1614479 results (page 12 of 64580)
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AK-MCS-C2 : Active Kriging Monte Carlo Simulation method with conformal certification for failure probability estimation
We introduce a novel active-learning framework for failure probability estimation in structural reliability analysis that integrates Active Kriging Monte Carlo simulation with conformal prediction. The proposed approach employs an adaptive cross-conformal strategy specifically designed for small-sample settings and kriging surrogate models using the J+GP conformal estimator. Unlike standard AK-MCS…
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HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin
Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD). However, current approaches typically treat VFMs as black-box teachers, relying exclusively on frame-wise feature similarity. Consequently, they do…
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Truncated Wigner dynamics of biclique quantum spin glasses
Quantum spin glasses are often considered testbeds for studying quantum optimization algorithms and as such have been the subject of various quantum advantage claims. Here we investigate the near adiabatic dynamics of biclique quantum spin glasses within the (discrete) truncated Wigner approximation (TWA). Benchmarks on small systems show that TWA recovers sample-to-sample fluctuations of the Edwa…
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Quasi-random graphs, subgraph counts and graph limits, again
We study properties of graphs (or rather graph sequences) saying that some restricted count of subgraphs is approximatively what is expected in a random graph. It has been shown by several authors that many such properties characterize quasi-random graphs, but there are also some exceptions. We continue here the line of investigation in Janson and Sós (2013), and introduce some new versions of the…
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Operator Learning for efficient Quantum Computation
An efficient implementation of quantum algorithms is often hindered by the lack of efficient primitives for operators and state preparation. This limits both the ability of near-term quantum hardware to simulate complex problems and the potential of fault-tolerant algorithms to achieve practical quantum advantage. To address this, we propose a full-stack variational framework that transforms arbit…
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Effective Dimension Governs Generalization in Quantum Kernel Vision Models
Recent quantum vision models-quantum vision transformers and quantum convolutional networks-report two striking but unexplained empirical phenomena: (i) ansatze with more, or more uniformly distributed, entanglement generalize better, and (ii) injecting quantum noise can improve test accuracy rather than degrade it. These observations are currently treated as curiosities, discovered by grid search…
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ReNikud: Audio-Supervised Hebrew Grapheme-to-Phoneme Conversion
Grapheme-to-phoneme (G2P) conversion for Modern Hebrew is needed for applications like text-to-speech (TTS), but is challenging due to the language's abjad writing system, which leaves vowels largely unwritten, creating substantial ambiguity. Standard approaches first predict vowel diacritics (nikud) to produce International Phonetic Alphabet (IPA) transcriptions, but this is limited: vocalization…
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Large spin splitting at ferromagnetic surfaces of bulk antiferromagnets
We use density functional theory and model Hamiltonians to reveal large spin splitting of bands localized at low-symmetry, ferromagnetic surfaces of bulk antiferromagnets (AFMs). There is great interest in finding new material platforms combining the robustness and ultrafast dynamics of AFMs with large, functional spin splitting which is often restricted to ferromagnets. Here, we show that a subse…
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Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs
Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks. However, their ability to comprehend negation remains underexplored, limiting deployment in real-world applications where models must explicitly identify what is false or absent, e.g., emergency responders need to locate non-flooded routes for evacuation. To comprehensively study thi…
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Quantum-Accelerated Self-Consistent Field: A Hybrid Algorithm
We present the Grover adaptive search self-consistent field (GAS-SCF) algorithm. GAS-SCF leverages quantum arithmetic to construct an efficient oracle that marks target states (Fock states) which improve upon some initial classical energy estimate. Amplitude amplification then increases the probability of measuring these states. This approach offers a theoretical quadratic speed-up for the optimiz…
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A Consistent Comparison of Intracluster Light Assembly in Simulations I. Redshift Evolution and Progenitor Galaxies
The tidal stripping of satellite galaxies and the stellar detritus ejected during galaxy mergers builds up a diffuse stellar component in galaxy clusters known as the intracluster light (ICL). We investigate ICL assembly in cluster-mass haloes ($M_{178c}\sim10^{14}-10^{15}$ M$_\odot$) using four different hydrodynamical simulations (Horizon-AGN, TNG100, The Three Hundred Gizmo-Simba 7K, and Hydran…
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Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detection
Cell-free DNA (cfDNA) is a promising avenue for non-invasive multicancer early detection (MCED), in that, it can enable multiple cancer detection simultaneously from a single blood draw, with particular sensitivity to cancers that currently lack established screening programs. Here we review the computational methods developed between 2022 and 2025 for cfDNA-based MCED. We focus on how fragmentomi…
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Qiskit Code Migration with LLMs
The rapid evolution of Quantum Development Kits (QDKs) introduces a specific form of technical debt that compromises code maintainability and hinders software reuse. In the specialized domain of Quantum Software Engineering (QSE), this challenge is intensified by the scarcity of high-quality training data and the high volatility of emerging frameworks, which often lead general-purpose Large Langua…
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Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI
Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi-modal mo…
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High-accuracy polarimetry for CMB: new frontiers with the POLOCALC project
Modern telescopes observing the Cosmic Microwave Background (CMB) polarization require an exquisite control of systematics to target Inflationary Gravitational Waves (IGW), Cosmic Birefringence (CB), and Primordial Magnetic Fields (PMF). The absolute polarization angle of the detectors is a critical parameter to disentangle the $E$-modes and $B$-modes of the CMB, allowing a correct detection of pr…
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Theory of uncertain probability: can we derive the probability density function of uncertain random experiments with continuously changing conditions?
