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.

We propose the Fourier Adaptive Lite Diffusion Architecture (FALDA), a novel probabilistic framework for time series forecasting. First, we introduce the Diffusion Model for Residual Regression (DMRR) framework, which unifies diffusion-based probabilistic regression methods. Within this framework, FALDA leverages Fourier-based decomposition to incorporate a component-specific architecture, enabling tailored modeling of individual temporal components. A conditional diffusion model is utilized to estimate the future noise term, while our proposed lightweight denoiser, DEMA (Decomposition MLP with AdaLN), conditions on the historical noise term to enhance denoising performance. Through mathematical analysis and empirical validation, we demonstrate that FALDA effectively reduces epistemic uncertainty, allowing probabilistic learning to primarily focus on aleatoric uncertainty. Experiments on six real-world benchmarks demonstrate that FALDA consistently outperforms existing probabilistic forecasting approaches across most datasets for long-term time series forecasting while achieving enhanced computational efficiency without compromising accuracy. Notably, FALDA also achieves superior overall performance compared to state-of-the-art (SOTA) point forecasting approaches, with improvements of up to 9%.

--This paper proposes CTP, a novel deep learning framework that integrates convolutional neural network(CNN), Transformer architectures, and physics-informed neural network(PINN) for ocean front prediction. Ocean fronts, as dynamic interfaces between distinct water masses, play critical roles in marine biogeochemical and physical processes. Existing methods such as LSTM, ConvLSTM, and AttentionConv often struggle to maintain spatial continuity and physical consistency over multi-step forecasts. Experimental results across south China sea(SCS) and Kuroshio(KUR) regions from 1993 to 2020 demonstrate that CTP achieves state-of-the-art(SOT A) performance in both single-step and multi-step predictions, significantly outperforming baseline models in accuracy, F 1 score, and temporal stability. I NTRODUCTION O CEAN fronts, characterized by sharp gradients in physical and biogeochemical properties such as temperature, salinity, and nutrient concentrations, are critical yet dynamic features of the global ocean [1]. These transitional zones, formed by the convergence of distinct water masses, play a pivotal role in regulating energy transfer, material cycling, and biological processes across marine ecosystems [2]. The study of fronts is essential for advancing understanding of ocean dynamics, as they act as hotspots for vertical mixing, influence large-scale circulation patterns, and modulate air-sea interactions that impact regional and global climate systems [3].

Yesterday, a user on X saw a viral post of Timothée Chalamet celebrating courtside at a Knicks game and had a simple question: Who was sitting next to him? The user tapped in Grok, X's proprietary chatbot, as people often do when they want help answering questions on the platform--the software functions like ChatGPT, except it can be summoned via reply to a post. And for the most part, Grok has performed reasonably well at providing responses. Chalamet was sitting with Kylie and Kendall Jenner, but here is how the chatbot replied: "I believe you're referring to a photo with Timothée Chalamet, but the context you mention doesn't seem to align with this image. The post discusses South African politics, which doesn't relate to Timothée or the people around him."