text:
Лу Ароника (; родился 8 января 1958 года) — американский писатель, автор книг по саморазвитию, креативности и психологии успеха, издатель научно-фантастической литературы. Наибольшую известность приобрел как редактор-составитель антологий серии Full Spectrum и соавтор британского эксперта по образованию сэра Кена Робинсона.

Биография
Лу Ароника выпускает научно-фантастическую, научно-популярную, историческую и бизнес-литературу с 1979 года, сотрудничая с ведущими книжными домами США (Bantam Books, Berkley Books и Avon Books). Первые же литературные опыты в жанре фэнтези сделали его популярным, чему значительно способствовали ежемесячные публикации в New York Times. Научно-фантастическую тему продолжила серия Full Spectrum, выпущенная в соавторстве с Эми Стут и Бетси Митчелл. В 1994 году эта работа получила World Fantasy Award за лучшую антологию. Благодаря издательской деятельности Лу Ароника увидели свет книги культовой эпопеи «Звездные войны». В 2003 году Лу Ароника основал собственную компанию The Fiction Studio, занимающуюся продвижением молодых литераторов.

Романы Ароника The Forever Year и Flash and Dazzle были изданы под псевдонимом Рональд Энтони и имели многомиллионные тиражи. Научно-популярные произведения The Element и Школа будущего. Как вырастить талантливого ребенка, созданные в соавторстве с британским экспертом по творческому образованию Кеном Робинсоном, стали бестселлерами и переведены на несколько языков.

Работы

Редактор
David Brin, Startide Rising (Bantam Spectra 1984)

Антологии
The Bantam Spectra Sampler (1985)

The first four books of the Full Spectrum series:
 Full Spectrum (1988) with Shawna McCarthy
 Full Spectrum 2 (1989) with Shawna McCarthy and Amy Stout and Pat LoBrutto
 Full Spectrum 3 (1991) with Amy Stout and Betsy Mitchell
 Full Spectrum 4 (1993) with Amy Stout and Betsy Mitche

Научно-популярная литература
 A Million Thanks (2005), with Shauna Fleming
 The Culture Code: An Ingenious Way to Understand Why People Around the World Live and Buy as They Do (2007), with Clotaire Rapaille
 Miraculous Health: How to Heal Your Body by Unleashing the Hidden Power of Your Mind (2008), with Dr. Rick Levy
 The Element: How Finding Your Passion Changes Everything (2009), with Sir Ken Robinson
 Conscientious Equity (2010), with Neal Ashbury
 Finding Your Element: How to Discover Your Talents and Passions and Transform Your Life (2013), with Sir Ken Robinson

Книги на русском языке
 Школа будущего. Как вырастить талантливого ребенка — М.: Манн, Иванов и Фербер, 2015. ISBN 978-5-00057-734-9
 Найти себя. Как выйти за рамки стереотипов и обрести свой путь — М.: Манн, Иванов и Фербер, 2014. ISBN 978-5-00057-133-0

Примечания

Ссылки

Лу Ароника на сайте Fiction Studio
Блог Лу Ароника

Родившиеся в 1958 году
Родившиеся 8 января
Лауреаты премии Всемирной премии фэнтези
Редакторы США
Писатели США
Издатели США


text:
Q: SQL (oracle) - how to query multiple changed values from multiple tables I'm trying to pull an audit of all the values that were changed in a specified time period from multiple tables. Is there a simpler way of writing the below?
select property_number, 'table1', count(*)
    from table1
   where start_date between to_date('07/01/2017','mm/dd/yyyy') and to_date('09/30/2017', 'mm/dd/yyyy')
group by property_number
union all
  select property_number, 'table2', count(*)
    from table2
   where start_date between to_date('07/01/2017', 'mm/dd/yyyy') and to_date('09/30/2017', 'mm/dd/yyyy')
group by property_number
union all
  select property_number, 'table3', count(*)
    from table3
   where start_date between to_date('07/01/2017', 'mm/dd/yyyy') and to_date('09/30/2017', 'mm/dd/yyyy')
group by property_number
union all
  select property_number, 'table4', count(*)
    from table4
   where start_date between to_date('07/01/2017', 'mm/dd/yyyy') and to_date('09/30/2017', 'mm/dd/yyyy')
group by property_number
union all
  select property_number, 'table5', count(*)
    from table5
   where start_date between to_date('07/01/2017', 'mm/dd/yyyy') and to_date('09/30/2017', 'mm/dd/yyyy')
group by property_number

A: Consider a CTE for a DRY-er solution.
WITH master_all AS
  (SELECT property_number, 'table1' AS "indicator"
   FROM table1
   UNION ALL
   SELECT property_number, 'table2'
   FROM table2
   UNION ALL
   SELECT property_number, 'table3'
   FROM table3
   UNION ALL
   SELECT property_number, 'table4'
   FROM table4
   UNION ALL
   SELECT property_number, 'table5'
   FROM table5)

SELECT property_number, indicator, Count(*) AS "Audit_Count"
FROM  master_all
WHERE start_date BETWEEN to_date('07/01/2017', 'mm/dd/yyyy')
                     AND to_date('09/30/2017', 'mm/dd/yyyy')
GROUP BY property_number, indicator;

But once again, consider a schema adjustment and append all similar structured tables with indicator field into one master table.

redpajama_set_name: RedPajamaStackExchange


text:
Florida's Role in Immigration Reform by Chatter Buzz
Articles Written: 46 - MORE ARTICLES FROM THIS AUTHOR
Florida's Role in Immigration Reform
Law,Travel & Tourism,Business
Florida is a pivotal player when it comes to the debate on immigration reform. Advocates on both sides of this issue have tried to pressure lawmakers in the sunshine state by means of large rallies, demonstrations, and massive gatherings at town hall meetings, according to the Sun Sentinel.
A former director of the Congressional Budget Office (CBO) estimated that if a bill for immigration reform is passed, there is the potential for the cumulative federal deficit to decrease by more than $2.5 trillion over the course of a decade. While that is great news for the United States as a whole, how will such a bill affect Florida? Depending on what Congress decides with regard to this bill, Florida could be affected in a variety of ways. Lawmakers have a few different choices to pick from.
a) They can move to deport undocumented immigrants. b) They can decide to stick with the status quo and not make changes. c) They can support the reform and fix the immigration system once and for all.
According to an article by Benjamin Johnson, the Executive Director of the American Immigration Council in Washington, Choice A would result in Florida losing approximately $43.9 billion in economic activity, $19.5 billion in gross state product (GSP), and a decrease in the number of jobs by 262,436. Mass deportation of undocumented immigrants would create a precipitous and large reduction in labor supply. Such a steep reduction in labor, a key factor in production, would lead to the state's economy contracting. The state's fiscal revenue would decline. If all of the undocumented immigrants are deported from the state, Florida's economy will suffer. In Florida alone, more than 60% of all undocumented immigrants have been working and making a living here for over a decade.
If Congress went for Choice B and stuck with the status quo, what would change? Nothing. Failure to reform the broken immigration system may not cause the economy to lose much, but continuing with the current immigration policies provides little room for economic improvement. Undocumented immigrants contribute to Florida's economy, and would continue to do so without reform. A study by Florida International University found that undocumented workers contribute an estimated $20 billion in state taxes. A similar study conducted by the Institute for Taxation and Economic Policy found that undocumented immigrants in Florida paid $806.8 million in local and state taxes in 2010. Due to their status, however, these undocumented immigrants are not able to contribute fully without becoming citizens.
If Congress goes for Choice C and decides to change the immigration system, Florida would benefit directly. In this situation where everyone would be documented and working, a report by the Institute on Taxation and Economic Policy states that a documented immigrant workforce in Florida would pay additional consumer and personal taxes. These taxes would exceed $41 million per year.
Orlando Immigration Attorney, Orlando Immigration LawyerMario Diaz-Balart, a Republican who represents parts of both Broward and Miami-Dade counties, told William E. Gibson from the Washington Bureau in this article, "It's going to get ugly at times, but I am cautiously optimistic we are going to get something done." He tried to persuade legislators to pass an immigration reform bill, just as Senator Marco Rubio did. They are pushing for this bill to be passed, which would give the estimated 825,000 undocumented immigrants in Florida a chance to earn their citizenship.
In the beginning of August, Republicans had a 33-member majority in the chamber. If a bill for reform hopes to pass, it needs the support of at least 17 Republicans. Six Republicans from Florida, other than Mario Diaz-Balart, seem to be "persuadable." Other Republicans, however, would only support such a bill if there were many strings and conditions attached to it. Florida plays an important role in the immigration reform debate, yet no reform bill has been passed. As an Orlando Immigration Attorney, I hope that the rest of Congress will review these benefits and pass the bill without further delay.
If you have questions about immigration reform, or any other immigration law issues, call Henry Lim at Lim & Associates, P.A. at (407) 897-8870.
Henry Lim, an Orlando Immigration Attorney, can help you with all of your USA immigration and citizenship law questions regarding a work visa, family based immigration, US student visa, visa lottery, visitor visa, E-2 Visa & E-1 Visa, L-1 Visa, H1B Visa, F-1 Student Visa, and more. Contact Henry Lim and Associates, the professional immigration law firm in Orlando, Florida at (407) 897-8870 to schedule an appointment. Proudly serving Orlando, Kissimmee, Volusia County, Brevard County, and all of Central Florida.
Related Articles - immigration, law, attorneys, immigration reform,

redpajama_set_name: RedPajamaCommonCrawl


Customize this bold loyalty card design for your salon or boutique.
Zebra loyalty cards can be used for any business from boutique to jewelers to restaurants. Reward your customers with a free item or discount on their next order with these eye-catching rewards cards. Select from 5, 6, 9 or 10 punches for the card. Change out the number of punches within the design studio (click on the Add/edit background image). Add your business name, logo, address, phone number and website. You can also mention the details on how your customers can receive a free item.


More than 35 years after Mariner 10 made its third and final flyby of the planet Mercury MESSENGER (short for MErcury, Surface, Space ENvironment, GEochemistry, and Ranging) became the first spacecraft to orbit the planet in March of 2011. Among the primary goals of the MESSENGER mission are to map its surface, determine the composition of the planet and its exosphere, and to constrain the structure of its interior and the nature of the planetary magnetic field. We will discuss highlights of some of MESSENGER's major discoveries and talk about the upcoming end of the mission.


