We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards process of each arm evolves according to an unknown Markovian rule, which is non-identical among different arms. At each time, a player chooses an arm out of N arms to play, and receives a random reward from a finite set of reward states. The arms are restless, that is, their local state evolves regardless of the player's actions. Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time t the arm that maximizes the expected immediate value. The objective is to develop an arm-selection policy that minimizes the regret. To that end, we develop the Learning under Exogenous Markov Process (LEMP) algorithm. We analyze LEMP theoretically and establish a finite-sample bound on the regret. We show that LEMP achieves a logarithmic regret order with time. We further analyze LEMP numerically and present simulation results that support the theoretical findings and demonstrate that LEMP significantly outperforms alternative algorithms.

It's no secret that the world is facing a climate crisis. Carbon emissions continue to climb yearly, and the earth is becoming warmer. If we don't take action soon, things could get even worse. Natural disasters will become more common and more destructive. The effects of climate change could become even more widespread across the globe.

Machine learning has been used to quickly discover some of the most promising materials for fluoride-ion batteries. The work could accelerate development of these batteries, which are tipped by some to rival, or even replace, lithium-based ones. In theory, fluoride-ion systems are ideal for batteries in everything from electric vehicles to consumer electronics. That's because fluoride ions are lightweight, small and highly stable. Fluoride is also cheaper than lithium and cobalt that are required for lithium-ion batteries.

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, the digital beamformer is performed by the hybrid antenna array at the frequency of operation of 10 GHz. The proposed digital beamformer, as a spatial filter, is capable of performing Direction of Arrival (DOA) estimation and beamforming. The most well-established machine learning technique of support vector machine (SVM) for the DoA estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, in the proposed beamformer, the QS-SVM classifier with a small regularizer has been used for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. The QS-SVM-based beamformer has been deployed in an FPGA board, as demonstrated in detail in this work. The implementation results have verified the strong performance of the QS-SVM-based beamformer in suppressing undesired signals, deep nulls with powers less than -10 dB in undesired signals, and transferring desired signals. Furthermore, we have demonstrated that the performance of the QS-SVM-based beamformer consists of other advantages of average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of about 100%. Index Terms Digital beamforming, Support vector machine, Minimum variance distortionless response, Linearly constrained minimum variance, Direction of arrival estimation, and FPGA, Spatial filter, Massive wireless communications.

Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian formalism provide a systematic framework to incorporate robustness through the distributional estimates and reason about uncertainty. Recent works have shown that approximate inference approaches that take the weight space uncertainty of neural networks to generate ensemble prediction are the state-of-the-art. However, architecture choices have mostly been ad hoc, which essentially ignores the epistemic uncertainty from the architecture space. To this end, we propose a Unified probabilistic architecture and weight ensembling Neural Architecture Search (UraeNAS) that leverages advances in probabilistic neural architecture search and approximate Bayesian inference to generate ensembles form the joint distribution of neural network architectures and weights. The proposed approach showed a significant improvement both with in-distribution (0.86% in accuracy, 42% in ECE) CIFAR-10 and out-of-distribution (2.43% in accuracy, 30% in ECE) CIFAR-10-C compared to the baseline deterministic approach.

Governments and international organizations the world over are investing towards the goal of achieving universal energy access for improving socio-economic development. However, in developing settings, monitoring electrification efforts is typically inaccurate, infrequent, and expensive. In this work, we develop and present techniques for high-resolution monitoring of electrification progress at scale. Specifically, our 3 unique contributions are: (i) identifying areas with(out) electricity access, (ii) quantifying the extent of electrification in electrified areas (percentage/number of electrified structures), and (iii) differentiating between customer types in electrified regions (estimating the percentage/number of residential/non-residential electrified structures). We combine high-resolution 50 cm daytime satellite images with Convolutional Neural Networks (CNNs) to train a series of classification and regression models. We evaluate our models using unique ground truth datasets on building locations, building types (residential/non-residential), and building electrification status. Our classification models show a 92% accuracy in identifying electrified regions, 85% accuracy in estimating percent of (low/high) electrified buildings within the region, and 69% accuracy in differentiating between (low/high) percentage of electrified residential buildings. Our regressions show $R^2$ scores of 78% and 80% in estimating the number of electrified buildings and number of residential electrified building in images respectively. We also demonstrate the generalizability of our models in never-before-seen regions to assess their potential for consistent and high-resolution measurements of electrification in emerging economies, and conclude by highlighting opportunities for improvement.

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available.

Artificial intelligence presents a major challenge to conventional computing architecture. In standard models, memory storage and computing take place in different parts of the machine, and data must move from its area of storage to a CPU or GPU for processing. The problem with this design is that movement takes time. You can have the most powerful processing unit on the market, but its performance will be limited as it idles waiting for data, a problem known as the "memory wall" or "bottleneck." When computing outperforms memory transfer, latency is unavoidable.

A new computational approach will improve understanding of different states of carbon and guide the search for materials yet to be discovered. Materials--we use them, wear them, eat them and create them. Sometimes we invent them by accident, like with Silly Putty. But far more often, making useful materials is a tedious and expensive process of trial and error. Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning (ML)--a type of artificial intelligence--and high performance computing.

Everyone knows that the Computer--an artificial intelligence (AI)-like entity--on a Star Trek spaceship does everything from brewing tea to compiling complex analyses of flux data. But how are they used at real research facilities? How can AI agents--computer programs that can act based on a perceived environment--help scientists discover next-generation batteries or quantum materials? Three staff members at the National Synchrotron Light Source II (NSLS-II) described how AI agents support scientists using the facility's research tools. As a U.S. Department of Energy's (DOE) Office of Science user facility located at DOE's Brookhaven National Laboratory, NSLS-II offers its experimental capabilities to scientists from all over the world who use it to reveal the mysteries of materials for tomorrow's technology.