperform existing methods with fewer data. We also show results for individual spatial and temporal streams with back-propagation. }
\label{tab:ablation}
\end{table}

 \begin{figure}[!h]
  \includegraphics[width=1\textwidth]{Figures/activations.png}

  \caption{Class activation maps of randomly selected samples from the MMX-Trailer-20 test partition. Each sub-figure represents a series of input tokens from the spatial encoder $h$. The class activation maps show pixel regions in red to blue, with red regions representing high activation and, therefore, greater contribution to the predicted class labels.}
  \label{fig:activation}
 \end{figure}

\textbf{Qualitative Results}  In Fig \ref{fig:activation} we show class activation maps of the input 2D CNN encoder, shown in Fig \ref{fig:sampling} as $h()$ and the predicted genres for a given input video. We observe that the model predicts the labels correctly and learns cohesive features for the input tokens. For example in Fig \ref{fig:activation} we see that the Family genre is predicted when we have a strong activation on animals faces. For the Horror classification, spatial features include people screaming and dark scenes. We also notice that text is an important feature for the classification task, acting as a strong temporal marker in the sequence of tokens. Class activation maps are obtained via Grad-CAM\cite{gradcam-paper} using the code provided by \cite{gradcam}. We refer the reader to the appendix for further qualitative results and ablation experiments.

\section{Conclusions}

We present a data and memory efficient spatio-temporal attention network for long video classification which combines the advantages of convolutional inductive bias with the computational advantages of transformer networks for the task of long video classification. We show that by using static \textit{image} and temporal \textit{context} convolutional tokens we are able to create a data efficient architecture capable of classifying videos of up to two minutes in length and trained on a single GPU while achieving SOTA results on a range of long-form video understanding tasks.

redpajama_set_name: RedPajamaArXiv


Signed into law on Oct. 17, 2018 by Governor Wolfe.
Primary Sponsor: Rep. Nesbit (Bulter and Mercer counties).
Introduced in the House on June of 2017, Passed by Senate on June of 2018.
Amends the Controlled Substance Act requiring that all Controlled Substance Prescriptions be sent electronically by Prescribers.
Exclusions: Veterinary, Compounds, Hospice, Temporary Electronic Issues.
Requires that regulations be promulgated within 180 days of the effective date to allow for implementation.
For the complete HB 353 Electronic Prescribing Requirements for Controlled Substances information: here.
To read full text: here.
Strongly recommend that all Pharmacists read the bill and look for coming DRAFT regulations. PSHP will keep you updated as this moves forward.
Signed into law on June 22, 2018 by Governor Wolfe.
Became effective in 60 days.
Relevance: to health systems and pharmacy programs that have or work with hospice or home care programs / agencies.
Medications must be disposed of in the home: transport from home is not permitted; a witness is required.
For complete SB 978: The Hospice and Home Health Prescription Medication Disposal Act: here.
Introduced in June 2017, Passed by Senate in June 2018.
Referred to House Professional Licensure Committee on June 19, 2018, Report Bill as Amended 9/24/18, amendments including adding podiatrists and changing wording on insurance coverage, and defining what can be provided via telemedicine. But removed from the table 9/25/18.
Pharmacists are included in the definition of "Provider" also known as "Health Care Provider" throughout the bill.
Providers that are licensed in Pennsylvania will be able to practice telemedicine as long as they follow the regulations developed by the corresponding licensure board.
Each board will be responsible to develop temporary regulations within 120 days after the proposed bill goes into effect and promulgate regulations for telemedicine within 24 months.
Providers must obtain informed consent from the patient.
Providers must administer an appropriate assessment when utilizing telemedicine services.
Providers may use interactive audio without video if used with store and forward.
A health insurer must reimburse a participating network provider the same amount as if the encounter was in person.
Services that would be covered by medical assistance if completed in person shall be covered if completed by telemedicine.
The current bill and its history can be found here.
Important for pharmacists to be considered independent practitioners and not to have the state board structure this in terms of collaborative practice. An area where PSHP would need to make recommendations to the Board (after reviewing NABP documents related to telemedicine). Our main priority was to help support the Board of Pharmacy in addressing the requirement for temporary regulations to implement the bill. We are developing a statement for the board the differentiates "dispensing" telepharmacy that usually includes remote oversight of pharmacy technicians, remote verification of medication orders or prescriptions, or use of technology for drug dispensing vs drug therapy management via telepharmacy that involves direct engagement with patients and use of their medications which would be relevant for the telemedicine bill regulations.
Primary Sponsor: Senator Rafferty (Berks, Chester, Montgomery – part of each).
Referred to Committee for Consumer Protection and professional Licensure on 2/27/2017, PSHP representatives were there for the hearing. But the legislation died in this committee.
Targeting the growing problem of prescription drug diversion.
Submit a criminal history background check.
PSHP Would want to encourage both Rafferty (Senator and Daley to better align their bills so there are not separate versions.
For complete bill information: here.
PSHP worked with HAP, PA Medical Society, the Emergency Medicine Group and directly with the Primary sponsor to amend this bills. It still appears that if would have resulted in a documentation burden for the justification of use.
PSHP legislative group is happy it did not more forward this legislative session.
Primary Sponsor Senator Yaw (Bradford, Lycoming, Sullivan, Susquehanna (part) and Union Counties).
Referred to Senate Health and Human Services in May 2017; Re-referred to the Appropriations Committee September 2017.
Passed Senate and referred to the House Health Committee in June 2018, where is remained until session ended.
An Act amending the act of April 14, 1972 (P.L.233, No.64), known as The Controlled Substance, Drug, Device and Cosmetic Act, further providing for definitions, providing for Pennsylvania Safe Effective Opioid Prescribing Advisory Council and further providing for promulgation of regulations.
PSHP was successful in working directly with Senator Yaw to amending the bill to add one health system and one community pharmacist.


