text:
As'ad AbuKhalil, arab. أسعد أبو خليل (ur. 16 marca 1960 w Tyrze) – libańsko-amerykański publicysta, pisarz oraz profesor nauk politycznych na Kalifornijskim Uniwersytecie Stanowym. Znany ze swoich komentarzy dotyczących bieżących wydarzeń na Bliskim Wschodzie.

Życiorys
Jego matka pochodziła z sunnickiej rodziny, ojciec natomiast z szyickiej. Pracował również jako sekretarz generalny libańskiego parlamentu, dlatego As'ad dorastał w Bejrucie. Tam również otrzymał Bachelor's degree (1981) i Master of Arts (1983) z nauk politycznych na Uniwersytecie Amerykańskim w Bejrucie. W 1988 obronił doktorat z systemów rządowych w perspektywie porównawczej na Georgetown University. Później wykładał na uniwersytetach, takich jak Tufts University, Georgetown University i George Washington University. Od 1993 wykłada na Kalifornijskim Uniwersytecie Stanowym w Stanislaus. AbuKhalil jest także profesorem wizytującym na University of California w Berkeley.

Poglądy
Określa siebie jako byłego marksistę-leninistę, a aktualnie jako anarchistę, który uniknął "pułapki dogmatyzmu".

Jest przeciwnikiem polityki zagranicznej prowadzonej przez Stany Zjednoczone, Iran i Arabię Saudyjską. Odnosi się również krytycznie do działalności Fatahu, Hamasu oraz wszystkich rywalizujących ze sobą frakcji w Libanie, w tym szyickiego Hezbollahu. W konflikcie izraelsko-palestyńskim opowiada się jednak po stronie palestyńskiej, wspierając pomysł powstania sekularystycznej Palestyny. Opisuje siebie jako antysyjonistę i wspiera działalność ruchu Boycott, Divestment and Sanctions (BDS), który powinien - według niego - dążyć do likwidacji Izraela, ponieważ "sprawiedliwość oraz wolność dla Palestyńczyków jest sprzeczna z istnieniem państwa izraelskiego.". W jego wypowiedziach pojawia się również ostra krytyka działalności izraelskiego lobby w Stanach Zjednoczonych.

AbuKhalil wobec sytuacji panującej w Libanie również odnosi się krytycznie. W wywiadzie dla New TV z 13 stycznia 2010  stwierdził, że "libański nacjonalizm - podobnie jak syjonizm - był oparty na rasizmie i pogardzie dla innych - czy to dla Libańczyków z innych stronnictw politycznych, czy dla innych Arabów... Libańczycy ze wszystkimi swoimi podziałami nigdy nie dowiedli, że chcą lub są zdolni do prawdziwej koegzystencji, koegzystencja w Libanie to współistnienie krwi, konfliktów i rozruchów społecznych."

The Angry Arab News Service
We wrześniu 2003 AbuKhalila uruchomił bloga, którego nazwał Angry Arab News Service. Nazwa została zaczerpnięta z frazy używanej przez producenta telewizyjnego, który chciał opisać poglądy AbuKhalila.

Wybrane publikacje

 Historical Dictionary of Lebanon (1998)
 Bin Laden, Islam & America's New "War on Terrorism" (2002)
 The Battle For Saudi Arabia: Royalty, Fundamentalism, and Global Power (2004)

Przypisy

Linki zewnętrzne
The Angry Arab News Service
As'ad AbuKhalil na Twitterze

Absolwenci Uniwersytetu Amerykańskiego w Bejrucie
Absolwenci Uniwersytetu Georgetown
Amerykańscy anarchiści
Amerykańscy myśliciele polityczni
Amerykańscy politolodzy
Amerykańscy publicyści
Antysyjoniści
Libańscy naukowcy
Libańscy pisarze
Ludzie urodzeni w Tyrze
Urodzeni w 1960
Wykładowcy uczelni w Stanach Zjednoczonych
Wykładowcy Uniwersytetu Georgetown


text:
Latest Release of Altair® PBS Professional® Offers Up to Tenfold Faster Performance for More Diverse and Dynamic HPC Workloads
Troy, Mich., August 11, 2020 – Altair (Nasdaq: ALTR), a global technology company providing solutions in product development, high-performance computing (HPC), and data analytics, today announced a major update to Altair® PBS Professional®, the market-leading HPC workload manager.
New features within the PBS Professional base scheduler enable customers to increase throughput as much as tenfold, manage costs and resources more accurately, and easily access efficient scaling through cloud bursting.
"For our users, the latest version of PBS Professional represents a significant increase in functionality and value. It will provide customers with the flexibility, scalability, and efficiency they need to flourish in a uniquely dynamic operating environment," said Sam Mahalingam, chief technology officer at Altair. "The addition of a hierarchical structure makes PBS Professional a truly full-spectrum proposition – accommodating workloads ranging from multi-core, long-running HPC jobs to high-throughput workloads that could potentially involve tens of millions of high-frequency jobs in a single day."
With the latest iteration of PBS Professional, users benefit from:
• Massively high throughput scheduling, with the flexibility to accommodate more diverse and dynamic workloads via hierarchical scheduling. PBS Professional now embraces both high-performance and high-throughput computing workloads. Users can employ a "personal" scheduler to queue their own jobs sequentially, all within the same overall scheduler.
• The ability to simulate workloads to find the most productive approach to scaling HPC resources.
• Allocation management to control and optimize budgets across multiple sites, departments, and HPC clusters by scheduling resources to match an organization's unique profile of costs and resources.
• Cloud bursting via a graphical user interface (GUI) that requires no specialist expertise, with the freedom to leverage on-demand resources from both public and private clouds.
For more details on the advantages of PBS Professional, visit https://www.altair.com/pbs-professional/. To learn more about Altair's HPC solutions, register for the Altair HPC Virtual Summit at https://web.altair.com/altair-virtual-hpc-summit.
About Altair (Nasdaq: ALTR)
Altair is a global technology company that provides software and cloud solutions in the areas of product development, high performance computing (HPC) and data analytics. Altair enables organizations across broad industry segments to compete more effectively in a connected world while creating a more sustainable future. To learn more, please visit www.altair.com.
Altair Corporate/ Americas/ Asia Pacific
Lindsay Savarese – +1 212.331.8417

redpajama_set_name: RedPajamaCommonCrawl


text:
Fran Viljem Lipić (Fran Viljem Lippich, Franz Wilhelm Lippich) (Spišská Nová Ves, Slovačka, 13. lipnja 1799. – Beč, 12. prosinca 1845.), slovenski liječnik hrvatskog podrijetla

Životopis
Rođen u Spišskoj Novoj Vesi. Otac Jožef je bio iz Boke Kotorske i umirovljen kao fizik šipaškog komitata. Fran Viljem Lipić je pohađao srednje škole u Podolini, Levoči i Košicama. Tih se godina bavio suvremenim jezicima, crtanjem i glazbom. Studirao medicinu u Pešti, a završio u Beču. Obranio je disertaciju  Observata de metritide septica in puerperas grassante. Živio je i radio u Sloveniji, Italiji i Austriji. U Sloveniji je živio i radio u Ljubljani kao gradski fizik i okružni liječnik (1823. – 34.), u Italiji je predavao praktičnu medicinu i ravnao internom klinikom u Padovi (1834. – 41.), a potom je u Beču predavao internu medicinu (1841. – 45.).

Zaslužnik je medicine Austro-Ugarske zbog promicanja novih metoda fizikalne dijagnostike (upotreba stetoskopa) u bolničkoj praksi. U Beču je reorganizirao kliničku nastavu. Jedan od pionira borbe protiv alkoholizma, protiv kojeg se borio na znanstvenim osnovama. Zanimao se za primjenu hipnoze u medicini.

Literatura

Slovački znanstvenici
Životopisi, Slovenija
Životopisi, Italija
Internisti
Ljubljana

redpajama_set_name: RedPajamaWikipedia


A preservative formulation meant to meet the challenges faced by extremely dynamic system of Metal Working Fluids. Designed by carrying out Mathematical Modelling on the average fluid-dynamics and then formulated. Innovative contributions by Melzer.
It is a tank-side preservative for pretreatment of Process Water used for diluting Metal Working Fluids. It enhances the life of the diluted MWF through prevention of microbial growth. Controlled toxicity and high safety.
It is a formaldehyde-donating compound for treating circulating fluids. It has broad spectrum activity against microorganisms including fungi. A conventional extra stabilised additive whose extended performance is considered by static evaluations though its actual usage in extremely dynamic system.


On Monday, the Centers for Medicare & Medicaid Services (CMS) and the American Medical Association (AMA) announced efforts to continue to help providers get ready for the October 1 ICD-10 deadline.
According to the AMA website, the two organizations will provide additional education through webinars, on-site training, articles, and national provider calls to help physicians and other health care providers learn about the updated codes and prepare for the transition.
CMS' free help includes the "Road to 10" aimed specifically at smaller physician practices with primers for clinical documentation, clinical scenarios, and other specialty-specific resources to help with implementation.
Posted in Industry News, Trends & Research, Medical Billing on July 6, 2015.


