1613351 results (page 3 of 64535)
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SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer from over-generalization and under-generalization problems, which are challenging to balance. To address this, we introduce multi-scale clustering to enhance reconstruction-based methods. At the representation level, we int…
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OneCanvas: 3D Scene Understanding via Panoramic Reprojection
Existing approaches to 3D scene understanding in Vision-Language Models (VLMs) either rely on complex, model-specific geometry encoders or large training budgets in pursuit of spatial reasoning. Instead, OneCanvas aggregates patch features from all views onto a single equirectangular panoramic canvas. Namely, each patch is unprojected to a 3D world coordinate using its depth and camera pose, then …
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Acceleration of an algebraic multigrid pressure solver using graph neural networks
Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities. This work introduces a data-driven algebraic multigrid (AMG) smoother that uses a modified graph convolutional isomorphism network (GCIN). The graph neural network predicts opti…
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Transformer Geometry Observatory TGO-I: Spectral Geometry Observatory
Despite the widespread adoption of Vision Transformers (ViTs) and their success across numerous computer vision applications, the fundamental understanding of their dimensional and representational geometry remains relatively underexplored. To address this gap, we introduce Transformer Geometry Observatory (TGO), a systematic framework of experiments and analysis pipelines designed to investigate …
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Ages of the resolved stellar populations inside the JWST/MIRI bubbles in NGC 628
JWST images in the MIRI filters are characterized by prominent interstellar bubbles, most of which are expected to be created by mechanical energy injected into the interstellar medium by dying massive stars. In this work, we use resolved stellar populations (RSPs) in JWST/NIRCam images of NGC 628 from the JWST-FEAST dataset to determine the demography of stellar populations within these bubbles b…
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A Taxonomy of Mental Health and Technology Needs for Alzheimer's and Dementia Caregivers
Family members caring for individuals with Alzheimer's disease and related dementias (AD/ADRD) provide the foundation of long-term care worldwide. In 2023, more than 11 million U.S. family and friends contributed 18 billion hours of unpaid care, often at the cost of their own physical and mental health. These informal caregivers -- also referred as the "invisible second patients" -- experience ele…
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TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions. We introduce TherapeuticsBench Preclinical Pharmacology (TxBench-PP), a verifiable benchmark for small-molecule preclinical pharmacology and the first focused slice of a broader Therape…
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Multi-Thermal CME Detection with ALMANAC
Reliable identification of low-coronal CME origins remains a key limitation in space weather forecasting with coronagraphs not directly resolving low-coronal signatures. We present a re-engineered multi-thermal implementation of the ALMANAC algorithm, designed to detect eruptive signatures in EUV observations. The framework extends the method to a multi-wavelength system, improving robustness agai…
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Seeing Through Occlusion: Deterministic Arm Kinematic Correction for Robot Teleoperation
Markerless, single-RGB-D-camera motion capture provides a low-cost and non-invasive alternative to conventional marker-based systems for robot teleoperation; however, depth estimation often degrades in the presence of self-occlusion, particularly during upper-limb motion. This paper presents an Arm Kinematic Correction (AKC) method that improves depth estimation by enforcing geometric constraints …
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STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training. We conduct a first-order gradient analysis of token-level entropy dynamics under GRPO and identify a token-level credit assignment mismatch: the per-token entropy variation decomposes…
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CodeSentinel: A Three-Layer Defense Against Indirect Prompt Injection in Code Contexts
Code large language models increasingly retrieve external code context from repositories, documentation, issue threads, and coding-agent environments, creating an indirect prompt-injection surface where attackers hide instructions in comments, strings, identifiers, or decoy code. We propose CodeSentinel, a three-layer inference-time sanitizer. It uses Tree-sitter to extract high-risk model-facing …
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Mobile Pedipulation for Object Sliding via Hierarchical Control on a Wheeled Bipedal Robot
In this letter, we present a hierarchical control framework that enables wheeled bipedal robots to perform planar object sliding tasks with their wheeled legs. The proposed approach formulates a nonlinear model predictive controller (NMPC) based on a reduced-order three rigid bodies (TRB) dynamical model that explicitly accounts for the hip roll degree of freedom and multiple wheel-environment con…
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Global Multi-ion Solar Wind Model. I. Ion Temperatures