Stevie talks about her 10-mile run through Central Park in New York city. I update everyone on how Andrew is doing with his half-marathon training. I summarize the Death Race that Iron Brandon (drink) suggested – no thanks! Some listeners sent in pictures of very cool medals. And we also talk about getting your mojo back.
Stevie and I talk about an organic gel called Chocolate #9, ambulance getting lost or being late at races, sales the anti-chafing lotion "Skin Sake", doing back to back long workouts, Disney for Marathon Weekend, and a good luck shout-out to IronBrandon. He's doing Ironman Louisville on August 29th.
Stevie is back from vacation! Scott Sloan joins us this week to talk about running his first ultramarathon. We also talk about chafing, herbal options for organic running fuel, Stevie's beach runs, my upcoming triathlons, wetsuit flotation, and a few other topics.
Chocolate #9, order and adult versus kid Sharkies. We also talk about the album of review photos on Facebook, a neck injury that interfered with my triathlon plans, massages and chiropractors, focusing on health over performance, and a noticeable change in sweat volume.
This episode is dedicated to Ben Davis and his weight loss journey. He has lost 120 pounds since January 2009. Last week, oncology he made and posted a video to chronicle his story. We wanted to get the inside scoop and he agreed, sick so here it is. Enjoy!
If you want to follow Ben's progress, check out bendoeslife.tumblr.com!
This week, diagnosis information pills I talk about my new Nike Run Free+ shoes and the Nike+ GPS App, my 18-mile run, Stevie's cross country pasta party, good luck to Geeks in Running Shoes' Jason with his upcoming half-marathon, and much more. Just four more weeks until the Chicago Marathon!
A bit of a failed experiment today. I took Niacin at the beginning of the show, for sale but never turned very red. Oh well. We also talk about the movie Spirit of the Marathon, migraine our listener's feelings about the weather, and my new running partner. At the end of the show, we play a call from Geeks in Running Shoes's Jason, who completed his first half-marathon on Sunday.
Only three more weeks until the Chicago Marathon!


text:
William Marshall Tredway (August 24, 1807 – May 1, 1891) was a U.S. Representative from Virginia.

Early life
Born near Farmville in Prince Edward County, Virginia, Tredway completed preparatory studies.
He was graduated from Hampden-Sydney College, Prince Edward County, Virginia, in 1827.

Career
After college studies, Tredway studied law, was admitted to the bar in 1830 and commenced practice in Danville, Virginia.

Tredway was elected as a Democrat to the Twenty-ninth Congress (March 4, 1845 – March 3, 1847) with 57.34% of the vote, defeating Whig John D. Cheatham. He was an unsuccessful candidate for reelection in 1846 to the Thirtieth Congress.

He served as delegate to the Democratic State convention in 1850.

In 1850, Tredway was elected to the Virginia Constitutional Convention of 1850. He was one of six delegates elected from the Southside delegate district made up of his home district of Pittsylvania County, as well as Halifax, and Mecklenburg Counties.

He served as member of the secession convention of Virginia in 1861. A conditional Unionist, he voted against secession on April 4 and for secession on April 17 following Lincoln's call for state militia to restore seized Federal property.

Tredway served as judge of the circuit court of Virginia 1870-1879. He resumed the practice of law in Chatham, Virginia.

Death
William Marshall Tredway died on May 1, 1891, in Chatham, Virginia. He was interred in Chatham Cemetery.

