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Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

Existence and uniqueness results for a nonlinear Budiansky-Sanders shell model

A nonlinear shell model is studied in this paper. This is a nonlinear variant of the Budiansky-Sanders linear shell model. Under some suitable assumptions on the magnitude of the applied force, we wil...

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This paper presents significant advancements in the mathematical theory of nonlinear shell models, specifically establishing existence and uniqueness results which are critical for engineers and material scientists. The novelty of extending previous work to various geometries and under new conditions enhances its applicability. The methodological rigor in proving these results adds to the overall strength of the findings, making it relevant for future research in related fields.

BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving

LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source operations research datasets lack d...

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The article introduces a novel algorithm (BPP-Search) that significantly enhances the reasoning capabilities of large language models (LLMs) in transforming natural language into mathematical models. The release of the StructuredOR dataset with comprehensive annotations is a substantial contribution to the field, as it addresses the existing gap in quality datasets for reinforcement learning. Furthermore, the advanced methodological approach, combining tree-of-thought reasoning and reinforcement learning techniques, showcases strong potential for practical applications and sets a new benchmark for future research.

A weighted scalar auxiliary variable method for solving gradient flows: bridging the nonlinear energy-based and Lagrange multiplier approaches

Two primary scalar auxiliary variable (SAV) approaches are widely applied for simulating gradient flow systems, i.e., the nonlinear energy-based approach and the Lagrange multiplier approach. The form...

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The article presents a novel integration of existing methods in the realm of gradient flows, showcasing methodological innovation that enhances stability and efficiency in numerical solutions. Its theoretical backing and validation through experiments strengthen its utility and potential application.

Receiver operating characteristic curve analysis with non-ignorable missing disease status

This article considers the receiver operating characteristic (ROC) curve analysis for medical data with non-ignorable missingness in the disease status. In the framework of the logistic regression mod...

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The study addresses a crucial gap in ROC curve analysis by dealing with non-ignorable missingness in disease status, which is a common issue in medical research. The methodological rigor is reinforced by the establishment of identifiability of model parameters and extensive simulations for validation. The application to real-world data (Alzheimer's disease) enhances its relevance, suggesting significant potential for impacting future studies in this domain.

One Mind, Many Tongues: A Deep Dive into Language-Agnostic Knowledge Neurons in Large Language Models

Large language models (LLMs) have learned vast amounts of factual knowledge through self-supervised pre-training on large-scale corpora. Meanwhile, LLMs have also demonstrated excellent multilingual c...

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The article addresses key limitations in current understanding of knowledge storage mechanisms within large language models (LLMs), specifically focusing on knowledge neurons. It presents a novel methodology (MATRICE) that seeks to improve knowledge localization accuracy, which is crucial for subsequent developments in the field. The construction of a new benchmark (RML-LAMA) enhances the generalizability of research in multilingual capabilities. The dual focus on methodological innovation and practical application makes this work highly relevant and impactful.

A Generalized Unified Skew-Normal Process with Neural Bayes Inference

In recent decades, statisticians have been increasingly encountering spatial data that exhibit non-Gaussian behaviors such as asymmetry and heavy-tailedness. As a result, the assumptions of symmetry a...

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The proposed Generalized Unified Skew-Normal (GSUN) process addresses a significant gap in the modeling of spatial data exhibiting non-Gaussian behaviors, which is a notable advancement in statistical methods. Its integration with neural Bayes inference and state-of-the-art deep learning techniques enhances its applicability and methodological rigor. The empirical validation against real-world data and the clear demonstration of its advantages over existing models suggest strong potential impact, although its novelty hinges on the established use of skewed distributions in spatial modeling.

Analysis of a Poisson-Nernst-Planck cross-diffusion system with steric effects

A transient Poisson-Nernst-Planck system with steric effects is analyzed in a bounded domain with no-flux boundary conditions for the ion concentrations and mixed Dirichlet-Neumann boundary conditions...

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This article presents a thorough mathematical analysis of a complex Poisson-Nernst-Planck system that incorporates steric effects, which is relevant for understanding ionic transport in confined environments. The novelty in modeling using a Lennard-Jones potential alongside rigorous proofs of global weak solutions and stability properties indicates significant advancements in the mathematical theory of electrochemistry and related fields. Furthermore, the numerical experiments provide practical insights that could inform experimental designs, enhancing its applicability.

Quantum-Classical Correspondence of Non-Hermitian Symmetry Breaking

Real-to-complex spectral transitions and the associated spontaneous symmetry breaking of eigenstates are central to non-Hermitian physics, yet their underlying physical mechanisms remain elusive. Here...

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This article presents a novel approach to understanding non-Hermitian physics through the lens of quantum-classical correspondence, utilizing robust methodologies such as complex path integral formalism and a generalized Gutzwiller trace formula. The insights into spectral transitions and symmetry breaking are likely to advance theoretical frameworks and directly inform experimental research in relevant systems.

Okamoto's symmetry on the representation space of the sixth Painlevé equation

The sixth Painlevé equation (PVI) admits dual isomonodromy representations of type 22-dimensional Fuchsian and 33-dimensional Birkhoff. Taking the multiplicative middle convolution o...

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The article presents a significant advancement in the understanding of the sixth Painlevé equation by associating dual isomonodromy representations with Okamoto's symmetry, utilizing a novel approach via cluster mutations. This connection not only enhances the theoretical framework but also opens pathways for future studies in related algebraic and geometric topics. Its methodological rigor and the potential for applications in both mathematics and theoretical physics elevate its relevance.

