Cyclone rapid intensification is the rapid increase in cyclone wind intensity, exceeding a threshold of 30 knots, within 24 hours. Rapid intensification is considered an extreme event during a cyclone, and its occurrence is relatively rare, contributing to a class imbalance in the dataset. A diverse array of factors influences the likelihood of a cyclone undergoing rapid intensification, further complicating the task for conventional machine learning models. In this paper, we evaluate deep learning, ensemble learning and data augmentation frameworks to detect cyclone rapid intensification based on wind intensity and spatial coordinates. We note that conventional data augmentation methods cannot be utilised for generating spatiotemporal patterns replicating cyclones that undergo rapid intensification. Therefore, our framework employs deep learning models to generate spatial coordinates and wind intensity that replicate cyclones to address the class imbalance problem of rapid intensification. We also use a deep learning model for the classification module within the data augmentation framework to di fferentiate between rapid and non-rapid intensification events during a cyclone. Our results show that data augmentation improves the results for rapid intensification detection in cyclones, and spatial coordinates play a critical role as input features to the given models. This paves the way for research in synthetic data generation for spatiotemporal data with extreme events. Introduction Over the past decade, the impacts of climate change have manifested in an alarming increase in the strength of tropical cyclones, characterised by elevated levels of precipitation and wind intensity, resulting in devastating consequences on a global scale [1, 2, 3]. Rappaport et al. [4] defined rapid intensification as a sudden surge in wind intensity exceeding 30 knots (35 miles / hour or 55 kilometres / hour) within 24 hours [5]. Forecasting the rapid intensification of high-category cyclones (Category 4 and 5) poses greater challenges due to their infrequent occurrence, in contrast to lower-category cyclones[6].

Fox News senior national correspondent Rich Edson has the latest on a Chinese pair charged with smuggling a'devastating' pathogen to the U.S. on'The Story.' By all appearances, the world is edging perilously close to the brink of a catastrophic global conflict. In just the past few days, five deeply troubling developments have emerged -- each significant on its own -- but taken together, they form a pattern too urgent to dismiss. Viewed in context, these events expose a rapidly deteriorating international order, where diplomacy is failing, deterrence is weakening, and the risk of multi-theater war is rising sharply. First, Ukraine's audacious drone strike deep inside Russian territory -- reportedly destroying or damaging a significant share of Russia's strategic bomber fleet -- bears the hallmarks of Western involvement.

Defense Minister Gen Nakatani called Saturday for closer defense cooperation among like-minded partners in the Indo-Pacific region in order to strengthen the global rules-based order and -- in an implicit criticism of China -- act as a counter to countries seeking to erode the status quo. The Japanese defense chief used a speech before scores of his counterparts and military brass in Singapore at the Shangri-La Dialogue, Asia's leading security conference, to push for closer cooperation and coordination, "while ensuring openness, inclusiveness and transparency, with an aim of restoring a rules-based international order in the Indo-Pacific region, strengthening accountability and promoting the international public good." Nakatani said the need to unite on defense cooperation was clear, pointing to Russia's invasion of Ukraine -- a violation of the U.N. charter -- and Beijing's moves in the disputed South China Sea, including its decision to openly ignore a 2016 international arbitral tribunal ruling that dismissed the country's claim to most of the strategic waterway.

Breakthroughs, discoveries, and DIY tips sent every weekday. In 1917, two US submarines collided off the coast of San Diego and submarine USS F-1 sank to the bottom of the Pacific Ocean, along with 19 crew members aboard. The horrible accident, whose wreckage was discovered in 1975, represents the US Naval Submarine Force's first wartime submarine loss. Now, researchers from Woods Hole Oceanographic Institution have captured new footage of the 1,300 feet-deep underwater archaeological site. "They were technical dives requiring specialized expertise and equipment," Anna Michel, a co-lead of the expedition and chief scientist at the National Deep Submergence Facility, said in a statement. "We were careful and methodical in surveying these historical sites so that we could share these stunning images, while also maintaining the reverence these sites deserve."

