Long short-term memory (LSTM) The long short-term memory (LSTM) is a special kind of recurrent neural network (Gers et al., 1999; Hochreiter & Schmidhuber, 1997), and is capable of processing sequential data with correlations between points that are far apart. On the one hand, similar to the standard recurrent neural network, the LSTM has a self-looped structure that allows the result of the previous step to participate in the calculation of the subsequent step. On the other hand, the LSTM possesses four interaction layers in its neurons, which makes it able to forget useless information and learn correlations between data points that are far away from each other in sequence. The LSTM is the state-of-the-art model for well log generation in previous studies (Zhang et al., 2018). This agrees well with the perspective of geoscience, since the well logs reflect a formation condition, which possesses internal continuity (spatial dependency). The sequential information in reservoirs is critical for well logs generation. Therefore, the LSTM constitutes the ideal foundation for building a new model for this type of geoscience problem.

In algorithmic decision making, recourse refers to individuals' ability to systematically reverse an unfavorable decision made by an algorithm. Meanwhile, individuals subjected to a classification mechanism are incentivized to behave strategically in order to gain a system's approval. However, not all strategic behavior necessarily leads to adverse results: through appropriate mechanism design, strategic behavior can induce genuine improvement in an individual's qualifications. In this paper, we explore how to design a classifier that achieves high accuracy while providing recourse to strategic individuals so as to incentivize them to improve their features in non-manipulative ways. We capture these dynamics using a two-stage game: first, the mechanism designer publishes a classifier, with the goal of optimizing classification accuracy and providing recourse to incentivize individuals' improvement. Then, agents respond by potentially modifying their input features in order to obtain a favorable decision from the classifier, while trying to minimize the cost of making such modifications. Under this model, we provide analytical results characterizing the equilibrium strategies for both the mechanism designer and the agents. Our empirical results show the effectiveness of our mechanism in three real-world datasets: compared to a baseline classifier that only considers individuals' strategic behavior without explicitly incentivizing improvement, our algorithm can provide recourse to a much higher fraction of individuals in the direction of improvement while maintaining relatively high prediction accuracy. We also show that our algorithm can effectively mitigate disparities caused by differences in manipulation costs. Our results provide insights for designing a machine learning model that focuses not only on the static distribution as of now, but also tries to encourage future improvement.

The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences. We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb's local context is most indicative of its aspectual class, and demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Lastly, we contribute a dataset of human--human conversations annotated with lexical aspect and present experiments that show the correlation of telicity with genre and discourse goals.

Nonnegative matrix factorization (NMF) is the problem of approximating an input nonnegative matrix, $V$, as the product of two smaller nonnegative matrices, $W$ and $H$. In this paper, we introduce a general framework to design multiplicative updates (MU) for NMF based on $\beta$-divergences ($\beta$-NMF) with disjoint equality constraints, and with penalty terms in the objective function. By disjoint, we mean that each variable appears in at most one equality constraint. Our MU satisfy the set of constraints after each update of the variables during the optimization process, while guaranteeing that the objective function decreases monotonically. We showcase this framework on three NMF models, and show that it competes favorably the state of the art: (1)~$\beta$-NMF with sum-to-one constraints on the columns of $H$, (2) minimum-volume $\beta$-NMF with sum-to-one constraints on the columns of $W$, and (3) sparse $\beta$-NMF with $\ell_2$-norm constraints on the columns of $W$.

Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset of relevant features that maximizes the performance of prediction models. However, such an approach typically requires knowledge of the multivariate probability distribution of all features and the target, and involves a challenging combinatorial optimization problem. Recent work has shown that any joint Probability Mass Function (PMF) can be represented as a naive Bayes model, via Canonical Polyadic (tensor rank) Decomposition. In this paper, we introduce a low-rank tensor model of the joint PMF of all variables and indirect targeting as a way of mitigating complexity and maximizing the classification performance for a given number of features. Through low-rank modeling of the joint PMF, it is possible to circumvent the curse of dimensionality by learning principal components of the joint distribution. By indirectly aiming to predict the latent variable of the naive Bayes model instead of the original target variable, it is possible to formulate the feature selection problem as maximization of a monotone submodular function subject to a cardinality constraint - which can be tackled using a greedy algorithm that comes with performance guarantees. Numerical experiments with several standard datasets suggest that the proposed approach compares favorably to the state-of-art for this important problem.