text:
Iggy Azalea Quotes
Reading and share top 22 Iggy Azalea famous quotes and sayings.
Top Iggy Azalea Quotes
Browse top 22 most favorite famous quotes and sayings by Iggy Azalea.
Favorite Iggy Azalea Quotes
1. "I do absolutely nothing, actually, believe it or not. People will probably hate me for saying that, but I guess I'm one of those lucky horrible people who, no matter what I eat, I don't gain a pound. My whole family is just like that. They're all skinny and tall, and I guess, so am I." ~ Iggy Azalea
2. "As far as people whose style interests me, I'd say Grace Kelly, Gwen, Spice Girls, Victoria Beckham, and that nanny called Fran." ~ Iggy Azalea
3. "Being talked about like a package - I feel like that all the time." ~ Iggy Azalea
4. "For me, visuals are as important as the music. I just love escapism and giving people something to escape to. To me, that's what art is." ~ Iggy Azalea
5. "I hated school so bad. I only liked art class during high school. I was always smart." ~ Iggy Azalea
6. "I like 'Ab Fab;' I love the crazy stuff they wear. That's my favourite! I love 'Ab Fab.'" ~ Iggy Azalea
7. "I love musk oil. There's something about musk that makes it memorable without being overpowering." ~ Iggy Azalea
8. "I love purple because my name is Amethyst." ~ Iggy Azalea
9. "I naturally want to be provocative." ~ Iggy Azalea
10. "I started rapping since, like, 14. But I've been obsessed with rap from when I was 11. I heard 'Baby Don't Cry,' I'll never forget." ~ Iggy Azalea
11. "I think most music provides the same messages - whether it be 'I'm unhappy' or 'I love a girl.' I just liked the package of rap music." ~ Iggy Azalea
12. "I think music talks to you on an emotional level, regardless of where you're from. I guess I related to the tempo of rap, the aggressiveness." ~ Iggy Azalea
13. "I'd known since about eleven that I wanted to live in America." ~ Iggy Azalea
14. "I'm irrational about all things creative, and I'm always late!" ~ Iggy Azalea
15. "Luxury lives in the finer details. It's a cloth napkin at a dinner table. It's a mint on your pillow before bed." ~ Iggy Azalea
16. "Music is art to me, and you don't censor art. You don't go into a museum and censor things." ~ Iggy Azalea
17. "My management team are all women. Most of the people at labels I liaise with are all women. It's pretty much all women all the time." ~ Iggy Azalea
18. "My real name is Amethyst. It sounds like a stage name. My mom is kind of crazy." ~ Iggy Azalea
19. "Not every artist is a role model." ~ Iggy Azalea
20. "T.I.'s my mentor; he's a really close friend of mine. I call him my brother like we talk on the phone all the time. He's helped me with my career." ~ Iggy Azalea
21. "There are many females in my life I couldn't function on a daily basis without who I've purposely put there that work with me." ~ Iggy Azalea
22. "There's an obligation to not lead people down the wrong path, but I hardly think me wearing short shorts on stage is creating monsters." ~ Iggy Azalea
Iggy Azalea Quotes Pictures
<< Aelred Of Rievaulx Quotes
Ross I Donaldson Quotes >>
Hyoi, it is through me that this has happened. It is the other hmana who have hit you, the bent two that brought me to Malacandra. We are only half hnau--Hyoi..." His speech died away into the inarticulate. He did not know the words for "forgive," or "shame," or "fault," hardly the word for "sorry." He could only stare into Hyoi's distorted face in speechless guilt. But the hross seemed to understand. It was trying to say something, and Ransom laid his ear close to the working mouth. Hyoi's dulling eyes were fixed on his own, but the expression of a hross was not even now perfectly intelligible to him."Hna--hma," it muttered and then, at last, Hman, hnakrapunt."
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The Certification of a company evaluates social, economic, and environmental aspects of its processes, activities, quality and conditions and well-being factors.
The ISO 14001 provides the requirements for what the organization must do to manage the processes that affect the impact of its activities on the environment.
The EU Eco-Management and Audit System (EMAS) is a tool, for voluntary use, developed by the European Commission, for that the companies and other organizations responsible, evaluate, inform, and improve the environmental impact EMAS is open to all types of organizations with desires to improve their environmental impact. It covers all economic and services sectors, and is applicable to the whole world.
OHSAS 18001 is a management system for safety and health in the workplace, which contributes to the improvement of the conditions and factors that may affect the well-being of any person in the physical environment of the company.
The ISO 9001 standard establishes the requirements that the organization must perform to manage the processes that affect the quality of its products and services.
The sustainability report is the main tool that an organization or company has to voluntarily communicate its impact on economic, environmental and social issues.
It can also be verified by a third party.
Certification which evaluates manufacturing in a responsible manner (acquisition or extraction of raw materials, safety and hygiene, economic management, etc.) of the products and their manufacturer.


text:
Call Of Duty: Black Ops 4's battle royale mode trailer features helicopters, explosions
Black Ops Royale
News by Jay Castello Contributor
Published on Sept. 8, 2018
Treyarch have shown off their first trailer for Call Of Duty: Black Ops 4's battle royale mode, which will be called Blackout. Take a peek at all the fire and chaos for yourself below:
A lot of the combat seems to take place in vehicles, which will presumably be useful for getting around the map that Treyarch are boasting is the biggest ever seen in Call Of Duty. There's also a brief tease that zombies will be making an appearance in the battle royale, as well as getting their own mode.
Battlefield V recently announced details about their battle royale mode, Firestorm, too. The two seem different enough: Firestorm will feature teams, objectives, and tanks, where Blackout appears to hew more closely to the format that's become traditional at this point. (At least, that guy who gruffly exclaims "I don't like anyone," before cross-map sniping someone doesn't seem like much of a team player.) The map in particular, also recently revealed, looks very Playerunknown's Battlegrounds on its face - the two island, double river format echoes Erangel if it were flipped 90 degrees.
On the other hand, the map features some old favourite Blops locations, like Nuketown, and there's a tonne on display in the Blackout trailer that isn't available in ol' Plunkbat. Wingsuits, grappling hooks, portable cover, and destructible helicopter gunships are all available to players who want to be the last one standing and/or to fall to their untimely deaths.
It's hard to tell how much of the chaos will carry across from trailer to game, but Treyarch are certainly selling their mode as something faster-paced and more hectic than others. It'll be available to play once Black Ops 4 releases on October 12.
Beenox
Jay Castello
Jay writes about video games, falls down endless internet rabbit holes, and takes a lot of pictures of flowers.


text:
\section{Introduction}

Unsupervised feature learning from high-dimensional data using deep
sparse feature extractors has been shown to yield state-of-the-art
performance in a number of benchmark datasets. The major advantage
of these approaches consists in the fact that they alleviate the need
of manually tuning feature design each time we consider a different
sensor modality, contrary to conventional approaches, such as optical
flow-based ones (e.g., HOG3D \cite{hog3d}, HOG/HOF \cite{hoghof}),
methods maximizing saliency functions in the spatiotemporal domain
(e.g., Cuboid \cite{Cuboid} and ESURF \cite{esurf}), methods based
on dense trajectory sampling (e.g., \cite{HengWang2011} and \cite{pipeline}),
and methods based on hierarchical template matching after Gabor filtering
and max pooling (e.g., \cite{jhuang}).

Indeed, unsupervised feature extractors are highly generalizable,
being capable of seamlessly learning effective feature representations
from observed data, irrespectively of the data nature and/or origin.
Due to this fact, there is a growing interest in such methods from
the computer vision and machine learning communities, with characteristic
approaches including sparse coding (SC) \cite{sc,sc2}, deep belief
networks (DBNs) \cite{dbn}, stacked autoencoders (SAEs) \cite{sae},
and methods based on independent component analysis (ICA) and its
variants (e.g., ISA \cite{isa} and RICA \cite{rica}).

In this work, we focus on unsupervised feature extractors based on
stacked convolutional ICA architectures, with application to action
recognition in video sequences. Our interest in these methods is motivated
by both experimental results, where such approaches have been shown
to yield state-of-the-art performance, as well as results from neuroscience,
where it has been shown that these algorithms can learn receptive
fields similar to the V1 area of visual cortex when applied to static
images and the MT area of visual cortex when applied to sequences
of images \cite{neurosc,neurosc2,neurosc3}.

A major drawback of existing deep learning architectures for feature
extraction concerns the requirement of \emph{a priori }provision of
the number of extracted latent features \cite{cdbn,cite1,cite2,cite3,isa}.
This need imposes considerable burden to researchers and practitioners,
as it entails training multiple alternative model configurations to
choose from, and application of cross-validation to determine optimal
model configuration for the applications at hand. Therefore, enabling
automatic data-driven determination of the most appropriate number
of latent features would represent a significant leap forward in the
field of deep unsupervised feature extraction approaches.

To address these issues, in this paper we initially introduce a nonparametric
Bayesian sparse formulation of ICA. Our model imposes an Indian buffet
process (IBP) \cite{ibp,ibp2} prior over the learned latent feature
matrix parameters, that naturally promotes sparsity, and allows for\emph{
automatically inferring the optimal number of latent features}. We
underline that the IBP prior is designed under the assumption of infinite-dimensional
latent feature representations, thus being capable of \emph{naturally
handling extraction of overcomplete representations} if the data requires
it, without suffering from degeneracies \cite{ibp-sb-vb}\footnote{As an aside, we also note that performing Bayesian inference over
the parameters of ICA-based models (instead of the point-estimates
obtained by existing approaches) allows for taking \emph{uncertainty}
into account during the learning procedure \cite{vbg}. Even though
this is not examined in this paper, such a capacity is theoretically
expected to yield much better performing models in cases learning
is conducted using \emph{limited }and \emph{scarce }datasets \cite{vbtmfa}.}. We dub the so-obtained model as \emph{IBP-ICA}.

We devise an efficient inference algorithm for our model under a \emph{hybrid
variational }inference paradigm, similar to \cite{hvb}. In contrast
to traditional variational inference algorithms, which require imposition
of truncation thresholds for the model or the variational distribution
over the extracted features \cite{vbg}, our method adapts model complexity
on the fly. In addition, variational inference scales much better
to massive datasets compared to Markov chain Monte Carlo (MCMC) approaches
\cite[Chapter 10]{BishopBook}, which do not easily scale, unless
one resorts to expensive parallel hardware.

Finally, we apply our IBP-ICA model to the problem of action recognition
in video sequences. For this purpose, we present a stacked convolutional
architecture for unsupervised feature extraction from data with spatiotemporal
dynamics, utilizing IBP-ICA models as its building blocks. Our convolutional
architecture, hereafter referred to as \emph{stacked convolutional
IBP-ICA} (SC-IBP-ICA) networks, is inspired
from related work on convolutional neural networks, e.g.
the 3D-CNN method \cite{3dcnn}, and is based on the approach
followed by existing convolutional extensions of unsupervised
feature extractors, e.g. convGRBM \cite{grbm} and ISA \cite{isa}.
Specifically, similar to \cite{grbm,isa}, our convolutional architecture
comprises training one unsupervised feature extractor (in our case,
one IBP-ICA model) on small spatiotemporal patches extracted from
sequences of video frames, and subsequently convolving this model
with a larger region of the video frames. Eventually, we combine the
responses of the convolution step into a single feature vector, which
is further processed by a pooling sublayer, to allow for translational
invariance. The so-obtained feature vectors may be further presented
to a similar subsequent processing layer, thus eventually obtaining
a deep learning architecture.

Our stacked model is greedily trained in a layerwise manner, similar
to a large number of alternative approaches proposed in the deep learning
literature \cite{dbn,cdbn,sae}. Our hybrid variational inference
algorithm for this model is completely \emph{heuristic parameter-free,}
thus obviating the need of parameter tuning, which is a major challenge
most deep unsupervised feature extractors are faced with.

The remainder of this paper is organized as follows: In Section 2,
we briefly review existing ICA formulations for unsupervised feature
extraction. In Section 3, we present our proposed method, and elaborate
on its inference and feature generation algorithms. In Section 4,
we experimentally demonstrate the advantages of the proposed approach:
we apply it to the Hollywood2, YouTube, and KTH action recognition
benchmarks. Finally, in the last section we summarize our results
and conclude this paper.

\section{ICA-based feature extractors}

In this section, we provide an overview of existing ICA-based feature
extractors, which are relevant to our approach. Let us denote as $\{\boldsymbol{x}_{n}\}_{n=1}^{N}$
a random sample of size $N$ comprising $D$-dimensional observations.
ICA, in its simplest form, models the observed variables $\boldsymbol{x}_{n},n=1,...,N,$
as
\begin{equation}
\boldsymbol{x}_{n}=\boldsymbol{G}\boldsymbol{y}_{n}+\boldsymbol{e}_{n}
\end{equation}
where $\boldsymbol{y}_{n}$ is a $K$-dimensional vector of latent
variables (latent features), $\boldsymbol{G}$ is a $D\times K$ matrix
of factor loadings (latent feature matrix), and $\boldsymbol{e}_{n}$
is the model error pertaining to modeling of $\boldsymbol{x}_{n}$.
ICA assumes that $(\boldsymbol{x}_{1},\boldsymbol{y}_{1}),(\boldsymbol{x}_{2},\boldsymbol{y}_{2}),...,(\boldsymbol{x}_{N},\boldsymbol{y}_{N})$
are independent, identically distributed (i.i.d). Further, the \emph{key}
characteristic of ICA that sets it apart from related approaches is
the additional assumption that the distinct components (features)
comprising the feature vectors $\boldsymbol{y}_{n}=[y_{nk}]_{k=1}^{K}$
are also i.i.d. For example, we may consider a simple $J$-component
mixture of Gaussians (MoG) prior, i.e.
\begin{equation}
y_{nk}\sim\sum_{j=1}^{J}\varpi_{kj}\mathcal{N}(0,s_{kj})
\end{equation}
with a Dirichlet prior imposed over the weight vectors $\boldsymbol{\varpi}_{k}=[\varpi_{kj}]_{j=1}^{J}$,
i.e.
\begin{equation}
p(\boldsymbol{\varpi}_{k}|\boldsymbol{\xi}_{k})=\mathrm{Dir}(\boldsymbol{\varpi}_{k}|\boldsymbol{\xi}_{k})
\end{equation}
 and a Gamma prior imposed over the inverse variances $s_{kj}^{-1}$
\begin{equation}
p(s_{kj}^{-1})=\mathcal{G}(s_{kj}^{-1}|\eta_{1},\eta_{2})
\end{equation}
 Finally, model error is usually considered to follow an isotropic
Gaussian distribution, reading
\begin{equation}
\boldsymbol{e}_{n}\sim\mathcal{N}(0,\phi^{-1}\boldsymbol{I})
\end{equation}

