Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care conditions that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. Specifically, we train a prognostic model on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects' predicted outcomes (their prognostic scores). We prove that, under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators. When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model. We demonstrate the approach using simulations and a reanalysis of an Alzheimer's Disease clinical trial and observe meaningful reductions in mean-squared error and the estimated variance. Lastly, we provide a simplified formula for asymptotic variance that enables power and sample size calculations that account for the gains from the prognostic model for clinical trial design.

Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release \textbf{PyRain}, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.

We study the role of the constraint set in determining the solution to low-rank, positive semidefinite (PSD) matrix sensing problems. The setting we consider involves rank-one sensing matrices: In particular, given a set of rank-one projections of an approximately low-rank PSD matrix, we characterize the radius of the set of PSD matrices that satisfy the measurements. This result yields a sampling rate to guarantee singleton solution sets when the true matrix is exactly low-rank, such that the choice of the objective function or the algorithm to be used is inconsequential in its recovery. We discuss applications of this contribution and compare it to recent literature regarding implicit regularization for similar problems. We demonstrate practical implications of this result by applying conic projection methods for PSD matrix recovery without incorporating low-rank regularization.

It's been two weeks since Google fired Timnit Gebru, a decision that still seems incomprehensible. Gebru is one of the most highly regarded AI ethics researchers in the world, a pioneer whose work has highlighted the ways tech fails marginalized communities when it comes to facial recognition and more recently large language models. Of course, this incident didn't happen in a vacuum. Case in point: Gebru was fired the same day the National Labor Review Board (NLRB) filed a complaint against Google for illegally spying on employees and the retaliatory firing of employees interested in unionizing. Gebru's dismissal also calls into question issues of corporate influence in research, demonstrates the shortcomings of self-regulation, and highlights the poor treatment of Black people and women in tech in a year when Black Lives Matter sparked the largest protest movement in U.S. history. In an interview with VentureBeat last week, Gebru called the way she was fired disrespectful and described a companywide memo sent by CEO Sundar Pichai as "dehumanizing." To delve further into possible outcomes following Google's AI ethics meltdown, VentureBeat spoke with five experts in the field about Gebru's dismissal and the issues it raises.

Learning sophisticated feature interactions is crucial for Click-Through Rate (CTR) prediction in recommender systems. Various deep CTR models follow an Embedding & Feature Interaction paradigm. The majority focus on designing network architectures in Feature Interaction module to better model feature interactions while the Embedding module, serving as a bottleneck between data and Feature Interaction module, has been overlooked. The common methods for numerical feature embedding are Normalization and Discretization. The former shares a single embedding for intra-field features and the latter transforms the features into categorical form through various discretization approaches. However, the first approach surfers from low capacity and the second one limits performance as well because the discretization rule cannot be optimized with the ultimate goal of CTR model. To fill the gap of representing numerical features, in this paper, we propose AutoDis, a framework that discretizes features in numerical fields automatically and is optimized with CTR models in an end-to-end manner. Specifically, we introduce a set of meta-embeddings for each numerical field to model the relationship among the intra-field features and propose an automatic differentiable discretization and aggregation approach to capture the correlations between the numerical features and meta-embeddings. Comprehensive experiments on two public and one industrial datasets are conducted to validate the effectiveness of AutoDis over the SOTA methods.

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying exiting research and by identifying gaps and opportunities for further research. To do that, we have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but seems to be still far from its peak.

Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability via symbolic representations for network models. In this paper, we relate recent and early research results in neurosymbolic AI with the objective of identifying the key ingredients of the next wave of AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI. We also identify promising directions and challenges for the next decade of AI research from the perspective of neural-symbolic systems.

Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios such as healthcare, however, usually only few records are available for each patient, and patients may show different responses to the same treatment, impeding the application of current RL algorithms to learn optimal policies. To address the issues of mechanism heterogeneity and related data scarcity, we propose a data-efficient RL algorithm that exploits structural causal models (SCMs) to model the state dynamics, which are estimated by leveraging both commonalities and differences across subjects. The learned SCM enables us to counterfactually reason what would have happened had another treatment been taken. It helps avoid real (possibly risky) exploration and mitigates the issue that limited experiences lead to biased policies. We propose counterfactual RL algorithms to learn both population-level and individual-level policies. We show that counterfactual outcomes are identifiable under mild conditions and that Q- learning on the counterfactual-based augmented data set converges to the optimal value function. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed approach.

Given a time-evolving tensor with missing entries, how can we effectively factorize it for precisely predicting the missing entries? Tensor factorization has been extensively utilized for analyzing various multi-dimensional real-world data. However, existing models for tensor factorization have disregarded the temporal property for tensor factorization while most real-world data are closely related to time. Moreover, they do not address accuracy degradation due to the sparsity of time slices. The essential problems of how to exploit the temporal property for tensor decomposition and consider the sparsity of time slices remain unresolved. In this paper, we propose TATD (Time-Aware Tensor Decomposition), a novel tensor decomposition method for real-world temporal tensors. TATD is designed to exploit temporal dependency and time-varying sparsity of real-world temporal tensors. We propose a new smoothing regularization with Gaussian kernel for modeling time dependency. Moreover, we improve the performance of TATD by considering time-varying sparsity. We design an alternating optimization scheme suitable for temporal tensor factorization with our smoothing regularization. Extensive experiments show that TATD provides the state-of-the-art accuracy for decomposing temporal tensors.

Robotic underwater gliders will be sent to investigate the massive iceberg presently on a collision course with the sub-Antarctic island of South Georgia, experts said. Dubbed A68a, the enormous mass -- some 87 miles (140 km) in length -- broke off from Antarctica's Larsen C Ice Shelf in 2017 and has been drifting north ever since. Scientists tracking the berg's progress via satellite warned that A68a -- propelled by the powerful circumpolar current -- could hit South Georgia within days. There is the chance that it could split into pieces beforehand -- MOD images taken from above the icy body have suggested that is already beginning to break up. But A68a is a hazard for wildlife -- having the potential to crush marine life on the island's ocean shelf and make waters inhospitable as it melts to release freshwater.