Superactivation of memory effects in a classical Markov environment

We investigate a phenomenon known as Superactivation of Backflow of Information (SBFI); namely, the fact that the tensor product of a non-Markovian dynamics with itself exhibits Backflow of Informatio...

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The article introduces a novel concept, Superactivation of Backflow of Information (SBFI), which presents significant implications in quantum information theory. The methodological rigor demonstrated through detailed mathematical analysis and the extension of established theories into new domains contributes to its high relevance. Its potential to inspire future work in both foundational quantum mechanics and practical quantum computing scenarios elevates its impact.

Variable selection via fused sparse-group lasso penalized multi-state models incorporating molecular data

In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across tr...

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The article presents a novel approach to variable selection in multi-state models, incorporating sophisticated statistical techniques to manage high-dimensional data, particularly in the context of cancer research. Its focus on covariate effects across different states reflects significant methodological rigor and potential applicability to real-world data challenges. The combination of sparse-group lasso with multi-state modeling is innovative and provides a fresh perspective that could inspire future studies.

Patient recruitment forecasting in clinical trials using time-dependent Poisson-gamma model and homogeneity testing criteria

Clinical trials in the modern era are characterized by their complexity and high costs and usually involve hundreds/thousands of patients to be recruited across multiple clinical centres in many count...

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This paper addresses a critical issue in clinical trial management, specifically patient recruitment, using an innovative approach to modeling recruitment rates. The introduction of a time-dependent Poisson-gamma model demonstrates methodological novelty that could enhance recruitment forecasting accuracy. The robustness of this approach, especially with its testing criteria, is significant given the complexities in clinical trials. However, the reliance on simulation for validation raises questions about its applicability to real-world clinical data.

NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds

Reconstructing continuous surfaces from unoriented and unordered 3D points is a fundamental challenge in computer vision and graphics. Recent advancements address this problem by training neural signe...

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The proposed NumGrad-Pull method addresses a significant challenge in 3D surface reconstruction by enhancing learning through innovative use of tri-plane representations and numerical gradients. The approach appears to enhance fidelity and training stability, showing rigorous experimental validation that strengthens its contribution. Its novelty and applicability in both computer graphics and computer vision warrant high relevance.

Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance

No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit sing...

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The article presents a novel approach to No-Reference Image Quality Assessment (IQA) that incorporates degradation and quality modeling distinctively, which is a significant advancement over previous methodologies. The integration of a restoration network with Representation-based Semantic Loss shows methodological rigor and an innovative framework, addressing a well-known challenge in the field. The extensive experimental validation against both synthetic and real-world datasets enhances the credibility and applicability of the proposed method, marking it as impactful for advancing IQA research.

Numerical Investigation of Instabilities in Over-pressured Magnetized Relativistic Jets

Context. Relativistic jets from Active Galactic Nuclei are observed to be collimated on the parsec scale. When the pressure between the jet and the ambient medium is mismatched, recollimation shocks a...

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The article presents novel findings related to instabilities in relativistic jets, utilizing advanced numerical simulations to investigate over-pressured conditions and their effects on magnetic fields. This focus on the transition between instabilities and its implications for observational phenomena in astrophysics adds significant value. The methodological rigor is commendable, and the applicability to existing astrophysical phenomena enhances its relevance.

Can LLMs be Good Graph Judger for Knowledge Graph Construction?

In real-world scenarios, most of the data obtained from information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graphs (KGs) remains a critic...

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This article presents a novel approach to Knowledge Graph construction using LLMs, addressing known limitations and challenges in existing methods. The introduction of innovative modules such as entity-centric iterative text denoising and knowledge-aware instruction tuning demonstrates methodological rigor and creativity. The empirical results showing superior performance over baseline methods enhance its relevance in practical applications.

Robust Bayesian Optimization via Localized Online Conformal Prediction

Bayesian optimization (BO) is a sequential approach for optimizing black-box objective functions using zeroth-order noisy observations. In BO, Gaussian processes (GPs) are employed as probabilistic su...

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The article presents a novel approach to improve Bayesian optimization by integrating conformal prediction to address model misspecification, which is a critical limitation in current optimization techniques. The theoretical performance guarantees enhance its credibility, and the experimental validation demonstrates its practical applicability. Its potential to significantly outperform existing methods under adverse conditions positions it as a key advancement in the field.

vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation

Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, si...

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The development of vesselFM represents a significant advancement in the field of medical image analysis, particularly in blood vessel segmentation. Its ability to generalize across different imaging modalities without the need for extensive annotations highlights its practicality and innovation. The methodological rigor showcased in training with diverse datasets further supports its robustness and potential utility in clinical settings, making it relevant for future research and application in medical imaging.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models

Large-scale pre-trained vision models are becoming increasingly prevalent, offering expressive and generalizable visual representations that benefit various downstream tasks. Recent studies on the eme...

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The article presents novel insights into monocular depth perception mechanisms in large vision models, which is significant for understanding their emergent properties. The introduction of a new benchmark, DepthCues, enhances methodological rigor and offers avenues for future research in model evaluation and improvement. The findings are applicable across different contexts, particularly in enhancing depth estimation capabilities in vision models, hence their relevance to practical applications.

Assessing Electricity Network Capacity Requirements for Industrial Decarbonisation in Great Britain

Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countrie...

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The article addresses a critical and often overlooked aspect of industrial decarbonisation by assessing the necessity for increased electricity network capacity. The use of a robust modeling approach (Net Zero Industrial Pathways model) to predict future demands adds methodological rigor. Its focus on the implications for net-zero targets and specific regional concerns enhances its relevance. However, while it provides valuable insights, further exploration into potential solutions or policy implications for addressing network capacity constraints could elevate its impact further.