Along these lines, several researchers have also considered more complex
assumptions regarding the model likelihood expression. For instance,
a \emph{nonlinear likelihood }assumption has been adopted in \cite{isa},
yielding $\boldsymbol{x}_{n}\approx\sigma(\boldsymbol{G}\boldsymbol{y}_{n})$,
where $\sigma$ is some nonlinear function (e.g., quadratic). Eventually,
the training algorithm of the model reduces to a minimization problem
that takes the form
\begin{equation}
\underset{\boldsymbol{G}}{\mathrm{min}}\sum_{k=1}^{K}\sum_{n=1}^{N}h(\boldsymbol{G}_{:,k}^{T}\boldsymbol{x}_{n})
\end{equation}
where the form of the function $h(\cdot)$ follows from the form of
the postulated likelihood and prior assumptions, and $\boldsymbol{G}_{:,k}$
is the $k$th column of $\boldsymbol{G}$. Usually, the minimization
problem (6) is solved under the additional orthonormality constraint
\begin{equation}
\boldsymbol{G}\boldsymbol{G}^{T}=\boldsymbol{I}
\end{equation}
This constraint is imposed so as to ensure non-degeneracy, i.e., to
prevent the bases in the factor loadings matrix $\boldsymbol{G}$
from becoming \emph{degenerate}. However, it is effective only in
cases of \emph{undercomplete} or \emph{complete} representations,
i.e., the number of latent features does \emph{not} exceed the number
of observed features ($K\leq D$) \cite{rica}.

As we discussed in Section 1, the capacity of extracting overcomplete
latent feature representations is a significant merit for unsupervised
feature learning algorithms. As such, it is important that ICA can
be effectively employed when postulating $K>D$. A computationally
efficient method that resolves this issue was proposed in \cite{rica};
it consists in replacing the orthonormality constraint (7) with a
soft reconstruction cost which measures the difference between the
original observations $\{\boldsymbol{x}_{n}\}_{n=1}^{N}$ and the
reconstructions obtained by a linear autoencoder, where the encoding
and decoding weights are tied to the feature matrix $\boldsymbol{G}$
learned by the model. The resulting method, dubbed reconstruction
ICA (RICA), yields the minimization problem
\[
\underset{\boldsymbol{G}}{\mathrm{min}}\frac{\xi}{N}\sum_{n=1}^{N}||\boldsymbol{G}\boldsymbol{G}^{T}\boldsymbol{x}_{n}-\boldsymbol{x}_{n}||_{2}^{2}+\sum_{k=1}^{K}\sum_{n=1}^{N}h(\boldsymbol{G}_{:,k}^{T}\boldsymbol{x}_{n})
\]
where $\xi$ is a regularization parameter, and $\boldsymbol{G}_{:,k}$
is the $k$th column of $\boldsymbol{G}$.

As discussed in \cite{rica}, under this scheme some bases of $\boldsymbol{G}$
may still degenerate and become zero, because the reconstruction constraint
can be satisfied with only a complete subset of features\footnote{The authors of \cite{rica} resorted to introducing an additional
norm ball constraint to resolve this issue.}. In addition, we note that the imposed reconstruction error constraints
suffer from a weak point that has been extensively studied in the
autoencoder (AE) literature: specifically, the optimal reconstruction
criterion of AEs may merely lead to the trivial solution of just copying
the input to the output, that yields very low reconstruction error
in a given training set combined with extremely poor modeling and
generalization performance \cite{ae-issues} (performing training
using a \emph{noise-corrupted} version of the original observations
is a solution commonly used in AE literature to prevent this from
happening \cite{ae-issues}).

\section{Proposed Approach}

\subsection{IBP-ICA}

\subsubsection{Model Formulation}

Let us consider a set of $D$-dimensional observations $\{\boldsymbol{x}_{n}\}_{n=1}^{N}$.
We model this dataset using ICA, adopting the conventional assumptions
(1) - (5). However, in contrast to the conventional model formulation,
we specifically want to examine the case where the dimensionality
$K$ of the latent feature vectors $\boldsymbol{y}_{n}$ tends to
infinity, $K\rightarrow\infty$. In other words, we seek to obtain
a nonparametric formulation for our model.

Under such an assumption, and imposing an appropriate prior distribution
over the latent feature matrix $\boldsymbol{G}$, we can obtain an
inference algorithm that allows for automatic determination of the
most appropriate number of latent features to model our data, and
performs inference over only this finite set \cite{ibp2}. For this
purpose, we impose a spike-and-slab prior over the components of the
\emph{latent feature matrix }$\boldsymbol{G}$:
\begin{equation}
p(g_{dk}|z_{dk};\lambda_{k})=z_{dk}\mathcal{N}(g_{dk}|0,\lambda_{k}^{-1})+(1-z_{dk})\delta_{0}(g_{dk})
\end{equation}
where $\lambda_{k}$ is the precision parameter of the (Gaussian)
prior distribution of the $k$th base in $\boldsymbol{G}$, $\delta_{0}(\cdot)$
is a \emph{spike} distribution with all its mass concentrated at zero
(delta function), and the discrete latent variables $z_{dk}$ indicate\emph{
latent feature activity, }being equal to one if the $k$th latent
feature contributes to generation of the $d$th observed dimension
(i.e., the latent feature is \emph{active}), zero otherwise.
Note that a similar prior has been previously adopted in the related,
factor analysis (FA)-based latent feature model of \cite{related}.
The \emph{key} difference between FA- and ICA-based models
is that in FA the prior over the latent feature vectors is a spherical
Gaussian; in contrast, in ICA  we impose independent priors over
each latent feature taking the form of a more complex distribution
(a Gaussian mixture in our work, see Eqs. (2)-(4)).

Spike-and-slab priors \cite{ssl} are commonly used to introduce \emph{sparsity
}in the modeling procedure; combined with a nonparametric prior over
the matrix of discrete latent variables $\boldsymbol{Z}=[z_{dk}]_{d,k}$,
they also allow for defining a generative process for the number of
latent factors under a sparse modeling scheme. To this end, we utilize
the IBP prior \cite{ibp}; specifically, we adopt the \emph{stick-breaking}
construction of IBP \cite{ibp-sb-vb}. This is another \emph{key} difference
between our model formulation and the method of \cite{related}; the
major advantage of using the \emph{stick-breaking} construction consists
in allowing for obtaining a variational inference algorithm, which
is much more scalable to massive data compared to MCMC \cite{ibp-sb-vb}
(used in \cite{related}). We have:
\begin{equation}
z_{dk}\sim\mathrm{Bernoulli}(\pi_{k})
\end{equation}
 where
\begin{equation}
\pi_{k}=\prod_{i=1}^{k}v_{i}
\end{equation}
 and the prior over the stick-variables $v_{i}$ is defined as
\begin{equation}
v_{k}\sim\mathrm{Beta}(\alpha,1)
\end{equation}
 In (11), $\alpha$ is called the innovation hyperparameter, and controls
the tendency of the process to discover new latent features. We impose
a Gamma hyperprior over it, yielding
\begin{equation}
p(\alpha)=\mathcal{G}(\alpha|\gamma_{1},\gamma_{2})
\end{equation}
 Finally, we impose a Gamma prior over the precision parameters $\lambda_{k}$,
which reads
\begin{equation}
p(\lambda_{k})=\mathcal{G}(\lambda_{k}|c,f)
\end{equation}
 as well as a Gamma prior over the noise precision parameter $\phi$
\begin{equation}
p(\phi)=\mathcal{G}(\phi|a,b)
\end{equation}
 This concludes the definition of our IBP-ICA model.

\subsubsection{Inference Algorithm}

The formulation of our model using the stick-breaking construction
of IBP allows for performing inference by means of an efficient hybrid
variational algorithm, inspired from \cite{hvb}. Our approach combines:
(i) \emph{mean-field} variational inference \cite{vbg} for the model
parameters and latent variables, similar to existing models utilizing
the stick-breaking construction of the IBP, e.g. \cite{ibp-sb-vb};
and (ii) a \emph{local} Metropolis-Hastings (MH) step to sample from
the distribution over the number of (active) latent features pertaining
to each dimension of the observed data, inspired from \cite{ibp2}.

Let us denote as $q(\cdot)$ the obtained variational posteriors.
Following \cite{ibp2}, the proposed number of new features $K_{d}^{*}$
to be added to the number of active features $K_{d}$ pertaining to
the $d$th input dimension is sampled from the Poisson proposal distribution:
\begin{equation}
p(K_{d}^{*})=\mathrm{Poisson}\left(K_{d}^{*}\big|\frac{\mathbb{E}_{q(\alpha)}[\alpha]}{D-1}\right)
\end{equation}
 This proposal is accepted with probability
\begin{equation}
p_{d}^{*}=\mathrm{min}\left\{ 1,\frac{\theta_{d}^{*}}{\theta_{d}}\right\}
\end{equation}
 where \cite{ibp2}:
\begin{equation}
\theta_{d}^{*}=|\boldsymbol{M}_{d}^{*}|^{-N/2}\mathrm{exp}\left(\frac{1}{2}\sum_{n=1}^{N}\boldsymbol{m}_{nd}^{*T}\boldsymbol{M}_{d}^{*}\boldsymbol{m}_{nd}^{*}\right)
\end{equation}
\begin{equation}
\begin{aligned}\boldsymbol{m}_{nd}^{*}= & \mathbb{E}_{q(\phi)}[\phi]\boldsymbol{M}_{d}^{*-1}\boldsymbol{G}_{d,:}^{*T}\\
 & \times\left(x_{nd}-\mathbb{E}_{q(\boldsymbol{y}_{n}).q(\boldsymbol{G})}[\boldsymbol{G}_{d,:}\boldsymbol{y}_{n}]\right)
\end{aligned}
\end{equation}
\begin{equation}
\boldsymbol{M}_{d}^{*}=\mathbb{E}_{q(\phi)}[\phi]\boldsymbol{G}_{d,:}^{*T}\boldsymbol{G}_{d,:}^{*}+\boldsymbol{I}
\end{equation}
 the $1\times K_{d}^{*}$ variables $\boldsymbol{G}_{d,:}^{*}$ in
(18) and (19) are sampled from their prior (8), and
\begin{equation}
\theta_{d}=|\boldsymbol{M}_{d}|^{-N/2}\mathrm{exp}\left(\frac{1}{2}\sum_{n=1}^{N}\boldsymbol{m}_{nd}^{T}\boldsymbol{M}_{d}\boldsymbol{m}_{nd}\right)
\end{equation}
\begin{equation}
\begin{aligned}\boldsymbol{m}_{nd}= & \mathbb{E}_{q(\phi)}[\phi]\boldsymbol{M}_{d}^{-1}\mathbb{E}_{q(\boldsymbol{G})}[\boldsymbol{G}_{d,:}^{T}]\\
 & \times\left(x_{nd}-\mathbb{E}_{q(\boldsymbol{y}_{n}).q(\boldsymbol{G})}[\boldsymbol{G}_{d,:}\boldsymbol{y}_{n}]\right)
\end{aligned}
\end{equation}
\begin{equation}
\boldsymbol{M}_{d}=\mathbb{E}_{q(\phi)}[\phi]\mathbb{E}_{q(\boldsymbol{G})}[\boldsymbol{G}_{d,:}^{T}\boldsymbol{G}_{d,:}]+\boldsymbol{I}
\end{equation}
(the expressions of the expectations $\mathbb{E}_{q}[\cdot]$
can be straightforwardly derived by following the identities
pertaining to Gaussians, Gamma, and Beta distributions in
\cite[Appendix B]{BishopBook}). Note that this MH step is
of a \emph{local} (input dimension-wise) nature, and
thus is very fast. Further, the introduction of this local sampling
step is the reason why our algorithm does not require provision of
heuristic truncation thresholds, because MCMC samplers for nonparametric
Bayesian models can operate in an unbounded feature space \cite{mc2}.

Having updated the number of latent features, our inference algorithm
proceeds to obtain the variational posteriors over the latent feature
activity indicator variables $z_{dk}$. This is performed by maximization
of the variational free energy of the model over $q(z_{dk}=1)$, yielding
\begin{equation}
q(z_{dk}=1)=\frac{1}{1+\mathrm{exp}(-\omega_{dk})}
\end{equation}
 where
\begin{equation}
\begin{aligned}\omega_{dk}= & \sum_{i=1}^{k}\mathbb{E}_{q(v_{i})}[\mathrm{log}v_{i}]+\mathbb{E}_{q(\boldsymbol{v})}\left[\mathrm{log}\left(1-\prod_{i=1}^{k}v_{i}\right)\right]\\
 & +\mathbb{E}_{q(\lambda_{k}),q(g_{dk})}[\mathrm{log}\mathcal{N}(g_{dk}|0,\lambda_{k}^{-1})]
\end{aligned}
\end{equation}

Further, the posterior over the latent feature matrices is obtained
by maximization of the variational free energy over $q(g_{dk})$,
yielding
\begin{equation}
\begin{aligned}q(g_{dk})= & q(z_{dk}=1)\mathcal{N}(g_{dk}|\tilde{\mu}_{dk},\tilde{\lambda}_{dk}^{-1})\\
 & +\left(1-q(z_{dk}=1)\right)\delta_{0}(g_{dk})
\end{aligned}
\end{equation}
where
\begin{equation}
\tilde{\lambda}_{dk}=\mathbb{E}_{q(\phi)}[\phi]\sum_{n=1}^{N}\mathbb{E}_{q(y_{nk})}[y_{nk}^{2}]+\mathbb{E}_{q(\lambda_{k})}[\lambda_{k}]\;\forall d
\end{equation}
\begin{equation}
\begin{aligned}\tilde{\mu}_{dk}=\tilde{\lambda}_{dk}^{-1}\mathbb{E}_{q(\phi)}[\phi]\sum_{n=1}^{N} & \mathbb{E}_{q(y_{nk})}[y_{nk}]\\
 & \times\left(x_{nd}-\mathbb{E}_{q(\boldsymbol{y}_{n}),q(\boldsymbol{G})}[\boldsymbol{G}_{d,:}\boldsymbol{y}_{n}]\right)
\end{aligned}
\end{equation}

Similarly, the posterior over the learned latent features yields
\begin{equation}
q(y_{nk})=\mathcal{N}(y_{nk}|\tilde{m}_{nk},\tilde{s}_{nk})
\end{equation}
 where
\begin{equation}
\tilde{s}_{nk}^{-1}=\mathbb{E}_{q(\phi)}[\phi]\mathbb{E}_{q(\boldsymbol{G})}[\boldsymbol{G}_{:,k}^{T}\boldsymbol{G}_{:,k}]+\sum_{j=1}^{J}\zeta_{nkj}\mathbb{E}_{q(s_{kj})}[s_{kj}^{-1}]
\end{equation}
\begin{equation}
\tilde{m}_{nk}=\tilde{s}_{nk}\mathbb{E}_{q(\phi)}[\phi]\mathbb{E}_{q(\boldsymbol{G})}[\boldsymbol{G}_{:,k}^{T}]\boldsymbol{x}_{n}
\end{equation}
In the above expressions, $\zeta_{nkj}$ are the source model component
posteriors, which read
\begin{equation}
\zeta_{nkj}\propto\mathrm{exp}\big(\mathbb{E}_{q(\boldsymbol{\varpi}_{k})}[\mathrm{log}\varpi_{kj}]+\mathbb{E}_{q(s_{kj})}[\mathrm{log}\mathcal{N}(y_{nk}|0,s_{kj})]\big)
\end{equation}
 where the posterior over the mixture component weights yields

\begin{equation}
q(\boldsymbol{\varpi}_{k}|\tilde{\boldsymbol{\xi}}_{k})=\mathrm{Dir}(\boldsymbol{\varpi}_{k}|\tilde{\boldsymbol{\xi}}_{k})
\end{equation}
 with $\tilde{\boldsymbol{\xi}}_{k}=[\tilde{\xi}_{kj}]_{j=1}^{J}$,
and
\begin{equation}
\tilde{\xi}_{kj}=\xi_{kj}+\sum_{n=1}^{N}\zeta_{nkj}
\end{equation}
 while the posterior over the $s_{kj}$ obtains
\begin{equation}
q(s_{kj}^{-1})=\mathcal{G}(s_{kj}^{-1}|\tilde{\eta}_{kj1},\tilde{\eta}_{kj2})
\end{equation}
 where
\begin{equation}
\tilde{\eta}_{kj1}=\eta_{1}+\frac{1}{2}\sum_{n=1}^{N}\zeta_{nkj}
\end{equation}
 and
\begin{equation}
\tilde{\eta}_{kj2}=\eta_{2}+\frac{1}{2}\sum_{n=1}^{N}\zeta_{nkj}\mathbb{E}_{q(y_{nk})}[y_{nk}^{2}]
\end{equation}

The rest of the model posteriors take expressions identical to existing
variational inference algorithms for nonparametric Bayesian formulations
of ICA, e.g., \cite{ibp-sb-vb}. For completeness sake, we provide
these expressions in the Appendix. An outline of the inference algorithm
of IBP-ICA is provided in Alg. 1.

\begin{algorithm}
\protect\caption{IBP-ICA inference algorithm. }

\begin{enumerate}
\item Select the number of source model components $J$, as well as the
hyperparameters of the imposed model priors: $a,b,\gamma_{1},\gamma_{2},c,f$,
$\eta_{1}$, $\eta_{2}$, and $\xi_{kj},\;\forall k,j$.
\item For $MAXITER$ iterations or until convergence of the variational
free energy of the model, \textbf{do}:\end{enumerate}
\begin{itemize}
\item Perform the MH step (15)-(16) to update the number of generated latent
features.
\item Update the variational posterior over the latent feature activity
variables, $q(\boldsymbol{Z})$, using (23).
\item Update the posteriors $q(y_{nk})$, $q(\boldsymbol{\varpi}_{k})$,
$q(s_{kj}^{-1})$, $\forall n,k,j$, using (28)-(36).
\item Update the posteriors $q(\lambda_{k})$, $q(v_{k})$, $q(\phi)$,
$q(\alpha)$, $\forall k$, using the expressions given in the Appendix.
\item Update the posteriors $q(\boldsymbol{G})=\{q(g_{dk})\}_{d,k}$ using
(25)-(27).\end{itemize}
\end{algorithm}

\begin{figure}

\centering{}%
\includegraphics[scale=0.34]{Drawing1}\protect\caption{The proposed SC-IBP-ICA architecture. It is trained by using some
randomly selected video patches, from each of which we extract an
input vector $\boldsymbol{x}$. Pooling is performed between the first
and the second layer over small neighborhoods comprising $p$ adjacent
first layer units. The outputs of the first layer are presented as
inputs to the second layer (after whitening).}

\end{figure}

\subsection{Stacked Convolutional IBP-ICA Networks}

Let us now turn to the problem of action recognition in video sequences.
Since videos are sequences of images (frames), to address this task
using our model we simply extract the component frames of the video
sequences at hand and flatten them into a vector, which is eventually
presented to our model as the observed input. However, a problem with
this model setup is the resulting extremely high dimensionality of
the input observations, which induces an excessive increase to the
number of inferred model parameters, and, hence, the computational
complexity of the inference algorithm of our model.

To alleviate this computational burden, we opt for resorting to a
\emph{stacked convolutional IBP-ICA network}
architecture, inspired from local receptive field networks
(e.g., \cite{isa,cdbn}). Specifically, instead of processing the
whole video frames using a single IBP-ICA model,
we use multiple convolved copies of an IBP-ICA model to process smaller
(overlapping) patches of the video frame sequences. This way, we manage
to dramatically reduce the number of inferred model parameters, and,
hence, the imposed computational complexity. The output feature vectors
of the postulated component IBP-ICA models are further processed by
a pooling sublayer, similar to conventional
convolutional neural network architectures. The introduction of pooling
into our convolutional IBP-ICA network endows it with the merit of
translational invariance, which is a desideratum in the context of
action recognition applications in video sequences. Finally, we stack
multiple layers of our convolutional IBP-ICA network to obtain a deep
learning architecture. Our deep architecture allows for capturing
features that correspond to both lower and higher level analysis of
the spatiotemporal dynamics in the observed data; this way, it extracts
much richer information to train a classifier with, compared to shallow
models \cite{sae} .

Training of our proposed SC-IBP-ICA network proceeds as follows: Initially,
we train an IBP-ICA model on \emph{small video patches}. Subsequently,
we build a \emph{convolutional network} of IBP-ICA models, by \emph{replicating}
and applying copies of the learned IBP-CA model to\emph{ different
overlapping patches }of the input video frame sequences (with an additional
\emph{pooling} sublayer on top). Finally, we feed the output of our
convolutional IBP-ICA network to a similarly trained \emph{subsequent}
convolutional IBP-ICA network, thus creating a deep learning architecture
through \emph{stacking}\footnote{In our experiments, input data are additionally pre-processed and
whitened using PCA, exactly as described in \cite{isa,rica}.}. As a final note, we underline that training of a whole SC-IBP-ICA
network is performed in a greedy layerwise manner, similar to many
existing convolutional deep learning architectures (e.g., \cite{cdbn}).

A graphical illustration of the proposed SC-IBP-ICA network and its
training procedures is provided in Fig. 1.

\subsection{Feature Generation Using SC-IBP-ICA}

Given a learned SC-IBP-ICA network, feature generation from observed
video frame sequences is performed similar to conventional deep convolutional
networks, by means of feedforward computation. Specifically, based
on the expression of the variational posterior $q(y_{nk})$ derived
in Eq. (28), to perform \emph{feature generation} we feedforward the
input vectors $\boldsymbol{x}_{n}$ by application of Eq. (30), using
the \emph{already} \emph{obtained }values of $\mathbb{E}_{q(\phi)}[\phi]$,
$\mathbb{E}_{q(\boldsymbol{G})}[\boldsymbol{G}_{:,k}^{T}\boldsymbol{G}_{:,k}]$,
and $\mathbb{E}_{q(\boldsymbol{G})}[\boldsymbol{G}_{:,k}^{T}]$. Note
that, from the simple feedforward computation form of Eq. (30), it
directly follows that using our method to generate features is extremely
fast, with costs identical to existing state-of-the-art approaches,
e.g. \cite{isa}.

\begin{table}
\protect\caption{Mean average precision on the Hollywood2 dataset.}

\centering{}%
\begin{tabular}{|c|c|}
\hline
Algorithm & Average Precision\tabularnewline
\hline
\hline
Harris3D + HOG/HOF \cite{pipeline} & 45.2\%\tabularnewline
\hline
Hessian + ESURF \cite{pipeline} & 38.2\%\tabularnewline
\hline
Cuboids + HOG/HOF \cite{pipeline} & 46.2\%\tabularnewline
\hline
convGRBM \cite{grbm} & 46.6\%\tabularnewline
\hline
Dense + HOG3D \cite{pipeline} & 45.3\%\tabularnewline
\hline
ISA (with reconstruction & \multirow{2}{*}{54.6\%}\tabularnewline
penalty) \cite{rica} & \tabularnewline
\hline
SC-IBP-ICA (1 layer) & 47.8\% (286)\tabularnewline
\hline
SC-IBP-ICA (2 layers) & 53.5\% (286 - 198)\tabularnewline
\hline
\end{tabular}
\end{table}

\begin{table}
\protect\caption{Average accuracy on the YouTube dataset.}

\centering{}%
\begin{tabular}{|c|c|}
\hline
Algorithm & Accuracy\tabularnewline
\hline
\hline
HAR + HES  & \multirow{2}{*}{71.2\%}\tabularnewline
+ MSER + SIFT \cite{pipeline2} & \tabularnewline
\hline
Harris3D + Grads.  & \multirow{2}{*}{71.2\%}\tabularnewline
+ PCA + Heuristics \cite{pipeline2} & \tabularnewline
\hline
ISA \cite{isa} & 75.8\%\tabularnewline
\hline
SC-IBP-ICA (1 layer) & 73.6\% (291.7)\tabularnewline
\hline
SC-IBP-ICA (2 layers) & 75.4\% (291.7 - 197.9)\tabularnewline
\hline
\end{tabular}
\end{table}

\begin{table}
\protect\caption{Average accuracy on the KTH dataset.}

\centering{}%
\begin{tabular}{|c|c|}
\hline
Algorithm & Accuracy\tabularnewline
\hline
\hline
Harris3D + HOG/HOF \cite{pipeline} & 91.8\%\tabularnewline
\hline
Hessian + ESURF \cite{pipeline} & 81.4\%\tabularnewline
\hline
Cuboids + HOG3D \cite{pipeline} & 90.0\%\tabularnewline
\hline
HMAX \cite{jhuang} & 91.7\%\tabularnewline
\hline
3D-CNN \cite{3dcnn} & 90.2\%\tabularnewline
\hline
convGRBM \cite{grbm} & 90.0\%\tabularnewline
\hline
ISA \cite{isa} & 93.9\%\tabularnewline
\hline
SC-IBP-ICA (1 layer) & 92.3\% (293)\tabularnewline
\hline
SC-IBP-ICA (2 layers) & 93.4\% (293 - 195)\tabularnewline
\hline
\end{tabular}
\end{table}

\begin{table}
\protect\caption{Average feature extraction time in our experiments (Hollywood2 dataset).}

