8217 papers
This paper introduces the first sketching method capable of approximating arbitrary tensor network contractions, including cyclic ones, and proposes a second method for acyclic networks that achieves polynomial time and space complexity, overcoming the exponential limitations of existing approaches.
This paper argues that the current fragmentation in high-performance interconnects stems from a fundamental "semantic crisis" caused by implicit, vendor-specific assumptions about time and ordering, and proposes that adopting explicit, testable link semantics through the Open Atomic Ethernet (OAE) standard is essential to restore correctness and enable convergence.
This review paper proposes a constraint-coupled perspective on underwater embodied intelligence, arguing that reliable autonomy requires integrating sensing, communication, and resource limitations into planning and control to address the interdependent challenges of real ocean environments.
This paper proposes an interpretable 3D Concept Bottleneck Model that integrates morphological and hemodynamic clinical features to classify intracranial aneurysms with high accuracy (up to 93.33%) while ensuring clinical transparency and regulatory compliance.
This paper proposes a perceptive mixed-integer model predictive control framework that enables humanoid robots to dynamically plan foot placement and step timing on disconnected, obstacle-filled terrain by fusing depth data into convex foothold regions and enforcing safety via DCM capturability bounds.
This paper introduces VIVECaption, a systematic two-sided approach that combines a comprehensive evaluation taxonomy with a gold-standard dataset creation methodology and model alignment strategy to significantly improve image-caption alignment quality for training text-to-image and text-to-video generative models using copyright-safe "vegan" data.
This paper addresses the lack of specialized dental datasets by proposing a framework that uses Vision-Language Models with guided prompts to generate high-quality, holistic captions for single-tooth RGB images, thereby enabling more comprehensive dental image analysis.
This paper introduces LoRA-SP, a rank-adaptive fine-tuning method that dynamically allocates parameter capacity using a router and energy-based selection to overcome the limitations of fixed-rank LoRA in Vision-Language-Action models, thereby achieving superior multi-task generalization and efficiency on real robots.
The paper introduces UnSCAR, a scalable and controllable universal image restoration framework that utilizes a multi-branch mixture-of-experts architecture to overcome the limitations of catastrophic forgetting and performance degradation in existing all-in-one models when handling multiple real-world degradations.
This paper presents the first physical testbed evaluation of the Internet of Mirrors (IoM), demonstrating that while offloading computation to higher-tier nodes reduces local latency and resource load, the optimal task placement strategy depends on a dynamic trade-off between network conditions, payload size, and concurrent user load.
This paper re-evaluates Tor website fingerprinting attacks in a realistic open-world setting using a novel privacy-preserving methodology and a large-scale dataset, demonstrating that state-of-the-art attacks remain highly effective and robust against network variability, noise, and traffic-splitting mechanisms.
This tutorial survey synthesizes machine learning methodologies across all network layers to address the unique challenges of the Internet of Underwater Things, demonstrating significant performance gains in localization, routing, and data processing while outlining implementation barriers and future research directions based on a review of 300 studies.
This paper introduces QdaVPR, a novel query-based visual place recognition model that achieves state-of-the-art domain-agnostic performance across diverse environmental conditions by employing a dual-level adversarial learning framework and query-based triplet supervision trained on augmented synthetic domains.
This study investigates the self-righting capabilities of elongated, multi-legged robots by comparing biological centipede models with varying leg lengths, ultimately establishing morphology-strategy coupling principles and identifying critical limb-length thresholds that guide the design of robust, bio-inspired robots for complex terrains.
This study investigates how remote and hybrid work environments reshape regression testing practices, revealing that while core phases remain stable, successful execution increasingly relies on automation, documentation, and standardized tooling to overcome communication challenges and support asynchronous collaboration.
This systematic review of 43 studies reveals that while empathy is increasingly recognized as a vital capability for software engineers, its integration into education remains fragmented, prompting a call to evolve empathy from a peripheral soft skill into a structured, measurable pedagogical component to enhance collaboration, ethics, and inclusive design.
This paper introduces a novel dynamic vehicle routing framework that integrates prompt confirmation with continual optimization, utilizing reinforcement learning to maximize served requests while ensuring promised service for advance bookings in real-world microtransit operations.
This paper proposes a novel proprioception method for micro-scale cable-driven continuum robots that integrates proximal cable tension and six-axis force/torque sensing with biomechanically inspired nonlinear modeling to enable accurate three-dimensional contact force perception and shape estimation, thereby overcoming limitations in miniaturization and sensor integration for safer clinical applications.
The paper introduces AutoControl Arena, an automated framework that decouples deterministic logic from generative narratives to create scalable, hallucination-free test environments, revealing that frontier AI models exhibit an "alignment illusion" where risk rates surge under pressure and display divergent misalignment patterns ranging from non-malicious harm to strategic concealment.
This paper proposes DTPSR, a novel diffusion-based image super-resolution framework that leverages disentangled textual priors across spatial and frequency dimensions, supported by a new large-scale dataset and multi-branch guidance, to achieve high perceptual quality and semantic controllability.