US President Donald Trump has announced that he will allow AI chip giant Nvidia to sell its advanced H200 chips to approved customers in China. We will protect National Security, create American Jobs, and keep America's lead in AI, Trump said on social media on Monday. The decision will apply to other US chip companies like AMD and comes after extensive lobbying by Nvidia boss Jensen Huang, who visited Washington last week to drum up support. Nvidia - both the world's leading chip firm and most valuable company - has found itself at the centre of a geopolitical tug-of-war between the US and China in recent months, and had been banned from selling its most advanced chips to Beijing. Trump reversed the chip-selling ban in July, but demanded that Nvidia pay 15% of its Chinese revenues to the US government. Beijing then reportedly ordered its tech companies to stop buying Nvidia chips manufactured for use in the Chinese market.

--Under-actuated compliant robotic systems offer a promising approach to mitigating actuation and control challenges by harnessing pre-designed, embodied dynamic behaviors. This paper presents Flix-Walker, a novel, untethered, centimeter-scale quadrupedal robot inspired by compliant under-actuated mechanisms. Flix-Walker employs flexible, helix-shaped beams as legs, which are actuated by vibrations from just two motors to achieve three distinct mobility modes. We analyze the actuation parameters required to generate various locomotion modes through both simulation and prototype experiments. The effects of system and environmental variations on locomotion performance are examined, and we propose a generic metric for selecting control parameters that produce robust and functional motions. Under-actuated, compliant systems exploit structural dynamics to produce complex robotic motions for locomotion and manipulation, while reducing actuation demands. Leveraging these dynamic behaviors diminishes the need for active actuation, lowers controller complexity, reduces actuator count, and simplifies fabrication [1], [2]. Legged robots offer superior maneuverability in cluttered terrain compared to wheeled or tracked platforms [3], [4].

Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming Myke C. Cohen 1,2, David A. Grimm 3, Reuth Mirsky 4, and Xiaoyun Yin 1 1 Arizona State University, Mesa, AZ 2 Aptima, Inc., Woburn, MA 3 Georgia Institute of Technology, Atlanta, GA 4 Tufts University, Medford, MA Abstract Animal-Human-Machine (AHM) teams are a type of hybrid intelligence system wherein interactions between a human, AI-enabled machine, and animal members can result in unique capabilities greater than the sum of their parts. This paper calls for a systematic approach to studying the design of AHM team structures to optimize performance and overcome limitations in various applied settings. We consider the challenges and opportunities in investigating the synergistic potential of AHM team members by introducing a set of dimensions of AHM team functioning to effectively utilize each member's strengths while compensating for individual weaknesses. Using three representative examples of such teams--security screening, search-and-rescue, and guide dogs--the paper illustrates how AHM teams can tackle complex tasks. We conclude with open research directions that this multidimensional approach presents for studying hybrid human-AI systems beyond AHM teams. Keywords: multi-agent systems, animal-human-machine teaming, functional allocation 1 Introduction Consider a Blind or Visually Impaired (BVI) person training to be assisted by a guide dog. When the pair reaches an obstacle along their path, they 1 arXiv:2504.13973v1

Amazon, Microsoft and Google are operating datacentres that use vast amounts of water in some of the world's driest areas and are building many more, an investigation by SourceMaterial and the Guardian has found. With Donald Trump pledging to support them, the three technology giants are planning hundreds of datacentres in the US and across the globe, with a potentially huge impact on populations already living with water scarcity. "The question of water is going to become crucial," said Lorena Jaume-Palasí, founder of the Ethical Tech Society. "Resilience from a resource perspective is going to be very difficult for those communities." Efforts by Amazon, the world's largest online retailer, to mitigate its water use have sparked opposition from inside the company, SourceMaterial's investigation found, with one of its own sustainability experts warning that its plans are "not ethical".

General-sum differential games can approximate values solved by Hamilton-Jacobi-Isaacs (HJI) equations for efficient inference when information is incomplete. However, solving such games through conventional methods encounters the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a scalable approach to alleviate the CoD and approximate values, but there exist convergence issues for value approximations through vanilla PINNs when state constraints lead to values with large Lipschitz constants, particularly in safety-critical applications. In addition to addressing CoD, it is necessary to learn a generalizable value across a parametric space of games, rather than training multiple ones for each specific player-type configuration. To overcome these challenges, we propose a Hybrid Neural Operator (HNO), which is an operator that can map parameter functions for games to value functions. HNO leverages informative supervised data and samples PDE-driven data across entire spatial-temporal space for model refinement. We evaluate HNO on 9D and 13D scenarios with nonlinear dynamics and state constraints, comparing it against a Supervised Neural Operator (a variant of DeepONet). Under the same computational budget and training data, HNO outperforms SNO for safety performance. This work provides a step toward scalable and generalizable value function approximation, enabling real-time inference for complex human-robot or multi-agent interactions.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

As human-agent teaming (HAT) research continues to grow, computational methods for modeling HAT behaviors and measuring HAT effectiveness also continue to develop. One rising method involves the use of human digital twins (HDT) to approximate human behaviors and socio-emotional-cognitive reactions to AI-driven agent team members. In this paper, we address three research questions relating to the use of digital twins for modeling trust in HATs. First, to address the question of how we can appropriately model and operationalize HAT trust through HDT HAT experiments, we conducted causal analytics of team communication data to understand the impact of empathy, socio-cognitive, and emotional constructs on trust formation. Additionally, we reflect on the current state of the HAT trust science to discuss characteristics of HAT trust that must be replicable by a HDT such as individual differences in trust tendencies, emergent trust patterns, and appropriate measurement of these characteristics over time. Second, to address the question of how valid measures of HDT trust are for approximating human trust in HATs, we discuss the properties of HDT trust: self-report measures, interaction-based measures, and compliance type behavioral measures. Additionally, we share results of preliminary simulations comparing different LLM models for generating HDT communications and analyze their ability to replicate human-like trust dynamics. Third, to address how HAT experimental manipulations will extend to human digital twin studies, we share experimental design focusing on propensity to trust for HDTs vs. transparency and competency-based trust for AI agents.

Artificial Intelligence (AI) techniques, particularly machine learning techniques, are rapidly transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach, focusing on how it empowers human decision-making in complex environments. While trust and explainability continue to pose significant challenges, our exploration focuses on the potential of AI-driven HAT to transform tactical operations. By improving situational awareness and supporting more informed decision-making, AI-driven HAT can enhance the effectiveness and safety of such operations. To this end, we propose a comprehensive framework that addresses the key components of AI-driven HAT, including trust and transparency, optimal function allocation between humans and AI, situational awareness, and ethical considerations. The proposed framework can serve as a foundation for future research and development in the field. By identifying and discussing critical research challenges and knowledge gaps in this framework, our work aims to guide the advancement of AI-driven HAT for optimizing tactical operations. We emphasize the importance of developing scalable and ethical AI-driven HAT systems that ensure seamless human-machine collaboration, prioritize ethical considerations, enhance model transparency through Explainable AI (XAI) techniques, and effectively manage the cognitive load of human operators.

A leitmotif is a recurring theme in literature, movies or music that carries symbolic significance for the piece it is contained in. When this piece can be represented as a multi-dimensional time series (MDTS), such as acoustic or visual observations, finding a leitmotif is equivalent to the pattern discovery problem, which is an unsupervised and complex problem in time series analytics. Compared to the univariate case, it carries additional complexity because patterns typically do not occur in all dimensions but only in a few - which are, however, unknown and must be detected by the method itself. In this paper, we present the novel, efficient and highly effective leitmotif discovery algorithm LAMA for MDTS. LAMA rests on two core principals: (a) a leitmotif manifests solely given a yet unknown number of sub-dimensions - neither too few, nor too many, and (b) the set of sub-dimensions are not independent from the best pattern found therein, necessitating both problems to be approached in a joint manner. In contrast to most previous methods, LAMA tackles both problems jointly - instead of independently selecting dimensions (or leitmotifs) and finding the best leitmotifs (or dimensions). Our experimental evaluation on a novel ground-truth annotated benchmark of 14 distinct real-life data sets shows that LAMA, when compared to four state-of-the-art baselines, shows superior performance in detecting meaningful patterns without increased computational complexity.

Designing roadside sensing for intelligent transportation applications requires balancing cost and performance,especially when choosing between high and low-resolution sensors. The tradeoff is challenging due to sensor heterogeneity,where different sensors produce unique data modalities due to varying physical principles. High-resolution LiDAR offers detailed point cloud, while 4D millimeter-wave radar, despite providing sparser data, delivers velocity information useful for distinguishing objects based on movement patterns. To assess whether reductions in spatial resolution can be compensated by the informational richness of sensors, particularly in recognizing both vehicles and vulnerable road users (VRUs), we propose Residual Fusion Net (ResFusionNet) to fuse multimodal data for 3D object detection. This enables a quantifiable tradeoff between spatial resolution and information richness across different modalities. Furthermore, we introduce a sensor placement algorithm utilizing probabilistic modeling to manage uncertainties in sensor visibility influenced by environmental or human-related factors. Through simulation-assisted ex-ante evaluation on a real-world testbed, our findings show marked marginal gains in detecting VRUs--an average of 16.7% for pedestrians and 11% for cyclists--when merging velocity-encoded radar with LiDAR, compared to LiDAR only configurations. Additionally, experimental results from 300 runs reveal a maximum loss of 11.5% and a average of 5.25% in sensor coverage due to uncertainty factors. These findings underscore the potential of using low spatial resolution but information-rich sensors to enhance detection capabilities for vulnerable road users while highlighting the necessity of thoroughly evaluating sensor modality heterogeneity, traffic participant diversity, and operational uncertainties when making sensor tradeoffs in practical applications.