Decision Systems (United States)

When the radio signal propagates in the near-field (NF), the distance can be estimated by the curvature of the wavefront. This brings new opportunities for sensing and communication, but a computational burden as well. This paper first establishes a theoretical foundation for computationally efficient NF feature (i.e., angle-of-arrival (AOA) and distance) estimation. We reveal that the computation of feature estimates can be reduced without sacrificing the estimation accuracy, if the distance between the receiver and transmitter (or scatterer) exceeds approximately one quarter of the Rayleigh distance. Based on this criterion, we introduce a zone-wise search-based algorithm that reduces computation by restricting the high-dimensional (HD) search to the angle-distance search space where the criterion is not met. In the other search space, sequential low-dimensional (LD) searches are carried out. Along with the algorithm, the estimation-theoretic analysis for NF feature estimation is provided to present the theoretical performance limit. The simulation results demonstrate that even under severe NF effect, the accuracy of the proposed algorithm reaches that of the HD search-based algorithm, which represents a realistically achievable upper bound. Nevertheless, the computational complexity of the proposed algorithm is significantly lower than the HD search-based algorithm.

Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating-base configuration makes them susceptible to real-world uncertainties, presenting substantial challenges in locomotion control. Deep reinforcement learning has emerged as a viable alternative for developing robust locomotion controllers. However, approaches relying solely on proprioception often sacrifice collision-free locomotion, as they require front-foot contact to detect stairs and adapt the gait. Meanwhile, incorporating exteroception necessitates a precisely modeled map observed by exteroceptive sensors over time. This work proposes a novel method for fusing proprioception and exteroception through a resilient multi-modal reinforcement learning framework. The proposed method yields a controller demonstrating agile locomotion on a quadrupedal robot across diverse real-world courses, including rough terrains, steep slopes, and high-rise stairs, while maintaining robustness in out-of-distribution situations.

The main form of freeway traffic congestion is the familiar stop-and-go wave, characterized by wide moving jams that propagate indefinitely upstream provided enough traffic demand. They cause severe, long-lasting adverse effects, such as reduced traffic efficiency, increased driving risks, and higher vehicle emissions. This underscores the crucial importance of artificial intervention in the propagation of stop-and-go waves. Over the past two decades, two prominent strategies for stop-and-go wave suppression have emerged: variable speed limit (VSL) and jam-absorption driving (JAD). Although they share similar research motivations, objectives, and theoretical foundations, the development of these strategies has remained relatively disconnected. To synthesize fragmented advances and drive the field forward, this paper first provides a comprehensive review of the achievements in the stop-and-go wave suppression-oriented VSL and JAD, respectively. It then focuses on bridging the two areas and identifying research opportunities from the following perspectives: fundamental diagrams, secondary waves, generalizability, traffic state estimation and prediction, robustness to randomness, simulation scenarios for strategy validation, and field tests and practical deployment. We expect that through this review, one area can effectively address its limitations by identifying and leveraging the strengths of the other, thus promoting the overall research goal of freeway stop-and-go wave suppression.

We develop link rate control policies to minimize the queueing delay of packets in overloaded networks. We show that increasing link rates does not guarantee delay reduction during overload. We consider a fluid queueing model that facilitates explicit characterization of the queueing delay of packets, and establish explicit conditions on link rates that can minimize the average and maximum queueing delay in both single-hop and multi-stage (switching) networks. These min-delay conditions require maintaining an identical ratio between the ingress and egress rates of different nodes at the same layer of the network. We term the policies that follow these conditions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rate-proportional</i> policies. We further generalize the rate-proportional policies to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queue-proportional</i> policies, which minimize the queueing delay asymptotically based on the time-varying queue length while remaining agnostic of packet arrival rates. We validate that the proposed policies lead to minimum queueing delay under various network topologies and settings, compared with benchmarks including the backpressure policy that maximizes network throughput and the max-link-rate policy that fully utilizes bandwidth. We further remark that the explicit min-delay policy design in multi-stage networks facilitates co-optimization with other metrics, such as minimizing total bandwidth, balancing link utilization and node buffer usage. This demonstrates the wider utility of our main results in data center network optimization in practice.

