This research aims to help scientists develop a better understanding of COVID-19. They will use the data to understand how fast the virus is spreading in specific areas, identify high risk areas, and to identify who is most at risk by better understanding symptoms linked to health conditions. The app gives researchers an opportunity to see how symptoms evolve over time in different risk groups, and to find patterns to who gets a mild disease. This information will be very important if there is a second wave of the virus later this year or next year. The app launched in the UK and 1.3 million people are already logging their symptoms.

Three-quarters of patients would recommend artificial intelligence as a component of clinical decision-making for skin cancer, according to a survey. "The use of artificial intelligence (AI) is expanding throughout the field of medicine," Caroline A. Nelson, MD, of the department of dermatology at Yale School of Medicine, and colleagues wrote, adding that researchers are investigating the use of AI in classifying skin lesions. "Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood." The researchers conducted semi-structured interviews with 48 patients in general dermatology clinics at Brigham and Women's Hospital and melanoma clinics at Dana-Farber Cancer Institute to determine how patients think about the risks, benefits, strengths and weakness of AI as it pertains to skin cancer screening. They also aimed to determine how patients feel about the differences between human and AI clinical decision-making.

The late Stephen Hawking called artificial intelligence the biggest threat to humanity. But Hawking, albeit a revered physicist, was not a computer scientist. Elon Musk compared AI adoption to "summoning the devil." But Elon is, well, Elon. And there are dozens of movies that depict a future in which robots and artificial intelligence go berserk.

Artificial Intelligence is either a silver shot for each issue on the planet or the ensured reason for the end of the world, contingent upon whom you address. The fact of the matter is probably going to be unmistakably progressively unremarkable. Artificial intelligence is a tool and like numerous technological breakthroughs before it, it will be utilized for good and for terrible. Artificial intelligence is progressively being utilized to impact the products we purchase and the music and movies we appreciate; to protect our money; and, dubiously, to settle on hiring decisions and procedure criminal behaviour. The Western world has been digitized for more, so there are more records for AIs to parse.

Apple launched a new app and website to inform the public about COVID-19. It includes a screening tool to help people figure out if they've caught the coronavirus, and the information is based on guidance from the Centers for Disease Control and Prevention, Apple says. The App was created in partnership with the CDC, the White House Coronavirus Task Force and FEMA "to make it easy for people across the country to get trusted information and guidance at a time when the U.S. is feeling the heavy burden of COVID-19," according to Apple. Both the app and website ask users to answer questions to help people figure out if they need to be tested. For instance, it asks if you have chest pain, difficulty breathing, dizziness, slurred speed or difficulty waking up.

To effect behavior change a successful algorithm must make high-quality decisions in real-time. For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users. While machine learning offers solutions for certain stylized settings, such as when batch data can be processed offline, there is a dearth of approaches which can deliver high-quality solutions under the specific constraints of mHealth. We propose an algorithm which provides users with contextualized and personalized physical activity suggestions. This algorithm is able to overcome a challenge critical to mHealth that complex models be trained efficiently. We propose a tractable streamlined empirical Bayes procedure which fits linear mixed effects models in large-data settings. Our procedure takes advantage of sparsity introduced by hierarchical random effects to efficiently learn the posterior distribution of a linear mixed effects model. A key contribution of this work is that we provide explicit updates in order to learn both fixed effects, random effects and hyper-parameter values. We demonstrate the success of this approach in a mobile health (mHealth) reinforcement learning application, a domain in which fast computations are crucial for real time interventions. Not only is our approach computationally efficient, it is also easily implemented with closed form matrix algebraic updates and we show improvements over state of the art approaches both in speed and accuracy of up to 99% and 56% respectively.

Distributed methods have attracted a lot of attention in the statistics and machine learning communities recently. There are several reasons for this, the most prominent ones being that they provide a way of dealing with large datasets and with privacy considerations. The theoretical literature on distributed methods is still rather minimal at the moment. A number of papers have recently investigated fundamental performance limits in distributed models, in particular pointing out issues that occur in high-dimensional or nonparametric problems, see for instance [1, 2, 4, 8, 16, 17, 21, 24, 27]. For example, optimal rates in distributed function estimation depend on the amount of communication that is allowed, and the relation of that amount with the regularity of the unknown function. The lower bounds obtained in [25] and [28] and the subsequent adaptation results in [25] show that in particular, automatically adapting to the smoothness of the unknown function is a complicated issue in communication restricted distributed settings. In the present paper we study this problem from a different, we think relevant and interesting perspective, not restricting communication a priori, but asking for rate-optimal procedures that require minimal communication.

A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template based approaches, but does not require domain knowledge and is much more scalable.

We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides. Our framework is robust to data heterogeneity, addressing the slow convergence problem traditional federated learning methods face when the data is non-i.i.d. across clients. We test the theory empirically and find substantial performance gains over baselines.

This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model to extract the features of the fonts character. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions.