321 papers
This paper proposes a community-informed, multidisciplinary approach to developing AI tools for analyzing police body-worn camera footage, emphasizing the integration of diverse stakeholder perspectives to ensure democratic accountability and transparency.
This paper proposes a Holographic Intelligence Surface (HIS) assisted Integrated Sensing and Communication (ISAC) system utilizing continuous-aperture arrays and an alternating optimization algorithm to significantly enhance multi-target sensing performance while satisfying multi-user communication requirements, outperforming traditional discrete-array-based systems.
This paper proposes a goal-oriented scheduling framework for real-time remote inference over networks with two-way delays, modeling the problem as a Semi-Markov Decision Process to derive index-based threshold policies that jointly optimize packet freshness, length, and transmission timing, thereby significantly reducing inference error compared to traditional age-based scheduling.
This paper proposes DDKL-PT, a distributed data-driven framework where multi-agent systems achieve consensus on global dynamics by exchanging local deep Koopman model estimates derived from private partial trajectories, enabling accurate model predictive control without sharing raw training data.
This paper proposes a robust stabilization framework for linear Markov-jumping hyperbolic PDEs by combining backstepping control with neural operator approximations, proving mean-square exponential stability under parameter uncertainty and validating the approach through freeway traffic control simulations.
This paper introduces a novel differentially private framework for estimating cumulative distribution functions using polynomial projection and sparse matching pursuit methods, which offer superior performance and adaptability for decentralized and streaming data scenarios compared to existing approaches.
This paper presents the first systematic survey of Audio-Language Models (ALMs), offering a comprehensive review of their architectures and training objectives across speech, music, and sound, while establishing a unified research landscape to guide future developments and practical applications.
The paper presents SHIELD, a host-independent framework that leverages deep filesystem features parsed at the storage controller level to achieve tamper-resistant, real-time ransomware detection and mitigation with over 97% accuracy and minimal impact on benign applications.
This paper presents a compact, low-profile nested hexagonal metamaterial sensor operating in S and X-bands that achieves high sensitivity (9.55%) and precise permittivity characterization for materials with dielectric constants between 1 and 6, as validated through both simulation and experimental testing.
This paper challenges the notion that time-varying dynamics are necessary for selectivity in State Space Models by introducing the Geometric SSM, which leverages geometric control theory to achieve pattern recognition and temporal memory through invariant subspaces while maintaining efficient LTI-based training.
This paper proposes a novel multi-agent reinforcement learning algorithm that enhances sample efficiency by combining a decomposed centralized critic with decentralized ensemble learning, utilizing ensemble kurtosis for selective exploration, a truncated TD() variant for reduced-variance off-policy training, and a mixed on/off-policy actor approach to outperform state-of-the-art baselines on standard benchmarks.
This paper utilizes contraction theory to establish that semicontraction in the observable space is a sufficient condition for different network structures of nonlinear dynamical systems, such as Kuramoto oscillators, to become indistinguishable when only partial nodal measurements are available.
This paper investigates the limitations of uniquely identifying network structures in linear dynamical systems from partial measurements by characterizing the space of consistent networks through observability properties, demonstrating that observing over 6% of nodes in random networks achieves approximately 99% edge classification accuracy while linking structural identifiability to the spectral properties of an augmented observability Gramian.
This paper presents a lightweight, model-free approach for real-time slip detection and tire-road friction coefficient estimation in autonomous racing that relies solely on IMU, LiDAR, and control inputs, demonstrating accurate performance across varying friction levels without requiring complex models or training data.
This paper introduces CoGuide, a novel diffusion-based framework that addresses spatial inverse problems with non-differentiable and partially specified forward operators by reformulating likelihood guidance in a contrastively learned embedding space to achieve robust and consistent reconstructions.
This paper proposes a framework for managing conflicts between concurrent xApps in Open RAN by combining explainable machine learning and causal inference to identify conflicting control parameters, model their interactions via causal graphs, and quantify their impacts on network performance.
This paper proposes a scalable multi-period sparse optimization method that leverages circuit-theory formulations and persistency constraints to proactively identify persistent vulnerability locations across a sequence of power grid blackouts under increasing stress, thereby enhancing system resilience through early warning diagnosis.
This paper proposes and experimentally validates a conductive amplitude and phase matrix framework that enables the emulation of multiple targets with arbitrary radar profiles for testing integrated sensing and communication (ISAC) base stations equipped with large-scale antenna arrays using radar target simulators with limited interface ports.
This paper proposes a semantics-guided fuzzy control framework that leverages Large Language Models to compress multimodal observations into interpretable tokens for robust, GPS-denied underwater navigation and semantic communication-based coordination among multi-robot swarms.
This paper proposes a joint sparsity and beamforming design for Reconfigurable Distributed Antennas and Reflecting Surface (RDARS)-aided systems, deriving closed-form optimal sparsity for specific user scenarios and developing a weighted minimum mean-square error-based alternating optimization algorithm for general cases to maximize sum rate while enabling low-complexity element configuration.