See also
William Tredway (Canadian politician)

References

Bibliography

External links

1807 births
1891 deaths
People from Prince Edward County, Virginia
Virginia lawyers
Politicians from Danville, Virginia
People from Chatham, Virginia
Virginia Secession Delegates of 1861
Democratic Party members of the United States House of Representatives from Virginia
19th-century American politicians
19th-century American lawyers

redpajama_set_name: RedPajamaWikipedia


text:
August 26, 2019 | By Leena Kim
Mexicans Less Receptive to Central American Migrants
Mexican attitudes towards Central American migrants are changing as the dispute between the US and Mexico over how to handle the migration issue continues.
August 1, 2019 | By Craig Kafura
In Korea-Japan Dispute, Japanese Public Backs Export Controls
Relations between Japan and South Korea are in freefall, with the two key US allies in Asia engaged in a steadily escalating economic conflict.
July 29, 2019 | By Bettina Hammer
Americans Aren't Fans of Arms Sales
The United States has long been the tops arms supplier in the world. Yet public opinion data shows that Americans aren't fans of U.S. arms sales.
Little Admiration for the United States among MENA Publics
Most Americans believe that respect and admiration for the United States are instrumental in achieving US foreign policy goals. But a new poll finds publics in the Middle East and North Africa continue to view the United States unfavorably.
July 9, 2019 | By Bettina Hammer
Peace to Prosperity Misses the Mark with Palestinians
At the June 25-26 Bahrain Peace to Prosperity Workshop, Jared Kushner presented the first component of a U.S. peace plan for the Middle East. But how does this plan sit with the Palestinian public?
May 13, 2019 | By Karl Friedhoff, Leena Kim
Moon's Approval Down, North Korea Not a Primary Driver
Approval rates for Moon Jae-in are sliding, but his North Korea policy is not one of primary drivers.
April 11, 2019 | By Dina Smeltz, Brendan Helm
Scholars vs the Public: Collapse of the INF Treaty
In early February 2019, the United States withdrew from the Intermediate-Range Nuclear Forces (INF) Treaty following President Trump's October 2018 (and the Obama administration's July 2014) accusations that Russia was failing to comply with the treaty. Russia withdrew from the treaty the next day.
Findings from a February 2019 Chicago Council on Global Affairs general public survey and a December 2018 Teaching, Research, and International Policy (TRIP) survey of International Relations (IR) scholars around the world illustrate how these different populations perceive the collapse of the INF Treaty.
April 8, 2019 | By Karl Friedhoff
Elite-Public Gap on China May be Decreasing
The foreign policy elite and the general public have long viewed the potential threat of China very differently. That gap may may now be in decline.
March 12, 2019 | By Craig Kafura
Expert Panel Survey: US Focus on the Denuclearization of North Korea
Despite expectations for the meeting between Donald Trump and Kim Jong Un, their recent summit in Hanoi ended with no agreement toward denuclearization. With that in mind, we asked our panel of foreign policy experts whether the United States should continue to focus primarily on denuclearization, or shift to arms control and non-proliferation.
February 28, 2019 | By Dina Smeltz
Announcing the Council's Foreign Policy Experts Panel
The Council's Lester Crown Center on US Foreign Policy is launching a series of flash polls to share expert insights on policy debates driving today's news.
Opinion Landscape Not Ideal for New Mideast Peace Plan
At a Middle East conference this month in Warsaw, Jared Kushner, President Trump's son-in-law and Mideast adviser, said that the administration will unveil its much-vaunted Middle East peace plan after the April 9 Israeli elections.
February 11, 2019 | By Karl Friedhoff
America the Dangerous
The Trump administration has taken a hard line on China, but has failed to convince the American public or many allies to follow suit. Instead, publics around the world now see the United States as a major threat.
February 8, 2019 | By Dina Smeltz
Majority of Nicaraguans Support Early Elections
Recent surveys about the political crisis in Nicaragua
February 4, 2019 | By Karl Friedhoff
South Korean Public Ready to Call Trump's Bluff
President Trump's demand that South Korea dramtically increase its burden sharing is uniting South Korean across the politica and age spectrum.
January 9, 2019 | By Karl Friedhoff
History Continues Unabated between Japan and South Korea
Publics in South Korea and Japan agree on the problems that need to be resolved, but there's little optimism they can find solutions.


text:
Antirrhinum molle ist eine Pflanzenart aus der Gattung der Löwenmäuler (Antirrhinum) in der Familie der Wegerichgewächse (Plantaginaceae).

