1273993 results (page 129 of 50960)
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Cosmological evolution of interacting dark energy with a CPL equation of state
This paper examines interacting dark energy models within the Chevallier-Polarski-Linder (CPL) parametrization, emphasizing both theoretical structure and observational viability. Two commonly adopted interaction terms are considered: $Q = βH ρ_{de}$ and $Q = βH ρ_c$. We derive exact analytic solutions that describe how the dark sector evolves. These solutions involve incomplete gamma functions an…
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Surrogate-Based Co-Design Coupling Analysis for Floating Offshore Wind Turbines
This work presents a design coupling analysis (DCA) framework to investigate the interactions among control and plant design variables in floating offshore wind turbine (FOWT) and to support the formulation of tractable control co-design (CCD) optimization strategies. DCA provides quantitative information that reveals the relationships and dependencies among design variables and to objective funct…
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Rethinking Trust Region Bayesian Optimization in High Dimensions
Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate lengthscale design can cause the local Gaussian process (GP) model within the trust region to degenerate, leading to suboptimal performance in high dimensions. In this work, we show that TuRBO's local GP may remain either…
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Institutions for the Post-Scarcity of Judgment
Each major technological revolution inverts a particular scarcity and rebuilds institutions around the shift. The near-consensus diagnosis of the AI revolution holds that AI collapses the cost of prediction while judgment remains scarce. This Opinion argues the inversion has now flipped: competent-looking judgment (selecting, ranking, attributing, certifying) is produced at scale and at marginal c…
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Agreement coefficients for continuous variables: A review
Agreement coefficients provide a fundamental framework for quantifying the concordance between two or more measurement methods applied to the same continuous variable. Unlike correlation, which measures the strength of a linear relationship, agreement focuses on assessing whether measurements are numerically similar, capturing both precision and accuracy. This review provides a comprehensive overv…
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AnemiaVision: Non-Invasive Anemia Detection via Smartphone Imagery Using EfficientNet-B3 with TrivialAugmentWide, Mixup Augmentation, and Persistent Patient History Management
Anemia affects over one billion people globally and remains severely under-diagnosed in low-resource regions where laboratory blood tests are inaccessible. This paper presents AnemiaVision, an end-to-end web-based system for non-invasive anemia screening from smartphone photographs of the palpebral conjunctiva and fingernail beds. The proposed pipeline fine-tunes a pre-trained EfficientNet-B3 back…
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The Oort Cloud as a Gravitational Detector for Primordial Black Holes
Planetary systems can act as sensitive gravitational detectors for dark matter. We investigate the gravitational scattering of Oort cloud objects by primordial black holes (PBHs) as a potential component of the Galactic dark matter halo. Calculating the rates at which PBH encounters eject objects from the Oort cloud or inject them into Earth crossing orbits, we find a linear scaling $Γ\propto m_{\…
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Understanding teens' self-beliefs when learning to construct and deconstruct AI/ML systems: Developing a survey instrument
Despite growing calls to foster AI literacy, there are few available survey instruments designed for children and youth that study computational empowerment alongside construction and deconstruction activities. In such activities, learners' beliefs about their abilities and attributes can impact their engagement. In this paper, we introduce and validate a survey instrument with constructs related …
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On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation
Preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such preferences control the transformation of attacks into defeats, and different approaches to doing so result in different reductions from a PAF to an AAF. In this paper we consider a PAF inverse problem which takes an argumentation graph, a labelling …
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Solving Einstein's Equation Numerically on Manifolds with Non-Orientable Spatial Slices
This paper presents solutions to Einstein's equation -- and the numerical methods used to construct them -- that describe simple cosmological models on manifolds with compact non-orientable spatial slices. These solutions have been constructed on a selection of manifolds having positive, negative, and vanishing spatial scalar curvatures. One example is shown to be indistinguishable locally from a …
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The Power of Power Law: Asymmetry Enables Compositional Reasoning
Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggests that reweighting or curating data towards a uniform distribution may help models better learn these long-tail skills, we find a counterintuitive result: across a wide range of compositional reasoning tasks, such as state tracking and multi-step a…
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Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on finite state spaces, which is infeasible in high-dimensional discrete configuration spaces or ill-posed in continuous domains. We propose Score-Repellent Monte Carlo (SRMC) framework that summarizes trajectory history by …
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Modeling Epidemic Spread with Strategic Vaccination and Socialization: a Mean Field Game Analysis
