Bucharest, Romania – Last Wednesday, a Russian drone attack on Ukraine's grain port infrastructure shook Romania, a NATO member. The force of the attack on the Izmail port, across the Danube River from the Eastern European nation, was so intense that the windows of some village homes in southeastern Romania shattered. Even though she lives far from the county of Tulcea, where the impact was felt, 28-year-old Alexandra, a paralegal from the capital Bucharest, is concerned. "We share a border with Ukraine and the conflict could expand at any moment," she told Al Jazeera. Russia has launched several attacks on Danube ports since pulling out of the wartime Black Sea grain deal.

The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved. Sorted L-One Penalized Estimation (SLOPE) is a generalization of the lasso with appealing statistical properties. In spite of this, the method has not yet reached widespread interest. A major reason for this is that current software packages that fit SLOPE rely on algorithms that perform poorly in high dimensions. To tackle this issue, we propose a new fast algorithm to solve the SLOPE optimization problem, which combines proximal gradient descent and proximal coordinate descent steps. We provide new results on the directional derivative of the SLOPE penalty and its related SLOPE thresholding operator, as well as provide convergence guarantees for our proposed solver. In extensive benchmarks on simulated and real data, we show that our method outperforms a long list of competing algorithms.

Summary: A new computational model predicts how information deep inside the brain could flow from one network to another, and how neural network clusters can self optimize over time. Researchers at the Cyber-Physical Systems Group at the USC Viterbi School of Engineering, in conjunction with the University of Illinois at Urbana-Champaign, have developed a new model of how information deep in the brain could flow from one network to another and how these neuronal network clusters self-optimize over time. Their work, chronicled in the paper "Network Science Characteristics of Brain-Derived Neuronal Cultures Deciphered From Quantitative Phase Imaging Data," is believed to be the first study to observe this self-optimization phenomenon in in vitro neuronal networks, and counters existing models. Their findings can open new research directions for biologically inspired artificial intelligence, detection of brain cancer and diagnosis and may contribute to or inspire new Parkinson's treatment strategies. The team examined the structure and evolution of neuronal networks in the brains of mice and rats in order to identify the connectivity patterns.

In response to the coronavirus health crisis, USC researchers have made a hard pivot, adapting labs and lessons learned from treating other diseases to help check the virus and save lives. At their disposal are numerous technologies that give a human advantage, despite the fast-break spread of COVID-19 once it exited central China and spread across the globe. The disease has afflicted thousands of Californians and poses a serious risk to public health and the world economy. Tools such as supercomputers, software apps, virtual reality, big data and algorithms are now in play. They are using the tools to find ways to search and destroy coronavirus DNA, turn smartphones into personal protection devices and use people-friendly simulators to help cope with the crush of medical cases.

If a Russian or Chinese Anti-Satellite (ASAT) weapon streamed into space and exploded U.S. military satellites, friendly forces would instantly become very vulnerable to significant and extremely destructive enemy attacks….-- Any, all or part of this could happen in as little as 10 to 15 minutes once a satellite attacking missile is launched from the ground. Lives will hang in the balance as alerts are sent through U.S. command and control and decision-makers scramble to determine the best countermeasure with which to protect its space assets. Space war is no longer a distant prospect to envision years down the road --- it is here. Recognizing the seriousness of this vulnerability, the Pentagon, U.S. Space Command, Missile Defense Agency and industry are moving quickly to integrate Machine Learning and AI into space-based systems and technology.

If a Russian or Chinese Anti-Satellite (ASAT) weapon streamed into space and exploded U.S. military satellites, friendly forces would instantly become very vulnerable to significant and extremely destructive enemy attacks - space-based infrared missile detection could be destroyed, GPS communications could be knocked out, guided weapons could jam and derail before hitting their targets and war-critical command and control could simply be "taken out." Any, all or part of this could happen in as little as 10 to 15 minutes once a satellite attacking missile is launched from the ground. Lives will hang in the balance as alerts are sent through U.S. command and control and decision-makers scramble to determine the best countermeasure with which to protect its space assets. Space war is no longer a distant prospect to envision years down the road --- it is here. Recognizing the seriousness of this vulnerability, the Pentagon, U.S. Space Command, Missile Defense Agency and industry are moving quickly to integrate Machine Learning and AI into space-based systems and technology.

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In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity---meaning that the fraction of variables with a non-vanishing effect tends to a constant, however small---this cannot really be the case, even when the design variables are stochastically independent. We demonstrate that true features and null features are always interspersed on the Lasso path, and that this phenomenon occurs no matter how strong the effect sizes are. We derive a sharp asymptotic trade-off between false and true positive rates or, equivalently, between measures of type I and type II errors along the Lasso path. This trade-off states that if we ever want to achieve a type II error (false negative rate) under a critical value, then anywhere on the Lasso path the type I error (false positive rate) will need to exceed a given threshold so that we can never have both errors at a low level at the same time. Our analysis uses tools from approximate message passing (AMP) theory as well as novel elements to deal with a possibly adaptive selection of the Lasso regularizing parameter.