\centering{}%
\begin{tabular}{|c|c|}
\hline
Algorithm & Seconds/Frame\tabularnewline
\hline
\hline
HOG3D & 0.20\tabularnewline
\hline
ISA \cite{isa} (1 layer)  & 0.13\tabularnewline
\hline
ISA \cite{isa} (2 layers)  & 0.40\tabularnewline
\hline
SC-IBP-ICA (1 layer) & 0.12\tabularnewline
\hline
SC-IBP-ICA (2 layers) & 0.38\tabularnewline
\hline
\end{tabular}
\end{table}

\section{Experiments}

In this section, we experimentally investigate how SC-IBP-ICA compares
to the current state-of-the-art in action recognition. To perform
our experimental investigations, we use three publicly available action
recognition benchmarks, namely Hollywood2 \cite{hollywood}, KTH actions,
and YouTube actions \cite{pipeline2}. Our experimental setup
is the same as in \cite{isa,rica}, adopting exacty the same data
preprocessing/postprocessing steps. After extracting local features
by means of SC-IBP-ICA, we subsequently perform \emph{vector quantization} of
the obtained feature vectors using K-means. Finally, we use these
discretized feature vectors to train an SVM classifier \cite{svm-book}
employing a $\chi^{2}$ kernel.

We adopt the same dataset splits and evaluation metrics as in \cite{pipeline,pipeline2}.
Specifically, Hollywood2 human actions dataset contains 823 train
and 872 test video clips organized into 12 action classes; each video
clip may have more than one action label. We utilize the produced
feature vectors to train 12 binary SVM classifiers, one for each action.
We use the final average precision (AP) metric for our evaluations,
computed as the average of AP for each classifier run on the test
set. Youtube actions dataset contains 1600 video clips organized into
11 action classes. These video clips have been split into 25 folds
which we use to perform 25-fold cross-validation. Note that, from
each split, we use only videos indexed 01 to 04, except for the biking
and walking classes, where we use the whole datasets. Finally, KTH
actions dataset contains 2391 video samples organized into 6 action
classes. We split these samples into a test set containing subjects
2, 3, 5, 6, 7, 8, 9, 10, 12, and a training set containing the rest
of the subjects. We use the produced feature vectors to train a multi-class
SVM.

We evaluate SC-IBP-ICA architectures comprising one and two layers,
to examine how extra layer addition affects model performance. Regarding
selection of the size of receptive fields of our model, \emph{the
first layer is of size 16$\times$16 (spatial) and 10 (temporal),
while the second one is of size 20$\times$20 (spatial) and 14 (temporal)},
similar to \cite{isa,pipeline}. The size of the output of the pooling
layers is identical to the size of the output of the IBP-ICA models
that feed it, i.e. the number of latent features our method discovers.
Training is performed on 200,000 video blocks, randomly sampled from the training
set of each dataset. We perform dense sampling with \emph{50\% overlap
in all dimensions}. In cases of 2-layer architectures, we train the
used SVM classifiers by combining the features generated from \emph{both}
layers. This setup retains more representative features compared to
using only the features from the \emph{top} layer, corresponding to
a coarse-to-fine analysis of the observations \cite{cdbn}.

Our obtained results are provided in Tables 1-3. Note that the performances
of the competing methods reported therein have been cited from \cite{isa}
and \cite{rica}. The reported results of ISA were obtained with 300 latent features on the first
layer, and 200 latent features on the second layer (these have been
heuristically found to yield the best performance
among a large set of evaluated alternatives).

In Tables 1-3, we also provide the number of latent features \emph{automatically}
discovered by our method (in parentheses, beside the accuracy figures).
Note that the reported performance results and the corresponding numbers
of discovered latent features pertaining to the YouTube dataset, illustrated
in Table 2, are means over the 25 splits of the dataset into training
and test sets (folds) provided by its creators. We observe that our
method obtains performance similar to the state-of-the-art, while
also allowing for automatic inference of the appropriate number of
generated features. We also observe that addition of a second layer
is auspicious in all cases, corroborating similar findings in the
literature.

In Table 4, we depict the computational costs of our approach regarding
feature extraction from the Hollywood2 (test) dataset (run as a single
thread)\footnote{We run these experiments on an Intel Xeon 2.5GHz Quad-Core CPU with
64GB RAM. Our source codes were written in MATLAB R2014a.}. It is clear that feature generation using our method takes time
similar to existing ICA-based approaches, namely the ISA method presented
in \cite{isa}, as theoretically expected (the small computational
advantage of our method is presumably due to the lower number of latent
features compared to \cite{isa}).

Further, we examine model generalization performance under a \emph{transfer
learning} setting: In real-world settings, a feature extraction system
pre-trained on samples from a set of video sequences will be expected
to perform well on any previously unseen input video sequence, with
\emph{no} samples of it included in its training set. To perform this
kind of evaluations, we train our SC-IBP-ICA network on video blocks
randomly sampled from the KTH dataset, and evaluate its performance
on the Hollywood2 dataset. Under this setup, our method yields a mean
average precision equal to 51.9\%. Compare this result to the performance
obtained by ISA \cite{isa} under the same experimental setup, which
yields a mean average precision equal to 50.8\%.

Finally, an interesting question concerns how IBP-ICA model performance
changes in case we use cross-validation to perform model selection
instead of sampling from the related posteriors over the number of
features {[}Eqs. (15)-(22){]}. To investigate this, we repeat our
experiments using the Hollywood2 dataset in the following way: We
perform model training without sampling the number of features, which
is considered a given constant. We repeat this experiment multiple
times, with different numbers of features each time; we try configurations
comprising 250-350 features on the first layer, and 150-250 features
on the second layer, with a step of 5 features between consecutive
evaluated models. Model selection is performed on the grounds of the
accuracy obtained in the available test set.

Our findings are illustrated in Table 5; as we observe, cross-validation
yields a slightly better model performance than our fully-fledged
nonparametric Bayesian approach. However, these mediocre gains come
at the price of significant computational costs: Specifically, the
computational gain from skipping the updates of the posterior over
the number of latent features constitutes only a meager 16.2\% of
the total training time. On the other hand, the aforementioned cross-validation
procedure required evaluating 40 different model configurations, i.e.
repeating model training 40 times. Therefore, model selection by means
of our nonparametric Bayesian approach offers an overwhelmingly favorable
complexity/accuracy trade-off compared to an exhaustive cross-validation
technique.

In the same vein, another interesting question concerns comparison
of the proposed hybrid variational inference algorithm of our model
with the straightforward alternative of Markov chain Monte-Carlo (MCMC)
inference. To examine this aspect, we rerun our experiments by properly
adapting the MCMC algorithm outlined in \cite{related} in the context
of our model. As we observed, our proposed algorithm requires one
order of magnitude less time to converge, for a negligible
performance deterioration compared to MCMC.

\begin{table}
\protect\caption{Mean average precision on the Hollywood2 dataset by application of
cross-validation.}

\centering{}%
\begin{tabular}{|c|c|c|}
\hline
Algorithm & Average Precision & Model Size\tabularnewline
\hline
\hline
SC-IBP-ICA (1 layer) & 48.1\% & 295\tabularnewline
\hline
SC-IBP-ICA (2 layers) & 53.9\% & 295-205\tabularnewline
\hline
\end{tabular}
\end{table}

\section{Conclusions}

In this paper, we introduced a deep convolutional nonparametric Bayesian
approach for unsupervised feature extraction. The main building block
of our approach is a nonparametric Bayesian formulation of ICA, dubbed
IBP-ICA. Our method imposes a spike-and-slab prior over the factor
loadings matrices, driven by an IBP prior over the latent feature
activity indicators. This way, it allows for automatic \emph{data-driven}
inference of the most appropriate number of latent features. This
is in \emph{stark} contrast with all existing methods, such as DBNs
\cite{dbn}, ICA variants \cite{isa,rica}, and SAEs \cite{sae},
where hand-tuning the number of extracted latent features is an \emph{essential}
part of the application of these methods to real-life tasks. It is also
substantially different from the related approach of \cite{betaprocess}, where, instead of the
spike-and-slab prior used in this work, a simpler Beta-Bernoulli process
prior is employed; this formulation of \cite{betaprocess}
does \emph{not} allow for performing feature generation via
\emph{simple} feedforward computation [in the sense of Eq. (30)].
Hence, feature generation in \cite{betaprocess} requires much higher
computational costs compared to state-of-the-art deep learning
approaches and our method. In addition, our approach can model latent feature
distributions of arbitrary complexity (approximated via
mixtures of Gaussians, Eq. (2)), as opposed to \cite{betaprocess}
which postulates a simplistic spherical Gaussian prior.

We devised an efficient variational inference algorithm for our model.
Our method is very easy to train because
(batch) variational inference does not need any tweaking with heuristics
such learning rates and convergence criteria. In this regard, our
method lies on exactly the opposite side of the spectrum compared
to conventional approaches based on neural networks: Our method entails
\emph{no }need of selection of training algorithm heuristics, whatsoever,
while training neural networks is a tedious procedure requiring a
great deal of hand-tuning of several heuristics (e.g., learning
rate, weight decay, convergence parameters, inertia).

We evaluated our approach using three well-known action recognition
benchmarks, adopting a standard video processing pipeline (e.g., \cite{neurosc2}).
As we showed, our method yields results similar to the state-of-the-art
for these benchmarks, while imposing competitive computational costs
for \emph{feature generation}. These results corroborate that
nonparametric Bayesian models can offer a viable alternative
to existing deep feature extractors, and at the same time mitigate
some of the major hurdles deep nets are confronted with, regarding
data-driven selection of model size during inference, and learning
algorithm parameters fine-tuning.

\section*{Appendix}

We have \begin{equation}
q(\lambda_{k})=\mathcal{G}(\lambda_{k}|\tilde{c}_{k},\tilde{f}_{k})
\end{equation}
 where
\begin{equation}
\tilde{c}_{k}=c+\frac{1}{2}\sum_{d=1}^{D}q(z_{dk}=1),\;\;\tilde{f}_{k}=f+\sum_{d=1}^{D}\mathbb{E}_{q(g_{dk})}[g_{dk}^{2}]
\end{equation}
and \begin{equation}
\begin{aligned}q(\phi)=\mathcal{G}\big(\phi\big|a+\frac{ND}{2},b+ & \sum_{n=1}^{N}(\boldsymbol{x}_{n}-\mathbb{E}_{q(\boldsymbol{y}_{n}),q(\boldsymbol{G})}[\boldsymbol{G}\boldsymbol{y}_{n}])^{T}\\
 & \times(\boldsymbol{x}_{n}-\mathbb{E}_{q(\boldsymbol{y}_{n}),q(\boldsymbol{G})}[\boldsymbol{G}\boldsymbol{y}_{n}])\big)
\end{aligned}
\end{equation}
For the stick-variables $v_{k}$, we adopt the approximations \cite{ibp-sb-vb}:
\begin{equation}
q(v_{k})=\mathrm{Beta}(v_{k}|\tilde{\tau}_{k},\hat{\tau}_{k})
\end{equation}
\begin{equation}
\begin{aligned}\tilde{\tau}_{k}= & \sum_{m=k+1}^{K}\left(D-\sum_{d=1}^{D}q(z_{dm}=1)\right)\sum_{i=k+1}^{m}q_{i}\\
 & +\sum_{d=1}^{D}\sum_{m=k}^{K}q(z_{dm}=1)+\mathbb{E}_{q(\alpha)}[\alpha]
\end{aligned}
\end{equation}
\begin{equation}
\hat{\tau}_{k}=1+\sum_{m=k}^{K}\left(D-\sum_{d=1}^{D}q(z_{dm}=1)\right)q_{k}
\end{equation}
 where we denote $K\triangleq\underset{d}{\mathrm{max}}\;K_{d}$,
\begin{equation}
\begin{aligned}q_{k}\propto\mathrm{exp} & \big(\psi(\hat{\tau}_{k})+\sum_{i=1}^{k-1}\psi(\tilde{\tau}_{i})-\sum_{i=1}^{k}\psi(\tilde{\tau}_{i}+\hat{\tau}_{i})\big)\end{aligned}
\end{equation}
 and $\psi(\cdot)$ is the Digamma function. Finally, the innovation
hyperparameter yields: $q(\alpha)=\mathcal{G}(\alpha|\tilde{\gamma}_{1},\tilde{\gamma}_{2})$,
where $\tilde{\gamma}_{1}=\gamma_{1}+K-1$ and $\tilde{\gamma}_{2}=\gamma_{2}-\sum_{k=1}^{K-1}[\psi(\tilde{\tau}_{k})-\psi(\tilde{\tau}_{k}+\hat{\tau}_{k})]$.

\small

\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
State fire marshal urges carbon monoxide awareness
Watauga Democrat
RALEIGH – In light of the recent infant deaths in North Carolina as a possible result of carbon monoxide poisoning, Insurance Commissioner and State Fire Marshal Mike Causey is issuing a statewide warning: Beware of the silent killer, check your carbon monoxide alarm. According to the Centers for Disease Control and Prevention,
"Our hearts and prayers go out to the families involved in this tragedy, especially to those whose children died,"
5 carbon monoxide safety tips
Along with the cold temperatures, snow and ice, winter also brings unpredictable dangers, including the threat of carbon monoxide (CO) poisoning. According to the Journal...
Napa Valley Register 2020-01-06
OUR VIEW: Protect your family from CO poisoning
There are families in our community mourning the loss of three people last weekend who are believed to have died from carbon monoxide poisoning. While we can say little to...
Crossville Chronicle 2020-01-09
Keep your Family Safe from Carbon Monoxide this Winter
Illinois Fire Departments responded to nearly 9,000 carbon monoxide incidents in 2018 Springfield, Ill- Cold temperatures mean Illinoisans will be turning up their...
Public Technologies 2020-01-07
Investigators find carbon monoxide leaks in public housing
DURHAM, N.C. (AP) — Inspectors discovered that about 40% of apartments at a North Carolina public housing community where residents have been hospitalized and two babies...
Lebanon Express 2020-01-09
BTS (방탄소년단) - HOME (Color Coded Lyrics Eng/Rom/Han/가사)
Home - หน้ากากแอปเปิ้ลหนอน | The Mask Mirror
Michael Bublé - Home [Official Music Video]
Home (2015) - Fixing My Mistakes Scene (9/10) | Movieclips
Martin Garrix feat. Bonn - Home (Official Video)
Edward Sharpe & The Magnetic Zeros - Home (Official Video)
Home (2015) - Boov Do Not Dancing Scene (4/10) | Movieclips
Blake Shelton - Home (Official Music Video)
Tests: No carbon monoxide in baby deaths at housing complex
DURHAM, N.C. (AP) — North Carolina's state medical examiner's office said Thursday that it found no sign of carbon monoxide poisoning in the deaths of two infants at a public housing community where the gas has forced evacuations. The Office of the Chief Medical Examiner issued a news release Thursday saying that the two infants who lived in McDougald Terrace in Durham had...
News & Record
Investigators find carbon monoxide leaks in NC public housing
DURHAM — Inspectors discovered that about 40% of apartments at a public housing community where residents have been hospitalized and two babies have died had appliances that were emitting carbon monoxide, a housing authority official confirmed. Durham officials inspected 70 occupied apartments at McDougald Terrace and identified furnaces, hot water heaters and stoves that were...
Winston-Salem Journal
Beware of carbon monoxide poisoning
SUNNYSIDE — When temperatures drop and electrical power fails from too much demand or from downed lines, don't bring a generator into the house to power portable heaters, warns Sunnyside Fire Marshall Bill Harris. "Keep the generator outside and use a properly rated electric cord to run inside heaters," he urges. Small gas engines, camp stoves, charcoal grills brought into the...
Sunnyside Daily Sun
Machine Gun Kelly, X Ambassadors & Bebe Rexha - Home (from Bright: The Album) [Official Video]
The official video of \"Home\" by Machine Gun Kelly, X Ambassadors & Bebe Rexha from \'Bright: The Album\' \'Bright The Album\' available now: https://Atlantic.lnk.to/BrightID Watch Bright exclusively on Netflix now! Subscribe for more official content from Atlantic Records: https://Atlantic.lnk.to/subscribe Follow Machine Gun Kelly https://www.facebook.com/machinegunkellymusic/ https://www.instagram.com/machinegunkelly https://twitter.com/machinegunkelly Follow X Ambassadors https://www.facebook.com/XAmbassadors https://www.instagram.com/xambassadors https://twitter.com/XAmbassadors Follow Bebe Rexha https://www.facebook.com/beberexha https://twitter.com/BebeRexha https://www.instagram.com/beberexha Follow Bright: https://facebook.com/BrightMovie https://instagram.com/bright https://twitter.com/BrightNetflix Follow Atlantic Records: https://facebook.com/atlanticrecords https://instagram.com/atlanticrecords https://twitter.com/AtlanticRecords The official Atlantic Records YouTube Channel is home to the hottest in hip-hop, rock, pop, R&B, indie, musicals and soundtracks. With over 70 years of global recorded music history, Atlantic Records' legacy and passion for artistry continues with Top 40 hitmakers like Wiz Khalifa, Sean Paul, Trey Songz, Bruno Mars, Charlie Puth, Janelle Monáe, and B.o.B. Atlantic Records prides itself for working on Motion Picture Soundtracks, such as "The Greatest Showman", "Suicide Squad" and "The Fate of the Furious." As well as Musical Soundtracks including "Dear Evan Hansen," and Grammy and Tony Award Winning "Hamilton". It is home to worl...
All Rights Administered by BigHit Entertainment Here you can download wit: https://wit.onelink.me/YogN/jaeguchibp • Artist: BTS (방탄소년단) • Song: HOME • Album: \'MAP OF THE SOUL: PERSONA\' • Released: 19.04.12 Engtrans: 김유경 (vegaren) .............................................................................. • Members: Jin, Suga, J-Hope, RM, Jimin, V, Jungkook • My bias: Suga .............................................................................. • REQUEST SONG HERE: https://goo.gl/9G5rQx • FOLLOW ME: https://www.facebook.com/jaeguchiofficial/ ................................................................................ • No copyright infringement intended / Don\'t reupload • TAGS: BTS HOME BTS HOME lyrics BTS HOME color coded lyrics HOME lyrics BTS HOME line distribution BTS HOME english trans BTS HOME mv BTS HOME showcase BTS HOME audio BTS HOME live BTS HOME dance 防弾少年団 HOME日本語字幕 BTS Mikrokosmos Lyrics BTS Make it right Lyrics BTS home Lyrics BTS Jamais vu lyrics BTS Dionysus lyrics 방탄소년단 HOME 가사 방탄소년단 가사...
พบกับรายการ THE MASK MIRROR ได้ทาง ช่องเวิร์คพอยท์ หมายเลข 23 ทุกวันพฤหัสบดี ตั้งแต่เวลา 20.05 น . และติดตามข้อมูลเพิ่มเติมของรายการได้ที่ เว็บไซต์: https://www.workpointtv.com เฟสบุ๊ค: www.facebook.com/TheMaskSingerTH...
New album ❤ available now: http://wbr.ec/love Michael Bublé - Home [Official Music Video] Listen to Home on the album \"It\'s Time\": https://wbr.ec/itstime Connect with Michael: Website: http://www.michaelbuble.com Facebook: http://www.facebook.com/michaelbuble Instagram: https://www.instagram.com/michaelbuble/ Twitter: http://www.twitter.com/michaelbuble \"Home\" Lyrics: Another summer day Has come and gone away In Paris and Rome But I wanna go home, mmm May be surrounded by A million people I Still feel all alone I just wanna go home Oh, I miss you, you know And I\'ve been keeping all the letters That I wrote to you Each one a line or two I\'m fine baby, how are you? Well I would send them but I know That it\'s just not enough My words were cold and flat And you deserve more than that Another aeroplane Another sunny place I\'m lucky I know But I wanna go home Mmm, I got to go home Let me go home I\'m just too far From where you are I wanna come home And I feel just like I\'m living someone else\'s life It\'s like I just stepped outside When everything was going right And I know just why you could not Come along with me That this was not your dream But you always believed in me Another winter day Has come and gone away In even Paris and Rome And I wanna go home Let me go home And I\'m surrounded by A million people I I still feel alone Oh, let me go home Oh, I miss you, you know Let me go home I\'ve had my run Baby, I\'m done I gotta go home Let me go home It\'ll all be all right I\'ll be home tonight I\'m coming back home Michael Bublé It\'s Time The Wedding Date...
Home - Fixing My Mistakes: Oh (Jim Parsons) tries to return an important artifact to the Gorg in order to save earth. BUY THE MOVIE: https://www.fandangonow.com/details/movie/home-2015/MMV8B1C9FD4B94EA181C65F366642AD9E74C?cmp=Movieclips_YT_Description Watch the best Home scenes & clips: https://www.youtube.com/playlist?list=PLZbXA4lyCtqrIyUzz3HaU8pWJ4l5SSrcr FILM DESCRIPTION: After a hive-minded alien race called the Boov conquer the Earth, they relocate the planet\'s human population -- all except for a little girl named Tip (Rihanna), who\'s managed to hide from the aliens. When Tip meets a fugitive Boov called Oh (Jim Parsons), there\'s mutual distrust. However, Oh is not like his comrades; he craves friendship and fun. As their distrust fades, the pair set out together to find Tip\'s mother, but, unbeknown to them, the Gorg -- enemies of the Boov -- are en route. CREDITS: TM & © Dreamworks Animation (2015) Cast: Jennifer Lopez, Jim Parsons, Rihanna Screewriter: Matt Ember, Tom J. Astle Director: Tim Johnson Watch More: ► Fresh New Clips: http://bit.ly/2taDWqW ► Classic Trailers: http://bit.ly/2qTCxHF ► Hot New Trailers: http://bit.ly/2qThrsF ► Clips From Movies Coming Soon: http://bit.ly/2FrP8VL ► Indie Movie Clips: http://bit.ly/2qTZMRE ► Deleted Scenes: http://bit.ly/2ARbLPJ ► Bloopers: http://bit.ly/2qYmBnc ► Celebrity Interviews: http://bit.ly/2D4tzw4 Fuel Your Movie Obsession: ► Subscribe to MOVIECLIPS: http://bit.ly/2CZa490 ► Watch Movieclips ORIGINALS: http://bit.ly/2D3sipV ► Like us on FACEBOOK: http://bit.ly/2DikvkY ► Follow us on TWITTER: http://bit.ly/2mgka...
Martin Garrix - Home (feat. Bonn) ♫ Home (feat. Bonn) is out now: http://stmpd.co/MGBHMID ♫ Used To Love is out now: http://stmpd.co/MDTLID Premiered my new track Home featuring Bonn at Tomorrowland 2019 as a surprise and so happy I can release it already! Turn on subtitles for the official lyrics! 💬 Subscribe to my channel: http://stmpd.co/MGYTSID and turn on notification so you never miss one of my videos! 🔔 Follow Martin Garrix: Facebook: http://facebook.com/MartinGarrix Twitter: http://twitter.com/MartinGarrix Instagram: http://instagram.com/MartinGarrix YouTube: http://youtube.com/MartinGarrix Follow STMPD RCRDS YouTube: http://www.youtube.com/c/stmpdrcrds Facebook: https://www.facebook.com/STMPDRCRDS/ Instagram: https://www.instagram.com/stmpdrcrds/ Twitter: https://twitter.com/stmpdrcrds Spotify: http://stmpd.co/ST50SPID 🎶 Martin Garrix Top Music Videos: Summer Days: https://youtu.be/8OwVaewDtS8 High On Life: https://youtu.be/Lpjcm1F8tY8 Ocean: https://youtu.be/BDocp-VpCwY Scared To Be Lonely: https://youtu.be/e2vBLd5Egnk In The Name Of Love: https://youtu.be/RnBT9uUYb1w There For You: https://youtu.be/pNNMr5glICM So Far Away: https://youtu.be/o7iL2KzDh38 Animals: https://youtu.be/IPYTxAHeR_o Written by Martijn Garritsen and Damian Karsznia Directed by Damian Karsznia Producer: Mees Roozen DOP: Julian Lomaga 1st AC: Maric Dam Associate Producer: Quita Ruijgrok Production ass.: Annerose van Strijen, Idris Bakker Gaffers : Jan van Galen, Wiegert de Vries Art Director: Daylene Kroon Set Dresser: Denise Bassahan Make-Up Artist: Luna van Herwijnen Make-Up Ass.:...
There\'s a fan campaign to get Jarvis\' Running The World up the charts in time for Christmas...Are you listening? ➤ http://bit.ly/2PA2KoR Taken from Edward Sharpe & the Magnetic Zeros\' debut album \'Up From Below\', released on Rough Trade Records: http://smarturl.it/upfrombelow Co-director Ryan Gall Produced by The Riot House https://www.facebook.com/themagneticzeros https://twitter.com/EdwardSharpe http://edwardsharpeandthemagneticzeros.com/ Rough Trade Records on the web: https://shopusa.roughtraderecords.com/ https://www.facebook.com/roughtraderecords/ https://twitter.com/RoughTradeRecs http://www.roughtraderecords.com/ Listen to Best of Rough Trade 2019 ➤ http://bit.ly/2PGkeib...
Provided to YouTube by Big Hit Entertainment Co. Ltd. HOME · ko - BTS (방탄소년단) / en - BTS MAP OF THE SOUL : PERSONA ℗ Big Hit Entertainment Released on: 2019-04-12 Auto-generated by YouTube....
Home - Boov Do Not Dancing: Oh (Jim Parsons) finds himself unable to stop his body moving when Tip (Rihanna) turns on the radio. BUY THE MOVIE: https://www.fandangonow.com/details/movie/home-2015/MMV8B1C9FD4B94EA181C65F366642AD9E74C?cmp=Movieclips_YT_Description Watch the best Home scenes & clips: https://www.youtube.com/playlist?list=PLZbXA4lyCtqrIyUzz3HaU8pWJ4l5SSrcr FILM DESCRIPTION: After a hive-minded alien race called the Boov conquer the Earth, they relocate the planet\'s human population -- all except for a little girl named Tip (Rihanna), who\'s managed to hide from the aliens. When Tip meets a fugitive Boov called Oh (Jim Parsons), there\'s mutual distrust. However, Oh is not like his comrades; he craves friendship and fun. As their distrust fades, the pair set out together to find Tip\'s mother, but, unbeknown to them, the Gorg -- enemies of the Boov -- are en route. CREDITS: TM & © Dreamworks Animation (2015) Cast: Jim Parsons, Rihanna Screewriter: Matt Ember, Tom J. Astle Director: Tim Johnson Watch More: ► Fresh New Clips: http://bit.ly/2taDWqW ► Classic Trailers: http://bit.ly/2qTCxHF ► Hot New Trailers: http://bit.ly/2qThrsF ► Clips From Movies Coming Soon: http://bit.ly/2FrP8VL ► Indie Movie Clips: http://bit.ly/2qTZMRE ► Deleted Scenes: http://bit.ly/2ARbLPJ ► Bloopers: http://bit.ly/2qYmBnc ► Celebrity Interviews: http://bit.ly/2D4tzw4 Fuel Your Movie Obsession: ► Subscribe to MOVIECLIPS: http://bit.ly/2CZa490 ► Watch Movieclips ORIGINALS: http://bit.ly/2D3sipV ► Like us on FACEBOOK: http://bit.ly/2DikvkY ► Follow us on TWITTER: http://bit.ly/2mgkaHb ► ...
Blake Shelton - Home (Official Music Video) From the album Pure BS Check out the Blake Shelton Official Music Videos Playlist! https://bit.ly/2DlxP9P Subscribe to Blake's channel for all the best and latest official music videos, official audio, albums, behind the scenes, live performances and more! https://bit.ly/2Hjua0h See Blake on the road! https://www.blakeshelton.com/tour Get your Blake Shelton merchandise here! https://store.blakeshelton.com/ Explore Blake's iconic music catalog! https://BlakeShelton.lnk.to/discography Stay in touch with Blake! Website: http://www.blakeshelton.com/ Facebook: https://www.facebook.com/blakeshelton/ Twitter: https://twitter.com/blakeshelton Instagram: https://www.instagram.com/blakeshelton/ The official YouTube channel of Blake Shelton. Warner Music Nashville's Blake Shelton is a country music heavyweight with over 25 #1 hit songs like "God Gave Me You", "Boys \'Round Here", "Who Are You When I'm Not Looking", "Honey Bee" and "I'll Name The Dogs". His iconic catalog also includes 6 #1 albums such as Red River Blue, Bringing Back The Sunshine, If I'm Honest and Texoma Shore. A celebrated singer, songwriter, musician and coach of Team Blake on hit TV show "The Voice", Blake has redefined what it means to be an entertainment superstar. And if that weren't enough, Blake also dabbles in the finer things by way of his Ole Red bar and restaurants, inspired by hit song \"Ol\' Red\", and his award-winning Smithworks vodka. With every turn, Blake continues to expand his own and country music\'s horizons. #BlakeShelton #TeamBlake #Pu...
The official video of \"Home\" by Machine Gun Kelly, X Ambassadors & Bebe Rexha from \'Bright: The Album\' \'Bright The Album\' available now: https://Atlantic.lnk.to/BrightID Watch Bright exclusively on Netflix now! Subscribe for more official content from Atlantic Records: https://Atlantic.lnk.to/sub
All Rights Administered by BigHit Entertainment Here you can download wit: https://wit.onelink.me/YogN/jaeguchibp • Artist: BTS (방탄소년단) • Song: HOME • Album: \'MAP OF THE SOUL: PERSONA\' • Released: 19.04.12 Engtrans: 김유경 (vegaren) ...................................................................
พบกับรายการ THE MASK MIRROR ได้ทาง ช่องเวิร์คพอยท์ หมายเลข 23 ทุกวันพฤหัสบดี ตั้งแต่เวลา 20.05 น . และติดตามข้อมูลเพิ่มเติมของรายการได้ที่ เว็บไซต์: https://www.workpointtv.com เฟสบุ๊ค: www.facebook.com/TheMaskSingerTH
New album ❤ available now: http://wbr.ec/love Michael Bublé - Home [Official Music Video] Listen to Home on the album \"It\'s Time\": https://wbr.ec/itstime Connect with Michael: Website: http://www.michaelbuble.com Facebook: http://www.facebook.com/michaelbuble Instagram: https://www.instagram.co
Home - Fixing My Mistakes: Oh (Jim Parsons) tries to return an important artifact to the Gorg in order to save earth. BUY THE MOVIE: https://www.fandangonow.com/details/movie/home-2015/MMV8B1C9FD4B94EA181C65F366642AD9E74C?cmp=Movieclips_YT_Description Watch the best Home scenes & clips: https://www
Martin Garrix - Home (feat. Bonn) ♫ Home (feat. Bonn) is out now: http://stmpd.co/MGBHMID ♫ Used To Love is out now: http://stmpd.co/MDTLID Premiered my new track Home featuring Bonn at Tomorrowland 2019 as a surprise and so happy I can release it already! Turn on subtitles for the official lyric
There\'s a fan campaign to get Jarvis\' Running The World up the charts in time for Christmas...Are you listening? ➤ http://bit.ly/2PA2KoR Taken from Edward Sharpe & the Magnetic Zeros\' debut album \'Up From Below\', released on Rough Trade Records: http://smarturl.it/upfrombelow Co-director Ryan Gall
Provided to YouTube by Big Hit Entertainment Co. Ltd. HOME · ko - BTS (방탄소년단) / en - BTS MAP OF THE SOUL : PERSONA ℗ Big Hit Entertainment Released on: 2019-04-12 Auto-generated by YouTube.
Home - Boov Do Not Dancing: Oh (Jim Parsons) finds himself unable to stop his body moving when Tip (Rihanna) turns on the radio. BUY THE MOVIE: https://www.fandangonow.com/details/movie/home-2015/MMV8B1C9FD4B94EA181C65F366642AD9E74C?cmp=Movieclips_YT_Description Watch the best Home scenes & clips:
Blake Shelton - Home (Official Music Video) From the album Pure BS Check out the Blake Shelton Official Music Videos Playlist! https://bit.ly/2DlxP9P Subscribe to Blake's channel for all the best and latest official music videos, official audio, albums, behind the scenes, live performances and mo