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Apple's Amsterdam store evacuated and temporarily closed after the iPad's battery exploded and released potentially harmful substances into the atmosphere.
Reported iCulture, Apple employees protected the iPad and exploded, then pierced the battery in a sand container. Fortunately, there were no fire, smoke, or serious injury. However, three employees who experienced dyspnea were treated by the first responder.
Due to the potentially released chemicals, the store is closed to allow firefighters to ventilate the space and remove harmful vapors.
It has nothing to do with the explosion of this iPad battery, but since Apple has launched the iPhone battery exchange program, there have been several such incidents in the last few months. The Swiss and Spanish Apple stores evacuated this year, after a hot event on the iPhone battery. Fortunately, as with today's incident, there were no serious injuries, but Apple employees suffered minor burns.
As usual, please do not swell the swollen battery. Please contact the Apple Store or your local Apple Authorized Service Provider as soon as possible.


text:
HomeNewsCampusCommunity responds to MO Hall
Community responds to MO Hall
October 21, 2018 Travis Maiden Campus, News
Missouri Hall's received a new dining hall renovation last summer to change the decor and kitchen. With brand new tables, chairs and food stations, the cafeteria now offers a new dining experience for students and staff like Missouri Hall Director Zac Burden.
Burden said he eats in the dining hall every day and is impressed with how good the new decor looks and how quickly it was all finished. He said one of the best changes was removing the wall separating the service area and dining areas because it was impossible to see what was being served and if there was a line for it.
Burden said he likes the new arrangement of food and the introduction of the new serving area in the middle of the hall.
"I think it made mealtime much more of an exploration," said Burden. "The idea is that I can go here, go there and keep looking around to take a look at everything. It really creates a better dining experience."
Burden also said the cafeteria, which opened in 1965, was in dire need of an upgrade. He said several of the fixtures like the salad bar, condiment bar and drinking station dated back to the 1990s, and the wires back in some areas were still running on fuses.
Burden said the Sodexo staff did an excellent job turning the construction site into a functional cafeteria over the span of a couple days. He said the team always seems to work hard, and it would not have been possible without Missouri Hall dining manager John Dahlman, who has worked on campus since 1986.
Dahlman said the kitchens needed the renovation because the infrastructure was falling apart. He said he is happy with the new appliances behind the counter.
"Copper and cast iron pipes don't last forever," Dahlman said. "For example, [it] was February a couple of years ago when the electricity went out. One of our drain lines that's over the top of the electrical part of the building had rotted through, so the water had ran into the electricity, which wasn't a good mix."
Dahlman said it is harder to work because of the new stations around the hall, but his staff can handle the pressure. Now that every area has its own line, it can be difficult to fully staff each area. He said nothing can really be changed because of how expensive stone countertops are.
Dahlman said the hall looks much nicer with a brand new color scheme and furniture. He said he was surprised the old, wooden chairs lasted so long since they were from 1989. Dahlman said even though the surroundings have changed, the best thing to him will always be the different faces he gets to see, like Missouri Hall resident junior Alexandra Miller.
Miller said she used to eat at the dining hall everyday during her freshman year. Now she rarely finds herself sitting down in the cafeteria. She said the food was the worst out of the halls last year, but it had the best arrangement of food and seating.
Miller said the cafeteria might look fancier now, but it feels less functional.
"The tables and chairs are too different, and I don't like the thing in the middle," Miller said. "I eat a lot of carrots and spinach, so now I have to duck down there and reach far because there's a glass plate thing right above me. I can only imagine how it's like for people who are tall to try reaching down there."
Miller said she liked the Missouri Hall dining area before the renovation because it felt like it was more community-based compared to Ryle and Centennial halls' restaurant dining experience. She said the other two halls felt like restaurants, which she wasn't interested in.
Miller said the hall looks much better and the food has improved tremendously compared to how it was before. Miller said she liked how many new options have opened up because of the new serving areas.
After being cut, wrestling raised enough to compete this season
Golf looks to improve as end of season looms


Girl from the North Country - Bob Dylan songs feature in new Conor McPherson play at Old Vic.
There's enough poetry and narrative intensity in Bob Dylan's music to tell all kinds of stories. But now, in a new 'play with music', writer Conor McPherson (The Weir) weaves Dylan's songs into a story about one family's fate during the Great Depression.
Named after Dylan's 1963 song, Girl From the North Country, the play is set in Duluth, Minnesota during the 1930s. It follows 'a family adrift'.
Nick Laine runs a guesthouse. Every day, he creeps closer to defaulting on its a mammoth mortgage. His wife Elizabeth is lost in a haze of early-onset dementia. Their son is a have-a-go Hemmingway, drinking his way through literary aspirations, unemployment and loneliness. Marianne, the black baby who was abandoned in one of the guest rooms, is all grown up with no partner and her own child growing inside her.
Waifs and runaways come and go, all with their own stories and secrets. The whole cast of 20 actors and musicians is impressive (particularly in capturing the Duluth dialect). But it's Shirley Henderson who especially stands out, playing distant wife and mother Elizabeth with humour, heart and a strange, impish strength.
'Pain comes in all kinds: physical, spiritual and indescribable,' we are told in the opening narration. And, over the course of two and half hours, every kind of suffering shimmers with life and is offset with bitter, brief flashes of hope.
There's a striking symbiosis between the stories and Dylan's songs. The bluesy base, haunting strains of harmonica and lyrical beauty are artfully entwined through the drama.
Girl From the North Country is not a story written around the classics or a case of 'Mr Tambourine Man' shoehorned into a script. It's emphatically not a musical or a greatest hits compilation to titillate fans. (This Dylan-disciple theatre reviewer and her can't-name-one-of-his-songs companion enjoyed the show in equal abundance).
McPherson takes tracks spanning 1963-2012 and inserts them into one winter in 1934. He describes it as 'a conversation between the songs and the story'; they illuminate each other and embody the storytelling heart of folk music.


Players can negotiate different bonuses into their contracts and while they often get reported, the variety and mechanics of player bonuses are not often explained. This post will explain the different types of player bonuses, how they affect a player's cap hit, and the basics of how they work.
It's easiest to start with a couple of bonus rules that are applied to all bonuses. All players are eligible for all types of bonuses, even players who are rookies on a rookie scale contract, so long as the bonuses do not cause the player's salary to be above his max (or 120% of the rookie scale for first round picks) or count toward his minimum (except as provided in footnote  below). Bonuses are typically given when a player signs a new contract, though they can also be given to players who are signing a rookie or veteran extension.
For unrestricted free agents or restricted free agents signing a new contract with their current team, the maximum amount a Signing Bonus can provide for is 15% of the compensation called for by the contract, excluding other bonus amounts. For restricted free agents who are signing an offer sheet from another team, the maximum amount a team can offer as a Signing Bonus is 10% of the compensation called for by the offer sheet, not including other bonus amounts. Trade Bonuses and International Player Bonuses follow the same rules – 10% of the player's contract for a restricted free agent signing an offer sheet and 15% of the player's contract for anyone else.
Player contracts can also provide for bonuses that will be paid out if the player participates in an offseason skill and/or condition program or the one of the NBA summer leagues. The maximum amount of such bonus is 20% of the player's base compensation for the upcoming NBA season. If the bonus is dependent on participation in an offseason program, such program cannot exceed 2 weeks in length.
Of course, a lot can happen in an NBA offseason that is out of a player's control or conflicts with the participation requirements. Players can negotiate around this and give themselves carve-outs wherein a team will waive the participation requirements upon the occurrence of certain events. Any contract with offseason participation bonuses may provide a carve-out where the bonus will still be paid if (i) the team waives the participation requirement, (ii) the player trains or plays games with his national team during the offseason, or (iii) the player is injured or has any other medical condition that prevents the player's participation in a summer league or skill and conditioning program.
Performance Bonuses are split into two categories: Likely Bonuses and Unlikely Bonuses. Likely Bonuses are bonuses based on a benchmark that was achieved by the player in the previous season. For example, if a player is coming off a season where he scored 22 ppg, and his new contract has a performance bonus if the player scores 20 ppg, this bonus would be a Likely Bonus. Unlikely Bonuses, on the other hand, are based on benchmarks that were not achieved in the player's most recent season.
Likely Bonuses are be applied to the player's salary and count against team salary. If and when the player does not achieve the stated benchmark, the amount of the Likely Bonus falls off of the player's cap hit. Unlikely Bonuses are not applied to a player's salary unless or until the player (or his team) achieves the relevant benchmark. Because Unlikely Bonuses are not immediately included in team salary but may be earned, they must fit into a team's cap space at the time of signing. In order to accurately calculate such team's cap space, all unlikely bonuses that could be paid out during the upcoming season must be considered. For example, if a team wants to sign a player to a $10M per year contract with a $1M Unlikely Bonus in year 1, the team needs to have $11M in cap space during that year. If that team has $13M in cap space and $3M in Unlikely Bonuses dedicated to current players in that year, they cannot sign a free agent for $10M plus $1M in Unlikely Bonuses.
All Performance Bonuses can be a maximum of 15% of a player's salary in a season. Over the course of a contract, Performance Bonus raises work the same as other raises applicable to the relevant player. For example, for Bird and Early Bird Free Agents, Performance Bonuses can be increased by 7.5% per year. For Non-Bird Free Agents and other team's free agents, Performance Bonuses can be increased 4.5% per year.
Player contracts can also have bonuses for achieving physical or academic benchmarks. These could be bonuses for reaching a certain weight or for earning a college degree. Boris Diaw, for example, famously had a weight-based bonus in his Spurs contract that would net him an extra $500,000 if he maintained a certain weight by different deadlines throughout the 2014-15 season.
The CBA is unclear on how these bonuses are applied or how large they can be. They are defined in the same CBA clause as the offseason participation bonuses, but the restrictions on those bonuses are not applied to Physical or Academic Achievement Bonuses. No part of the CBA mentions anything further on the rules and restrictions applied to Physical or Academic Achievement Bonuses. It is probably safe to assume, however, that they have most of the same characteristics of other bonuses or are at least not more favorable or more restrictive than any other bonus type.
 The Excluded International Player Payment Amount is the amount an NBA team can pay to an international team or organization to buyout a player's international contract so he can sign with whoever holds his draft rights. These payment amounts are not included in team salary if they stay below the amount set forth in the CBA. Any money exceeding this amount is treated as a bonus to the international player and will count against the salary cap.
 The International Player Bonus is the only bonus that can be considered salary for the purposes of determining whether a player is paid the required minimum salary.
 If the contract is fully non-guaranteed (for a Signing bonus) or has no guaranteed years remaining (for a Trade bonus), the entire bonus will be applied to the team's salary cap for the first year (for a Signing bonus) or the year the player is traded (for a Trade bonus).
 If the contract has an Early Termination Option (ETO), the bonus is applied to all guaranteed years of the contract except for the ETO year.
 Trade Bonuses are only applied the first time a player is traded. If a player has a $1M trade bonuses and is traded in Y2 and Y4 of his contract, he will only receive the $1M bonus for the Y2 trade.
 Of course this means that even if an NBA team doesn't care about international payments counting against their salary cap, the amount they can pay to buyout their Eurostash players is restricted to the Excluded International Player Payment Amount plus 15% of the player's contract.
 This includes MVP, Finals MVP, DPOY, 6MOY, Most Improved Player, All-NBA honors, NBA All-Defense honors, and being selected to the All-Star team.
 All raises are a percentage increase over the first year's salary or bonus and are not compounded.


