6348 papers
This paper proposes Online FISTA, a computationally and memory-efficient algorithm that incrementally reconstructs Synthetic Aperture Radar (SAR) images through sparse coding without storing all raw data, thereby enabling real-time downstream tasks like Automatic Target Recognition for autonomous drones.
PRISM introduces a streaming human motion generation framework that employs a joint-factorized latent space and noise-free condition injection within a single foundation model to overcome representation entanglement and error accumulation, thereby unifying text-to-motion, pose-conditioned, and long-horizon sequential synthesis with state-of-the-art performance.
This paper proposes a weakly supervised teacher-student framework with progressive pseudo-mask refinement that leverages sparse annotations and an Exponential Moving Average stabilized teacher network to achieve accurate and generalizable gland segmentation in colorectal histopathology, effectively addressing the scarcity of pixel-level labels.
This paper proposes a reachability-based framework that utilizes Signal Temporal Logic and an STL feasibility filter to verify and decompose LLM-translated human commands, enabling the identification of infeasible subformulas and the generation of informative feedback to ensure reliable and safe human-autonomy teaming.
This paper proposes a unified, real-time carbon-aware building energy management system that integrates a Transformer-based forecasting model with a mixed-integer linear program to co-optimize grid imports, storage, and flexible demand across day-ahead and real-time markets, achieving a 22.5% emission reduction with only a 1.7% cost increase.
This paper demonstrates that Generative AI tools, specifically the open-source CAI framework, have fundamentally disrupted consumer robot cybersecurity by automating the discovery of critical vulnerabilities across diverse devices like lawnmowers, exoskeletons, and window cleaners, thereby exposing a dangerous asymmetry between democratized offensive capabilities and lagging defensive measures.
This paper presents a simplified and fully analyzed sublinear-time algorithm that achieves a -approximation of the average degree in bounded arboricity graphs using queries, thereby recovering logarithmic factors lost in previous work and offering a modified version with query complexity.
This paper reviews and compares various R packages and methods for conducting Sentiment Analysis in educational contexts, demonstrating their practical application in processing student feedback to monitor teaching effectiveness and enhance learning experiences.
This paper proposes a hybrid direct-iterative method for solving KKT linear systems in interior point optimization that replaces the communication-heavy, pivoting-dependent LDL^T factorization with a GPU-efficient strategy combining Cholesky factorization for smaller positive definite systems and iterative Schur complement solves, thereby achieving superior performance on large-scale problems.
This paper proposes the Olfactory-Auditory augmented Bug algorithm (OA-Bug) for swarm robots to effectively explore denied environments without GNSS or central processing, demonstrating through simulations and real-world experiments that it achieves significantly higher search coverage (96.93%) compared to existing methods like SGBA.
This paper establishes nearly optimal sample complexity bounds for testing the -clique property and -colorability in the dense graph model by applying new extensions of the graph container method.
This paper proposes a real-time two-stream RGB-D object detection model featuring a dynamic RBF-weighted depth-based hyper-involution and a trainable fusion layer to effectively overcome challenges in extracting and combining photometric and depth features, achieving state-of-the-art performance on the NYU Depth V2 benchmark.
This paper introduces TT-SNN, a novel framework that leverages Tensor Train decomposition and a parallel computation pipeline to significantly reduce the parameter size, computational cost, and energy consumption of Spiking Neural Networks during training while maintaining accuracy on both static and dynamic datasets.
This paper reveals a significant performance disparity where Large Language Models excel at generation tasks but struggle with evaluation, often producing unfaithful judgments even in areas where they lack competence, thereby challenging the assumption that generative proficiency guarantees evaluative reliability.
This study proposes and validates an enhanced orthogonality sampling method using multiple sources to effectively identify small objects in limited-aperture inverse scattering problems, overcoming the limitations of single-source approaches through theoretical analysis and experimental data from the Fresnel Institute.
This paper addresses the online busy time scheduling problem on heterogeneous machines by presenting an 8-competitive algorithm for unit-length jobs, establishing a lower bound of 2 on the competitive ratio, and extending the solution to a ratio of less than 16 for jobs of uniform length .
This paper presents a fixed-parameter tractable algorithm that resolves the open question of whether a graph can be covered by fewer than vertex-disjoint paths by outputting either a minimum path cover or an independent set certifying the cover's near-optimality, while also providing the first polynomial-time Hamiltonian path decision for graphs with independence number at most three.
Through a mixed-methods study of 179 Rust developers, this paper reveals that while unsafe code is primarily used out of necessity and avoided when possible, current tooling limitations regarding foreign function calls and encapsulation create significant challenges, highlighting the urgent need for improved verification tools to ensure soundness in multi-language applications.
This paper proposes a Mixed Integer Linear Program and a learning-based approach to optimize mode scheduling, transmit power, power splitting, and decoding order in wirelessly powered IoT networks using RSMA, achieving near-optimal packet transmission counts and significantly outperforming competing methods.
Fuse4Seg introduces a novel bi-level optimization framework for multi-modal medical image fusion that dynamically aligns feature extraction with downstream segmentation tasks through semantic gradients, thereby overcoming the limitations of traditional visual-centric methods to achieve superior tumor boundary preservation and clinical interpretability.