Transportation network design often involves multiple stakeholders with diverse priorities. We consider a system with a hierarchical multi-agent structure, featuring self-optimized subnetwork operators at the lower level and a central organization at the upper level. Independent regional planning can lead to inefficiencies due to the lack of coordination, hindering interregional travel and cross-border infrastructure development, while centralized methods may struggle to align local interests and can be impractical to implement. To support decision making for such a system, we introduce an iterative VCG-based mechanism for multi-regional network design that fosters cooperation among subnetwork operators. By leveraging the Vickery-Clarke-Groves (VCG) mechanism, the framework determines collective investment decisions and the necessary payments from both operators and the central organization to achieve efficient outcomes. A case study on the European Railway System validates the effectiveness of the proposed method, demonstrating significant improvements in overall network performance through enhanced cross-region cooperation.

Metal-air batteries, leveraging earth-abundant metals (Zn, Al, Li, Mg, Fe) and ambient oxygen, promise exceptionally high theoretical energy densities, cost-efficiency, and minimal environmental impact. This review highlights advances in electrode architectures, catalysts, and electrolytes that enhance ORR/OER kinetics, rechargeability, and cycle life. We examine metal-specific innovations addressing anode corrosion in Al-air, dendrite suppression in Zn-air, stability of discharge products in Li-air, passivation mitigation in Mg-air, and redox-mediated architectures in Fe-air systems. Applications spanning electric vehicles, grid storage, portable electronics, and emergency backup underscore the technology's versatility, while market projections (USD <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$400 \mathrm{M} \rightarrow$</tex> USD 1.24 B by 2027) highlight growing demand. Finally, we discuss key challenges in material stability, electrolyte management, and system integration, and outline future research directions-nanotechnology-driven materials, enhanced rechargeability techniques, and policy frameworks-to propel sustainable metal-air batteries toward large-scale deployment.

In the shift toward the quantum computing era, the foundational principles of classical cybersecurity, particularly in the realm of cryptographic algorithms, are facing unprecedented challenges. This demands comprehensive reevaluation and redesign of cryptographic infrastructures to withstand quantum adversarial attacks. With the emergence of the quantum Internet, a new approach to secure communication is possible, utilizing quantum properties that have no counterpart in classical systems. As the quantum Internet facilitates the exchange of quantum information, data publication protocols become essential in anonymizing and protecting privacy-sensitive data in quantum communication networks. This paper proposes two controlled quantum anonymous communication (QAC) protocols for publishing classical and quantum information on an Internet server (IS) with the assistance of a communication service provider. The first protocol allows for the controlled publication of classical information without revealing the publisher’s identity such that an adversary, even with access to all network resources, cannot trace the publication source—i.e., achieving perfect untraceability. The second protocol enables anonymous publication of quantum information on an IS in a controlled and untraceable manner. These protocols serve as essential building blocks for advancing the quantum Internet, which has the potential to transform communication and information exchange methods. We provide a detailed anonymity analysis of these QAC protocols for data publication, ensuring that the published symbol or qudit information remains untraceable to its publisher. Moreover, the performance analysis in terms of publication error probability, fidelity, and degree of anonymity in noisy environments demonstrates the robustness of the protocols against noise and adversarial attacks.

Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging highcapacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30% cost improvement and 80 % reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.Project website and code: https://acc25art.github.io

SummaryModeling cellular responses to genetic perturbations is a significant challenge in computational biology. Measuring all gene perturbations and their combinations across cell types and conditions is experimentally challenging, highlighting the need for predictive models that generalize across data types to support this task. Here we present MORPH, a MOdular framework for predicting Responses to Perturbational cHanges. MORPH combines a discrepancy-based variational autoencoder with an attention mechanism to predict cellular responses to unseen perturbations. It supports both single-cell transcriptomics and imaging outputs and can generalize to unseen perturbations, combinations of perturbations, and perturbations in new cellular contexts. The attention-based framework enables inference of gene interactions and regulatory networks, while the learned gene embeddings can guide the design of informative perturbations. Overall, we envision MORPH as a flexible tool for optimizing perturbation experiments, enabling efficient exploration of the perturbation space to advance understanding of cellular programs for fundamental research and therapeutic applications.