We study a game-theoretic model of epidemic control in a large population with finitely many groups and non-cooperative individuals. In the model, individuals jointly choose their socialization levels and vaccination rates, and vaccination is subject to a linear individual cost structure. We derive a forward-backward ordinary differential equations (FBODE) system that characterizes the mean field …
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VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
Diffusion models produce high-quality synthetic data but suffer from slow inference. We propose 3D Variable-Step Denoising Diffusion Probabilistic Model (VS-DDPM) a framework engineered to maintain generative quality while accelerating inference by several factors. We tested our approach on four tasks (missing MRI, tumor removal, MRI-to-sCT, and CBCT-to-sCT) within the BraTS2025 and SynthRAD2025 c…
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Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge
While the next-token prediction (NTP) paradigm enables large language models (LLMs) to express their intrinsic knowledge, its sequential nature constrains performance on specialized, non-generative tasks. We attribute this performance bottleneck to the LLMs' knowledge expression mechanism, rather than to deficiencies in knowledge acquisition. To address this, we propose Self-Knowledge Re-expressio…
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Large language model-enabled automated data extraction for concrete materials informatics
The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets. Here, we introduce a generalizable large language model (LLM)-powered pipeline for automated extraction and structuring of materials data from unstructured scientific literature, using concrete materials as a representative and particularly challenging ex…
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AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current verifiers face a fundamental trade-off: LLM-based verifiers are expressive but hard to control and prone to error, while deterministic executable verifiers are reliable and interpretable but often limited in capability. We study the following questi…
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Secure eFPGA-Enabled Edge LLM Inference: Architectural and Hardware Countermeasures
Edge deployment of transformer-based models increasingly relies on ASIC accelerators due to their high performance and energy efficiency, achieved through optimized dataflows, specialized architectures, low-bitwidth computation, and efficient memory hierarchies. However, these advantages come with significant security vulnerabilities. ASIC-based DNN accelerators are susceptible to side-channel att…
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PExA: Parallel Exploration Agent for Complex Text-to-SQL
LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of t…
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Characterization of the Volatile Properties of 133P/Elst-Pizarro and Other Main-Belt Comets with JWST and Ground-Based Observations
We report results from an analysis of the volatile composition and evolution of main-belt comet (MBC) 133P/Elst-Pizarro using JWST NIRSpec and NIRCam observations and ground-based observations during its 2024 active apparition, and also assess the body of JWST MBC observations acquired to date. Using NIRSpec, we measure water vapor outgassing rates at two points in 133P's orbit, finding Q(H2O)=(1.…
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Emerging Diversity Among the Main-Belt Comets: Insights from JWST and Ground-Based Observations of 457P/Lemmon-PANSTARRS
We present JWST NIRSpec and NIRCam observations of 457P/Lemmon-PANSTARRS, a main-belt comet that displayed activity around its 2020 perihelion and that was observed to regain activity during its 2024 perihelion by a ground-based observing campaign. The previous successful measurements of water production from two main-belt comets by the JWST NIRSpec instrument confirmed the hypothesis that H2O res…
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Dynamical masses of young stellar objects with the VLBA: DYNAMO-VLBA: Radio binary stars in Orion
We present results from a multi-epoch Very Long Baseline Array (VLBA) survey conducted as part of the DYNAMO-VLBA project, aimed at measuring the dynamical masses of young stellar systems in the Orion complex. Our observations include 19 radio sources associated with 15 binary or multiple young systems. For four visual binaries in which both components were detected, the derived Keplerian orbits y…
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Quantitative modelling of type Ia supernovae spectral time series III: Implications for type Ia supernovae standardisation in cosmology
The physics driving type Ia supernovae (SNe~Ia) standardisation in cosmology remains poorly-understood. Recent advances however mean that it is now possible to systematically analyse the explosion properties of large numbers of cosmological SNe~Ia. To that end we use riddler, a machine learning based framework for rapidly modelling SNe~Ia based on realistic explosion simulations, to perform quanti…
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Quantitative modelling of type Ia supernovae spectral time series II: Exploring the diversity of thermonuclear explosion scenarios
Observations of type Ia supernovae (SNe Ia) have led to suggestions of multiple progenitor and explosion scenarios. Distinguishing between scenarios and tying specific SNe Ia to individual scenarios however has so far been challenging. Constraints on the explosion physics are often achieved through empirical modelling of SNe Ia spectra and qualitative assessments of the level of agreement. While t…
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Come Together: Analyzing Popular Songs Through Statistical Embeddings
Statistical modeling of popular music presents a unique challenge due to the complexity of song structures, which cannot be easily analyzed using conventional statistical tools. However, recent advances in data science have shown that converting non-standard data objects into real vector-valued embeddings enables meaningful statistical analysis. In this work, we demonstrate an approach based on lo…