text:
Washington river freed after world's biggest-ever dam removal
December 27, 2021 August 27, 2014 by dfonis
The Elwha River flows freely through what was Lake Mills and past the old Glines Canyon Dam, bottom, in Olympic National Park near Port Angeles, Wash.AP Photo/Elaine Thompson
The biggest dam-removal project in history is complete and Washington state's Elwha River is running freely for the first time in more than a century. A blast yesterday destroyed the final 30 feet of the 210-foot Glines Canyon Dam, completed in 1927, on the Olympic Peninsula.
The older, 108-foot Elwha Dam was destroyed last year as part of a project to restore what was once the best salmon river in the area.
The restoration has been "the dream of tribal members for a hundred years," a spokesman for the Lower Elwha Klallam Tribe told the Peninsula Daily News as the tribe celebrated.
The old dams lacked fish ladders and no longer produced significant amounts of electricity. Over the last few years of the restoration project, salmon have started returning to the river and more wildlife has followed.
Millions of tons of sediment that had been trapped behind the dams have created new habitats for wildlife downstream. "We're seeing all sorts of different creatures. It's fantastic," a US Geological Survey spokesman tells National Geographic, which notes that hundreds of smaller dams have been removed over the last few years, but the American West's thirst for water means other large ones are unlikely to go anytime soon.
(This "salmon cannon" could also be a lifesaver for fish.)
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text:
Morrissey Exposes Spotify For Blatantly Lying
in Alternative, News
It was less than two weeks after announcing his upcoming album Bonfire of Teenagers was no longer on the release calendar of Capitol Records that Morrissey unveiled a new single, "Rebels Without Applause," on … Capitol Records. He recently clarified claims by Spotify and YouTube.
Morrissey clarifies
The track has been part of Morrissey's live set for several months and has an unmistakable resemblance to the Smiths' 1986 song "Cemetry Gates," particularly in its main guitar motif and bass line. This is the artist's first new single in the "exactly three years" since "It's Over," according to his official website.
It has been noted that Morrissey is the sole lyricist for 'Rebels Without Applause', and for all of the tracks on Bonfire of Teenagers. However, it seems wrong information has been supplied to both Spotify and YouTube. So, Morrissey has clarified the same. His website noted:
"Contrary to information supplied to both Spotify and to YouTube, Morrissey is the sole lyricist for 'Rebels Without Applause', and for all of the tracks on Bonfire of Teenagers."
Bonfire of Teenagers has been largely completed for a year-and-a-half following a flurry of sessions at producer Andrew Watt's Beverly Hills, Calif., studio, backed by his core band of session collaborators Josh Klinghoffer and Chad Smith. Smith's Red Hot Chili Peppers bandmate Flea and longtime Morrissey band member Jesse Tobias also play on Bonfire, while Iggy Pop and Miley Cyrus contribute guest vocals on as-yet-unnamed songs. Smith and Klinghoffer also play on Pop's own new album, Every Loser, produced by Watt and due out Jan. 6.
Tags: MorrisseySpotify


text:
\section{Introduction}
\subsection{Motivation}
An inverse problem that occurs in a number of important applications involves recovering a signal $\mathbf{x} \in \mathbb{R}^N$ from a set of under-sampled measurements. This problem is formulated as recovering $\mathbf{x} \in \mathbb{R}^N$ from $\mathbf{y} = \boldsymbol \Gamma(\mathbf{x})$ where $\boldsymbol \Gamma(.):\mathbb{R}^N \to \mathbb{R}^M$ could be either a linear or non-linear function while $M\ll N$. Since this problem is ill-posed in general, one is able to recover $\mathbf{x}$ given $\mathbf{y}$ and $\boldsymbol \Gamma(.)$ only if $\mathbf{x}$ has some type of structure such that by applying $\boldsymbol \Gamma(.)$ its dimensionality can be reduced from $N$ without losing information. Many configurations for $\mathbf{x}$ and $\boldsymbol \Gamma(.)$ have been explored in the literature for this problem; however, one of the most useful ones is to have a sparse signal $\mathbf{x}$ and a linear $\boldsymbol \Gamma(.)$, i.e., $\mathbf{y} = \boldsymbol \Gamma(\mathbf{x})=\boldsymbol \Phi \mathbf{x}$  . Compressive sensing (CS) \cite{donoho2006compressed,baraniuk2007compressive, candes2006compressive} is a field that tries to solve this linear inverse problem in case that $\mathbf{x}$ has a sparse representation, i.e., there exists   an $N\times N$ basis matrix $\boldsymbol \Psi = [\boldsymbol \psi_1|\boldsymbol \psi_2|\ldots|\boldsymbol \psi_N]$ such that $\mathbf{x} = \boldsymbol \Psi \mathbf{s}$ and only $K \ll N$ of the coefficients $\mathbf{s}$ are nonzero. Therefore, CS is mainly concerned with the problem of recovering a $K$-sparse signal $\mathbf{x} \in \mathbb{R}^N$ from a set of under-sampled linear measurements, i.e., from  $M \ll N$ measurements acquired via $\mathbf{y} = \boldsymbol \Phi \mathbf{x} =  \boldsymbol \Phi  \boldsymbol \Psi \mathbf{s}$, where $\mathbf{y} \in \mathbb{R}^M$ is the measurement vector and $\boldsymbol \Phi \in \mathbb{R}^{M\times N}$ is the measurement matrix.

The measurement vector formulation $\mathbf{y} = \boldsymbol \Gamma(\mathbf{x})$ suggests that one should answer the following questions to compressively acquire a signal:
\begin{enumerate}[(i)]
\item How to recover the signal $\mathbf{x}$ from a given measurement vector $\mathbf{y}$ and operator $\boldsymbol \Gamma(.)$?
\item How to design the measurement operator $\boldsymbol \Gamma(.)$?
\item If we are concerned with any type of structure, How could we find a representation in which the signal $\mathbf{x}$ has that structure?
\end{enumerate}

In case of sparse $\mathbf{x}$ and linear $\boldsymbol \Gamma(.)$, CS framework answers these three questions in the following way:
\begin{enumerate}[(i)]
\item Using methods from convex optimization or greedy algorithms.
\item Using linear random matrices as measurement matrices.
\item Using pre-specified set of transformations or data-dependent basis such as wavelets, frames, and dictionaries.
\end{enumerate}

Although there has been a considerable progress in CS and particularly in the answers of aforementioned questions, our goal is to go beyond the state-of-the-art results. We approach this goal by incorporating a deep learning framework into structured signal recovery.

Deep learning is an emerging field mainly concerned with learning multiple levels of representation of data and coming up with higher levels of abstraction in it. In this paper we study the ability of deep neural networks to recover structured signals (in particular images) from their under-sampled random linear measurements. In other words, we study the performance of deep learning framework in recovering structured signals from their under-sampled measurements.

The motivation for this work is the great success of deep architectures in image representation. In particular, Hinton et al. showed in \cite{hinton2006reducing} that one can achieve dimensionality reduction in high-dimensional data by training a multilayer neural network called autoencoder. We compare the performance of deep learning approach with state-of-the-art algorithms for solving the CS problem and show that deep architectures can help us to outperform their results at least in certain cases.

In the following three paragraphs, we briefly describe how deep learning provides new opportunities to attack questions (i), (ii), and (iii) mentioned above. We specifically compare these opportunities with the answers to these questions given by CS framework.

The first question is about recovering the original signal from measurement vector and matrix. In order to recover a sparse signal $\mathbf{x} \in \mathbb{R}^N$ from its corresponding measurement vector $\mathbf{y} \in \mathbb{R}^M$, one needs to seek for the sparsest signal $\mathbf{\hat{x}}$ that agrees with the measurement vector $\mathbf{y}$
\begin{align}\label{eq:l0}
\mathbf{\hat{x}} = \arg \min_{\mathbf{x'}} \|\mathbf{x'}\|_0 ~~ \text{s.t.} ~~ \mathbf{y}=\boldsymbol \Phi \mathbf{x'},
\end{align}
where $\|.\|_0$ denotes the $\ell_0$-norm of a vector and counts the number of its nonzero elements. While it has been shown that by using only $\mathcal{O}(K)$ measurements this optimization can recover a $K$-sparse signal \cite{baron2005distributed}, solving \eqref{eq:l0} is an NP-hard problem. Therefore, researchers have replaced $\ell_0$-norm in \eqref{eq:l0} with its convex relaxation $\ell_1$-norm to convert \eqref{eq:l0} to a tractable and stable linear programming problem.
This linear program can be solved either based on convex optimization methods \cite{candes2006near} or iterative greedy algorithms \cite{donoho2009message, needell2009cosamp, blumensath2009iterative, beck2009fast} that are generally first order methods and as a result are more suitable for high-dimensional problems. In this paper, we replace these algorithms, i.e., the convex optimization based approaches and greedy iterative algorithms converging usually in hundreds of iterations with a feed-forward deep neural network. We show that as a result of using a feed-forward deep neural network we do not need to solve the linear program to recover $\mathbf{x}$ and hence we can have much faster signal recovery.

The second question is about designing measurement matrix. Traditional approaches in designing measurement matrix $\boldsymbol \Phi$ are based on focusing on desirable properties needed by $\boldsymbol \Phi$ to preserve information while doing dimensionality reduction, i.e., mapping $\mathbf{x} \in \mathbb{R}^N$ to $\mathbf{y} \in \mathbb{R}^M$ where $M \ll N$. One important property of measurement matrix that guarantees successful sparse signal recovery with very high probability is \textit{restricted isometry property} (RIP) \cite{candes2008restricted}. While checking whether or not a matrix has the RIP is an NP-Complete problem, random matrices whose elements are independent and identically distributed (i.i.d.) Gaussian or Bernoulli random variables, satisfy the RIP with very high probability given $M=\mathcal{O}(K\log(N/K))$. The main drawback of random measurements is that they are not optimally designed according to the signal under acquisition. Adaptive methods \cite{malloy2014near, haupt2009adaptive, haupt2012sequentially, haupt2009compressive} in which each measurement is designed based on the information obtained from previous measurements reduce uncertainty. The major problem with adaptive sequential measurements in CS is time complexity since each new measurement will depend on the information obtained from prior measurements. In this paper we show how deep neural networks can help us to adapt the measurements to the signal being under acquisition instead of taking random measurements and hence enhance the performance of the overall system.

