Neural Information Processing Systems http://nips.cc/

China's People's Armed Forces Maritime Militia deployed thousands of fishing vessels in coordinated formations that could disrupt global shipping lanes, analysts warn.

Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent (SGD) from random initialization. In the overparameterized setting, when the data comes from mixtures of well-separated distributions, we prove that SGD learns a network with a small generalization error, albeit the network has enough capacity to fit arbitrary labels. Furthermore, the analysis provides interesting insights into several aspects of learning neural networks and can be verified based on empirical studies on synthetic data and on the MNIST dataset.

The secret to this acrobatic skill lies in an extremely flexible part of the spine that allows cats to twist in the air and land safely. It's well established that when cats fall, they're able to land perfectly most of the time, nimbly maneuvering to right themselves before they hit the ground. Now, researchers at Japan's Yamaguchi University have advanced our understanding of this extraordinary ability, focusing on the mechanical properties of feline spines. What they found, as detailed in a recent study in the journal The Anatomical Record, is that those sure-footed landings are due in part to the fact that a cat's thoracic region is much more flexible than its lumbar region. While a cat's ability to rotate in the air without something to push again seems to defy the laws of physics, it's instead a complex righting maneuver.

Neural Information Processing Systems http://nips.cc/

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We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric. These constraints demand that we select similar actions or individuals with approximately equal probability [?], which may be at odds with optimizing reward, thus modeling settings where profit and social policy are in tension. We assume we learn about an unknown Mahalanobis similarity metric from only weak feedback that identifies fairness violations, but does not quantify their extent. This is intended to represent the interventions of a regulator who "knows unfairness when he sees it" but nevertheless cannot enunciate a quantitative fairness metric over individuals. Our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on T, while obtaining an optimal O( T) regret bound to the best fair policy.

Could Iran be using China's BeiDou system? Iraq's Iranian-backed Kataib Hezbollah has released drone video from an attack on the US's Victory Base near Baghdad International Airport. It's believed to be the first time the group has successfully used the FPV attack drone to skirt US defences. Iran's Space Research Centre severely damaged in strikes Thousands in Madrid protest'forgotten' Gaza, warn Iran war may spiral into

Human scene understanding uses a variety of visual and non-visual cues to perform inference on object types, poses, and relations. Physics is a rich and universal cue that we exploit to enhance scene understanding. In this paper, we integrate the physical cue of stability into the learning process by looping in a physics engine into bottom-up recognition models, and apply it to the problem of 3D scene parsing. We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples. We then present a novel architecture for 3D scene parsing named Prim R-CNN, learning to predict bounding boxes as well as their 3D size, translation, and rotation. With physics supervision, Prim R-CNN outperforms existing scene understanding approaches on this problem. Finally, we show that finetuning with physics supervision on unlabeled real images improves real domain transfer of models training on synthetic data.

Iranian Crown Prince Reza Pahlavi said he is ready to lead Iran's transition as soon as the Islamic Republic falls, announcing plans for a transitional system under his leadership.