6845 papers
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.
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.