Finally, the third question asks about finding a representation in which the original signal $\mathbf{x}$ has a specific structure. In CS framework, one is concerned with finding a basis $\boldsymbol \Psi$ in which $\mathbf{x}$ has a sparse representation. It is well known that $\boldsymbol \Psi$ could be chosen from a prespecified set of transformations. For example, natural images have sparse representation in wavelet basis or in the DCT domain \cite{mallat1999wavelet}. The main drawback of these prespecified bases is that they are handcrafted and as a result restrictive in capturing complex dependencies between different elements of a signal. More concretely, the main drawback of representing an image in wavelet domain is the assumption of independence between wavelet coefficients. This point has motivated researchers to develop models for capturing statistical dependencies in real-world signals \cite{crouse1998wavelet, de1997non}. However, these models are also handcrafted and hence do not necessarily capture more complex dependencies within a signal. The limitation in representation power of these prespecified transformations has lead researchers to seek for data-dependent basis, i.e., learning a transformation from a set of training examples \cite{aharon2006img}. Deep learning is a framework based on automating feature discovery and feature learning for many machine learning tasks. Accordingly, in this paper we use deep learning framework to automate the process of finding a representation for a class of signals being under acquisition. We show how the learned representation outperforms prespecified set of transformations. In particular, we focus on image data and show how deep neural networks outperform wavelet domain by providing a better representation to do dimensionality reduction.

We believe, to the best of our knowledge, that this paper is the first one trying to study structured signal recovery from  a set of under-sampled measurements by using deep learning framework. However, there have been several studies of using deep learning technique in solving the inverse problems. These studies have been focused on image denoising \cite{burger2012image}, removing noisy patterns from images \cite{eigen2013restoring}, and image super-resolution \cite{dong2014learning}.

The rest of this paper is organized as follows: Section \ref{sec:arc} introduces the network architecture we have used to solve the structured signal recovery problem. In Section \ref{sec:theory} we discuss the probabilistic interpretation for using stacked denoising autoencoders (SDA) in solving the structured signal recovery problem. Section \ref{sec:simul} contains the simulation results. Finally, Section \ref{sec:conclu} includes the conclusion of the paper.

\section{Stacked Denoising Autoencoders for Structured Signal Recovery}\label{sec:arc}
As we mentioned, natural images have sparse representation in wavelet basis or in the DCT domain. Therefore, they could be compressively acquired and reconstructed  using CS framework.  In this section we introduce our deep architecture for solving this CS recovery problem. Later in Section \ref{sec:simul} we compare the performance of the proposed method in this section with other state-of-the-art approaches from CS framework.

We divide our solution into two different scenarios. First, we consider fixed linear measurements that is the traditional CS measurement paradigm. Second, we introduce a new measurement paradigm, namely nonlinear adaptive compressive measurements, inspired by neural networks architecture and capability. Later in Section \ref{sec:simul} we will show that incorporating nonlinearity in the measurements enhance the overall recovery performance.
\subsection{SDA + Linear Measurement Paradigm}\label{sec:SDAL}
In linear measurement paradigm, the measurement vector $\mathbf{y}$ is represented as $\mathbf{y}=\boldsymbol \Phi \mathbf{x}$, i.e., each $\mathbf{y}_i$ ($1 \leq i \leq M$) is a linear combination of $\mathbf{x}_j$s ($1\leq j \leq N$). We consider the typical supervised learning framework where our training set $\mathcal{D}_{\rm train}$ has $l$ pairs consisting of original signals and their corresponding measurements, i.e.,  $\mathcal{D}_{\rm train} = \{(\mathbf{y}^{(1)},\mathbf{x}^{(1)}),(\mathbf{y}^{(2)},\mathbf{x}^{(2)}),\ldots,(\mathbf{y}^{(l)},\mathbf{x}^{(l)})\}$. Based on this training set, we would like to learn a nonlinear mapping from a measurement vector $\mathbf{y}$ to its original signal $\mathbf{x}$. We then test the performance of the trained deep architecture on our test set $\mathcal{D}_{\rm test}$ where it has $s$ pairs consisting of original signals and their corresponding measurements, i.e.,  $\mathcal{D}_{\rm test} = \{(\mathbf{y}^{(1)},\mathbf{x}^{(1)}),(\mathbf{y}^{(2)},\mathbf{x}^{(2)}),\ldots,(\mathbf{y}^{(s)},\mathbf{x}^{(s)})\}$.

Among the traditional sparse recovery algorithms, the ones that are greedy and iterative perform faster than the ones that are based on convex optimization techniques such as linear programming. Each iteration in these greedy or iterative algorithms includes a matrix-vector multiplication which has the computational cost of $\mathcal{O}(MN)$. This fact was an inspiration for us to design the SDA architecture such that its recovery speed would be competitive with the existing fast iterative algorithms along with being similar to an iterative message passing algorithm. Therefore, each layer of the SDA used for sparse recovery either has input size of $N$ (the ambient dimension of the original signal) and output size of $M$ (the dimension of the measurement vector) or vice versa. We use a 3-layer SDA where each layer applies a nonlinearity to the affine transformation of its input. More formally, the first hidden layer receiving measurement vector as its input is formulated as
\begin{align}\label{eq:1stLayer}
\mathbf{x}_{h_1}=\mathcal{T}(\mathbf{W}_1\mathbf{y}+\mathbf{b}_1),
\end{align}
where $\mathbf{W}_1 \in \mathbf{R}^{N\times M}$ and $\mathbf{b}_1 \in \mathbf{R}^N$ are the weight matrix and bias vector of the first layer, respectively. $\mathcal{T}(.)$ is the nonlinearity applied element-wise to the affine transform of input. We use sigmoid function as the nonlinearity; therefore, $\mathcal{T}(x)=\frac{1}{1+e^{-x}}$. Given the weight matrix $\mathbf{W}_1$ and the bias vector $\mathbf{b}$,  the computational cost for calculation of $\mathbf{x}_{h_1}$ is $\mathcal{O}(MN)$ according to \eqref{eq:1stLayer} that is the same as cost of one iteration of an iterative algorithm for CS recovery problem. In order to keep this computational cost at each layer, the second hidden layer and the output layers are formulated as
\begin{align}\label{eq:Layers}
\mathbf{x}_{h_2}=\mathcal{T}(\mathbf{W}_2\mathbf{x}_{h_1}+\mathbf{b}_2) \quad\text{and}\quad \mathbf{\hat{x}}=\mathcal{T}(\mathbf{W}_3\mathbf{x}_{h_2}+\mathbf{b}_3).
\end{align}
In \eqref{eq:Layers} $\mathbf{W}_2 \in \mathbf{R}^{M \times N}$ and $\mathbf{b}_2 \in \mathbf{R}^M$ are the weight matrix and bias vector of the second layer, respectively. Similarly, $\mathbf{W}_3 \in \mathbf{R}^{N \times M}$ and $\mathbf{b}_3 \in \mathbf{R}^N$ are the weight matrix and bias vector of the output layer, respectively. We denote the output of the SDA and its set of parameters by $\mathbf{\hat{x}}$ and $\Omega_{\text{L}}=\{\mathbf{W}_1,\mathbf{b}_1,\mathbf{W}_2,\mathbf{b}_2,\mathbf{W}_3,\mathbf{b}_3\}$, respectively. Therefore, we can define the nonlinear mapping $\mathbf{\hat{x}} = \mathcal{M}_{\text{L}}(\mathbf{y},\Omega_{\text{L}})$ and use the mean squared error (MSE) as the loss function for the training set $\mathcal{D}_{\rm train}$
\begin{align}\label{eq:lossFN}
\mathcal{L}(\Omega_{\text{L}})=\frac{1}{l}\sum_{i=1}^l \| \mathcal{M}_{\text{L}}(\mathbf{y}^{(i)},\Omega_{\text{L}})-\mathbf{x}^{(i)}\|_2^2.
\end{align}
We use backpropagation \cite{rumelhart1988learning} algorithm to minimize the loss function defined in \eqref{eq:lossFN}. Figure \ref{fig:SDA_L} shows the SDA structure fed by linear measurements of original signal.

\subsection{SDA + Nonlinear Measurement Paradigm}
The structure of the SDA for nonlinear measurement paradigm is almost the same as the one in Section \ref{sec:SDAL}. The only difference is that we consider the mapping from original signal to its measurement vector as one layer of the SDA. This extra layer will let SDA to adapt its structure to the training set $\mathcal{D}_{\rm train}$. Therefore, if we have enough data, e.g. lots of natural images (as in ImageNet dataset), we could be hopeful that the measurement matrix is well adapted to the class of signals being under acquisition. We denote this extra layer that is the first layer of the SDA by
\begin{align}\label{eq:1stNL}
\mathbf{y}=\mathcal{F}(\mathbf{W}_1\mathbf{x}+\mathbf{b}_1),
\end{align}
where $\mathcal{F}(.)$ is the nonlinearity we have used in order to take measurements from the original signal $\mathbf{x}$. $\mathcal{F}(.)$ can be either a sigmoid function used in other layers of the network or other types of nonlinearities or even identity function such that the measurements would be linear and at the same time adapted to the acquired signals just like traditional CS framework. We denote the parameter set of this SDA by $\Omega_{\text{NL}}=\{\mathbf{W}_1,\mathbf{b}_1,\mathbf{W}_2,\mathbf{b}_2,\mathbf{W}_3,\mathbf{b}_3,\mathbf{W}_4,\mathbf{b}_4\}$ and its output by $\mathbf{\hat{x}} = \mathcal{M}_{\text{NL}}(\mathbf{x},\Omega_{\text{NL}})$. The loss function corresponding to this SDA is similar to \eqref{eq:lossFN} with some minor changes
\begin{align}\label{eq:lossNL}
\mathcal{L}(\Omega_{\text{NL}})=\frac{1}{l}\sum_{i=1}^l \|\mathcal{M}_{\text{NL}}(\mathbf{x}^{(i)},\Omega_{\text{NL}})-\mathbf{x}^{(i)}\|_2^2.
\end{align}
Figure \ref{fig:SDA_NL} shows the SDA structure for non-linear measurement paradigm. The next section describes how these SDA structures could be related to the CS recovery problem from the probabilistic point of view.

