1273993 results (page 132 of 50960)
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Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most available longitudinal neuroimaging datasets are temporally sparse with a few follow-up scans per subject. This scarcity of temporal data limits our ability to model and accur…
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Entrywise Low-Rank Approximation and Matrix $p \rightarrow q$ Norms via Global Correlation Rounding
Given a matrix $A$, the goal of the entrywise low-rank approximation problem is to find $\operatorname{argmin} \|A-B\|_p$ over all rank-$k$ matrices $B$, where $\| \cdot \|_p$ is the entrywise $\ell_p$ norm. When $p = 2$ this well-studied problem is solved by the singular value decomposition, but for $p \neq 2$ the problem becomes computationally challenging. For every even $p > 2$ and every fixed…
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RFID-Based Non-Biometric Classroom Attendance System: Proxy Attendance Detection via Weight Sensor Integration
Attendance tracking in educational institutions, when conducted through traditional methods, leads to structural problems that consume instruction time and threaten academic integrity. Attendance durations spanning several minutes in primary and secondary education and exceeding ten minutes in higher education, combined with the proxy attendance problem of signing on behalf of someone else, demons…
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Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis
Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to characterize the internal structure of individual breaths. Traditional approaches treat airflow as a quasi-periodic signal and rely on global descriptors such as tidal volume or peak flow, obscuring sub-breath events that reflect neuromuscular coordina…
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Forecasting the occupancy of satellite megaconstellations in SKA observations
The Square Kilometre Array (SKA) is expected to start science operations in 2030 and by that time there could be up to 10$^5$ artificial satellites in Earth's orbit, comprising an increase of an order of magnitude compared to 2024. Most of these new satellites will belong to satellite megaconstellations aimed at providing communication services all over Earth. These satellites create radio frequen…
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CRAFT: Clustered Regression for Adaptive Filtering of Training data
Selecting a small, high-quality subset from a large corpus for fine-tuning is increasingly important as corpora grow to tens of millions of datapoints, making full fine-tuning expensive and often unnecessary. We propose CRAFT (Clustered Regression for Adaptive Filtering of Training data), a vectorization-agnostic selection method for training sequence-to-sequence models. CRAFT decomposes the joint…
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A Unified Framework for Multiple Exposure Distributed Lag Non-Linear Models for Air Pollution Epidemiology
This study quantifies the association between air pollution and mortality in Ontario, Canada. Exposure-response relationships in air pollution epidemiology are complex due to three features: time-lagged associations, non-linear associations, and multiple pollutants. To address the first two features, two distinct classes of distributed lag non-linear model (DLNM) have been proposed, but extending …
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WAsp: The Wideband (W) Adaptive-Scale Pixel (Asp) Deconvolution Algorithm for Interferometric Imaging
This paper introduces the Wide-band Asp-Clean (\texttt{WAsp}) algorithm, a novel scale-sensitive image reconstruction method tailored for wide-band imaging applications. This algorithm is particularly beneficial for thermal noise-limited imaging with aperture synthesis telescopes, where joint spatio-frequency modeling of the sky brightness distribution is critical. The \texttt{WAsp} algorithm repl…
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SS3D: End2End Self-Supervised 3D from Web Videos
We present SS3D, a web-scale SfM-based self-supervision pretraining pipeline for feed-forward 3D estimation from monocular video. Our model jointly predicts depth, ego-motion, and intrinsics in a single forward pass and is trained/evaluated as a coherent end-to-end 3D estimator. To stabilize joint learning, we use an intrinsics-first two-stage schedule and a unified single-checkpoint evaluation pr…
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CosmicDancePro -- Measuring LEO satellite's orbital decay and network connectivity implications during solar storms
The May 2024 solar superstorm highlighted the vulnerability of rapidly expanding low Earth orbit (LEO) satellite networks to severe space weather events. To systematically evaluate LEO network resilience, we introduce an open-source tool, CosmicDancePro. It enables a comprehensive analysis of the effects of solar storms in the LEO satellite network. It integrates real-world multimodal datasets, in…
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Quasinormal Modes and Neutrino Energy Deposition for a Magnetically Charged Black Hole in a Hernquist Dark Matter Halo
We investigate quasinormal modes, shadow observables, weak gravitational lensing, and neutrino--antineutrino annihilation for a static, spherically symmetric black hole that carries a nonlinear-electrodynamics magnetic charge and is embedded in a Hernquist dark-matter halo. The geometry is controlled by the black-hole mass $M$, magnetic charge $g$, and halo parameters $(α,β)$, and provides a simpl…
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Mobility Aware Power Control for VCSEL Based Indoor OWC
Optical wireless communication (OWC) is a promising technology for supporting data intensive services in indoor environments due to its large unregulated spectrum, high spatial reuse, and potential for multigigabit data rates. In particular, vertical cavity surface emitting laser (VCSEL) based systems enable highly directional transmission, allowing efficient spatial separation of users and improv…
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Dense Matter and Compact Stars in Strong Magnetic Fields
