Forecasting has undergone a profound transformation in the 21st century, driven by advancements in methodology, computational power, and data availability. The origins of this transformation can be traced back to the late 1990s, when the field of economics began grappling with the challenges and opportunities presented by what is now termed "Big Data"--the availability of large datasets with numerous predictors. This period marked the emergence of systematic efforts to develop tools capable of addressing the high-dimensional nature of these datasets with seminal contributions by Frank Diebold, Mario Forni, Marc Hallin, Marco Lippi, Lucrezia Reichlin, Jim Stock, and Mark Watson (see Reichlin, 2003; Watson, 2003; Diebold, 2003). They laid the foundation for a new era of forecasting, as presented at the World Meeting of the Econometric Society in the summer of 2000 (for a survey see De Mol et al., 2017; Diebold, 2021). Since these early contributions, the field of macroeconomic forecasting has experienced significant progress.

Deep learning based weather forecasting (DLWF) models leverage past weather observations to generate future forecasts, supporting a wide range of downstream tasks, including tropical cyclone (TC) trajectory prediction. In this paper, we investigate their vulnerability to adversarial attacks, where subtle perturbations to the upstream weather forecasts can alter the downstream TC trajectory predictions. Although research on adversarial attacks in DLWF models has grown recently, generating perturbed upstream forecasts that reliably steer downstream output toward attacker-specified trajectories remains a challenge. First, conventional TC detection systems are opaque, non-differentiable black boxes, making standard gradient-based attacks infeasible. Second, the extreme rarity of TC events leads to severe class imbalance problem, making it difficult to develop efficient attack methods that will produce the attacker's target trajectories. Furthermore, maintaining physical consistency in adversarially generated forecasts presents another significant challenge. To overcome these limitations, we propose Cyc-Attack, a novel method that perturbs the upstream forecasts of DLWF models to generate adversarial trajectories. First, we pre-train a differentiable surrogate model to approximate the TC detector's output, enabling the construction of gradient-based attacks. Cyc-Attack also employs skewness-aware loss function with kernel dilation strategy to address the imbalance problem. Finally, a distance-based gradient weighting scheme and regularization are used to constrain the perturbations and eliminate spurious trajectories to ensure the adversarial forecasts are realistic and not easily detectable.

Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection within a single learning process. Unlike traditional heuristic-driven filter or wrapper approaches, our RL agent adaptively selects features using a reward signal that explicitly integrates predictive performance with fairness considerations. This dynamic formulation allows the model to balance generalization, accuracy, and equity throughout the training process, rather than rely exclusively on pre-processing adjustments or post hoc correction mechanisms. In this paper, we describe the construction of a multi-component reward function, the specification of the agents action space over feature subsets, and the integration of this system with ensemble learning. We aim to provide a flexible and generalizable way to select features in environments where predictors are correlated and biases can inadvertently re-emerge.

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As some financial leaders fret publicly about the stock market falling to earth, Andrew Ross Sorkin's new book recounts the greatest crash of them all. As stocks plummeted on the morning of October 24th, 1929, a large crowd gathered on Wall Street outside of the New York Stock Exchange. Pat Bologna, a local shoeshiner whose life savings were invested in the market, dodged into a packed brokerage nearby. "Everybody is shouting," he later recalled. "They're all trying to reach the glass booth where the clerks are. Everybody wants to sell out. The boy at the quotation board is running scared. He can't keep up with the speed of the way stocks are dropping. The guy who runs it is Irish. I can't hear what he's saying. But a guy near me shouts, 'the sonofabitch has sold me out!' " The stock-market crash of 1929 occupies a dark but indelible place in the national imagination, and for good reason.

Google Maps, Waze and Apple Maps comparison reveals key differences in navigation accuracy, privacy policies and features to help users choose the best app.

Where an AI system is used, patients could face difficulties showing fault in the event of a negative outcome, experts say. Where an AI system is used, patients could face difficulties showing fault in the event of a negative outcome, experts say. The use of artificial intelligence in healthcare could create a legally complex blame game when it comes to establishing liability for medical failings, experts have warned. The development of AI for clinical use has boomed, with researchers creating a host of tools, from algorithms to help interpret scans to systems that can aid with diagnoses . AI is also being developed to help manage hospitals, from optimising bed capacity to tackling supply chains.

While working at NASA in 2003, Dr. Robert Ambrose, director of the Robotics and Automation Design Lab (RAD Lab), designed a robot with no fixed top or bottom. A perfect sphere, the RoboBall could not flip over, and its shape promised access to places wheeled or legged machines could not reach -- from the deepest lunar crater to the uneven sands of a beach. Two of his students built the first prototype, but then Ambrose shelved the idea to focus on drivable rovers for astronauts. When Ambrose arrived at Texas A&M University in 2021, he saw a chance to reignite his idea. With funding from the Chancellor's Research Initiative and Governor's University Research Initiative, Ambrose brought RoboBall back to life.

Plus: The Trump administration has laid off thousands of federal health workers. The pendant on Rebecca Jensen-Clem's necklace is composed of 36 silver hexagons entwined in a honeycomb mosaic. At the Keck Observatory, in Hawaii, just as many segments make up a mirror that spans 33 feet, reflecting images of uncharted worlds for her to study. Jensen-Clem, an astronomer at the University of California, Santa Cruz, works with the Keck Observatory to try to detect new planets without leaving our own. It's a pursuit that faces a vast array of obstacles, for example wind, and fluctuations in atmospheric density and temperature. At her lab among the redwoods, Jensen-Clem and her students experiment with new technologies and software to help overcome the challenges, and see into space more clearly.

Earth's turbulent atmosphere makes it hard to detect new planets from the ground. Astronomer Rebecca Jensen-Clem is working out how to find them anyway. The pendant on Rebecca Jensen-Clem's necklace is only about an inch wide, composed of 36 silver hexagons entwined in a honeycomb mosaic. At the Keck Observatory, in Hawaii, just as many segments make up a mirror that spans 33 feet, reflecting images of uncharted worlds for her to study. Jensen-Clem, an astronomer at the University of California, Santa Cruz, works with the Keck Observatory to figure out how to detect new planets without leaving our own. Typically, this pursuit faces an array of obstacles: Wind, fluctuations in atmospheric density and temperature, or even a misaligned telescope mirror can create a glare from a star's light that obscures the view of what's around it, rendering any planets orbiting the star effectively invisible.