Beschreibung
Antirrhinum molle ist ein Zwergstrauch, dessen niederliegende bis aufsteigende Stängel Längen von bis zu 40 cm erreichen. Stängel, Laubblätter und Blütenstiele sind drüsenlos flaumig bis wollig behaart, Kelch, Krone und die Früchte sind drüsig-flaumig behaart. Die unten gegenständig und oben wechselständig stehenden Laubblätter sind 8 bis 22 mm lang und 5 bis 20 mm breit, breit eiförmig bis elliptisch. Die Blattstiele sind 2 bis 6 mm lang.

Die Tragblätter ähneln den Laubblättern. Die Blütenstiele sind 3 bis 20 mm lang. Die Kelchzipfel sind 5 bis 8 mm lang, lanzettlich und spitz. Die Krone ist 25 bis 35 mm lang, weiß oder blass rosa gefärbt und mit einem gelben Gaumen versehen.

Die Früchte sind nahezu kugelförmige Kapseln mit einem Durchmesser von 7 bis 8 mm.

Die Chromosomenzahl beträgt 2n = 16.

Vorkommen und Standorte
Die Art ist im Osten und in der Mitte der Pyrenäen und in den angrenzenden Bergen im Nordosten Spaniens und Portugals verbreitet. Sie wächst auf kalkreichen Felsen und Wänden in Höhenlagen zwischen 500 und 1800 m.

Literatur

Einzelnachweise

Weblinks

Löwenmäuler
molle


At the Steele football game, band members Alyssa Wagner, Adam Cevallos and Senior color guard member, Aliyah Wotenia perform their UIL show eXpiriment 78070.
This past weekend on Saturday, the band and color guard competed in the Area D UIL Competition at Heroes Stadium.
Out of 33 of the best bands from South Central and Central Texas, the band and color guard made the top 10 finals that evening.
This is the first time in the school's history that the band and color guard has made the finals at a UIL Area Show in class 6A.
The band and color guard will make school history again by competing at the Bands of America Super Regional at the Alamo Dome in San Antonio this Friday.


text:
Indy Cornrows homepage Horizontal - White Indy Cornrows an Indiana Pacers community
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Pacers release 2022-23 NBA schedule
Indiana will kick off their 56th season on October 19, facing Washington in the first of three home games.
By Nathan S. Aug 17, 2022, 5:45pm EDT
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Geoff Burke-USA TODAY Sports
Despite some leaked games and preseason teasers, the NBA schedule has remained largely elusive into the third week of August, but two months ahead of the regular season tipoff on October 18, the league wide schedule has been released including 82 contests featuring the Indiana Pacers.
BOOM (shakalaka) BABY❕
our 2022-23 schedule has arrived.https://t.co/5bYkTYm0ah pic.twitter.com/nLAGVjB2bw
— Indiana Pacers (@Pacers) August 17, 2022
The October 18th opening night showcase will take place between four teams (Philadelphia/Boston & LA Lakers/Golden State) leading into the Pacers and the rest of the league to kickstart their season the following night, Wednesday the 19th with Indiana facing the Washington Wizards.
It's the eighth time in the last 10 years the Pacers will open things up at home, getting an interesting slate of games to open things up. Not only will be three straight game at Gainbridge Fieldhouse, but it will be three straight against lottery teams, giving a young Pacers team an interesting test to start the year, facing off against the San Antonio Spurs and Detroit Pistons on Friday and Saturday.
Unfortunately for Indiana's win loss record, even a successful start will carry over into a challenging set of games to follow, a five-game road trip against Philadelphia, Chicago, and a pair of games in Brooklyn. Indiana's lone nationally televised game will take place in this stretch, a Friday October 28th game against the Wizards on ESPN.
Heading into November, the Pacers will have their first of four four-game homestands to round out 2022, all season longs as the league heavily splits Indiana's home/away schedule to start the year. Tough games against Miami, New Orleans, Denver, and Toronto will be followed by two road games in Charlotte and Houston before four more home games, two against Orlando and one each against Minnesota and Brooklyn.
Following the Circle of Lights celebration on November 25, the Pacers will embark on an absolutely brutal seven-game West coast swing. The Pacers will be have a tough back-to-back against the Los Angeles Clippers and Lakers on the 27th and 28th before heading north to Sacramento to face Domantas Sabonis and the Kings.
After facing the Utah Jazz on December 2, Indiana will have another difficult back-to-back against the Portland Trail Blazers and Golden State Warriors before heading back to the Midwest to wrap things up against the Minnesota Timberwolves. The ensuing four game home stand will feature Golden State's lone Fieldhouse visit on December 14 before some more road action sends Indiana to the Garden to face Malcolm Brogdon and the Boston Celtics.
That will be the start of a tough schedule run for the Pacers, closing out 2022 with seven of eight games against playoff teams, including home matchups against the Atlanta Hawks on December 27 and an early New Year's Eve tip against the Clippers. In January, the Pacers will play seven home games, their first of the year vs. the Chicago Bulls on January 24 and Milwaukee Bucks on January 27, both following a four game West-ish swing including a challenging back-to-back against the Denver Nuggets and Phoenix Suns.
A home friendly schedule will keep the Pacers in Indianapolis for much of February, kicking things off with a three game homestand featuring games against the Lakers (February 2) and Kings (February 3). Boston will visit Indy for the only time on February 23. Indiana will hit the road following that game and spend much of March away from the Fieldhouse, playing 11 of 14 games on the road with only a pair of matchups against Philadelphia (March 6 & 18) and Houston (March 9) breaking up the traveling.
Returning home on March 27, the Pacers will close out with five of seven at home, starting with the Dallas Mavericks on March 25 and wrapping up against the Pistons on April 7. An April 9 road meeting against the New York Knicks will wrap things up for the Pacers, at least in the regular season should any higher asperations reveal themselves as the schedule progresses.
Feel free to dive deeper into the schedule here.
the fun begins in 63 days.
In addition to the lone ESPN game on October 28th, the Pacers will be showcased on NBA TV three times; at Brooklyn on October 31, at Portland on December 4, and at Boston on March 25, though that's always subject to change as the season progresses.
The Pacers will have 14 back-to-backs on the year and will have three (well, four) games against the same team with no additional travel, at Brooklyn in October, vs. Orlando in November, at Detroit in March as well as the Los Angeles back-to-back in November.
All in all, it's hard to look at this schedule and find the more favorable and less favorable stretches due to the uncertainty about what kind of win total the Pacers will be capable of. The three game opening stretch is particularly fascinating in that regard, but following that up with a tough road stretch, as well as splitting the 2022 portion of the schedule almost exclusively into home stands and road trips will no doubt provide their own challenges in terms of win/loss consistency no matter the level of the opposition.
On the bright side, Pacers basketball within sight, especially with training camp six weeks away and the preseason portion beginning on October 5, two road games against the Charlotte Hornets and New York and two home games against New York and Houston prepping Indiana for the start of the 22-23 season.
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Pacers final score: Magic light up Pacers 126-120 Indiana wasn't able to erase another slow start tonight, losing wire-to-wire in Orlando in a poor defensive showing. Bennedict Mathurin led all scorers with 26.
Pacers Links: Mark Boyle appreciation, Halibturton injury update The voice of the Pacers reached an impressive milestone over the weekend.