The global AI-based fever detection camera market size is USD1.28 billion by 2020 and is projected to reach USD 2.19 billion by 2027, exhibiting a CAGR of 8.0% during the forecast period. The worldwide surge in the growth of corona infected people has led to the emergence of advanced artificial intelligence-based fever detection cameras to monitor and detect human body temperature. Vaccine for coronavirus is still in its development stage and hence, the only way to reduce the spread of this pandemic is to isolate the infected person from the crowd. This type of camera is being considered as an efficient and effective device to identify a person with high temperature as fever is one of the symptoms of coronavirus. An individual with high temperature is further screened with virus-specific tests.

The history of how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread around the planet has been far from clear. Several narratives have been propagated by social media and, in some cases, national policies were forged in response. Now that many thousands of virus sequences are available, two studies analyzed some of the key early events in the spread of SARS-CoV-2. Bedford et al. found that the virus arrived in Washington state in late January or early February. The viral genome from the first case detected had mutations similar to those found in Chinese samples and rapidly spread and dominated subsequent undetected community transmission. The other viruses detected had origins in Europe. Worobey et al. found that early introductions into Germany and the west coast of the United States were extinguished by vigorous public health efforts, but these successes were largely unrecognized. Unfortunately, several major travel events occurred in February, including repatriations from China, with lax public health follow-up. Serial, independent introductions triggered the major outbreaks in the United States and Europe that still hold us in the grip of control measures. Science , this issue p. [571][1], p. [564][2] Accurate understanding of the global spread of emerging viruses is critical for public health responses and for anticipating and preventing future outbreaks. Here we elucidate when, where, and how the earliest sustained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission networks became established in Europe and North America. Our results suggest that rapid early interventions successfully prevented early introductions of the virus from taking hold in Germany and the United States. Other, later introductions of the virus from China to both Italy and Washington state, United States, founded the earliest sustained European and North America transmission networks. Our analyses demonstrate the effectiveness of public health measures in preventing onward transmission and show that intensive testing and contact tracing could have prevented SARS-CoV-2 outbreaks from becoming established in these regions. [1]: /lookup/doi/10.1126/science.abc0523 [2]: /lookup/doi/10.1126/science.abc8169

Dogs were the first domesticated animal, likely originating from human-associated wolves, but their origin remains unclear. Bergstrom et al. sequenced 27 ancient dog genomes from multiple locations near to and corresponding in time to comparable human ancient DNA sites (see the Perspective by Pavlidis and Somel). By analyzing these genomes, along with other ancient and modern dog genomes, the authors found that dogs likely arose once from a now-extinct wolf population. They also found that at least five different dog populations ∼10,000 years before the present show replacement in Europe at later dates. Furthermore, some dog population genetics are similar to those of humans, whereas others differ, inferring a complex ancestral history for humanity's best friend. Science , this issue p. [557][1]; see also p. [522][2] Dogs were the first domestic animal, but little is known about their population history and to what extent it was linked to humans. We sequenced 27 ancient dog genomes and found that all dogs share a common ancestry distinct from present-day wolves, with limited gene flow from wolves since domestication but substantial dog-to-wolf gene flow. By 11,000 years ago, at least five major ancestry lineages had diversified, demonstrating a deep genetic history of dogs during the Paleolithic. Coanalysis with human genomes reveals aspects of dog population history that mirror humans, including Levant-related ancestry in Africa and early agricultural Europe. Other aspects differ, including the impacts of steppe pastoralist expansions in West and East Eurasia and a near-complete turnover of Neolithic European dog ancestry. [1]: /lookup/doi/10.1126/science.aba9572 [2]: /lookup/doi/10.1126/science.abe7823

Deep learning pioneer Yoshua Bengio has provocative ideas about the future of AI. For the first part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.

For the first part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.