Over the past several decades, observations have shown that minor ions have a higher temperature and flow faster than protons in the solar wind. Theories based on turbulence have been developed that can explain many of these observed phenomena. We present our first step in developing a global multi-ion solar wind model with turbulence by including ion temperatures but not yet including differentia…
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A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single au…
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JWST-TST High Contrast: First Direct Spectroscopy of GJ 504 b reveals Clouds and Possible Metal Enrichment
Characterizing the coldest directly imaged companions through direct spectroscopy has only recently become possible with the James Webb Space Telescope. We present moderate-resolution (R $\sim$ 2,700) spectroscopic observations of the directly imaged planetary-mass companion (PMC), GJ 504 b, using the $JWST$/NIRSpec. As the coldest imaged PMC of the pre-JWST era GJ 504 b is too faint for ground-ba…
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Constant Time-Delay Leader Following with Neural Networks and Invariant Extended Kalman Filters for Arbitrary Trajectories
This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication, a common coordinate system, or global positioning. The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process, allowing accurate estimation of a leader ve…
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Pushing the Limits: Unlocking the Potential of Faster-than-Nyquist Signaling
Faster-than-Nyquist (FTN) signaling is gaining attention as a smart way to pack more data into limited spectrum by intentionally breaking the traditional symbol-spacing rules. This article takes a fresh look at FTN's potential to boost capacity, examining how performance varies across different acceleration factors and signal-to-noise ratio (SNR) definitions. Beyond the theory, we explore what it …
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Mechanism-Guided Selective Unlearning for RLVR-Induced Reasoning
We propose MAST (Mechanism-Aligned Selective Targeting), a mechanism-guided method for unlearning RLVR-induced reasoning with substantially lower collateral damage than standard full-parameter updates. In matched SFT/RLVR checkpoints on Qwen2.5-Math-1.5B and Qwen3-1.7B-Base, the SFT-to-RLVR increment differs sharply from the SFT update in token-level delta-log-probability, and full-parameter gradi…
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Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image data, leaving a gap in the domain of network intrusion detection, which relies heavily on tabular data. This work introduces XGBoost-Forget, an unlearning approach for the XGBoost…
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RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering
Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity), and tell a better system from a worse one (discriminative power). On open-ended, opinion-driven question answering, the two are in tension. We introduce RECOM (Reddit Evaluation for Correspondence of Models), a contam…
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No Two Developers Think Alike: How Problem-Solving Styles and Experience Shape Needs in Conversational Interaction with Copilot
Conversational LLM-based ``programming assistants'' provide a range of benefits to developers. However, recent studies demonstrate the variety in individual developers' needs regarding programming assistants, and challenges encountered by only specific groups of developers. In this study, we explore the role of cognitive diversity in shaping interactions with GitHub Copilot chat. Through a mixed-m…
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GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation
Pelvic segmentation is one of the most important and fundamental research problems in precise and intelligent diagnosis and treatment, as well as surgical planning and navigation for pelvic fractures. By combining an improved geodesic active contour model with deep neural networks, we propose GUMP-Net, an interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation, in…
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Generalised Eigenvalue Geometry of Semantic Adversarial Attacks
Recent empirical work shows that semantically equivalent paraphrases can fool financial sentiment classifiers: although a paraphrase remains close to the original under a strong reference embedding, it may shift the target model's representation enough to change the predicted class. Existing robustness theory either assumes a single-model threat model or focuses mainly on empirical attack algorith…
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Thermal Characterization of a 6-Positioner, 6.2-mm-Pitch Module for Stage-5 Telescopes
Ensuring thermal stability of robotic fiber positioners is essential for reliable operation in the real environments of Stage-5 telescopes, where temperature variations can influence mechanical behavior and impact fiber-target accuracy. We present the results of thermal qualification tests conducted on 6.2-mm-pitch robotic positioner modules developed for high-density fiber positioning in next-gen…
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Guarded Epoch Bloom Filters for Sliding-Window Membership
Approximate membership queries in streams often need recent-window semantics rather than membership over all items ever seen. This paper studies guarded epoch Bloom filters, a sliding-window alternative to counting and stable Bloom filters. The structure partitions a fixed bit budget into rotating epochs, inserts only into the current epoch, clears whole segments at epoch boundaries, and keeps one…