We investigate the estimation of an optimal transport map between probability measures on an infinite-dimensional space and reveal its minimax optimal rate. Optimal transport theory defines distances within a space of probability measures, utilizing an optimal transport map as its key component. Estimating the optimal transport map from samples finds several applications, such as simulating dynamics between probability measures and functional data analysis. However, some transport maps on infinite-dimensional spaces require exponential-order data for estimation, which undermines their applicability. In this paper, we investigate the estimation of an optimal transport map between infinite-dimensional spaces, focusing on optimal transport maps characterized by the notion of $γ$-smoothness. Consequently, we show that the order of the minimax risk is polynomial rate in the sample size even in the infinite-dimensional setup. We also develop an estimator whose estimation error matches the minimax optimal rate. With these results, we obtain a class of reasonably estimable optimal transport maps on infinite-dimensional spaces and a method for their estimation. Our experiments validate the theory and practical utility of our approach with application to functional data analysis.

Big trajectory data hold great promise for human mobility analysis, but their utility is often constrained by the absence of critical traveler attributes, particularly sociodemographic information. While prior studies have explored predicting such attributes from mobility patterns, they often overlooked underlying cognitive mechanisms and exhibited low predictive accuracy. This study introduces SILIC, short for Sociodemographic Inference with LLM-guided Inverse Reinforcement Learning (IRL) and Cognitive Chain Reasoning (CCR), a theoretically grounded framework that leverages LLMs to infer sociodemographic attributes from observed mobility patterns by capturing latent behavioral intentions and reasoning through psychological constructs. Particularly, our approach explicitly follows the Theory of Planned Behavior (TPB), a foundational behavioral framework in transportation research, to model individuals' latent cognitive processes underlying travel decision-making. The LLMs further provide heuristic guidance to improve IRL reward function initialization and update by addressing its ill-posedness and optimization challenges arising from the vast and unstructured reward space. Evaluated in the 2017 Puget Sound Regional Council Household Travel Survey, our method substantially outperforms state-of-the-art baselines and shows great promise for enriching big trajectory data to support more behaviorally grounded applications in transportation planning and beyond.

Causal inference determines cause-and-effect relationships between variables and has broad applications across disciplines. Traditional time-series methods often reveal causal links only in a time-averaged sense, while ensemble-based information transfer approaches detect the time evolution of short-term causal relationships but are typically limited to low-dimensional systems. In this paper, a new causal inference framework, called assimilative causal inference (ACI), is developed. Fundamentally different from the state-of-the-art methods, ACI uses a dynamical system and a single realization of a subset of the state variables to identify instantaneous causal relationships and the dynamic evolution of the associated causal influence range (CIR). Instead of quantifying how causes influence effects as done traditionally, ACI solves an inverse problem via Bayesian data assimilation, thus tracing causes backward from observed effects with an implicit Bayesian hypothesis. Causality is determined by assessing whether incorporating the information of the effect variables reduces the uncertainty in recovering the potential cause variables. ACI has several desirable features. First, it captures the dynamic interplay of variables, where their roles as causes and effects can shift repeatedly over time. Second, a mathematically justified objective criterion determines the CIR without empirical thresholds. Third, ACI is scalable to high-dimensional problems by leveraging computationally efficient Bayesian data assimilation techniques. Finally, ACI applies to short time series and incomplete datasets. Notably, ACI does not require observations of candidate causes, which is a key advantage since potential drivers are often unknown or unmeasured. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events.

Large language models such as GPT, Llama, Claude, and DeepSeek can be so fluent that people feel it as a "you," and it answers encouragingly as an "I." The models can write poetry in nearly any given form, read a set of political speeches and promptly sift out and share all the jokes, draw a chart, code a website. How do they do these and so many other things that were just recently the sole realm of humans? Practitioners are left explaining jaw-dropping conversational rabbit-from-a-hat extractions with arm-waving that the models are just predicting one word at a time from an unthinkably large training set scraped from every recorded written or spoken human utterance that can be found--fair enough--or a with a small shrug and a cryptic utterance of "fine-tuning" or "transformers!" These aren't very satisfying answers for how these models can converse so intelligently, and how they sometimes err so weirdly.

Alternators have recently been introduced as a framework for modeling time-dependent data. They often outperform other popular frameworks, such as state-space models and diffusion models, on challenging time-series tasks. This paper introduces a new Alternator model, called Alternator++, which enhances the flexibility of traditional Alternators by explicitly modeling the noise terms used to sample the latent and observed trajectories, drawing on the idea of noise models from the diffusion modeling literature. Alternator++ optimizes the sum of the Alternator loss and a noise-matching loss. The latter forces the noise trajectories generated by the two noise models to approximate the noise trajectories that produce the observed and latent trajectories. We demonstrate the effectiveness of Alternator++ in tasks such as density estimation, time series imputation, and forecasting, showing that it outperforms several strong baselines, including Mambas, ScoreGrad, and Dyffusion.