In a large bowl, mix first four ingredients.
In a small bowl, combine the remaining ingredients EXCEPT for avocado.
Pour dressing over the vegetables and cover. Refrigerate for at least eight hours or overnight.
Stir in avocados before serving.


The GBC Career and Technical Education Department and MTC scholarship will host an open house and informational night January 23 from 7-9 p.m.
The open house will provide information on the associate of applied science degree and certificate of achievement programs in diesel technology, industrial millwright technology, welding technology, electrical systems technology and instrumentation technology. These accelerated programs can be completed at GBC in less than a year, with courses taught in the morning (7 AM-12 PM), afternoon (12 PM-5 PM), and evening (5 PM -10 PM).
GBC will also be presenting information on the Maintenance Training Cooperative (MTC) scholarship. The MTC scholarship is a $5,000 scholarship and some scholarships include a paid internship with local mining and contracting companies.
To apply for admission into the CTE programs and the MTC Scholarship, students must apply to GBC, submit the GBC CTE application available at www.gbcnv.edu/mtc. The application also requires a resume, letter of intent, high school or college transcripts, and completion of a placement exam (ACT or SAT scores are acceptable). Applications are due March 15 for admission into the fall 2018 semester.
The live open house will be held at the GBC Elko Campus in Diekhans Center IT room 203.
Students in the GBC's area of outreach will also be able to attend via interactive video: GBC Battle Mountain Center Room 4, GBC Ely Center Room 108, GBC Pahrump Valley Center Room 120, and GBC Winnemucca Center Room 124.
For more information on the GBC CTE Programs, MTC Scholarship, or the open house, call Jonica Gonzalez, CTE College Credit Coordinator at 775-753-2303 or e-mail jonica.gonzalez@gbcnv.edu.


Restriction of Flying Regulations for Scottish Air Show at Ayr on 2 and 3 September 2017, detailed in AIC M074/2017. There will be additional airspace restrictions for displays by the Red Arrows on 3 September 2017; details in AIC M064/2017. All airspace restrictions will be notified by NOTAM.