1614479 results (page 8 of 64580)
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Computing Twin-Width via Treedepth and Vertex Integrity
Twin-width is a graph parameter that has become central to explaining the fixed-parameter tractability of first-order model checking across many graph classes. Despite its algorithmic importance, computing twin-width remains poorly understood: even recognizing graphs of twin-width at most four is NP-hard, and no fixed-parameter approximations parameterized by twin-width itself are known. A recent …
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Observation of alignment tensor effects in metastability-exchange collisions with highly polarized 3He ensembles
Highly polarized 3He ensembles prepared by metastability-exchange optical pumping (MEOP) have been widely used in precision measurements and fundamental physics. Metastability-exchange (ME) collisions, serving as the basis of MEOP, are traditionally described in terms of atomic orientation, while the significant contributions of metastable alignment tensor at high polarization remain unexplored. I…
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Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Representing microbial dynamics in process-based soil models is therefore critical to predict carbon cycling in soils, albeit highly challenging to inform from data. One promising approach to improve their parametrisation is the integration …
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Effective Faraday interaction between light and Helium-3 nuclear spins in a multi-pass cell
Helium-3 nuclear spins form an exceptionally stable quantum system with extremely long coherence time, offering exciting opportunities for quantum technologies. In particular, nuclear spin-squeezed states promise enhanced precision for sensing tasks and tests of new physics. A central challenge for all these applications is the realization of a controllable light-nuclear spin interface. Here we ex…
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Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs
We develop QCPIKAN, the first quantum-classical physics-informed Kolmogorov-Arnold network designed to solve partial differential equations (PDEs). Built upon Chebyshev-polynomial KAN layers and parameterized quantum circuits, this hybrid framework embeds physical constraints into the training loss to enforce physical consistency. Our theoretical investigations grounded in approximation theory pro…
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Recurrent neural networks approximate continuous functions
Classical approximation theorems ask for a new neural network whenever the target accuracy is improved. This paper studies the opposite possibility: can the network be chosen once and for all, and can accuracy be bought only by letting it run longer? We prove that this is possible for every continuous function on [-1,1]. More precisely, each such function is uniformly approximated by the time evol…
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A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
Virtual training environments are software-intensive systems in which reinforcement learning (RL) agents learn, adapt, and demonstrate meaningful behavior. Virtual training environments offer a safe and cost-efficient alternative to training agents in real-world settings. However, to converge, most realistic RL problems require training in multiple, mostly similar but slightly different environmen…
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Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems
Deep Transfer Learning (DTL) allows for the efficient building of Intelligent Fault Diagnosis Systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of labelled data. Obtaining such an amount of data can be challenging when dealing with machines or structures faults. This document proposes a novel approach to the design of vibration-based IFDS using DTL in condition of …
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Towards 3D karst underwater scene reconstruction from rotating sonar data
Karst aquifers provide critical freshwater resources but pose significant hazards due to their complex and poorly understood subsurface geometry. Mapping these environments is challenging because sonar data from underwater exploration is sparse and noisy, while navigation estimates suffer from drift limiting standard 3D reconstruction methods. We present a pipeline for reconstructing underwater ka…
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AgenticDB: Agentic Performance Reconfiguration for Database Workloads
Database configuration tuning is critical for workload performance, but practical tuning on real deployments remains difficult. Existing automatic tuners mostly formulate tuning as iterative search over DBMS knob values. This formulation leads to high execution cost, prematurely narrows the configuration space, and leaves practical requirements insufficiently addressed: diagnosing runtime bottlene…
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Exploiting More Than Symmetry in Variational Quantum Machine Learning
The success of variational quantum learning models crucially depends on choosing parametrizations that reflect the structure of the problem at hand. Symmetries provide one of the clearest such structures: whenever transformations of the input leave the desired outcome unchanged, this invariance should be built into the model rather than discovered during training. However, imposing a symmetry does…
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bioETH-Beacon: A Confidential On-Chain Genomic Beacon with Encrypted Counts, Filters, and Bounded Noise over a Fully Homomorphic EVM
The Global Alliance for Genomics and Health (GA4GH) Beacon protocol lets researchers ask whether a genomic variant has been observed in a participating cohort and receive aggregate variant-level counts. As Beacon networks grow, two privacy risks remain: host institutions can see plaintext queries, and repeated rare-variant queries can support membership-inference attacks. We present bioETH-Beacon,…
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Macroscopic Black-Hole Remnants in a Nonlocal Field Theory: Towards Hawking Radiation in SFT
