Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitz-continuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.

We study the problem of testing discrete distributions with a focus on the high probability regime. Specifically, given samples from one or more discrete distributions, a property $\mathcal{P}$, and parameters $0< \epsilon, \delta <1$, we want to distinguish {\em with probability at least $1-\delta$} whether these distributions satisfy $\mathcal{P}$ or are $\epsilon$-far from $\mathcal{P}$ in total variation distance. Most prior work in distribution testing studied the constant confidence case (corresponding to $\delta = \Omega(1)$), and provided sample-optimal testers for a range of properties. While one can always boost the confidence probability of any such tester by black-box amplification, this generic boosting method typically leads to sub-optimal sample bounds. Here we study the following broad question: For a given property $\mathcal{P}$, can we {\em characterize} the sample complexity of testing $\mathcal{P}$ as a function of all relevant problem parameters, including the error probability $\delta$? Prior to this work, uniformity testing was the only statistical task whose sample complexity had been characterized in this setting. As our main results, we provide the first algorithms for closeness and independence testing that are sample-optimal, within constant factors, as a function of all relevant parameters. We also show matching information-theoretic lower bounds on the sample complexity of these problems. Our techniques naturally extend to give optimal testers for related problems. To illustrate the generality of our methods, we give optimal algorithms for testing collections of distributions and testing closeness with unequal sized samples.

We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger and more diverse datasets, which in multiple cases increases robustness, but is still far from closing the performance gaps. Our results indicate that distribution shifts arising in real data are currently an open research problem. We provide our testbed and data as a resource for future work at https://modestyachts.github.io/imagenet-testbed/ .

China's Defense Ministry on Sunday blasted a critical U.S. report on the country's military ambitions, saying it is the U.S. instead that poses the biggest threat to the international order… Oman welcomes Bahrain's decision to normalize relations with Israel and hopes it will contribute to Israeli-Palestinian peace, Oman state media said on Sunday. Pushing for new roads to reelection, President Donald Trump is going on the offense this weekend in Nevada, which hasn't supported a Republican presidential candidate since 2004. Housing and Construction Minister Rabbi Yaakov Litzman threatened Sunday to resign if the government implements a lockdown over the Yamim Nora'im. The United Arab Emirates' Mohamed Bin Zayed University of Artificial Intelligence and Israel's Weizmann Institute of Science have agreed to work together, UAE state news agency WAM said on Sunday.… Hamas leader Ismail Haniyeh told Turkish media on Friday that Egypt is currently mediating a new prisoner exchange deal between Gaza Strip's rulers and Israel.

Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and usually a higher dimensional one ensures better reasoning capability. However, high-dimensional KGEs pose huge challenges to storage and computing resources and are not suitable for resource-limited or time-constrained applications, for which faster and cheaper reasoning is necessary. To address this problem, we propose DistilE, a knowledge distillation method to build low-dimensional student KGE from pre-trained high-dimensional teacher KGE. We take the original KGE loss as hard label loss and design specific soft label loss for different KGEs in DistilE. We also propose a two-stage distillation approach to make the student and teacher adapt to each other and further improve the reasoning capability of the student. Our DistilE is general enough to be applied to various KGEs. Experimental results of link prediction show that our method successfully distills a good student which performs better than a same dimensional one directly trained, and sometimes even better than the teacher, and it can achieve 2 times - 8 times embedding compression rate and more than 10 times faster inference speed than the teacher with a small performance loss. We also experimentally prove the effectiveness of our two-stage training proposal via ablation study.

Safety concerns on the deep neural networks (DNNs) have been raised when they are applied to critical sectors. In this paper, we define safety risks by requesting the alignment of the network's decision with human perception. To enable a general methodology for quantifying safety risks, we define a generic safety property and instantiate it to express various safety risks. For the quantification of risks, we take the maximum radius of safe norm balls, in which no safety risk exists. The computation of the maximum safe radius is reduced to the computation of their respective Lipschitz metrics - the quantities to be computed. In addition to the known adversarial example, reachability example, and invariant example, in this paper we identify a new class of risk - uncertainty example - on which humans can tell easily but the network is unsure. We develop an algorithm, inspired by derivative-free optimization techniques and accelerated by tensor-based parallelization on GPUs, to support efficient computation of the metrics. We perform evaluations on several benchmark neural networks, including ACSC-Xu, MNIST, CIFAR-10, and ImageNet networks. The experiments show that, our method can achieve competitive performance on safety quantification in terms of the tightness and the efficiency of computation. Importantly, as a generic approach, our method can work with a broad class of safety risks and without restrictions on the structure of neural networks.

Identity-based patterns for which a computational model needs to output some feature together with a copy of that feature are computationally challenging, but pose no problems to human learners and are common in world's languages. In this paper, we test whether a neural network can learn an identity-based pattern in speech called reduplication. To our knowledge, this is the first attempt to test identity-based patterns in deep convolutional networks trained on raw continuous data. Unlike existing proposals, we test learning in an unsupervised manner and we train the network on raw acoustic data. We use the ciwGAN architecture (Begu\v{s} 2020; arXiv:2006.02951) in which learning of meaningful representations in speech emerges from a requirement that the deep convolutional network generates informative data. Based on four generative tests, we argue that a deep convolutional network learns to represent an identity-based pattern in its latent space; by manipulating only two categorical variables in the latent space, we can actively turn an unreduplicated form into a reduplicated form with no other changes to the output in the majority of cases. We also argue that the network extends the identity-based pattern to unobserved data: when reduplication is forced in the output with the proposed technique for latent space manipulation, the network generates reduplicated data (e.g., it copies an [s] e.g. in [si-siju] for [siju] although it never sees any reduplicated forms containing an [s] in the input). Comparison with human outputs of reduplication show a high degree of similarity. Exploration of how meaningful representations of identity-based patterns emerge and how the latent space variables outside of the training range correlate with identity-based patterns in the output has general implications for neural network interpretability.

In recent years the phrase "data science" has become a buzzword in the tech industry. The demand for data scientists has surged since the late 1990s, presenting new job opportunities and research areas for computer scientists. Before we delve into the computer science aspect of data science, it's useful to know exactly what data science is and to explore the skills required to become a successful data scientist. Data science is a field of study that involves the processing of large sets of data with statistical methods to extract trends, patterns, or other relevant information. In short, data science encapsulates anything related to obtaining insights, trends, or any other valuable information from data.

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