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 7cm]{SDA_Linear.eps}
\caption{Stacked denoising autoencoders (SDA) for recovering a sparse signal from its linear measurements. This is equivalent to having a 3-layer neural network fed with linear measurements of the original signal and try to reconstruct it.}
\label{fig:SDA_L}
\end{center}
\end{figure}

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 7cm]{SDA_NL.eps}
\caption{SDA for recovering a sparse signal from its non-linear measurements. This is equivalent to having a 4-layer neural network taking non-linear and adaptive measurements from the original signal. The non-linear function used for taking measurements could be different from non-linearity used in other layers. However, it should be analytical tractable in order to fit in backpropagation framework.}
\label{fig:SDA_NL}
\end{center}
\end{figure}
\section{Probabilistic Relation Between SDA and Compressive Sensing}\label{sec:theory}
In this section we provide a probabilistic interpretation that explains the success of SDA in solving the structured signal recovery problem. As introduced in Section \ref{sec:arc}, the deep network that we are using to solve this problem is basically stacked version of denoising autoencoders. At the first layer, this deep network is fed by a training example that is either compressed measurements of an image in training set (Figure \ref{fig:SDA_L}) or the original image itself (Figure \ref{fig:SDA_NL}). The next layers are then fed by the latent representation (or output code) of the denoising autoencoder found on their corresponding previous layer. We perform an unsupervised pre-training on this deep architecture that is justified in \cite{bengio2007greedy}. The authors in \cite{bengio2007greedy} have explained how unsupervised pre-training helps the corresponding optimization problem in deep networks by initializing the weights in all layers in a region near a good local minimum of loss function.

In the stacked version of denoising autoencoders, the unsupervised pre-training phase is done one layer at a time. Each layer of this deep network is pre-trained as a denoising autoencoder. In other words, it is trained by minimizing the error in reconstructing its input (that is the output code of the previous layer) from the noisy version of it. As we proceed in the pre-training phase, once the first $t$ layers are trained, we can compute the corresponding latent representation (or output code) of the first $t$-layers and use it as an input in order to train the $t+1$-th layer.

An important aspect of this pre-training phase, is the connection between the Restricted Boltzmann Machine (RBM) \cite{hinton2002training} and denoising autoencoder. RBM is a generative model that can learn probability distribution underlying its input data. It has a set of visible units $\mathbf{v}$ and a set of hidden units $\mathbf{h}$. Since it is a energy-based model \cite{lecun2006tutorial}, it associate a scalar energy $E(\mathbf{v},\mathbf{h})$ to each configuration of the visible and hidden units. The joint probability distribution is defined as $\mathbb{P}(\mathbf{v},\mathbf{h})=\frac{1}{Z}e^{-E(\mathbf{v},\mathbf{h})}$, where $Z$ is called the partition function used for normalizing the probability distribution. Training an RBM is equivalent to configuring its energy function such that desirable configurations have low energy. As an example, the energy function corresponding to Gaussian-Bernoulli RBM \cite{krizhevsky2009learning} with real-valued visible units $\mathbf{v}$ and binary hidden units $\mathbf{h}$ is
\begin{align}\label{equ:energy}
&E(\mathbf{v},\mathbf{h}|\mathbf{W},\mathbf{b},\mathbf{c}) \\
&=\sum_{i=1}^{n_v}\frac{(v_i-b_i)^2}{2\sigma_i^2}-\sum_{i=1}^{n_v} \sum_{j=1}^{n_h} W_{ij}h_j\frac{v_i}{\sigma_i}-\sum_{j=1}^{n_h}c_j h_j, \nonumber
\end{align}
where $W_{ij}$ denote weights connecting visible and hidden units. $\sigma_i$ is the standard deviation associated with the $i$-th Gaussian visible unit. Finally, $b_i$ and $c_j$ denote biases corresponding to visible and hidden units. Training an RBM in this case is the process of adjusting weights $W_{ij}$ and biases $b_i$ and $c_j$ such that the probability distribution it represents fits the training data as well as possible.

The authors in \cite{kamyshanska2014potential} have shown the derivation of an energy function for autoencoders by interpreting them as dynamical systems \cite{seung1997learning}. In particular, the authors in \cite{kamyshanska2014potential} and \cite{vincent2011connection} have shown that the energy function of an autoencoder with sigmoidal hidden layer and real-valued observations is identical to the free energy of corresponding RBM with Gaussian visible units and binary hidden units.

Suppose that we want to train a denoising autoencoder with sigmoidal hidden layer to compress signals from a class of probability distribution. This training is equivalent to learning a set of weights and biases that will result in low energy for signals from that probability distribution. In other words, it is equivalent to adjusting set of weights and biases such that the reconstruction error it has for recovering signals (drawn from that probability distribution) from their compressed representation, is as small as possible.

As an example, suppose that in our training set $\mathcal{D}_{\rm train} = \{(\mathbf{y}^{(1)},\mathbf{x}^{(1)}),(\mathbf{y}^{(2)},\mathbf{x}^{(2)}),\ldots,(\mathbf{y}^{(l)},\mathbf{x}^{(l)})\}$, the original signals $\mathbf{x}^{(i)}$s are drawn from a probability distribution $\mathcal{P}$. As derived in \cite{kamyshanska2014potential} and \cite{vincent2011connection}, the energy function of a denoising autoencoder with sigmoidal hidden units is
\begin{align}
E(\mathbf{x})=\sum_{j}\log(1+\exp(W_{.j}^T\mathbf{x}+b_j))-\frac{1}{2}\|\mathbf{x}-\mathbf{c}\|_2^2+{\rm const},
\end{align}
where $W_{ij}$ denote weights connecting visible and hidden units, and $b_j$ and $c_i$ are biases for hidden layer and reconstruction layer. Training a denoising autoencoder based on the training set $\mathcal{D}_{\rm train}$ in this case is the process of adjusting the weights $W_{ij}$ and biases $b_j$ and $c_i$ such that the reconstruction error for any signal drawn from probability distribution $\mathcal{P}$ (and not necessarily in $\mathcal{D}_{\rm train}$) is as small as possible.

Similarly, suppose that we want to train a denoising autoencoder with sigmoidal hidden layer to decompress data that is originally (i.e., before compression) coming from the probability distribution $\mathcal{P}$. In this case, the autoencoder learns a set of weights and biases that will result in low energy for compressed signals drawn originally from the probability distribution $\mathcal{P}$. In other words, training is equivalent to adjusting set of weights and biases such that the reconstruction error it has for recovering compressed signals (drawn originally from the probability distribution $\mathcal{P}$) from their decompressed representation (in hidden layer), is as small as possible. This training will end up in retrieving the original signals (from the probability distribution $\mathcal{P}$) as decompressed representations in hidden layer since they are the origin of compressed data.

 This is fairly similar to the optimization problem in \eqref{eq:l0}. In \eqref{eq:l0} we have the measurement vector (compressed data), we know the original signal model ($k$-sparse), and the goal is to retrieve the original signal from the compressed measurements. The considerable difference though is the fact that in \eqref{eq:l0} we need an optimization algorithm to retrieve the signal from its measurements. However, in an autoencoder (or deep networks in general) we need to pass the compressed data into a trained feedforward network without any need to solve an optimization problem.

Once we are done with pre-training of all the layers, we perform supervised fine-tuning on the weights and biases of the pre-trained SDA. More precisely, we take the encoding part of each denoising autoencoder, stack them together, and use back-propagation algorithm to minimize the MSE on reconstructing the images from their compressed measurements.

\section{Simulation Results}\label{sec:simul}
In this section we study the performance of our proposed framework for structured signal recovery and compare them with the state-of-the-art results. We first describe the implementation of the proposed models. After that, we compare our method with other CS recovery algorithms. We do this comparison based on both the quality of reconstruction (\text{\sl{PSNR}}) and speed of recovery.
\subsection{Implementation}\label{sec:impl}
Autoencoders are very similar to multilayer perceptron (MLP) in structure. In other words, all the units in input layer of an autoencoder are connected to all the units in hidden layer and similarly all the units in hidden layer are connected to all the units in output layer. Therefore, as image size grows, we have to train a larger network as well. This issue poses a huge computational complexity on running the Backpropagation algorithm in addition to increasing the chance of overfitting.

As a result and instead, we design a neural network for recovering small sub-images (by sub-image we mean a large image patch) instead of a large image. However, this will not block our way to compressively measure and recover large images. We can decompose a large image into several non-overlapping or overlapping sub-images and compressively measure and recover each of them. In the case of non-overlapping sub-images, we will basically have a blocky reconstruction of the original image by putting reconstructed neighbor sub-images beside each other. On the other hand, if we are working with overlapping sub-images, we place each recovered sub-image at its corresponding location in the original image and average on the overlapping sub-images. Figure \ref{fig:subimages} shows a visualization of overlapping sub-images in a larger image.

In this paper, we have trained our deep neural network based on sub-images of size $32 \times 32$. We used natural images from the ILSVRC 2014 ImageNet dataset \cite{russakovsky2014imagenet} for both training and testing the network. For dataset preparation, we extracted the central $256\times256$ part of each image, turned it into grayscale, and chopped it into $32 \times 32$ sub-images. During the training phase, we did not use overlapping sub-images so as to have a diverse training set. However, during the test phase, we use overlapping sub-images and averaging method as described earlier in this section. We normalized each image pixel value such that they would be between 0 and 1. According to the results in \cite{glorot2010understanding}, we sampled the initial weights of our neural network from a uniform distribution $U\left[ -4\sqrt{\frac{6}{\text{fan}_{\text{in}}+\text{fan}_{\text{out}}}},4\sqrt{\frac{6}{\text{fan}_{\text{in}}+\text{fan}_{\text{out}}}}\right]$, where $\text{fan}_{\text{in}}$ and $\text{fan}_{\text{out}}$ are number of units in input and output layers, respectively.

As we mentioned earlier, we use denoising autoencoders as building blocks of our network. Therefore, we corrupt the input of each layer with a Gaussian noise having zero mean and standard deviation of 0.2 and let the layer to reconstruct its corrupted input as the pre-training phase. We then use backpropagation algorithm to fine-tune the weights and biases. We implemented our deep neural networks using Theano package \cite{bergstra2010theano} and used GPU on Amazon web service (AWS) platform.