We demonstrate that, for a large black hole of radius $a$, Hawking radiation terminates around the scrambling time $u_{\text{scr}} \equiv 2a \log(a/\ell)$ due to the nonlocal, exponential suppression of trans-Planckian interactions inherent in string field theory (SFT). Modifying a massless scalar field's interaction with a dynamical black hole background via the smearing operator $e^{\ell^2\Box}$…
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Entanglement structure of the dynamical phases in the sub-Ohmic spin-boson model
The sub-Ohmic spin-boson model exhibits three distinct dynamical regimes in its spin population dynamics, classified as coherent, incoherent, and pseudo-coherent. Whether these regimes correspond to distinct spin-bath entanglement structures remains an open question. Here we address this using tree tensor network states with projector-splitting time evolution (TTN-TDVP-PS), scanning a broad grid i…
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Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection
Pose-flow video anomaly detectors are attractive for one-class surveillance because they provide likelihood-based rankings for tracked skeleton windows. However, a single likelihood score may hide multimodal normal behavior and be sensitive to pose-observation noise. We study a frozen-detector setting in which the pose-flow backbone, cached skeleton tracks, and evaluation pipeline are fixed. Relia…
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Dice Relabeling Using Square-Sided Dice
We continue recent work of Chao, Gabel, Larson, and Nasr in using cyclotomic polynomials for dice relabeling. In their work, one idea they expand on is finding pairs of dice with different number of sides which maintain the sum frequency of two normal dice. We continue this idea in this paper by studying pairs of dice where the number of sides of each is a different perfect square (which we call "…
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Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models
Evaluating video generation with clean, pixel-based reward models disconnects evaluation from the noisy diffusion process and incurs massive VAE decoding costs. In this paper, we challenge this paradigm by asking a fundamental question: Can a powerful video generator inherently discriminate preferences directly from noisy latents? To answer this, we introduce \textbf{PRISM} (\textbf{P}reference \t…
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Evidence for candidate X-ray pulsations from the ultraluminous X-ray source NGC 7456 ULX-1
We report evidence for a candidate pulsational signal at $\sim0.22$~Hz from NGC7456 ULX-1, a previously identified ultraluminous X-ray source (ULX). The signal is identified in the 2023 XMM-Newton observation using independent timing techniques including accelerated searches, $Z^2_n$ statistics, and an orbital-demodulation analysis designed to restore phase coherence in the presence of binary moti…
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Quantum Batteries as Work Sources for Phase-Locked Parametric Amplification
Quantum batteries have been proposed as locally precharged work sources for superconducting quantum technologies, suggesting a route to reduce continuously supplied microwave drives. Here we ask whether the pump tone of a quantum-limited parametric amplifier can be replaced, or strongly duty-cycled, by a finite bosonic quantum battery. Quantizing the pump of a nondegenerate parametric amplifier ex…
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Intermittent turbulent fluctuations in solar coronal mass ejections
Localised regions of high intensity fluctuations are known to be signatures of intermittency in fluid and plasma turbulence. We investigate such turbulent spots using near-Earth {\em in-situ} spacecraft observations of a sample of 125 solar coronal mass ejections (CMEs). We present statistical results which suggest that the intensity of the strongest turbulent spot and the turbulent spot occurrenc…
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Diagonal Hessian Approximation Based on Conjugacy Condition for Noisy Derivative-Free Optimization Problems in High Dimensions
We consider large-scale noisy derivative-free optimization (DFO) problems in which only function values are available and gradient or subgradient information cannot be reliably estimated. Matrix-adaptation evolution strategies (MAES) and their limited-memory variants are among the most robust DFO methods under noise; however, their performance may deteriorate when the noise level is large. In such…
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GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI
Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices - selecting the "best" global model based only on validation data from participating hospitals - can lead to suboptimal deployment choices. We identify this critical failure mode as performance leakage, where the selected model overfits internal f…
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CUPID: Reconstructing UV Texture Maps for Interpretable Person-of-Interest Deepfake Detection
Deepfakes targeting a high-profile individual, known as Person-of-Interest (POI), are a threat to modern democracies and societies. Current POI deepfake detection methods still struggle to combine robustness to post-processing, efficiency and interpretability, focal aspects of modern deepfake detectors. In this paper we propose CUPID, a POI video deepfake detector that combines UV texture maps, a …
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Data-Driven Control from Poisoned Data: Fundamental Limitations and Secure DeePC
We study a data-driven control problem in the presence of arbitrary data poisoning attacks. We assume that a subset of offline output data is stored in unprotected locations and may be poisoned by an adversary. We first establish fundamental limitations for data-driven control arising from such poisoned data: poisoning attacks are not detected/identified from the dataset alone; unprotected data ar…
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CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection
Few-shot anomaly detection remains challenging due to limited training data. Multi-modal anomaly detection (MAD) offers a viable solution, leveraging 3D geometric cues to enrich 2D RGB representations and compensate for this scarcity. However, existing MAD methods apply spatially uniform feature processing, conflating stable macroscopic structures with high-frequency localized defect signals, exac…