In this paper, we present a novel adversarial lossy video compression model. At extremely low bit-rates, standard video coding schemes suffer from unpleasant reconstruction artifacts such as blocking, ringing etc. Existing learned neural approaches to video compression have achieved reasonable success on reducing the bit-rate for efficient transmission and reduce the impact of artifacts to an extent. However, they still tend to produce blurred results under extreme compression. In this paper, we present a deep adversarial learned video compression model that minimizes an auxiliary adversarial distortion objective. We find this adversarial objective to correlate better with human perceptual quality judgement relative to traditional quality metrics such as MS-SSIM and PSNR. Our experiments using a state-of-the-art learned video compression system demonstrate a reduction of perceptual artifacts and reconstruction of detail lost especially under extremely high compression.

The Internet is a giant, directed network of webpages pointing to other webpages. Facebook is an undirected network built via friendships between users. The ecological web is a directed network of different species with edges specified by'who-eats-whom' relationships. Protein-protein interactions are undirected networks consisting of pairs of baitprey proteins that bind to each other during coaffinity purification experiments arising in mass spectrometry analysis. In these application areas, it is often of interest to characterize a network using statistics such as the clustering coefficient, triangle density, or principal eigenvalues. There has been a substantial amount of work on approximating these quantities with small error on massive networks Assadi et al. (2018); Eden et al. (2017); Feige (2006); Goldreich and Ron (2008); Gonen et al. (2010); Kallaugher et al. (2019).

This paper studies the universal approximation property of deep neural networks for representing probability distributions. Given a target distribution $\pi$ and a source distribution $p_z$ both defined on $\mathbb{R}^d$, we prove under some assumptions that there exists a deep neural network $g:\mathbb{R}^d\rightarrow \mathbb{R}$ with ReLU activation such that the push-forward measure $(\nabla g)_\# p_z$ of $p_z$ under the map $\nabla g$ is arbitrarily close to the target measure $\pi$. The closeness are measured by three classes of integral probability metrics between probability distributions: $1$-Wasserstein distance, maximum mean distance (MMD) and kernelized Stein discrepancy (KSD). We prove upper bounds for the size (width and depth) of the deep neural network in terms of the dimension $d$ and the approximation error $\varepsilon$ with respect to the three discrepancies. In particular, the size of neural network can grow exponentially in $d$ when $1$-Wasserstein distance is used as the discrepancy, whereas for both MMD and KSD the size of neural network only depends on $d$ at most polynomially. Our proof relies on convergence estimates of empirical measures under aforementioned discrepancies and semi-discrete optimal transport.

Over past years, the easy accessibility to the large scale datasets has significantly shifted the paradigm for developing highly accurate prediction models that are driven from Neural Network (NN). These models can be potentially impacted by the radiation-induced transient faults that might lead to the gradual downgrade of the long-running expected NN inference accelerator. The crucial observation from our rigorous vulnerability assessment on the NN inference accelerator demonstrates that the weights and activation functions are unevenly susceptible to both single-event upset (SEU) and multi-bit upset (MBU), especially in the first five layers of our selected convolution neural network. In this paper, we present the relatively-accurate statistical models to delineate the impact of both undertaken SEU and MBU across layers and per each layer of the selected NN. These models can be used for evaluating the error-resiliency magnitude of NN topology before adopting them in the safety-critical applications.

Behavioural inhibition and shyness at infancy leads to a reserved, introverted personality by the time a person reaches their mid-twenties, new research shows. US neuroscientists found that infants with'behavioural inhibition' grew up to be reserved and have fewer human interactions aged 26. Individuals who showed sensitivity to making errors at the age of 15, meanwhile, later had a higher risk for internalizing anxiety and depression. The quarter-century-long experiment is evidence of the long-lasting impact of our internal processes at a young age, despite physical changes and years of life experience. 'While many studies link early childhood behaviour to risk for psychopathology, the findings in our study are unique,' said Daniel Pine, study author and chief of the National Institute of Mental Health Section on Development and Affective Neuroscience.

Microsoft Real Estate and Security (RE&S) is responsible for heating and cooling 115 buildings in the Puget Sound area. Microsoft Core Services and Engineering (CSEO) partnered with RE&S to improve the effectiveness of the schedules for their heating, ventilation, and air conditioning (HVAC) system to reduce costs and increase employee comfort. CSEO implemented machine learning to predict when employees will arrive into Microsoft buildings each morning and how long it will take for a building to reach its optimal comfort temperature. As a result, we were able to generate a dynamic HVAC schedule that resulted in significant cost savings and increased employee comfort for RE&S. We're continuing to implement machine learning in our buildings throughout the Puget Sound region and we're encouraging the rest of Microsoft to use machine learning to optimize operations and drive digital transformation.

The spread of the novel coronavirus that causes COVID-19 has prompted state and local governments around the U.S. to institute shelter-in-place orders and business closures. As millions suddenly find themselves confined to their homes, the shift has strained not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation's electrical grid, as well. U.S. electricity use on March 27, 2020 was 3% lower than it was on March 27, 2019, a loss of about three years of sales growth. Peter Fox-Penner, director of the Boston University Institute for Sustainable Energy, asserted in a recent op-ed that utility revenues will suffer because providers are halting shutoffs and deferring rate increases. Moreover, according to research firm Wood Mackenzie, the rise in household electricity demand won't offset reduced business electricity demand, mainly because residential demand makes up just 40% of the total demand across North America.

The COVID-19 coronavirus, since its initial outbreak in Wuhan, China, has quickly become a global pandemic, as declared by the World Health Organization (WHO). The number of confirmed cases has exceeded 577,531 globally as of March 27, 2020 and will continue to rise in the days and weeks ahead. The rapid increase in coronavirus cases and the inadequate amount of lab tests available for people suspected of infection have posed serious risks to public health and efforts in containing the virus. Simply put, it's challenging for healthcare professionals and government officials to allocate resources and stop the spread of the virus without knowing who is infected, where they are located, and how they are affected. The standard testing method for coronavirus is RT-PCR (reverse transcription-polymerase chain reaction); however, this test is not yet readily available in many parts of the world or in some cases can produce false negative results.

In this project, ShapingEDU will partner with Omdena's innovation platform to build an artificial intelligence solution to better understand -- and potentially recommend solutions to -- the student debt crisis. If you are a data scientist, data engineer, or domain expert you can apply to join the project and make a real-world impact. Student debt has reached crisis proportions. In the United States, student loan borrowers owed a collective $1.6 trillion in federal and private student loan debt as of March 2019, according to the Board of Governors of the Federal Reserve System. Sixty-five percent of the class of 2018 graduated with student debt, according to the data available from The Institute for College Access & Success, a nonprofit organization that works to improve higher education access and affordability.

As artificial intelligence (AI) technology continues to revolutionize how we live and work, semiconductor industry leader Advanced Micro Devices (AMD) is designing a potential new breakout. AMD stock is closing in on a new buy point as fellow chip industry and AI powerhouses Intel (INTC) and Nvidia (NVDA) also near new highs. With AMD stock, Nvidia stock and Intel stock showing strength, the semiconductor industry group now ranks No. 11 out of the 197 groups IBD tracks. Cirrus Logic (CRUS) and Inphi (IPHI) are also among the best chip stocks to watch. AMD and Nvidia both sport the highest-possible 99 Composite Rating, while Intel earns a still-impressive 93.