'We will go wherever they hide': Rooting out IS in Somalia A figure appears in the picture, moving through a valley. He has been to fetch water for his friends, says the drone operator. He is running and carrying something on his back, adds another soldier. The man on the screen is near a cave, which the army believes is a hideout for 50 to 60 IS fighters. The Puntland Defence Forces have about 500 soldiers stationed at this base in the north-east of Somalia. Ten years ago the barren and inhospitable landscape was home to only a few nomadic communities, but that changed when IS established a foothold here, shifting its focus to Africa as its fighters were driven out of their strongholds in Syria and Iraq.

'Lucy' probably needed to watch her back. Researchers led by the University of Iowa have described and named a new crocodile species that roamed a region in Africa more than 3 million years ago. The species is named Lucy's hunter, because it overlapped with the famed Lucy and her hominin kin and would have hunted them. Breakthroughs, discoveries, and DIY tips sent six days a week. Humans have contended with crocodiles for a long time.

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To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.

We show for the first time that this leads both to improved convergence bounds under standard assumptions.

We study adversarially robust transfer learning, wherein, given labeled data on multiple (source) tasks, the goal is to train a model with small robust error on a previously unseen (target) task. In particular, we consider a multi-task representation learning (MTRL) setting, i.e., we assume that the source and target tasks admit a simple (linear) predictor on top of a shared representation (e.g., the final hidden layer of a deep neural network). In this general setting, we provide rates on the excess adversarial (transfer) risk for Lipschitz losses and smooth nonnegative losses. These rates show that learning a representation using adversarial training on diverse tasks helps protect against inference-time attacks in data-scarce environments. Additionally, we provide novel rates for the single-task setting.

We propose a probabilistic perspective on adversarial examples, allowing us to embed subjective understanding of semantics as a distribution into the process of generating adversarial examples, in a principled manner.