Apotex Corp., a generic pharmaceutical company headquartered in Florida, was charged with fixing the price of the generic drug pravastatin, the Department of Justice announced today.  According to the one-count felony charge filed today in the U.S. District Court for the Eastern District of Pennsylvania in Philadelphia, Apotex and other generic drug companies agreed to increase and maintain the price of pravastatin, a commonly prescribed cholesterol medication that lowers the risk of heart disease and stroke.  The conspiracy began in May 2013 and continued through December 2015.

Top trainer Mike de Kock has warned of a “grim reality” featuring “loss of jobs and euthanasia” of significant parts of the horse population if the South African racing shutdown continues. On Tuesday, the South African government blocked plans for a resumption when it refused to allow the sport to take place under Lockdown Level […]

The post Trainer Mike de Kock Warns Of Mass Job Loss, Euthanasias In South Africa If Racing Shutdown Continues appeared first on Horse Racing News | Paulick Report.

Throughout the year, the NTRA will provide a guide to the best stakes races in North America and beyond. Races are listed in chronological order (all times Eastern). Full previews when available can be found through the link for each race. There may be no Kentucky Oaks or Kentucky Derby this weekend, but there is […]

The post Weekend Lineup: No Derby, But Plenty Of Action At Oaklawn appeared first on Horse Racing News | Paulick Report.

North Carolina researchers are now trying to spread the word about their tried-and-true decontamination method for surgical masks.

The post Researchers Have Found a Way to Sterilize and Reuse Face Masks During Pandemic appeared first on Good News Network.

The Malheur model for fire-caused delayed mortality is presented as an easily interpreted graph (mortality-probability calculator) as part of a one-page field guide that allows the user to determine postfire probability of mortality for ponderosa pine (Pinus ponderosa Dougl. ex Laws.).

Afer all this time there is finally a dropdown menu that doesn't use javascript, table, conditional comments, hacks, extra markup and works in all the major browsers including IE6.

This guide presents a classification of the deep canyon and subalpine riparian and wetland vegetation types of the Malheur, Umatilla, and Wallowa-Whitman National Forests. A primary goal of the deep canyon and subalpine riparian and wetland classification was a seamless linkage with the midmontane northeastern Oregon riparian and wetland classification provided by Crowe and Clausnitzer in 1997. The classification is based on potential natural vegetation and follows directly from the plant association concept for riparian zones. The 95 vegetation types classified across the three national forests were organized into 16 vegetation series, and included some 45 vegetation types not previously classified for northeastern Oregon subalpine and deep canyon riparian and wetland environments. The riparian and wetland vegetation types developed for this guide were compared floristically and environmentally to riparian and wetland classifications in neighboring geographic regions. For each vegetation type, a section was included describing the occurrence#40;s#41; of the same or floristically similar vegetation types found in riparian and wetland classifications developed for neighboring geographic regions. Lastly, this guide was designed to be used in conjunction with the midmontane guide to provide a comprehensive look at the riparian and wetland vegetation of northeastern Oregon.

Reports suggest that Cheryl is pushing for Girls Aloud to get back together after The Greatest Dancer was axed by the BBC

Newcastle United player Jonjo Shelvey's wife Daisy, who used to be in S Club Juniors, is now makeup artist in the city

iHEARMEDIA Top 40 WFLZ (933 FLZ)/TAMPA has announced that the “THEjoeSHOW” will now helm mornings, replace THE KANE SHOW, beginning JUNE 1st. “THEjoeSHOW,” is hosted … more

Kalydeco is the first medicine in Europe to treat the underlying cause of the disease

The Canadian Museum of History is Canada’s most-visited museum. It also houses the Canadian Children’s Museum, the Virtual Museum of New France and an IMAX theatre. Why it’s in the Showcase: A national cultural website using WordPress as its web...

The Smithsonian Institution’s National Museum of African Art focus is to inspire conversations about the beauty, power, and diversity of African arts and cultures worldwide.

Welcome to the EU Policy Blog, Microsoft Brussels’ platform for providing insights on the issues that impact Europe’s future in the digital age. This is a forum to discuss the latest trends, research and regulatory developments which are shaping how European citizens, businesses,...

Research Highlights: A robot was used to treat brain aneurysms for the first time. The robotic system could eventually allow remote surgery, enabling surgeons to treat strokes from afar. Embargoed until 11:15 a.m. Pacific Time / 2:15 p.m. Eastern ...

Research Highlights: A genetic risk score from a blood test identified more men age 50 and older who are at higher risk of an aortic aneurysm and could benefit from ultrasound screening. Weakness and bulging in the wall of the aorta, the major blood ...

With its opening on Jan. 11, 2011, the striking and grand Salvador Dali Museum entered a new era in its home along St. Petersburg, Florida's picturesque waterfront. The story of the Salvador Dali Museum is rich in detail and even some intrigue. So take your time and explore our special report to see for yourself why this museum and the surreal artist are now forever entwined with St. Petersburg's history.

