326 papers
This paper introduces a maximum-entropy random walk framework for directed hypergraphs that models both broadcasting and merging interactions through a Kullback-Leibler divergence projection, utilizing Sinkhorn-type iterations to derive transition kernels and analyze ergodicity for capturing complex higher-order flows.
O3N is the first purely visual, end-to-end framework for omnidirectional open-vocabulary 3D occupancy prediction that utilizes a Polar-spiral Mamba module, Occupancy Cost Aggregation, and Natural Modality Alignment to achieve state-of-the-art performance and robust generalization in open-world exploration.
This paper analyzes and optimizes simultaneous multi-modal covert communications in heterogeneous wireless networks by deriving optimal detection error probabilities for scenarios where an adversary either knows or is unaware of the selected modalities, and proposes a low-complexity modality selection technique that maximizes covertness while satisfying transmission rate constraints.
This paper proposes a novel event-triggered, data-based adaptation method for Model Predictive Control that utilizes statistical monitoring to detect performance degradation and selectively updates the controller via reinforcement learning and identification, thereby avoiding the pitfalls of continuous updates like catastrophic forgetting, and validates the approach on a district heating system benchmark.
This paper presents a decision-support method that combines modeling-to-generate-alternatives with power flow simulations to design diverse, cost-effective, and socially acceptable carbon-neutral district heating networks that minimize impacts on the electricity grid, as demonstrated through a Dutch case study.
This paper introduces Conformalized Data-Driven Reachability (CDDR), a framework that leverages the Learn Then Test procedure to provide Probably Approximately Correct (PAC) coverage guarantees for reachable set over-approximations in linear and nonlinear systems using only independent and identically distributed calibration data, thereby overcoming the limitations of existing deterministic methods that require known noise bounds or specific system parameters.
This paper presents a passive non-line-of-sight imaging method that utilizes a single photograph of a penumbra cast by a wall edge to reconstruct two-dimensional hidden scenes by introducing a range dimension and deriving a new forward model with corresponding inversion algorithms.
This paper proposes and validates a novel online method for estimating particle beam current directly from secondary electron count data using time-resolved measurements and joint estimation techniques, thereby eliminating the need for offline calibration and significantly improving micrograph accuracy and sample milling outcomes.
This paper introduces a passive range imaging method that jointly estimates object distance and intrinsic properties from hyperspectral thermal measurements by computationally separating atmospheric absorption effects from surface radiance, utilizing regularization and atmospheric modeling to recover range features in natural scenes without active illumination.
This paper proposes an online learning algorithm based on contextual multi-armed bandits to optimize bidding strategies for price-maker wind power producers, effectively addressing the computational complexity and market modeling challenges of traditional bilevel optimization approaches.
This paper proposes a task-oriented learning framework for automatic EEG denoising that eliminates the need for clean reference signals by using blind source separation and a learnable selector optimized via downstream task performance, achieving significant improvements in both classification accuracy and signal quality across diverse datasets.
This paper establishes the existence of, develops an algorithm for computing, and demonstrates the application of quadratizations that preserve the dissipativity (stability) properties of polynomial ODE systems, thereby enabling more reliable model analysis and control in fields such as systems theory and synthetic biology.
This paper proposes Style-Guided Motion Diffusion (SGMD), a novel framework that integrates a Transformer-based architecture with a Style Modulation module and spatial-temporal masking to generate realistic, music-aligned dance sequences that are both stylistically consistent and flexibly controllable for tasks like trajectory generation, in-betweening, and inpainting.
This paper presents an unsupervised adversarial deep-unfolding network that leverages a single high-resolution RGB image to super-resolve low-resolution MA-XRF scans of Old Master paintings, effectively overcoming data scarcity and acquisition time limitations while outperforming existing state-of-the-art methods.
The paper proposes ExSampling, an integrated system combining a recording application and a Deep Learning environment to enable the real-time ensemble performance of field-recorded environmental sounds through automated sound mapping to Ableton Live tracks.
This paper proposes a novel spatial IDFT technique to eliminate beam squint in massive phased arrays by counteracting inherent array-induced distortions, while also analyzing the underlying causes of squint and demonstrating how OFDM modulation can mitigate resulting signal quality issues.
This paper proposes a radar target tracking framework utilizing random finite sets and multiple hypothesis tracking to effectively mitigate main-beam range deception jamming, specifically range gate pull-off attacks, by modeling jammer-induced biases and false detections to maintain accurate target tracking and enable attack detection without relying on additional signal features.
This paper introduces ResSR, a computationally efficient, unsupervised model-based method for multispectral image super-resolution that decouples spectral and spatial processing to achieve high-quality reconstruction with significantly reduced computational cost compared to existing approaches.
This paper explores the application of Generative Diffusion Models (GDMs) in massive MIMO communications by reviewing their theoretical foundations, analyzing recent advancements including a case study on near-field channel estimation, and outlining future research directions and challenges for enhancing channel state information acquisition in 5G and 6G networks.
This paper provides a comprehensive survey of the evolution of anti-deception jamming techniques for radar systems, categorizing current strategies into prevention, detection, and mitigation while highlighting future research directions in distributed, cognitive, and AI-enabled radar technologies.