This paper aims to explore the formation mechanism of probability distribution in situations where the differences among random experiments are distinguishable, and these differences continue to evolve along with the dynamic changes in conditions and their mechanisms of action. To this end, we are motivated to devise a new theoretical system -- theory of uncertain probability (TUP) with Kolmogorov…
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Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying
Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations. To overcome this challenge, self-supervised pre-training is a possible solution, with contrastive learning dominant for location encoders. Those approaches usually align geographic coordinates with just one additional modality. We propose two multimodal contrastive learning architectures: Multi…
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Applications of quantum annealing to magnetic dipole hyperfine structure constants: First results beyond energies for atoms
We report the first results of the magnetic dipole hyperfine structure (HFS) constants of neutral $\mathrm{Li}$, Li-like $\mathrm{Be}$, neutral $\mathrm{Na}$, and Na-like $\mathrm{Mg}$ using a modified version of the Quantum Annealer Eigensolver (QAE) algorithm on D-Wave's quantum hardware. The results are benchmarked against relativistic configuration interaction with multiconfiguration Dirac Har…
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MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions. However, current medical large language models and retrieval-augmented generation systems often rely on single-step prompting or retrieval, which can be fragile when clinical evidence is distributed across long electronic health records, …
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Techno-Economic Analysis of Shared Mobile Storage for Demand Charge Reduction
This paper investigates the techno-economic viability of shared electric vehicle (EV) fleets for demand charge reduction under practical logistical and operational constraints. Unlike idealized models that overlook transit overheads, we propose a high-fidelity fleet management framework that explicitly accounts for the spatio-temporal coupling of energy consumption, labor costs for EV drivers, and…
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Implicit Semantic-Aware Communication Based on Hypergraph Reasoning
Semantic-aware communication has emerged as a transformative paradigm for next-generation communication systems, shifting the fundamental goal from transmitting bit-level symbols to reliably recovering and understanding the semantic meaning of information. Previous studies have demonstrated that representing the semantic content of source messages as graph-based structures can significantly improv…
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ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation
Imperfectly supervised video polyp segmentation (VPS) aims to learn dense, temporally consistent masks from inexpensive supervision, including weak annotations (points, scribbles) and semi-supervision with few densely labeled frames. This setting is clinically valuable but challenging due to weak contrast, ambiguous boundaries, motion blur, and specular highlights, compounded by sparse pixel-level…
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Multi-objective design of photon blockade for bright single-photon sources
High-quality single-photon sources, realized through saturable emitters, photon blockade, or heralded pair generation, are indispensable building blocks for photonic quantum platforms. Although these mechanisms suppress multiphoton emission through distinct principles typically captured by analytical models, their practical implementation is constrained by conflicting requirements for purity, brig…
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N-Version Programming with Coding Agents
This paper revisits the classical concept on N-version programming in the setting of contemporary AI coding agents. Revisiting the seminal Knight-Leveson experiment, we study whether diversity across agent systems, models, and implementation languages creates diverse failure modes. Using the Knight-Leveson's, Launch Interceptor Program Specification, we evaluate 48 agent-generated implementations …
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Modularity-Free Conflict-Averse Training for Generalized PINNs
Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives. Despite their advances, training PINNs remains fragile: recent conflict-averse optimization schemes alleviate gradient interference between residual and boundary losses, but we show that their effectiveness deteriorates as model capacity increases. I…