data keys (including KEK, ZCMK/ZMK, Session Key).
Purpose and peculiarities of each of these keys, keys hierarchy and interdependence are discussed. Issuer and Certification Authority certificates purpose and peculiarities are discussed as well.
smart card and POS terminal.
The list, purpose and interconnection os keys applied in each transaction are discussed.
Finally, the responsibilities of each role of key management process are discussed (including security officers responsibilities).


People used to tell me, "God will bring you a husband when you least expect it!"
That never made sense to me, because I was never not praying for or keeping my eyes open for a husband. I wasn't desperate. I wasn't chasing down every potential candidate. But I desired marriage and I was always aware.
So how in the world would God bring me a husband when I "least expected it"?
I wrote this in May of 2016 and, for whatever reason, I never actually posted it. It just sat in my drafts...until now. Because Joy is something worth sharing about. Enjoy!
"Oh!" I gasped as I looked down at the street from the second story window. "It's raining! Look how beautiful it makes the world!"
My friend laughed. "You're cute, you know that? Most people dislike rain."
Later, as we prayed, my friend said, "Thank you, Lord, for giving Yaasha such joy..."
And it hit me like a spray of sunlight through clouds. My joy was a gift.
Some time ago, I walked upon a beach, breathed upon by soft darkness, draped in the glimmer of an infinity of stars, touched by the wet sand beneath my feet, and in the company of a young lady whose compassion and friendship has blessed me immensely.
I felt the Lord's presence as I have only rarely felt it before.
And for half a moment, I felt a twinge of alarm.
When I broke my nose playing high school soccer, my whole game changed. Up until then, people had commented that I was an aggressive go-getter. My coach assigned me as a striker because, even though I couldn't really kick straight, I could move the ball up the field so that a more accurate teammate could score for the team. I wasn't afraid to get into a tangle with an opponent in an attempt to steal the ball.
One day, I approached an opponent who had the ball and prepared to swipe it from her. She kicked--and the ball blasted right into my face.
"I don't understand," I told my friend through my tears. "I thought that I'd be over this by now. What is wrong with me, that I can't just move past this?"
"Honey," my friend replied gently. "You've been living with this situation for years. You think you're going to heal in just a few months?"
Yes. Yes, I did think I would. I didn't want to believe that I was so weak. What design could God possibly have in my weakness?
Fear has been a near-constant battle for me in the last few months.
Recently, my life changed significantly. When I came to grips with a number of difficult things I had been denying or minimizing over the years, it all led to making significant changes in my life. Suddenly, I found myself in a completely unfamiliar place, surrounded by strangers, battling constant doubts and fears, unemployed, and uprooted from almost every familiar or comforting thing I knew.
Every tomorrow seemed like a yawning chasm of the unknown.
Every day of 2016, I opened my eyes to a new morning and God's Spirit whispered, "Treasure this."
He did not say why, but I sensed a preparing in my heart. I felt a whisper of change boiling in my future, like storm-clouds brewing just beyond sight. Whether weeks or years away, this change would alter the life I had built.
Christian, How Do You Suffer?
This post first appeared on LessonsFromPain.com on February 17, 2016.
This week my community lost a 23-year-old young man. He was ice-fishing on the lake with friends and, through a series of circumstances, was alone in sub-zero temperatures when his truck broke through the ice. He extricated himself and tried to make it to shore. He never made it.
How do I know if a man is worth developing a relationship with? How do I know if he has the character to be a good husband? Well, here are my top six ways to know if a guy is worth it.
Note: I hesitated to post this, because of the fear that people would mistake it as a "This is my laundry list of qualifications for my future husband," which is not at all the intent. I wrote it--and now share it--because I think my thought processes here have some value for the young woman who seeks guidance in these matters and wants to set her standards responsibly.
I started this post with a totally different title. I wanted to write about energy and how variable it is for me, with the usual mix of honesty about a rough situation and optimism for the future. And I started to write, "I can handle any situation so long as I have the energy to do what I want."
But that made me think. What do I want?