Compact stars serve as natural systems where matter exists at densities far beyond those achievable in laboratory experiments. Among them, magnetars are expected to possess interior magnetic fields that may reach values of the order of $10^{17}-10^{18}$ G. These extreme conditions are expected to alter the microscopic and macroscopic properties of dense matter. In this review, we examine how stron…
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How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications
The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses including the EU AI Act, NYC Local Law 144, and Colorado's AI Act. While existing research examines bias through technical or regulatory lenses, both perspectives overlook a fundamental challenge: modern AI hiring systems operate within complex supply chains…
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BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering
A common approach to question answering with retrieval-augmented generation (RAG) is to concatenate documents into a single context and pass it to a language model to generate an answer. While simple, this strategy can obscure the contribution of individual documents, making attribution difficult and contributing to the ``lost-in-the-middle'' effect, where relevant information in long contexts is …
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Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe
Graph neural networks achieve strong node-classification accuracy, but learned message passing entangles ego attributes, neighborhood smoothing, high-pass graph differences, class geometry, and classifier-boundary effects inside opaque representations. This obscures why nodes are classified as they are and which graph-learning mechanisms a dataset requires. We propose WG-SRC, a white-box signal-…
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Measuring Epistemic Unfairness for Algorithmic Decision-Making
Algorithmic systems increasingly function as epistemic infrastructures that govern the conditions of interpretative access and social belief. Yet, mainstream auditing strategies operationalize fairness primarily in predictive terms - error rates, calibration, or group-level parity - leaving epistemic harms under-theorized and under-measured. We propose a quantitative framework for evaluating forms…
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Inferring Equivalence Classes from Legacy Undocumented Embedded Binaries for ISO 26262-Compliant Testing
Equivalence class partitioning is a well-established test design technique mandated by safety standards such as ISO~26262 for systematic testing of safety software. In industrial practice, however, its application to legacy undocumented embedded firmware is often hindered by incomplete or outdated functional specifications. This paper proposes a binary-level methodology for inferring output-orie…
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Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
Batch processes are inherently transient and typically nonlinear, motivating nonlinear model predictive control (NMPC). However, adopting NMPC is hindered by the cost and unavailability of dynamic models. Thus, we propose to use Gaussian Processes (GP) in a model-learning NMPC scheme (GP-MLMPC) for batch processes. We initialize the GP-MLMPC using data from a single initial trajectory, e.g., from …
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Exploring the diversity of kilonovae with 3D radiative transfer I. The polar direction
We present 3D kilonova radiative transfer simulations for a series of binary neutron star merger models. The masses of the neutron stars are varied as well as the total mass of the system and two different equations of state were used (SFHO and DD2), producing a range in dynamical ejecta masses and elemental abundance patterns. In this paper, we focus on the bolometric light curves and spectra in …
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Curvature of optimal transport with respect to the cost and applications to inverse optimal transport
We study the inverse optimal transport problem of recovering the ground cost from an optimal transport plan. In discrete settings, this problem reduces to inverse linear programming and is intrinsically ill-posed, exhibiting non-identifiability and flat directions. We show that in the continuous setting, the regularity of the marginals fundamentally alters the structure of the inverse problem. Ass…
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Penalised and constrained geodesics in geometric control theory
In many problems in optimal control, one seeks to minimise an objective function subject to constraints on the velocity of the system. Imposing these constraints directly -- the ``hard-constrained'' approach -- is often analytically and computationally challenging. A natural alternative is to penalise violations of the constraints, solving a sequence of ``soft-constrained'' problems indexed by a p…
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The Sound of the Universe: A Resonant Gravitational Instability Driven by Baryon-Dark Matter Relative Drift
Dark matter and baryons acquire a relative velocity after decoupling in the early Universe. Baryons are gravitationally unstable only above their Jeans scale, while cold dark matter (CDM) is unstable on all scales. We show for the first time that their relative drift triggers a resonant gravitational instability that drives sound waves in baryons. When the projected DM drift is subsonic, the stabl…
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Cuts and Gauges for Submodular Width
Submodular width is a central structural measure governing the complexity of conjunctive query evaluation. In this paper we recast submodular width in geometric terms. We how that submodular width can be approximated, up to a factor $3/2$, by a new branchwidth parameter defined in terms of edge separations in the hypergraph and the costs induced on them by admissible submodular functions. This ref…
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Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings
Shapley values are a cornerstone of explainable AI, yet their proliferation into competing formulations has created a fragmented landscape with little consensus on practical deployment. While theoretical differences are well-documented, evaluation remains reliant on quantitative proxies whose alignment with human utility is unverified. In this work, we use a unified amortized framework to isolate …