Tour of the new boston Tea Party museum

Portraits of entrepreneurs Quebecois, for whom the question of profit goes after those working conditions, personal development and improvement of the environment. How to do more with less? Enrich themselves ... but differently. --------------------------------------------- Des portraits d'entrepreneurs québecois, pour qui la question du profit passe après celles des conditions de travail, de l’épanouissement personnel et de l’amélioration de l’environnement. Comment faire mieux avec moins ? S’enrichir… mais différemment.

Through MLB history, plenty of players have returned to the teams where they became stars. Let's take a look back at 10 of the most memorable ones.

Traumatic brain injury (TBI) can have lifelong and dynamic effects on health and wellbeing. Research on the longterm consequences emphasises that, for many patients, TBI should be conceptualised as a chronic health condition. Evidence suggests that functional outcomes after TBI can show improvement or deterioration up to two decades after injury, and rates of all-cause mortality remain elevated for many years. Furthermore, TBI represents a risk factor for a variety of neurological illnesses, including epilepsy, stroke, and neurodegenerative disease. With respect to neurodegeneration after TBI, post-mortem studies on the long-term neuropathology after injury have identified complex persisting and evolving abnormalities best described as polypathology, which includes chronic traumatic encephalopathy. Despite growing awareness of the lifelong consequences of TBI, substantial gaps in research exist. Improvements are therefore needed in understanding chronic pathologies and their implications for survivors of TBI, which could inform long-term health management in this sizeable patient population.

A study to comparatively examine the effects of sports-related concussions (SRC) on athletes in Major League Soccer (MLS) and the English Premier League (EPL) in terms of incidence, return to play (RTP), performance, and career longevity.

ADRIENNE EUGINA DOOLIN HOWARD
Cedar Rapids


Adrienne Eugina Doolin Howard, 75, daughter of Pearl A. Doolin and Emmitt Eugene Doolin, was born June 16, 1944. She departed this life Thursday, April 30, 2020, of complications from COVID-19, after a lengthy stay at Living Centers Nursing Home Facility in Cedar Rapids, Iowa.
She was born in St. Louis, Mo., and had a passion for soul food, cooking, music
and her church. She reared four children in East St. Louis, Ill.
She was preceded in death by children, Howard E. Doolin, Sr. and Viola E. Howard; and siblings, Burdell M., Madeline and Regina Doolin.
Adrienne E. Howard is survived by two sons, Emmitt J. Doolin of Marion, Iowa, and David C. Washington of Carbondale, Ill.; siblings, Dedric, Aaron and Emmitt E. (Michelle) Doolin; Steven Bacon; Derek, Kyle and Lori Doolin; Louisia (Eric) Harrison, Donna Jackson and Stephanie Doolin Bacon; many grandchildren and great-grandchildren and many other relatives, family members and friends.
The family expresses a special thank you to her brother, Burdell's widow, Christine Arenas Doolin, who met our mother 18 years ago and touched her life in many ways.
A family memorial will be planned at a later date.
Service by Officer. www.officerfh.com.

And while we’re in the process of missing European architecture… ????
—
4 more days left to catch my Lightroom presets for 50% off! ⌛️ (at Copenhagen, Denmark)

https://design4users.com/neuomorphism-mobile-design/

The pandemic has changed the daily lives of everyone. How we work, how we shop, and how we interact with each other are all shifting. Comparing life as it is now with how it used to be can lead to sadness or despair and what's called "ambiguous loss."

The new UI trend known as Neumorphism (with Skeuomorphism roots) has gotten a more consistent shape in the last period, is another beautiful approach to design user interfaces that look soft and is...

Tags: Jukebox, music, neural network, OpenAI

2019 was the year of the “ethical source” licenses – or ‘open source with a moral clause’ licenses. It was also the year many in the open source movement labeled any attempt at adding moral clauses to open source licenses not only made them not open source licenses, but were a dangerous attack on the […]

The post Value Neutrality and the Ethics of Open Source appeared first on MOR10.

The pandemic has changed the daily lives of everyone. How we work, how we shop, and how we interact with each other are all shifting. Comparing life as it is now with how it used to be can lead to sadness or despair and what's called "ambiguous loss."

