A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste, MIT and Duke University researchers report. A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste, according to researchers at MIT and Duke University. Using machine learning, the researchers identified crosslinker molecules that can be added to polymer materials, allowing them to withstand more force before tearing. These crosslinkers belong to a class of molecules known as mechanophores, which change their shape or other properties in response to mechanical force. "These molecules can be useful for making polymers that would be stronger in response to force. You apply some stress to them, and rather than cracking or breaking, you instead see something that has higher resilience," says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering at MIT, who is also a professor of chemistry and the senior author of the study.

This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a machine-learning technique from features extracted from the observers together with input and output data to predict the SOC. The SOC predictions returned by the VS are supplied to an Extended KF (EKF) as output measurements along with the cell terminal voltage, combining the two paradigms. A data-driven calibration strategy for the noise covariance matrices of the EKF is proposed. Experimental results show that the designed approach is beneficial w.r.t. SOC estimation accuracy and smoothness.

An artificial intelligence model appeared to predict the likelihood of unplanned hospitalizations during chemoradiation therapy among a cohort of patients with various cancer types. The results, presented at American Society for Radiation Oncology Annual Meeting, showed the model, which used daily step counts measured through wearable devices as a proxy to monitor patients' health, provided physicians with a real-time method to provide personalized care. About 10% to 20% of patients who undergo outpatient radiation or chemoradiation require acute care via an ED visit or hospital admission during their course of treatment. These unplanned hospitalizations can cause treatment delays and stress that may affect clinical outcomes, according to a press release. "Wearable devices allow for continuous, objective capture of patient-generated health data outside of the clinical setting, which minimizes travel and has the potential to have a more realistic and equitable assessment of a person's health status," Isabel Friesner, clinical data researcher at University of California, San Francisco, said during the presentation.

It is easy to recognize the benefits of the machine-learning approach to, for example, testing chemicals and materials for toxicity -- an area that we work on as a combined team of computer scientists and chemists. First, the need is obvious when you consider that less than 1% of the chemicals registered for commercial use in the United States have undergone toxicity characterization, whether they are used for medicinal purposes or for fracking. Moreover, there are many scientific, ethical, and economic advantages to replacing the animals currently used in toxicity tests with non-animal test systems, and great speed and cost advantages in using computer systems. Second, material and chemical usage has increased to 60 billion tonnes per year during the twentieth century2, underscoring the advantages of a rapid machine-learning approach for toxicity characterization. Finally, the number of materials and chemicals that can be designed digitally far exceeds the number that have been well characterized. For example, our estimates based on the number of material combinations with six surfaces exceed trillions, while the organic chemicals based on only hexanes exceed 1030 (Figure 1), clearly indicating the vastness of possibilities.

Face detection in unrestricted conditions has been a trouble for years due to various expressions, brightness, and coloration fringing. Recent studies show that deep learning knowledge of strategies can acquire spectacular performance inside the identification of different gadgets and patterns. This face detection in unconstrained surroundings is difficult due to various poses, illuminations, and occlusions. Figuring out someone with a picture has been popularized through the mass media. However, it's miles less sturdy to fingerprint or retina scanning. The latest research shows that deep mastering techniques can gain mind-blowing performance on those two responsibilities. In this paper, I recommend a deep cascaded multi-venture framework that exploits the inherent correlation among them to boost up their performance. In particular, my framework adopts a cascaded shape with 3 layers of cautiously designed deep convolutional networks that expect face and landmark region in a coarse-to-fine way. Besides, within the gaining knowledge of the procedure, I propose a new online tough sample mining method that can enhance the performance robotically without manual pattern choice.

Technology investment company and incubator Red Cell Partners announced today the launch of Zephyr AI, a company that leverages large data sets to inform both clinical care and the development of new targeted precision therapies. The management team of the new company consists of CEO Yisroel Brumer, formerly of the office of the Secretary of Defense; Executive Chairman Grant Verstandig, who most recently served Chief Digital Officer at UnitedHealth Group; and Chief Technology Officer Jeff Sherman, who was the machine learning architect at Rally Health, which was acquired in 2017 by UnitedHealth's Optum unit. According to a press release announcing its launch, Zephyr AI will look to improve patient outcomes while lowering costs by integrating "artificial intelligence with extensive datasets to upend traditional'guess and test' drug development and personalized medicine processes to unearth novel therapeutics, new applications for existing therapeutics, and advanced biomarkers for individualized treatments." The potential new company gave a hint at its direction earlier in the year via the publication of two papers by the founders in the journal Oncogene that detailed the company's technology and it's performance. "These findings demonstrate that Zephyr AI can already identify novel-use cases for existing therapeutics in cancer," company CTO Sherman.

As Albert Einstein famously said, "If I had an hour to solve a problem, I would spend 55 minutes on defining the problem and five minutes on the solution." This is ever so true when setting up machine-learning (ML) projects.

Disinformation has become a central feature of the COVID-19 crisis. According to a recent poll, false or misleading information about the pandemic reaches close to half of all online news consumers in the U.K. As this type of malign information and high-tech "deepfake" imagery can spread so fast online, it poses a risk to democratic societies worldwide by increasing public mistrust in governments and public authorities -- a phenomenon referred to as "truth decay." New research, however, highlights new ways to detect and dispel disinformation online. There are several factors that may account for the rapid spread of disinformation during the COVID-19 pandemic. Given the global nature of the pandemic, more groups are using disinformation to further their agendas.

Over recent weeks, the global business system has been heavily impacted by the outbreak of Covid-19, obliging companies to activate strategies in line with governments' directives. Decision makers have been tested by the increasing pressures stemming from the international arena, where uncertainties put them in a risky position along the supply chains, emphasizing that operating in such global environment certainly means getting access to a larger number of opportunities, as well as being victim of a domino effect in front of turbulent circumstances deployed far away. These changes could also lead to a rethink of some paradigms and dynamics that have typically characterized companies – even at the top level. In this framework, the technological trends that have transformed business realities over recent years could knock on the boardrooms' doors to strengthen their responsiveness and resilience before, during and after an emergency. Indeed, even if these bodies have been an under-researched "black box" for a long time, the moment to revitalize their role has come.

Geophysics Department, Stanford University, Stanford, California, USA In this study we develop a single-station deep-learning approach for fast and reliable estimation of earthquake magnitude directly from raw waveforms. We design a regressor composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. Our network can predict earthquake magnitudes with an average error close to zero and standard deviation of 0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems.