--This study investigates how autonomous vehicle (A V) technology is perceived by pedestrians and bicyclists in Pittsburgh. Using survey data from over 1200 respondents, the research explores the interplay between demographics, A V interactions, infrastructural readiness, safety perceptions, and trust. Findings highlight demographic divides, infrastructure gaps, and the crucial role of communication and education in A V adoption. Autonomous vehicle (A V) integration into urban settings has sparked serious concerns about how these vehicles may affect vulnerable road users, especially pedestrians and cyclists. It is critical to comprehend the comfort, safety, and views of these road users as autonomous vehicles (A Vs) are tested and used more frequently in places like Pittsburgh. Sharing the road with autonomous vehicles poses special risks for pedestrians and cyclists because of their exposure and lack of physical protection. Among these issues are worries regarding A Vs' capacity to recognize and react to their motions, especially in situations with a lot of traffic or unpredictability. Furthermore, concerns and discomfort may be exacerbated by the inadequacy of the current urban infrastructure to facilitate the safe coexistence of A Vs and non-motorized users.

Modeling stochastic differential equations (SDEs) is crucial for understanding complex dynamical systems in various scientific fields. Recent methods often employ neural network-based models, which typically represent SDEs through a combination of deterministic and stochastic terms. However, these models usually lack interpretability and have difficulty generalizing beyond their training domain. This paper introduces the Finite Expression Method (FEX), a symbolic learning approach designed to derive interpretable mathematical representations of the deterministic component of SDEs. For the stochastic component, we integrate FEX with advanced generative modeling techniques to provide a comprehensive representation of SDEs. The numerical experiments on linear, nonlinear, and multidimensional SDEs demonstrate that FEX generalizes well beyond the training domain and delivers more accurate long-term predictions compared to neural network-based methods. The symbolic expressions identified by FEX not only improve prediction accuracy but also offer valuable scientific insights into the underlying dynamics of the systems, paving the way for new scientific discoveries.

The heatwave currently blanketing two-thirds of the United States with record-setting temperatures is straining the nation's power system. On Monday, Con Edison, New York City's power provider, urged residents to conserve electricity. It reduced power voltage to the borough of Brooklyn by 8 percent as it made repairs; it did the same to areas in the boroughs of Staten Island and Queens yesterday. Thousands also lost power as the grid could not handle the strain. Comparable outages have been felt around much of the East Coast and Midwest including in the states of Virginia and New Jersey.

Mogadishu, Somalia – Ending the United States' "forever wars" was a major slogan of Donald Trump's 2024 election campaign, during which he and many of his supporters spoke out against American resources and lives being put to waste in conflicts across the globe. But on February 1, a mere 10 days after being inaugurated for a second time, President Trump announced that the US had carried out air strikes targeting senior leadership of ISIL (ISIS) in Somalia. "These killers, who we found hiding in caves, threatened the United States," his post on X read. This marked Trump's first military action overseas, but it wouldn't be his last. In the time since, the US has provided weapons and support to Israel in its wars in Gaza and across the Middle East; it has launched strikes on Yemen; and even attacked Iran's nuclear facilities.

Three Ukrainian soldiers raced across a field on a quad bike in eastern Ukraine, weaving at 100 kilometers an hour to avoid the attack drone chasing them from the sky. One fired a shotgun upward, blasting the tiny craft into pieces. This time it is just a training exercise. But with Russia having gained an upper hand in front line drone warfare for the first time since it invaded, Kyiv's troops are practicing hard.

Russia and Ukraine have swapped drone strikes, with at least three people reportedly killed by Moscow near the shared border. Strikes were reported overnight on Tuesday in several areas of Ukraine, as well as in Moscow. The attacks are the latest in a series of intensifying hostilities as the efforts of the United States to broker a ceasefire have stalled, with Russia appearing eager to take advantage, as global attention is dominated by the war between Israel and Iran. A Russian drone attack on a village in Sumy killed an eight-year-old boy and two adults, and injured another three people, the military administration of the region said. Drone strikes also wounded five people in Kharkiv and four others in the Dnipropetrovsk region, local authorities said.