Dr Alka Mittal has joined as Director (HR) of Oil and Natural Gas Corporation Limited. A post graduate in Economics, MBA (HRM) and Doctorate in Commerce and Business Studies, she joined ONGC as a Graduate Trainee in 1985 and brings with her an extremely rich experience spanning over three decades.
Dr Mittal is the first woman to hold the charge of a full-time Director in ONGC's history. She is also on the board of ONGC Mangalore Petrochemicals Limited (OMPL) as ONGC nominee Director since August 2015.
Prior to joining as Director (HR), Dr Mittal held the post of Chief Skill Development (CSD) of the Company. In her capacity as CSD she streamlined the activities and brought in uniformity in the working of the Skill Development Centres of ONGC. During this period, she has also implemented the National Apprenticeship Promotion Scheme (NAPS) in ONGC engaging more than 5000 apprentices across all work centres. Previously she worked as Head CSR at Corporate Office and took up major CSR projects across India.
Earlier, she has led the HR-ER functions in various capacities across regions including Vadodara, Mumbai, Delhi and Jorhat, and was also the Head of Corporate Communications of ONGC during 2009. She has a special penchant for training and mentoring, and has trained more than 11000 GTs of ONGC since 2001 on "Corporate Governance" as part of their induction program.


text:
Jesse Weaver Shipley
|Professor of African and African American Studies
John D. Willard Professor of African and African American Studies and Oratory; Chair of Program in African and African American Studies; Affiliated Department of Anthropology
Jesse Weaver Shipley is an ethnographer, filmmaker, and artist. His work explores the links between aesthetics and politics by focusing on performances and popular cultures in the midst of changing economic regimes and forms of sovereignty. He has conducted field research in Ghana, Nigeria, South Africa, Britain, and the United States focusing on the complex realities of urban life, labor, race, gender, mobility, and new media technologies. He is the author of two books Living the Hiplife: Celebrity and Entrepreneurship in Ghanaian Popular Music and Trickster Theatre: The Poetics of Freedom in Urban Africa. His writings appear in journals including Public Culture, American Ethnologist, Journal of Popular Music Studies, American Anthropologist, Cultural Anthropology, and Social Text. He has contributed to africasacountry.com, Chimurenga, and thehill.com. His film work includes feature documentaries Living the Hiplife: Musical Life in the Streets of Accra and Is It Sweet? Tales of an African Superstar in New York; multi-channel video installations Black Star and High Tea; and (in collaboration with Khadija von Zinnenburg Carroll) the multimedia installation Investigated. He is currently working on a book on political aesthetics and another on the relationship between parody and violence. He is also working on a film on world champion female boxers.
A.B. Brown University
M.A. University of Chicago
Ph.D. University of Chicago


text:
Mack Gordon
Morris Gittler
(1904-06-21)June 21, 1904
Warsaw, Congress Poland, Russian Empire
February 28, 1959(1959-02-28) (aged 54)
Frank Sinatra, Dean Martin, Sammy Davis, Jr., The Rat Pack, Etta James, Nat King Cole, Harry Warren, Mel Tormé, Rosemary Clooney, Barbra Streisand, Christina Aguilera, Harry Revel
Mack Gordon (born Morris Gittler; June 21, 1904 – February 28, 1959)[1] was an American composer and lyricist for the stage and film. He was nominated for the best original song Oscar nine times in 11 years, including five consecutive years between 1940 and 1944, and won the award once, for "You'll Never Know".[2] That song has proved among his most enduring, and remains popular in films and television commercials to this day. "At Last" is another of his best-known songs.
2 Selected songs
3 Original works for Broadway
Gordon was born in Warsaw, then part of the Russian Empire. He moved to New York City as a child. He appeared as a vaudeville actor and singer in the late 1920s and early 1930s but his songwriting talents were always paramount. He formed a partnership with English pianist Harry Revel that lasted throughout the 1930s. In the 1940s he worked with a string of other composers including Harry Warren.
The Internet Movie Database states that Gordon's songs have been used in the soundtrack of over 100 films, Gordon writing specifically for at least 50 of them. His catalogue includes more than 120 songs sung by some of the world's most famous and talented performers such as Frank Sinatra, Nat King Cole, Dean Martin, Sammy Davis, Jr., Etta James, Glenn Miller, Barbra Streisand, Mel Tormé, Christina Aguilera and many more. His close friendship with many of his artists (such as Frank Sinatra and the Rat Pack) and his ability to write lyrics that were timeless allowed him to become one of the most famous members of the world of music and a legendary lyricist.[citation needed] His exhibit in the Songwriters Hall of Fame says he was "arguably one of the most successful lyricists to write for the screen".
Dying in 1959, he was entombed in the Corridor of Immortality at Home of Peace Cemetery.
Selected songs
1942 sheet music cover,"At Last", as recorded by Glenn Miller and His Orchestra from the movie Orchestra Wives, Leo Feist, New York.
"A Lady Loves"
"A Star Fell Out of Heaven"
"A Tree Was a Tree"
"All About Love"
"An Old Straw Hat"
"An Orchid to You"
"At Last"
"Baby, Won't You Say You Love Me"
"Chattanooga Choo-Choo" – nominee for 1941 Academy Award for Best Original Song[2]
"Cigarettes, Cigars"
"Danger, Love at Work"
"Did You Ever See a Dream Walking?"
"Doin' the Uptown Lowdown"
"Down Argentine Way" – nominee for 1940 Academy Award for Best Original Song
"From the Top of Your Head to the Tip of Your Toes"
"Goodnight My Love"
"Help Yourself to Happiness"
"I Can't Begin to Tell You" – nominee for 1946 Academy Award for Best Original Song
"I Feel Like a Feather in the Breeze"
"I Had the Craziest Dream"
"I Played Fiddle for the Czar"
"I'm Making Believe" – nominee for 1944 Academy Award for Best Original Song
"I've Got a Date With a Dream"
"I've Got a Gal in Kalamazoo" – nominee for 1942 Academy Award for Best Original Song
"If You Feel Like Singing, Sing"
"In Old Chicago"
"It Happened In Sun Valley"
"It Happens Every Spring"
"It Was a Night in June"
"It's Swell of You"
"Listen to the German Band"
"Lookie, Lookie, Lookie, Here Comes Cookie"
"Love Thy Neighbor"
"Mam'selle"
"May I?"
"My Heart is an Open Book"
"My Heart Tells Me"
"Never in a Million Years"
"On the Boardwalk at Atlantic City"
"Once in a Blue Moon"
"Once Too Often"
"Paris in the Spring"
"Serenade in Blue"
"She Reminds Me of You"
"Somebody Soon"
"Somewhere in the Night"
"Stay As Sweet As You Are"
"Sunny Southern Smile"
"Takes Two to Make a Bargain"
"Thanks for Everything"
"The More I See You"
"There Will Never Be Another You"
"There's a Lull in My Life"
"Through a Long and Sleepless Night" – nominee for 1949 Academy Award for Best Original Song
"Time on My Hands"
"Underneath the Harlem Moon"
"What Did I Do"
"When I'm With You"
"Wilhelmina" – nominee for 1950 Academy Award for Best Original Song
"Without a Word of Warning"
"With My Eyes Wide Open, I'm Dreaming"
"You Do" – nominee for 1947 Academy Award for Best Original Song
"You Make Me Feel So Young"
"You Say the Sweetest Things Baby"
"You'll Never Know" – winner for 1943 Academy Award for Best Original Song, from Hello, Frisco, Hello
Original works for Broadway
Fast and Furious (1931) – revue – primary lyricist
Smiling Faces (1932) – musical – lyricist
Strike Me Pink (1933) – revue – contributing dialogue-writer
^ "MACK GORDON, 54, LYRICIST, IS DEAD". New York Times. Mar 1, 1959. p. 86 – via ProQuest.
^ a b "Mack Gordon". Academy Awards Database. The Academy of Motion Picture Arts and Sciences. Archived from the original on July 17, 2013. Retrieved July 16, 2013.
Mack Gordon discography at Discogs
Mack Gordon on IMDb
Mack Gordon in the Songwriter's Hall of Fame
Mack Gordon at the Internet Broadway Database
Mack Gordon recordings at the Discography of American Historical Recordings.


Since the beginning of the year the State Bank of Viet Nam has been increasing the reference rate of the đồng against the US dollar, and the Vietnamese currency has appreciated 0.43 per cent as a result.
The buying and selling prices of the dollar are now at VNĐ23,200.
The central bank has bought nearly US$5 billion worth of the greenback in the first two months of the year, increasing its foreign reserves significantly.
The inflow this year has been worth $150 million.
Foreign direct investment has risen 2.5 times from the same period last year to $8.47 billion.
Overseas remittances also saw a sharp increase.
The central bank is allowing the đồng to strengthen though there is no pressure on the currency and foreign exchange reserves are plentiful.
In 2016, Việt Nam decided to announce a daily reference rate for the đồng against the dollar each day, shifting from a fixed rate, to enable more flexibility.
The adjustment is based on daily changes to eight foreign currencies that strongly affect the Vietnamese economy: China's yuan, Europe's yuro, Japan's yen, South Korea's won, Taiwan's new dollar, Thailand's baht, Singapore's dollar, and the US dollar.
But experts said though the reference rate is announced daily, there is a big gulf between it and the rate in the market.
The dollar has, in the unofficial market, been much weaker than in the banking system.
For instance, on March 11 the buying price of the dollar on the unofficial market was VNĐ23,205 compared to VNĐ23,160-23,180 in the official channel, while the selling price was VNĐ23,215 compared to VNĐ23,170-23,190.
Market observers said the Vietnamese currency weakened slower on the official market because the SBV capped its buying price at VNĐ23,200 to the dollar based on the central rate.
Some analysts disagreed with the central bank's policy.
But many others said agencies in charge of monetary policy have their own reasons to decide on the reference rate.
The central bank wants to narrow the gap between the đồng and other currencies in the region.
In 2018 the đồng weakened by only 2.2-2.3 per cent against the dollar while many other currencies in the region fell sharply. For instance, the Chinese yuan fell by 5 per cent.
To weaken the đồng, the central bank bought large quantities of the greenback. Exporters are not complaining since their products are becoming more competitive with the depreciation of the Vietnamese currency.
The analysts also said the central bank's decision to lower the central rate and weaken the đồng is also aimed at aligning the currency closer to the market rate.
This would help the central bank gradually realise its goal of doing away with the current policy of setting the reference exchange rate and having a +/- 3 per cent band within which banks can set their rates.
Analysts believe that 2020 will be a do-or-die year for banks since the SBV has decreed that they must all meet global capital adequacy norms.
According to Circular No 41/2016/TT-NHNN, banks must have a capital adequacy ratio (CAR) of at least 8 per cent from January 1, 2020, as stipulated under BASEL II standards.
Four years ago the central bank selected the first 10 commercial banks to pilot Basel II standards. But to date only VIB and Vietcombank have joined the pilot scheme successfully while other lenders involved in the plans failed.
Last year, 18 out of the country's 34 banks received approval from their shareholders to hike their capital, with their combined increase expected to be nearly VNĐ63 trillion ($2,74 billion).
Banks in the country still derive their revenues mainly -- 70 and 80 per cent -- from credit activities.