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 5.5cm]{subfigure2.eps}
\caption{Overlapping sub-images of a larger image.}
\label{fig:subimages}
\end{center}
\end{figure}

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 9cm]{joined.eps}
\caption{Test images for Table \ref{table:PSNR}.}
\label{fig:Images}
\end{center}
\end{figure}

\begin{table}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c || c  c  c  c  c c | }
 \hline
 &  L-SDA &  NL-SDA & D-AMP &  O-NL-SDA  &  Tiled D-AMP & TV\\ [0.5ex]
 \hline\hline
 \textbf{Damselfly}& \textit{29.01} & 30.32 & \textbf{45.97} & \textit{30.85} & 30.51 & 27.46 \\
 \hline
 \textbf{Birds}& 24.93 & \textit{26.19} & 25.58 & \textbf{26.62} & 24.45  & 21.46 \\
 \hline
 \textbf{Rabbit}& 25.42 & \textit{26.80} & 26.37& \textbf{27.24} & 25.04& 19.79\\
 \hline
 \textbf{Turtle}& 31.07 & 33.79 & \textit{34.17} & \textbf{34.65} & 32.08 & 27.16\\
 \hline
 \textbf{Dog}& 19.76 & \textit{21.03} & 19.71& \textbf{21.55} & 18.45& 13.96 \\
 \hline
 \textbf{Eagle Ray}& 25.00& \textit{26.18} & 25.37 & \textbf{26.57} & 24.68& 16.30 \\
 \hline
 \textbf{Boat}& 29.67& 31.96& \textbf{41.75} & 33.11 & \textit{33.49} & 27.63\\
 \hline
\textbf{Monkey}& 28.33& 29.74 & \textbf{34.00} &  \textit{30.32} & 28.66 & 26.47\\
\hline
\textbf{Panda} & 19.82& \textit{20.68} & 19.66 & \textbf{21.00} & 18.61 & 18.31\\
\hline
\textbf{Snake} & 16.42 & \textit{17.39} & 16.33& \textbf{17.72} & 15.46 & 10.42 \\
\hline
\end{tabular}}
\caption{ Quality of reconstruction ({\em PSNR} in  {\sl dB}) for different images in Figure \ref{fig:Images} and different algorithms. The under-sampling ratio ($\frac{M}{N}$) is assumed to be constant and equal to 0.25 in all the cases.}
\label{table:PSNR}
\end{center}
\end{table}

\subsection{Comparison with Other Methods}
In this section we compare the performance of structured signal recovery using deep neural networks with other recovery algorithms. These other algorithms include
\begin{itemize}
\item  One of the state-of-the-art methods that is the denoising-based approximate message passing (D-AMP) \cite{metzler2014denoising}.
\item The total variation (TV) minimization \cite{candes2006robust} which is famous for its intriguing properties for image recovery.
\item The parameterless approximate message passing (P-AMP) \cite{mousavi2013parameterless} employing sparsity in wavelet domain.
\item the tiled version of D-AMP (Tiled D-AMP).
\end{itemize}
By the Tiled D-AMP we mean D-AMP being applied to the non-overlapping sub-images similar to what we mentioned for the SDA-based methods in Section \ref{sec:impl}. We introduce the Tiled D-AMP for the sake of fairness in here. Since due to huge computational complexity we could not train the SDAs for recovering large images, we wanted to compare the performance of the D-AMP if it is applied in the same way that we apply the SDA-based methods, i.e., recovering images from blocky reconstruction of smaller sub-images.

Table \ref{table:PSNR} shows the summary of results for recovering 10 different images in Figure \ref{fig:Images}.  As is clear from Table \ref{table:PSNR} there is not an obvious winner among different methods. In some cases, the L-SDA (SDA + Linear Measurements) and NL-SDA (SDA + Non-linear Measurements) and O-NL-SDA (SDA + Overlapping Non-linear Measurements) have better performance in comparison with the D-AMP and min-TV.

More specifically and according to our simulation results, whenever the acquired image has an irregular structure such that there are not to many similar patches in the image, then the L-SDA, NL-SDA, and O-NL-SDA have better performance comparing to the D-AMP and min-TV.

For example, in Figure \ref{fig:Images} the dog and panda images have irregular structure and texture. As we can see in Table \ref{table:PSNR} for these images, all the recovery methods based on deep learning have a better performance in comparison with the D-AMP which is one of the state-of-the-art CS recovery algorithms. On the other hand, the damselfly and boat images have very smooth and regular structure and hence lots of similar patches. As a result, we see from Table \ref{table:PSNR} that the D-AMP is outperforming methods based on deep learning since the D-AMP is utilizing these similar patches to enhance the image reconstruction.

One more interesting point about the Table \ref{table:PSNR} is that in all cases except one, the NL-SDA and O-NL-SDA are outperforming the Tiled D-AMP. This fact shows the potential of deep learning and the fact that if we were able to train huge networks in a reasonable time or coming up with another network structure, we might be able to outperform the D-AMP in almost all the cases. We leave this problem as an avenue for future work.

Figures \ref{fig:reconMonkey}, \ref{fig:reconDog}, and \ref{fig:reconFork} show 3 examples of set of reconstructed images. As we can see, in Figure \ref{fig:reconDog} that we do not have a regular and smooth structure, the NL-SDA and O-NL-SDA are outperforming the D-AMP. In Figure \ref{fig:reconFork} that has both regular and irregular textures, we can see that the D-AMP is outperforming the L-SDA and NL-SDA; however, the O-NL-SDA is outperforming the D-AMP. Finally, in Figure \ref{fig:reconMonkey} that mostly has a smooth and regular texture, we see that the D-AMP is outperforming SDA-based methods although the O-NL-SDA is outperforming the Tiled D-AMP.

Although there is not a clear winner among the different methods from the reconstruction quality point of view, our simulation results show that the L-SDA and NL-SDA beat the other methods from the reconstruction time perspective. This is clear both intuitively and mathematically since in all the other methods, we need to solve an optimization problem. We solve this optimization problem by using either convex optimization techniques (e.g. linear programming) or greedy algorithms. However, the L-SDA and NL-SDA do not need to solve any optimization problem. They just use a feed-forward neural network to recover images from their measurements. Table \ref{table:SEC} shows reconstruction time for different recovery algorithms and different under-sampling ratios. As we can see, for under-sampling ratio of 0.4, the NL-SDA and L-SDA are almost 1,000,000 times faster than the D-AMP.
\begin{figure}[t!]
\begin{center}
\includegraphics[width= 9cm]{joined_1111_v2.eps}
\caption{Reconstructed monkey image using different algorithms with $\frac{M}{N}=0.4$. Clockwise from upper left: SDA+Linear Measurements ({\sl PSNR=29.96 dB}). SDA+Nonlinear Measurements ({\sl PSNR=31.15 dB}). D-AMP ({\sl PSNR=38.48 dB}). TV ({\sl PSNR=29.67 dB}). Tiled D-AMP ({\sl PSNR=31.56 dB}). Overlapping SDA+Nonlinear Measurements (\sl{PSNR=32 dB}).
}
\label{fig:reconMonkey}
\end{center}
\end{figure}

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 9cm]{joined_530_v2.eps}
\caption{Reconstructed dog image using different algorithms with $\frac{M}{N}=0.3$. Clockwise from upper left: SDA+Linear Measurements ({\sl PSNR=20.19 dB}). SDA+Nonlinear Measurements ({\sl PSNR=21.27 dB}). D-AMP ({\sl PSNR=20.25 dB}). TV ({\sl PSNR=14.68 dB}). Tiled D-AMP ({\sl PSNR=18.54 dB}). Overlapping SDA+Nonlinear Measurements ({\sl PSNR=21.88 dB}).}
\label{fig:reconDog}
\end{center}
\end{figure}

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 9cm]{joined_1527_v2.eps}
\caption{Reconstructed Food and Fork image using different algorithms with $\frac{M}{N}=0.25$. Clockwise from upper left: SDA+Linear Measurements ({\sl PSNR=29.45 dB}). SDA+Nonlinear Measurements ({\sl PSNR=31.79 dB}). D-AMP ({\sl PSNR=31.90 dB}). TV ({\sl PSNR=25.16 dB}). Tiled D-AMP ({\sl PSNR=30.57 dB}). Overlapping SDA+Nonlinear Measurements ({\sl PSNR=32.46 dB}).}
\label{fig:reconFork}
\end{center}
\end{figure}

Figure \ref{fig:PT} shows the plot of average probability of successful recovery for different under-sampling ratios and different recovery algorithms. In order to calculate the probability of successful recovery we have used 1881 Monte Carlo samples. For each under-sampling ratio $\delta$ and for the $j$-th Monte Carlo sample, we define the success variable $\varphi_{\delta,j} = \mathbb{I}\left(\frac{\|\mathbf{\hat{x}}^{(j)}-\mathbf{x}^{(j)}\|_2}{\|\mathbf{x}^{(j)}\|_2} \leq 0.01\right)$ where $\mathbf{x}^{(j)}$ is the $j$-th Monte Carlo sample, $\mathbf{\hat{x}}^{(j)}$ denotes the corresponding recovered image, and $\mathbb{I}(.)$ denotes the indicator function. We then define the empirical success probability as $P_{\delta} = \frac{1}{s}\sum_{j=1}^{s}\varphi_{\delta,j}$. As we can see in Figure \ref{fig:PT}, for small under-sampling ratios (less than 0.06), SDA-bases methods are outperforming the D-AMP. In addition, for larger under-sampling ratios the D-AMP is outperforming SDA-based methods. Nevertheless, SDA-based methods (specially the NL-SDA) are outperforming the Tiled D-AMP, P-AMP (employing sparsity in wavelet domain), and TV minimization in a large range of under-sampling ratios. Of course, comparing SDA-based methods with the Tiled D-AMP is a fairer comparison rather than comparing them with D-AMP alone.

Finally, Figure \ref{fig:bp} denotes the convergence curve of fine tuning step in training our deep neural network. It shows the average PSNR (in dB) on test images over different iterations of backpropagation algorithm. This figure shows that for the under-sampling ratio of 0.06, the NL-SDA method has started to outperform the D-AMP method after $3.5\times10^4$ running of backpropagation algorithm. The jumps in this plot is due to feeding the neural network with new training data.

\section{Conclusion and Future Work}\label{sec:conclu}

In this work, we developed a new framework for sensing and recovering structured signals. This framework is able to learn a structured representation from training data, support both linear and mildly nonlinear measurements, and efficiently computes a signal estimate. In particular, we used a stacked version of denoising autoencoders, as an unsupervised feature learner. We showed how SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.

We should note that GRBMs treat different components of the input image vector as conditionally independent given the hidden layer state. This is an important limitation in modeling natural images using GRBM (and denoising autoencoders correspondingly). One important direction for future work is to come up with a model that can easily be extended to large images. In addition, it should capture relationships between pixel intensities rather than assuming them independent conditioned on the hidden layer. This model will let us to outperform the D-AMP algorithm in the cases that SDA-based methods introduced in this paper were not able to.
\begin{figure}[t!]
\begin{center}
\includegraphics[width= 9.5cm]{PT_V2.eps}
\caption{Average probability of successful signal recovery for different under-sampling ratios and different algorithms. In order to calculate the probability of successful recovery we have used 1881 Monte Carlo samples. If we denote the original signal by $\mathbf{x}$ and the recovered signal by $\mathbf{\hat{x}}$, then we call a recovery successful if $\frac{\|\mathbf{\hat{x}}-\mathbf{x}\|_2}{\|\mathbf{x}\|_2} \leq 0.01$. }
\label{fig:PT}
\end{center}
\end{figure}

\begin{figure}[t!]
\begin{center}
\includegraphics[width= 9.5cm]{bp_v1.eps}
\caption{Convergence of Backpropagation over different iterations and comparing the test result with other methods. In this plot average PSNR is calculated over 1881 test images each acquired with under-sampling ratio of 0.6, i.e., $\frac{M}{N}=0.06$.}
\label{fig:bp}
\end{center}
\end{figure}

\begin{table}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c || c  c  c  c  c c| }
 \hline
 $\frac{M}{N}$&    L-SDA &  NL-SDA & D-AMP &  O-NL-SDA &  Tiled D-AMP &  TV \\ [0.5ex]
  \hline\hline
 0.06& \textbf{0.002} & \textbf{0.002} & 74.79 & 1.01& 43.99& 45.94 \\
 \hline
 0.1& \textbf{0.002 } & \textbf{0.002 } & 92.21 & 1.03  & 50.00 & 39.82 \\
 \hline
 0.25 & \textbf{0.002 } & \textbf{0.002 } & 108.61 & 1.07 & 50.84  & 43.33\\
 \hline
 0.4 & \textbf{0.002 } & \textbf{0.002 } & 1900.68  & 1.22 & 39.55  & 38.83  \\
 \hline

\end{tabular}}
\caption{Running time (in sec.) for recovering the dog image for different under-sampling ratios and different algorithms. Numbers with bold face show the winner in each row.}
\label{table:SEC}
\end{center}
\end{table}

\bibliographystyle{IEEETran}

redpajama_set_name: RedPajamaArXiv


text:
TVS Renault
TV Sundaram Iyengar & Sons Pvt Ltd,
Near KSRTC Bus Stand,
Mavoor Road, Kozhikode,
binoy.paulosetvs.in
renault.com
9:00 AM - 6:00 PM, SUNDAY HOLIDAY
Cash,credit Card,debit Card,UPI
T V Sundram Iyengar & Sons, established in 1911, is the holding company of the TVS group and is the largest automobile corporate dealer in India. The service focused company provides employment to over 10,000 people with revenue of around INR 8000 Crores. It operates through three divisions, viz., TVS, Sundaram Motors and Madras Auto Service.
Being the trading and distribution arm of the group, the business activities of TVS & Sons include dealerships for Automobile vehicles, sales & service of products for special applications like Construction & Material handling.
The company manages Joint Ventures in Sri Lanka and Bangladesh for automobile distribution, dealership business through its subsidiary company in South Africa and vehicle servicing business in Saudi Arabia.
The Dealership Business focuses on sales and distribution of commercial vehicles, utility & sports utility vehicles, passenger cars representing various automobile vehicle manufacturers such as Ashok Leyland, General Motors, Honda, Mahindra & Mahindra, Mahindra Navistar, Mercedes Benz, Renault, Volkswagen and off highway equipment manufactured by Escorts, JLG, Ingersoll Rand and Pal Finger. The company has more than 150 outlets and sells over 60,000 vehicles and service reporting exceeds 600,000 vehicles per annum.
TVS & Sons also has two subsidiary companies that include TVS Logistics Services Limited – India's leading third party logistics service provider and TVS Automobile Solutions Limited – India's largest after-market service provider for passenger cars, which also operates the brand MyTVS.
Renault Kwid | Renault Duster | SUV | Renault Cars | Renault Service | Renault Dealers in Kozhikode | Renault Car Dealers in Kozhikode | Car Dealers in Kozhikode | Renault Service Centers in Kozhikode | Car Dealerships


text:
CPU Fan Error Press F1 Error When Booting Your Computer: How To Fix
When Windows stops loading and a black or blue technical window appears, it can confuse most users. But sometimes the problem can be solved quite simply.
♥ ON TOPIC: Best Lightroom Equivalents: 10 Unsubscribed Apps for Windows and Mac.
What does the "CPU Fan Error! Press F1 "
Users may encounter an error whose full text literally looks like this:
CPU Fan Error! Press F1 to Resume or CPU Fan Error! Press F1 to Run SETUP.
If you translate this phrase, you can understand its meaning. After turning on and at the beginning of the boot, the computer goes through a self-test, as a result of which an error with the operation of the fan on the processor was revealed. So the computer prompts the user to fix the problem by entering the UEFI settings (BIOS) or ignore it and continue working.
More often than not, you just want to press the F1 key, as the computer suggests, and continue with the normal boot. However, in this case, the user takes risks – you need to be really sure that there are no problems with cooling the processor, and it will not overheat.
♥ ON TOPIC: How to find a forgotten Wi-Fi password on a Windows computer: 4 ways.
Why does the "CPU Fan Error! Press F1 "
At the very beginning of the computer and later, the UEFI system (or BIOS) provides the correct voltage for the equipment and monitors its operation, including the fan speed. Their status is determined by detecting the rotational speed of the blades, which can be adjusted in the UEFI (BIOS).
A mistake "CPU Fan Error" arises when the system detects a reduced number of revolutions of the cooler or its complete stop. Needless to say, the processor's active cooling system provides operating temperatures for it. Without a cooler, any time-consuming task will quickly lead to overheating of the device, so it is no coincidence that the computer warns the user about the problem.
♥ ON TOPIC: How to upload photos and videos to Instagram from Mac or Windows computer.
How to fix "CPU Fan Error! Press F1 "
1. See if the fan (cooler) is spinning
If the system signals a fan rotation error, it is logical to assume that something is physically wrong with it. To do this, you will have to examine the system unit from the inside. You will need to carefully remove its cover and see if, in principle, the fan rotates. It may well be that it is jammed due to dust, or the cables from the power supply that have gotten between the blades are interfering with it.
Most likely, the situation can be easily resolved. The wires can be removed and tied up with plastic ties, while dust can be easily removed with a brush or blowing compressed air into the cooler. If the fan clogs up quite hard, then it will have to be dismantled for a while. To do this, you will either need to unscrew the screws or snap off the latches. Of course, such a check should be carried out with the computer de-energized from the power supply.
♥ ON TOPIC: How to speed up your Windows 10, 8, 7 PC: 20 tips.
2. Is the fan connected correctly?
The fan on physical examination may be clean and even spin easily when moved by hand. But the cooler does not want to spin when the computer is running. It may just be incorrectly connected to the correct connector on the motherboard.
The connector itself has a special key, which seems to prevent it from being inserted in an unnatural way into the connector. But people can be inattentive or just do it for the first time. Some, however, contrive to make a mistake, applying, moreover, physical strength. And the three fan wires drive inexperienced users into a stupor at all.
In addition, it happens that users plug the three-pin plug of the cooler into the four-pin connector on the motherboard. Everything seems to be working and spinning, but the system cannot recognize the RPM sensor and the "CPU Fan Error" error appears. In this case, you need to select a compatible cooler or disable fan rotation control in the UEFI (BIOS) (see point 5 below).
In the picture on the left – the connector on the motherboard, on the right – fan headers with different numbers of pins. The pin assignment is always identical: 1 – "minus", 2 – "plus", 3 – speed sensor, 4 – speed control
Sometimes the mistake lies in connecting the cooler to the wrong connector. On a motherboard (especially an advanced one), there can be several such fans at once. The CPU cooler connector is marked as CPU_FAN, but there is a similar one nearby CHA_FAN…
Only this connector is suitable for case fans. Any cooler must be connected exclusively to the connector intended for it, and you will have to check the coincidence of the slots on the connectors. The computer can be turned on – everything will work as expected.
♥ ON TOPIC: svchost.exe loads memory in Windows 7, 8, 10: what is this process and how to solve the problem.
3. Increase the number of revolutions of the fan
You can increase the fan speed programmatically in the BIOS (UEFI). The error we are describing may just arise due to the low speed – it seems to the system that the cooler simply does not rotate.
It is likely that you have manually changed these settings at some point in the past. In this case, you will need to disable the function called Smart Q ‑ FAN (it may be called differently) to intelligently control the fan speed. You can save the settings and exit by pressing the F10 key.
In the same section, you can usually find the rotational speed of the cooler at which the error will no longer be displayed. The user will have to gradually increase the speed, exit the BIOS (UEFI) while saving the settings, reboot and look at the result.
♥ ON TOPIC: Windows computer freezes? Hotkeys to help solve the problem.
4. Replace the fan
Sometimes, which rarely happens, the fan may not rotate due to its own breakdown. You will need to check if the device is working. This can be done by applying a voltage of 5 to 12 volts to the connector. In such a situation, a Krona battery or four AA batteries connected in series will do.
The negative pole of the battery must be connected to the first contact of the connector (black wire), while the positive pole must be connected to the second contact (yellow or red wire). If the fan does not turn on when connected to such a circuit, then it can be considered physically broken. You need to buy a new one.
♥ ON TOPIC: How to disable Windows 10 login password: working methods.
5. Activate Ignore RPM monitoring
An unpleasant mistake can be avoided even if the cooler is physically connected to the wrong connector on the motherboard, or the user deliberately limited the number of revolutions to reduce noise. It is necessary to disable the rotation control function in the BIOS (UEFI), which will help resolve the situation.
But it is only necessary to take such a step when realizing the risks of processor overheating. It will not be superfluous to configure additional temperature control of working devices using specialized programs, for example, SpeedFan.
In BIOS (UEFI), find the option CPU FAN Fail Warning (or similar, for example, CPU FAN Speed, etc.) and disable it by setting the value Disabled or Ignored… Save your settings and restart your computer.
Filed Under: Uncategorized Tagged With: booting, computer, CPU, error, Fan, fix, Press


2) participate fully in completing a PowerPoint based on research conducted by your team on a top company to work for, to examine its original and current leadership, values and profitability, and to create and submit a PowerPoint according to criteria and suggestions provided.
http://www.greatplacetowork.com/best-companies/100-best-companies-to-work-for (Links to an external site.)Links to an external site.
What is the company known for? What does it do right? What are its mission and values? Does it work toward social responsibility?
What is the company's background—what is the source of its leadership, conscience and approach to social responsibility? What is the rationale for employee-friendly endeavors? What is the history behind its good governance?
Who leads this company toward excellence presently? What are that person's values? Are the values stated by the leader(s)? Do those values drive the company and carry over to its employees? What are the leadership qualities of the people responsible for its success?
Is the company profitable? Can you be a caring organization and still be profitable? (You may need to look for a financial report in Business Source Complete available in the Chamberlain library database).
Submit your team assignment deliverable/product to the team discussion, the Files section of the Course Menu for all to see, and to the Team Assignment area. The presentation is posted in the team discussions so that you can all work on it, and the team leader or designee should post a final copy in the Files section of the Course Menu so other teams can see it. Each participating team member should post the same final copy in his or her own Week 3 Team Assignment area so that it can be graded.


text:
Zio Paperone e la Stella del Polo (Back to the Klondike) è una storia a fumetti della Walt Disney, scritta e disegnata da Carl Barks e pubblicata per la prima volta nel marzo 1953 sul secondo numero di Uncle Scrooge. Considerata una delle più belle storie di Carl Barks, nonché una delle migliori storie a fumetti Disney di sempre, viene ricordata per l'esordio del personaggio di Doretta Doremì e per il primo riferimento al passato di Paperon de' Paperoni come cercatore d'oro nello Yukon.