Sparse attacks are to optimize the magnitude of adversarial perturbations for fooling deep neural networks (DNNs) involving only a few perturbed pixels (i.e., under the l0 constraint), suitable for interpreting the vulnerability of DNNs. However, existing solutions fail to yield interpretable adversarial examples due to their poor sparsity. Worse still, they often struggle with heavy computational overhead, poor transferability, and weak attack strength. In this paper, we aim to develop a sparse attack for understanding the vulnerability of CNNs by minimizing the magnitude of initial perturbations under the l0 constraint, to overcome the existing drawbacks while achieving a fast, transferable, and strong attack to DNNs. In particular, a novel and theoretical sound parameterization technique is introduced to approximate the NP-hard l0 optimization problem, making directly optimizing sparse perturbations computationally feasible. Besides, a novel loss function is designed to augment initial perturbations by maximizing the adversary property and minimizing the number of perturbed pixels simultaneously. Extensive experiments are conducted to demonstrate that our approach, with theoretical performance guarantees, outperforms state-of-the-art sparse attacks in terms of computational overhead, transferability, and attack strength, expecting to serve as a benchmark for evaluating the robustness of DNNs. In addition, theoretical and empirical results validate that our approach yields sparser adversarial examples, empowering us to discover two categories of noises, i.e., "obscuring noise" and "leading noise", which will help interpret how adversarial perturbation misleads the classifiers into incorrect predictions. Our code is available at https://github.com/fudong03/SparseAttack.

The automation of composite sheet layup is essential to meet the increasing demand for composite materials in various industries. However, draping plans for the robotic layup of composite sheets are not robust. A plan that works well under a certain condition does not work well in a different condition. Changes in operating conditions due to either changes in material properties or working environment may lead a draping plan to exhibit suboptimal performance. In this paper, we present a comprehensive framework aimed at refining plans based on the observed execution performance. Our framework prioritizes the minimization of uncompacted regions while simultaneously improving time efficiency. To achieve this, we integrate human expertise with data-driven decision-making to refine expert-crafted plans for diverse production environments. We conduct experiments to validate the effectiveness of our approach, revealing significant reductions in the number of corrective paths required compared to initial expert-crafted plans. Through a combination of empirical data analysis, action-effectiveness modeling, and search-based refinement, our system achieves superior time efficiency in robotic layup. Experimental results demonstrate the efficacy of our approach in optimizing the layup process, thereby advancing the state-of-the-art in composite manufacturing automation.

As Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making in the real world, it is essential to know when to trust LLM decisions. Existing LLM Uncertainty Quantification (UQ) methods are primarily designed for single-turn question-answering formats, resulting in multi-step decision-making scenarios, e.g., LLM agentic system, being underexplored. In this paper, we introduce a principled, information-theoretic framework that decomposes LLM sequential decision uncertainty into two parts: (i) internal uncertainty intrinsic to the current decision, which is focused on existing UQ methods, and (ii) extrinsic uncertainty, a Mutual-Information (MI) quantity describing how much uncertainty should be inherited from preceding decisions. We then propose UProp, an efficient and effective extrinsic uncertainty estimator that converts the direct estimation of MI to the estimation of Pointwise Mutual Information (PMI) over multiple Trajectory-Dependent Decision Processes (TDPs). UProp is evaluated over extensive multi-step decision-making benchmarks, e.g., AgentBench and HotpotQA, with state-of-the-art LLMs, e.g., GPT-4.1 and DeepSeek-V3. Experimental results demonstrate that UProp significantly outperforms existing single-turn UQ baselines equipped with thoughtful aggregation strategies. Moreover, we provide a comprehensive analysis of UProp, including sampling efficiency, potential applications, and intermediate uncertainty propagation, to demonstrate its effectiveness. Codes will be available at https://github.com/jinhaoduan/UProp.

This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely studied and used in machine learning, statistics, and numerical analysis. Connections and equivalences between them are reviewed for fundamental topics such as regression, interpolation, numerical integration, distributional discrepancies, and statistical dependence, as well as for sample path properties of Gaussian processes. A unifying perspective for these equivalences is established, based on the equivalence between the Gaussian Hilbert space and the RKHS. The monograph serves as a basis to bridge many other methods based on Gaussian processes and reproducing kernels, which are developed in parallel by the two research communities.