In any uncertain time there, we can both feel anxious, upset, curious and we can also look for opportunities. Opportunities for reinvention, for connection, and community in ways we haven’t seen before. That’s the theme of today’s episode with some of my good friends Mark and Brian Canlis + James Beard Award Winning Chef Brady Williams in a conversation we recorded for CreativeLive TV. Mark and Brian run an iconic restaurant in Seattle named Canlis. It’s been a family business for over 70 years. Faced with these uncertain times, they share how they’ve reinvented their business 3 times over the last couple of months. No matter what industry you’re in, their story of overcoming obstacles, problem solving and heart is wisdom for all of us. Enjoy! FOLLOW CANLIS: instagram | website Listen to the Podcast Subscribe   Watch the Episode This podcast is brought to you by CreativeLive. CreativeLive is the world’s largest hub for online creative education in photo/video, art/design, music/audio, craft/maker, money/life and the ability to make a living in any of those disciplines. They are high quality, highly curated classes taught by the world’s top experts — Pulitzer, Oscar, Grammy Award winners, New York Times best selling authors […]

The post Resilience + Reinvention with Canlis Restaurateurs appeared first on Chase Jarvis Photography.

St-Donat, Bas-St-Laurent, Québec

St-Donat, Bas-St-Laurent, Québec

We study a mean field approximation for the 2D Euler vorticity equation driven by a transport noise. We prove that the Euler equations can be approximated by interacting point vortices driven by a regularized Biot-Savart kernel and the same common noise. The approximation happens by sending the number of particles $N$ to infinity and the regularization $epsilon$ in the Biot-Savart kernel to $0$, as a suitable function of $N$.

We deal with various Diophantine equations involving the Euler totient function and various sequences of numbers, including factorials, powers, and Fibonacci sequences.

Among all dissipative solutions of the multi-dimensional isentropic Euler equations there exists at least one that minimizes the acceleration, which implies that the solution is as close to being a weak solution as possible. The argument is based on a suitable selection procedure.

We define local indices for projective umbilics and godrons (also called cusps of Gauss) on generic smooth surfaces in projective 3-space. By means of these indices, we provide formulas that relate the algebraic numbers of those characteristic points on a surface (and on domains of the surface) with the Euler characteristic of that surface (resp. of those domains). These relations determine the possible coexistences of projective umbilics and godrons on the surface. Our study is based on a "fundamental cubic form" for which we provide a closed simple expression.

We study a two-stage reneging queue with Poisson arrivals, exponential services, and two levels of exponential reneging behaviors, extending the popular Erlang A model that assumes a constant reneging rate. We derive approximate analytical formulas representing performance measures for the two-stage queue following the Markov chain decomposition approach. Our formulas not only give accurate results spanning the heavy-traffic to the light-traffic regimes, but also provide insight into capacity decisions.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence). Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent. We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences. We conclude by relating these results to broader concerns about the relationship between comprehension (i.e. typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.

Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models often contain a huge number of parameters and may not meet the requirements of computation resources in real-world applications. To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost. To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search. Such a hierarchical controller is able to simultaneously find promising cell-level blocks and network-level positions of upsampling layers. Moreover, to design compact architectures with promising performance, we build a joint reward by considering both the performance and computation cost to guide the search process. Extensive experiments on five benchmark datasets demonstrate the superiority of our method over existing methods.

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1 imes$ and $2 imes$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.

The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is under debate. In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift. In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this. Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size. Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem. This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an AP@0.5 of 78.67\% in our empirical evaluation.

We present a new system, EEV, for verifying binarized neural networks (BNNs). We formulate BNN verification as a Boolean satisfiability problem (SAT) with reified cardinality constraints of the form $y = (x_1 + cdots + x_n le b)$, where $x_i$ and $y$ are Boolean variables possibly with negation and $b$ is an integer constant. We also identify two properties, specifically balanced weight sparsity and lower cardinality bounds, that reduce the verification complexity of BNNs. EEV contains both a SAT solver enhanced to handle reified cardinality constraints natively and novel training strategies designed to reduce verification complexity by delivering networks with improved sparsity properties and cardinality bounds. We demonstrate the effectiveness of EEV by presenting the first exact verification results for $ell_{infty}$-bounded adversarial robustness of nontrivial convolutional BNNs on the MNIST and CIFAR10 datasets. Our results also show that, depending on the dataset and network architecture, our techniques verify BNNs between a factor of ten to ten thousand times faster than the best previous exact verification techniques for either binarized or real-valued networks.

In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) - one trained on transcripts of speech produced by healthy participants and the other trained on transcripts from patients with dementia - as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance. However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (DementiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables. In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.

Domain of mathematical logic in computers is dominated by automated theorem provers (ATP) and interactive theorem provers (ITP). Both of these are hard to access by AI from the human-imitation approach: ATPs often use human-unfriendly logical foundations while ITPs are meant for formalizing existing proofs rather than problem solving. We aim to create a simple human-friendly logical system for mathematical problem solving. We picked the case study of Euclidean geometry as it can be easily visualized, has simple logic, and yet potentially offers many high-school problems of various difficulty levels. To make the environment user friendly, we abandoned strict logic required by ITPs, allowing to infer topological facts from pictures. We present our system for Euclidean geometry, together with a graphical application GeoLogic, similar to GeoGebra, which allows users to interactively study and prove properties about the geometrical setup.

In recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.