Nella storia compare Doretta Doremì (grande amore di Paperone) cinica e spietata, proprietaria del saloon di Dawson, la città mineraria dove Paperone andava a depositare l'oro raccolto nella sua miniera. La storia, comunque, partendo dall'espediente della scarsa memoria di Paperone, porta i paperi nel Klondike, giocando sull'esile equilibrio tra il papero scorbutico e taccagno che vuole tornare nei luoghi della sua gioventù da minatore per riscuotere un vecchio debito e quello innamorato e romantico che vuole tornare per sapere che fine ha fatto il suo primo amore.

Trama
Paperone soffre di vuoti di memoria che gli causano diversi problemi: si è dimenticato non solo l'ammontare del suo denaro che ha appena finito di contare, ma persino l'aspetto e il nome di suo nipote Paperino, il quale gli suggerisce di farsi curare. Paperone non vuole, in quanto le visite mediche costano, ma subito dopo, venendo colpito da una trappola antiladro di cui si era dimenticato, si rende conto della gravità del problema e si reca da un medico, che gli diagnostica come patologia il svitatorum rotellorum (in originale blinkus of the thinkus,  con il significato di "momentanea perdita di concentrazione" o "vuoto di memoria", tradotto pedissequamente su Zio Paperone numero 1 con blinkus del thinkus) e gli prescrive delle pillole in grado di fargli recuperare la memoria. Paperone, presa la prima di queste pillole, inizia a riacquistare la memoria, non solo recente, ma anche di avvenimenti avvenuti decenni prima che aveva completamente rimosso: in particolare ricorda di aver nascosto sottoterra cinquant'anni prima delle pepite in Klondike e che la scintillante Doretta Doremì, una sua vecchia "fiamma", gli deve 1000 dollari, che con gli interessi sono diventati un miliardo.

Paperone decide quindi di partire con i nipoti per il Klondike per recuperare le pepite e per riscuotere il debito. Arrivati a destinazione, per risparmiare sui costi, Paperone rinuncia a prendere l'aereo e decide di recarsi a Dawson a piedi. A metà strada, però, i paperi incontrano il pilota dell'aereo, che aveva fatto scalo in quel punto, e scoprono che Paperone è il proprietario della linea, dunque potevano viaggiare in aereo gratis, causando l'irritazione di Paperino. Paperone, però, aveva dimenticato di essere il proprietario della linea aerea perché, per risparmiare, non aveva preso le pillole per la memoria.

Una volta arrivati a Dawson, Paperone, una volta entrato nella sala da ballo, racconta ai suoi nipoti quello che era successo cinquant'anni prima: Doretta l'aveva drogato e derubato della sua gigantesca Pepita Uovo d'Anatra e di altre pepite, per cui, dopo una violenta rissa con i suoi scagnozzi, Paperone costrinse Doretta a restituirgli le pepite, ma lei aveva già sperperato quelle piccole, quindi, Paperone le fece firmare una cambiale, ma poi la "rapì" e la fece lavorare un mese nella sua concessione, affinché comprendesse che quello che sottraeva ai cacciatori d'oro, non era solo oro, ma ore e ore di impegno e sudore. Il mattino dopo, i paperi partono per la concessione ma arrivati lì scoprono che ora ci abita una vecchia armata di fucile che possiede un orso e che non li vuole nel suo terreno e li caccia via a fucilate. Dopo vari tentativi falliti, i paperi riescono a catturare l'orso e a disarmare la vecchia scoprendo che è Doretta.

Paperone, contentissimo per svariate ragioni, va a riscuotere il debito e la priva di tutti i suoi beni. Doretta però gli lascia solo alcuni gioielli e l'atto di proprietà della propria casa, avendo devoluto ai poveri tutti i propri beni e adesso dovrà andare all'ospizio per poveri. Pieno di sensi di colpa, Paperone cerca di rimediare, facendole una proposta: se Doretta vincerà una gara con lui a chi trova più oro entro un certo tempo, riavrà indietro la concessione e anche tutto l'oro che troverà. Paperone, per assicurarsi di perdere, suggerisce ai nipoti di convincerla a scavare proprio nel posto dove erano sotterrate le pepite. Quando poi Doretta trova l'oro, Paperone finge di disperarsi, sostenendo che, poiché non aveva preso le pillole per la memoria, si era dimenticato che proprio in quel punto vi era l'oro. Ma Paperino, non convinto, conta le pillole rimanenti e scopre che non è vero: lo zio ha preso la medicina e solo finto di non ricordare. La storia finisce con Paperino e i nipoti che sorridono su come lonzio abbia davvero il cuore per amare qualcuno.

Storia editoriale
Barks scrisse la storia in seguito al suo secondo divorzio, in un periodo in cui viaggiava frequentemente. In un negozio di Seattle, trovò una copia di Klondike '98, una raccolta di fotografie, realizzata da Ethel Anderson Becker, sulla corsa all'oro del Klondike. Barks scrisse in seguito che la lettura di questa raccolta di fotografie gli diede l'idea per la realizzazione della scena della rissa e del rapimento di Doretta. Tuttavia la storia venne censurata dall'editore americano che ritenne inadatta per una pubblicazione Disney la scena della rissa e del rapimento di Doretta. Furono allora tagliate in totale 5 tavole (oltre alla scena della rissa e del rapimento, lunga quattro tavole, venne tagliata anche mezza tavola in cui Paperone ammetteva che non aveva rinnovato la concessione, per cui il terreno che rivendicava e abitato ora da Doretta non era più legalmente suo, oltre ad altre vignette, equivalenti a mezza pagina di contenuto, in modo da tagliare un numero intero di pagine).

Solo nel 1981, quando è stato possibile recuperare quasi tutte le tavole eliminate, La Stella del Polo ha visto la luce in un'edizione integrale (contenuta nella raccolta "Uncle Scrooge His Life & Times"), alla quale, però, mancavano all'appello un numero di vignette equivalente a mezza pagina, sostituite da un paio di strisce schizzate dallo stesso Barks in base ai suoi ricordi sulla storia (in seguito Daan Jippes, partendo proprio da queste quattro vignette, ridisegnò la scena perduta: l'opera dell'artista olandese può essere vista su ZP n. 77). Nel 2021 è stata pubblicata sulla testata svedese Kalle Anka Klassiker una delle vignette mancanti, rinvenuta nel 2020, il cui contenuto è tuttavia completamente diverso dalle quattro vignette schizzate da Barks, ambientate in prossimità della sala da ballo di Dawson, mentre la vignetta ritrovata è ambientata in prossimità della concessione di Paperone, poco prima della scoperta che non era disabitata. Ciò è spiegabile supponendo che Barks non ricordasse perfettamente il contenuto delle vignette andate perdute, che probabilmente non erano contigue tra loro.

Le diverse edizioni italiane riflettono le due versioni della storia. Ne volumetto "Vita e dollari di Paperon De Paperoni" (Oscar Mondadori, 1968) compare la versione censurata. Solo diciannove anni dopo, in "Zio Paperone", n. 1 (1987) viene pubblicata la versione completa [32 pagine]. Per la pubblicazione delle diverse versioni, vedi anche Pagina relativa del sito INDUCKS. La versione integrale è stata poi pubblicata in italiano anche sul volume omonimo della serie I Classici del Fumetto di Repubblica (2003), sul volume n. 6 de La grande dinastia dei paperi (2008) e sul n. 1 di Uack! (2014).

Citazioni alla storia in altre opere
In un episodio del fumetto PKNA (numero 13, "La notte più buia"), nel corso di un'invasione aliena, Camera 9 cerca di rassicurare due anziani paperi, i quali però sostengono di non essere impauriti, bensì divertiti, e che non si divertivano così tanto "dai tempi della Stella del Polo", quando "arrivò a Dawson quel giovanotto con le basette", chiaro riferimento alla vicenda di Zio Paperone.

Nella Saga di Paperon de' Paperoni di Don Rosa, questi racconta ciò che accadde prima degli avvenimenti del flashback, l'insediamento di Paperone nel Klondike (L'argonauta del Fosso dell'Agonia Bianca), gli avvenimenti del mese di prigionia di Doretta (La prigioniera del fosso dell'agonia bianca), il periodo successivo a tali avvenimenti (Cuori nello Yukon) e anche il seguito della storia principale di Barks (Zio Paperone e l'ultima slitta per Dawson) in cui Doretta ristruttura la Bolla d'Oro e diventa un'albergatrice.

L'episodio è anche base dell'episodio "Ritorno al Klondike" di DuckTales - Avventure di paperi, e ispira (assieme alla storia La prigioniera del Fosso dell'Agonia Bianca) l'episodio "Il mistero del Fosso dell'Agonia Bianca" del reboot DuckTales.

Note

Collegamenti esterni

Storie a fumetti di Paperino
Storie a fumetti di Zio Paperone
Storie a fumetti di Qui, Quo, Qua
Carl Barks