3462 papers
This paper presents a generic framework that guarantees polynomial-time decidability for fixed-negation fragments of first-order theories, demonstrating its effectiveness by proving that weak Presburger arithmetic and related restricted theories are efficiently decidable, in contrast to the NP-hardness of more restricted fragments of full Presburger arithmetic.
This paper proposes MORCoRA, a multi-objective search-based technique that recommends code quality-improving refactoring sequences along with suitable reviewers by simultaneously optimizing for expertise and workload availability to ensure the refactorings can be promptly reviewed.
This paper presents a 1.6-fJ/spike subthreshold analog Leaky Integrate-and-Fire neuron fabricated in 28 nm CMOS, which achieves a 300 kHz spiking frequency at 250 mV and demonstrates 82.5% accuracy on the MNIST dataset when used in a quantized Spiking Neural Network, validating its potential for energy-efficient embedded machine learning.
This mixed-methods study investigates the lifecycle and sustainability of taboo knowledge artifacts on Wikipedia, revealing that their successful development relies on resilient leadership, engaged organizations, and emergent governance to navigate conflict and limited identifiability.
This paper introduces the Multimodal Laryngoscopic Video Analyzing System (MLVAS), a novel framework that integrates audio keyword spotting with diffusion-refined video segmentation to automatically extract key metrics and features for the objective assisted diagnosis of Vocal Fold Paralysis.
This paper explores how integrating artificial intelligence into software testing transforms the generation and validation of test cases by enhancing efficiency, accuracy, and scalability while addressing challenges related to data quality, model transparency, and the balance between automation and human oversight.
This paper proposes a safe navigation framework for bipedal robots that combines deep reinforcement learning with Koopman operator-based model predictive control to linearize complex nonlinear dynamics in a lifted space, thereby achieving more accurate trajectory prediction and improved success rates in dense, narrow environments.
This paper presents InterMind, a large language model-powered system that enhances depression assessment by integrating multi-role interactions among doctors, patients, and families while employing retrieval-augmented generation and chain-of-thought techniques to improve diagnostic accuracy, interpretability, and reduce hallucinations.
The paper proposes PiVOT, a visual prompting mechanism that leverages a pretrained CLIP foundation model to automatically generate and refine online visual prompts, thereby enhancing generic object tracking by effectively suppressing distractors through contrastive guidance.
This paper presents S^2Sim, a novel system that automatically diagnoses and repairs distributed routing configuration errors by selectively simulating symbolic configuration variants to identify intent-compliant fixes, demonstrating high efficiency across networks ranging from 10 to 1,000 nodes.
This paper introduces Pose Prior Learner (PPL), an unsupervised method that utilizes hierarchical memory to learn and distill general pose priors for object categories, thereby improving pose estimation accuracy and robustness against occlusion without requiring additional human annotations.
ExpGest is a novel diffusion-based framework that generates expressive, controllable full-body gestures by leveraging synchronized audio and text guidance, along with a specialized noise emotion classifier, to overcome the limitations of existing methods that often produce stiff, upper-body-only movements.
This paper investigates revenue-maximizing mechanisms for a selfish mediator in bilateral trade with interdependent valuations, establishing an impossibility trilemma between incentive compatibility, obedience, and informativeness, and subsequently characterizing optimal threshold-based mechanisms under conditions of constant seller costs or mediator veto power.
This paper introduces a new Pebble Game variant that models arbitrary in-degree DAGs through partial computations, proving that determining optimal pebbling strategies under this model is NP-complete even for simple cases while also outlining approximation algorithms for special scenarios.
This paper establishes that uniform nondeterministic lower bounds for fundamental problems like k-SAT and MAX-3-SAT imply the existence of combinatorial objects with high complexity, specifically yielding win-win circuit lower bounds for monotone functions and constructing families of matrices and tensors with high rigidity and rank.
This study employs a comparative computational analysis of a two-year longitudinal dataset to demonstrate how the distinct platformization practices of TikTok and Spotify shape the characteristics of hit songs while revealing evidence of cross-platform influence where Spotify popularity may precede trends on TikTok.
This paper demonstrates that competing algorithmic agents using multi-armed bandit learning can spontaneously develop "naive collusion" through action synchronicity, even without knowledge of competitors, with the likelihood of such outcomes depending critically on whether the agents employ deterministic, greedy-in-the-limit, or persistently random policies.
Prithvi-EO-2.0 is an open-source, multi-temporal geospatial foundation model trained on 4.2 million global time series samples that significantly outperforms its predecessor and other existing models across diverse Earth observation tasks, from disaster response to ecosystem monitoring, through the integration of temporal and location embeddings and a Trusted Open Science development approach.
iLLaVA is a novel approach that achieves comprehensive end-to-end acceleration of Large Vision-Language Models by jointly optimizing the image encoder and LLM through a token merging strategy that recycles discarded information, resulting in significant throughput gains and reduced latency while maintaining or improving accuracy.
This paper establishes that the computational complexity of finding a pure Nash Equilibrium in general Tullock contests with heterogeneous contestants is determined by the number of players with medium elasticity (r_i ∈ (1, 2]), proving the problem is polynomial-time solvable when this number is logarithmically bounded but NP-complete otherwise, while offering an FPTAS for the latter case.