1273993 results (page 127 of 50960)
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Nonlinear balanced truncation model reduction through scalable Taylor series
The theory of nonlinear balanced truncation provides a system-theoretic framework for model reduction that preserves important properties such as stability, controllability, and observability. We present a scalable algorithm for computing reduced-order models based on the nonlinear balancing theory. The approach is based on polynomial approximations using the Kronecker product representation, buil…
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Itô tracers: continuous-trajectory Lagrangian particles for Eulerian hydrodynamics
Lagrangian tracer particles have long been used to track the history of individual gas parcels in hydrodynamical codes. Particles advected by the cell-centered velocity carry no representation of underlying numerical diffusion, and thus exhibit systematic bias. The Monte-Carlo (MC) tracer resolves this with discrete probabilistic cell-to-cell, flux-based jumps, at the cost of trajectories that are…
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Within-person prediction of depressive symptom change using year-long Screenome data and CES-D assessments
Predicting whether an individual's depressive symptoms will worsen, remain stable, or improve over the coming weeks can enable earlier and more targeted care, yet prospective within-person trajectory prediction remains largely unaddressed in digital phenotyping. We combine fortnightly CES-D assessments with over 100 million screenshots captured every five seconds via the Stanford Screenomics platf…
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Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction
Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting applications, e.g., collaborative robots for rehabilitation. Safety is critical for the successful deployment of this kind of robotic system. In recent years, Control Barrier Function (CBF) has emerged as an effective approach to enforce safety guarante…
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Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning
Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fragile. Methods such as DenseMixer and ESFT mitigate router collapse with dense mixing or auxiliary load-balancing losses, but these introduce noisy gradients that often degrade performance. In preliminary experiments, we systematically pruned experts …
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Remote Concolic Multiverse Debugging -- Extended Version with Additional Appendices
Debugging nondeterministic programs is inherently difficult, particularly in microcontroller environments where execution paths can diverge unpredictably due to external sensor inputs. Traditional debugging techniques often fail to capture or reproduce this nondeterministic behavior effectively. Multiverse debugging has emerged as a compelling technique to debug nondeterministic programs, allowing…
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The Decay of Impact with Network Distance in Linear Diffusion Processes
Many processes related to status, power, and influence within social networks have been modeled using forced linear diffusion models; examples include the highly successful Friedkin-Johnsen model of social influence, the status/power scores of Katz and Bonacich, and the widely used network autocorrelation model. While a basic assumption of such models is that the impact of one individual on anothe…
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Equivariant Filter for Radar-Inertial Odometry
Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address these limitations, this letter proposes an Equivariant Filter (EqF) for RIO based on a Lie group symmetr…
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Vision as looking and seeing through a bottleneck
Progress in vision research has been slower downstream than upstream of primary visual cortex (V1). Traditional frameworks have largely overlooked a central constraint: only a tiny fraction of retinal input is recognized. Thus, to a first approximation, vision is better formulated as looking and seeing through a bottleneck. Looking, mainly by the peripheral visual field, selects visual information…
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Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due to pronounced volatility driven by seasonal demand patterns, geopolitical developments, and shifting macroeconomic conditions. The nonlinear dynamics and frequent regime changes can limit the effectiveness of traditional time-series models. In this …
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Sampling distributions for complex design variance estimators in a Fay-Herriot model
Fay-Herriot (FH) models with variance smoothing typically use chi-squared sampling distributions for the design variance estimators. This choice is only valid under strong assumptions on the population and the sampling design, and the choice of sampling distribution is understudied for complex survey designs such as the stratified two-stage clustering design used by the Demographic and Health Surv…
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Computational Method for Desensitized Optimal Guidance Using Direct Collocation
A computational method is developed for desensitized optimal guidance using adaptive Gaussian quadrature collocation. The method computes a reference trajectory that reduces the sensitivity to uncertainties in the dynamic model by augmenting the objective functional to explicitly penalize the sensitivity of the state with respect to uncertain parameters. Using this desensitized reference trajector…
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A Systematic Approach for Large Language Models Debugging
Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper introduces a systematic approach for LLM debu…
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Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness. We address this by constructing a time-stamped dataset of benign and malicious Android apps and introducing a timestamp-verification procedure to ensure temporal accuracy. We th…
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Sharp condition-number bounds for growth factors of Higham matrices in Gaussian elimination
Higham's conjecture on the growth factor of complex symmetric positive definite matrices is a longstanding problem in the stability theory of Gaussian elimination without pivoting. It asserts that every complex matrix $A=B+iC$ with $B$ and $C$ real symmetric positive definite, is called Higham matrix and has growth factor $ρ_n(A)<2$. In 2013, Drury [Linear Algebra Appl. \textbf{439} (2013), no.~10…
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CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
Two-stage recommender systems first choose a candidate generator and then rank items within the generated set. Because the generator decides which items are available to the ranker, changing the generator changes both the policy value and the data support used to estimate that value. This creates an offline selection problem that standard single-stage objectives do not capture: a policy may look g…
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Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery
Accurate classification of tropical tree species from unoccupied aerial vehicle (UAV) imagery remains challenging due to high species diversity and strong visual similarity among species at typical image resolutions (centimeters per pixel). In contrast, models trained on close-up citizen science photographs captured with smartphones achieve strong plant species classification performance. Recent a…
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AmaraSpatial-10K: A Spatially and Semantically Aligned 3D Dataset for Spatial Computing and Embodied AI
Web-scale 3D asset collections are abundant, but rarely deployment-ready. Assets ship with arbitrary metric scale, incorrect pivots and forward axes, brittle geometry, and textures that do not support relighting, which limits their utility for embodied AI, robotics simulation, game development, and AR/VR. We present AmaraSpatial-10K, a dataset of over 10,000 synthetic 3D assets designed for downst…
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Complex SGD and Directional Bias in Reproducing Kernel Hilbert Spaces
Stochastic Gradient Descent (SGD) is a known stochastic iterative method popular for large-scale convex optimization problems due to its simple implementation and scalability. Some objectives, such as those found in complex-valued neural networks, benefit from updates like in SGD and Gradient Descent (GD) with a newly defined ``gradient'' that allows for complex parameters. This complex variant of…
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DeepSignature: Digitally Signed, Content-Encoding Watermarks for Robust and Transparent Image Authentication
AI-powered generative models have significantly expanded the possibilities for editing, manipulating, and creating high-quality images. Particularly, images that falsely appear to originate from trusted sources pose a serious threat, undermining public trust in image authenticity. We propose DeepSignature, a novel approach that integrates the guarantees of digital signatures with the capabilities …
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Approximating Energy-Constrained Drone Delivery Packing Problem for Last-Mile Logistics
Collaboration between drones and trucks in a last-mile delivery system offers numerous benefits and reduces many challenges of the traditional delivery system. Here, we introduce Drone-Delivery Packing Problem, where a set of parcels, associated with delivery intervals and cost, should be delivered to customer locations. The system comprises a set of identical drones and battery stations along tru…
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Integrated Lander-Propulsion-GNC Framework for Autonomous Lunar Powered Descent
This paper presents an integrated lander-propulsion-GNC framework for autonomous lunar powered descent. The BUG VTVL test vehicle serves as the reference platform, with the YUNT V0 throttleable bipropellant engine providing variable thrust across a wide operating envelope, integrated with a real-time successive convexification guidance solver. The vehicle design accounts for structural configurati…
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On-Device Vision Training, Deployment, and Inference on a Thumb-Sized Microcontroller
This paper presents a complete, end-to-end on-device vision machine learning pipeline, comprising data acquisition, two-layer CNN training with Adam optimization, and real-time inference, executing entirely on a microcontroller-class device costing $15-40 USD. Unlike cloud-based workflows that require external infrastructure and conceal the computational pipeline from the practitioner, this system…
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GenAssets: Generating in-the-wild 3D Assets in Latent Space
High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting…
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Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads
Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-market matching efficiency and supporting personalized employment services. To the best of our knowledge, this work presents the first Chinese JobSkillNER dataset for recruitment texts. We propose annotation guidelines tailo…