8464 papers
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.
AuthFace is a novel blind face restoration framework that achieves highly authentic results by fine-tuning a text-to-image diffusion model on a curated 1.5K high-resolution professional photography dataset with photography-guided annotations, while employing a time-aware latent facial feature loss to minimize artifacts in critical facial areas.
PPLLaVA addresses the computational inefficiency of long-video understanding by introducing a prompt-guided pooling strategy that aggressively compresses visual tokens while preserving instruction-relevant semantics, achieving state-of-the-art performance with up to 18x token reduction.
This paper analyzes a unique three-year dataset of over 3,000 unhoused individuals in King County, WA, revealing a concerning trend of declining social connectivity and increasing isolation driven by displacement and population growth, which underscores the urgent need for policies that foster community building and reduce social fragmentation.
This paper introduces an efficient and accurate non-linear programming framework that integrates hierarchical decision-making with inverse kinematic planning and control by leveraging sparse structures and the -norm to solve complex problems like simultaneous end-effector and grasp selection without relying on heavy mixed-integer computations.
This paper presents a high-performance, SIMD-optimized algorithm for computing positional population counts (pospopcnt) across AVX2, AVX-512, and ASIMD architectures, achieving near memory-bound speeds even for small input arrays through improved algorithmic structure and efficient bit-parallel accumulation.
The paper proposes CSP-FT, a characteristic-specific partial fine-tuning strategy that selectively updates only the most and least relevant layers of LLM-based TTS models to achieve superior emotion and speaker adaptation with significantly faster training and reduced catastrophic forgetting compared to full fine-tuning.
This paper proposes and analyzes two adaptive algorithms for maximizing time-average utility in multi-channel multiple access systems with unknown link failure probabilities, offering a trade-off between fast convergence with high computational cost and slower convergence with efficient implementation, while also providing specialized solutions for single-channel settings and non-adaptive approaches.
FeatureGS enhances 3D Gaussian Splatting by introducing an eigenvalue-based geometric loss term that significantly improves geometric accuracy, reduces floater artifacts and storage requirements by 90%, and enables direct mesh reconstruction while maintaining high photometric quality.
This paper introduces PoI, a framework that enhances Scene Coordinate Regression for visual localization by combining 3D Gaussian Splatting with diffusion-based refinement and a progressive pixel-level filtering strategy to generate and selectively utilize reliable novel views for robust training.
{\lambda}Scale is an efficient serverless inference system that accelerates large language model scaling by leveraging high-speed RDMA networks for fast model multicast and enabling "execute-while-load" distributed inference, thereby significantly reducing tail latency and costs compared to state-of-the-art solutions.
This paper argues that Artificial Intelligence in urban governance does not merely restrict or enhance bureaucratic discretion but redistributes it across institutional levels, necessitating a framework of "accountable discretion" and specific guiding principles to balance improved service delivery with the mitigation of new risks like algorithmic opacity and fragmented responsibility.
This paper introduces SCOTT, a sparse convolutional tokenizer combined with a MIM-JEPA training framework, which enables Vision Transformers to learn robust self-supervised representations from scratch on small-scale, fine-grained datasets, thereby challenging the necessity of big data and massive computational resources for effective vision representation learning.
This paper proposes CrackIVF, an adaptive, partition-based index for vector search in data lakes that progressively optimizes itself based on query workloads, enabling immediate query responses without upfront index construction while eventually matching the performance of conventional indexes.
This paper demonstrates that a simple iterative LQR algorithm using MuJoCo dynamics and finite-difference derivatives can achieve effective, real-time whole-body model-predictive control for quadruped and humanoid robots in the real world with minimal sim-to-real tuning, thereby lowering the barrier for future research.
The paper introduces NAMI, a Bridged Progressive Rectified Flow Transformer framework that significantly accelerates image generation and reduces inference time by 64% through a multi-resolution, spatially cascaded architecture with a BridgeFlow module, while maintaining state-of-the-art quality and introducing the NAMI-1K benchmark for evaluation.
ECLARE is an open-source, self-supervised super-resolution method that enhances anisotropic 2D MR volumes by estimating slice profiles and learning in-plane mappings without external data, thereby overcoming domain shift and outperforming existing techniques in both signal recovery and downstream tasks.