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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!

A comparative study of uncertainty quantification methods in gust response analysis of a Lift-Plus-Cruise eVTOL aircraft wing

Wind gusts, being inherently stochastic, can significantly influence the safety and performance of aircraft. This study investigates a three-dimensional uncertainty quantification (UQ) problem to expl...

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The article addresses a critical area in aerospace engineering by analyzing the impact of wind gust uncertainties on eVTOL aircraft, which represents a significant advancement given the growing interest in urban air mobility. The use of a comprehensive aeroelastic model coupled with various uncertainty quantification methods reflects methodological rigor and relevance to current challenges in aircraft safety and design. The insights into the variability of structural responses under uncertainty can guide future research and design improvements, making it impactful for both academic and applied fields.

Global well-posedness and scattering for the massive Dirac-Klein-Gordon system in two dimensions

We prove global well-posedness and scattering for the massive Dirac-Klein-Gordon system with small and low regularity initial data in dimension two. To achieve this, we impose a non-resonance conditio...

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The paper presents significant advancements in the global well-posedness and scattering theories for a complex system, the massive Dirac-Klein-Gordon system, which is a critical aspect in mathematical physics. The focus on global well-posedness with small and low regularity initial data indicates a strong methodological rigor that contributes to the existing literature. The non-resonance condition imposed on the masses adds novelty and specificity, which could enable further research to explore related systems and conditions.

Influence of growth parameters on the superconducting transition temperature in granular aluminum films

This study investigates the influence of various growth parameters on normal-state resistivity and superconducting transition temperature Tc of granular aluminum films. Specifically, we focus on the e...

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The study presents novel insights into the correlation between growth parameters and superconducting properties, which is crucial for enhancing the performance of superconducting materials. The systematic approach to analyze the effects of different parameters on resistivity and transition temperature indicates methodological rigor. The implications for optimizing granular aluminum superconductors reflect practical applicability in both research and technology development, particularly in electronics and quantum computing domains.

Channel Coding based on Skew Polynomials and Multivariate Polynomials

This dissertation considers new constructions and decoding approaches for error-correcting codes based on non-conventional polynomials, with the objective of providing new coding solutions to the appl...

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The dissertation presents innovative constructions and decoding approaches for error-correcting codes leveraging skew and multivariate polynomials, which are notable for their potential applications in quantum computing and distributed systems. The focus on dual-containing codes and locally recoverable codes signifies advancements in error correction methods, which is crucial given the growing reliance on fault-tolerant storage systems and secure communication in quantum contexts. The methodological rigor and exploration of new coding strategies suggest this work could significantly influence future research in coding theory and related applications.

Nuclear cross sections from low-energy interactions

We present a method to calculate neutron scattering cross sections for deformed nuclei using many--body wavefunctions described with multiple reference states. Nuclear states are calculated with the g...

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This article presents a novel approach to calculate neutron scattering cross sections, addressing a significant gap in the understanding of low-energy nuclear interactions. The use of many-body wavefunctions with multiple reference states and the systematic comparison with experimental results bolster the methodological rigor of the study, highlighting its potential for advancing theoretical and experimental nuclear physics.

Vision Language Models as Values Detectors

Large Language Models integrating textual and visual inputs have introduced new possibilities for interpreting complex data. Despite their remarkable ability to generate coherent and contextually rele...

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This paper addresses a significant gap in the field of vision-language models by exploring how well these models align with human annotators' perceptions. Its novel approach of analyzing home environment scenarios with a comparison of multiple models introduces important insights into the model's capabilities and limitations. The implications for practical applications such as social robotics and human-computer interaction enhance its relevance and potential impact on future research.

Ribbing patterns in inertial rotary drag-out

We report pattern formation in an otherwise non-uniform and unsteady flow arising in high-speed liquid entrainment conditions on the outer wall of a wide rotating drum. We show that the coating flow i...

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The study presents a novel insight into the dynamics of ribbing patterns in rotary drag-out processes, which has implications for fluid dynamics and material processing fields. The comprehensive experimental and numerical approach adds to its rigor, making it a potentially significant contribution.

Multi-functional Wafer-Scale Van der Waals Heterostructures and Polymorphs

Van der Waals heterostructures have promised the realisation of artificial materials with multiple physical phenomena such as giant optical nonlinearities, spin-to-charge interconversion in spintronic...

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The article presents a significant advancement in the field of van der Waals heterostructures by demonstrating the controlled epitaxial growth of diverse quantum materials, which improves material scalability and functionality. The focus on combining properties of topological insulators, transition metal dichalcogenides, and ferromagnets, as well as exploring polymorphic effects, enhances novelty and applicability in multiple applications such as nonlinear optics and spintronics. The methodological rigor in utilizing coherent phase-resolved terahertz measurements also underlines the robustness of the findings.

Learning to Relax Nonconvex Quadratically Constrained Quadratic Programs

Quadratically constrained quadratic programs (QCQPs) are ubiquitous in optimization: Such problems arise in applications from operations research, power systems, signal processing, chemical engineerin...

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This paper presents a novel machine learning approach to improve the solving of nonconvex QCQPs by dynamically selecting appropriate relaxation techniques. The integration of machine learning with optimization is a growing area of interest, and the authors provide empirical evidence supporting their method, which enhances practical applicability across various real-world problems. The methodological rigor and relevance to substantial applications in multiple fields strengthen its impact.

Sur l'application de Quillen pour la cohomologie modulo 2 de certains groupes finis

Let GG be a finite group. In a famous article, Quillen describes an F\mathrm{F}-isomorphism between commutative N\mathbb{N}-graded F2\mathbb{F}_{2}-algebras $...

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This article presents a significant application of Quillen's $ ext{F}$-isomorphism, demonstrating it as an actual isomorphism for several important families of groups. Its novelty lies in extending the understanding of the relationship between group cohomology and algebra structures associated with these group families. The methodological rigor seems solid as it deals directly with well-defined categories in algebraic topology and group theory, making the theoretical implications relevant for both fields, and potentially influencing future research on group representations and computational group theory.

Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States

Although large language models (LLMs) have transformed our expectations of modern language technologies, concerns over data privacy often restrict the use of commercially available LLMs hosted outside...

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This article addresses a critical area of importance given the EU's data privacy regulations, specifically in evaluating the performance of open-weight language models for underrepresented Baltic languages. The novelty lies in the localized approach to LLMs, which is significant for both linguistic diversity and socio-political contexts in Europe. The rigorous evaluation of multiple models across various tasks adds methodological robustness, enabling practical implications for governmental and defense sectors.

Mixing times for the open ASEP at the triple point

We consider mixing times for the open asymmetric simple exclusion process (ASEP) at the triple point. We show that the mixing time of the open ASEP on a segment of length NN for bias paramete...

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The article presents a thorough investigation of mixing times in the open asymmetric simple exclusion process (ASEP), which has significance in statistical mechanics and probability theory. Its findings on the mixing time at the triple point are both novel and contribute to a deeper understanding of the ASEP, bridging several analytical methods that address a complex topic. The combination of techniques and the implications for bounds on mixing times suggest a high level of methodological rigor and broad applicability, which can inspire future research in related domains.

Scalable calibration for partially observed individual-based epidemic models through categorical approximations

The computational cost of exact likelihood evaluation for partially observed and highly-heterogeneous individual-based models grows exponentially with the population size, therefore inference relies o...

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This article presents a novel and efficient computational method for addressing a well-known problem in epidemic modeling. The deterministic recursive approach offers significant improvements in scalability and computational efficiency while maintaining robust calibration performance. Its empirical validation across various models and its application to a real-world data set add to its methodological rigor and applicability. This work has high potential for practical implementation, advancing both theoretical and applied fields in epidemiology.

Shapiro Steps Observed in a Two-Dimensional Yukawa Solid Modulated by a One-Dimensional Vibrational Periodic Substrate

Depinning dynamics of a two-dimensional (2D) solid dusty plasma modulated by a one-dimensional (1D) vibrational periodic substrate are investigated using Langevin dynamical simulations. As the uniform...

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The article presents a novel investigation into the dynamics of a 2D Yukawa solid under specific modulations, revealing significant findings in the form of Shapiro steps which have implications for understanding phase transitions and dynamic behavior in soft condensed matter systems. The methodological approach using Langevin simulations is rigorous and applicable, potentially inspiring future explorations into similar systems or conditions. Its findings could have broader applications in materials science and plasma physics.

Kinetic theory of decentralized learning for smart active matter

Smart active matter describes agents which can process information according to their individual policy to solve predefined tasks. We introduce a theoretical framework to study a decentralized learnin...

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The article presents a novel theoretical framework for decentralized learning in smart active matter, which could greatly influence the understanding of agent interactions in complex systems. The introduction of hydrodynamic equations for policy dynamics is an innovative approach, and the agreement with agent-based simulations underscores its robustness. The applicability to both evolutionary dynamics and robotics broadens its relevance.

Enabling high mass accretion rates onto massive main sequence stars by outer envelope mass removal

Using the one-dimensional numerical code MESA, we simulate mass accretion at very high rates onto massive main sequence stars, M=30, 60, 80 Mo, and find that these stars can accrete up to 10% of their...

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This research presents a novel approach to understanding mass accretion in massive stars by integrating the concept of simultaneous mass removal via jets. The use of numerical simulations in MESA adds methodological rigor and the findings have implications for several astrophysical phenomena. Its relevance is further enhanced by addressing an important gap in current stellar evolution models, thereby influencing future research in this area.

Reducing Proxy Discrimination

Today, there is no clear legal test for regulating the use of variables that proxy for race and other protected classes and classifications. This Article develops such a test. Decision tools that use ...

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The article addresses a pressing issue of proxy discrimination, providing a framework that could significantly influence policy and legal standards. Its comparative approach and consideration of algorithmic fairness are timely, given the increasing reliance on algorithms across various sectors. This methodological contribution could guide lawmakers and developers toward more equitable practices, enhancing its impact on future research and applications.

Sequential Monte Carlo for Noncausal Processes

This paper proposes a Sequential Monte Carlo approach for the Bayesian estimation of mixed causal and noncausal models. Unlike previous Bayesian estimation methods developed for these models, Sequenti...

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The article introduces a novel application of Sequential Monte Carlo methods specifically addressing the challenging problem of noncausal processes in Bayesian estimation. Its methodological advancements, particularly the proposed identification methodology, show significant promise for improving efficiency and accuracy in model estimation. The simulation studies reinforce the robustness of the presented methods, suggesting practical applicability and relevance in the field of econometrics and statistics. However, the impact might be somewhat limited by the specificity of the application to certain types of data and processes, which could restrict broader adoption without further validation in diverse contexts.

A GPU Implementation of Multi-Guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization

Swarm intelligence optimization algorithms have gained significant attention due to their ability to solve complex optimization problems. However, the efficiency of optimization in large-scale problem...

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The novel approach of implementing the Multi-Guiding Spark Fireworks Algorithm on a GPU platform showcases significant advancements in optimization techniques for neural networks. Its emphasis on computational efficiency and the clear benchmarking against existing methods highlight its methodological rigor and practical applicability. The open-source availability of the code enhances its potential for widespread use and further research in the area.

When is Cat(Q) cartesian closed?

An example in the paper "Exponentiable functors between quantaloid-enriched categories" is mistaken: it is not true that the category of categories and functors enriched in any free quantalo...

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The paper addresses a significant misconception in the mathematical understanding of quantaloid-enriched categories and cartesian closed categories. Its focus on providing a unified characterization adds to the foundational knowledge in category theory, driving potential advancements in this niche subfield. The clarity of the corrections and the new examples enhance its contribution to existing literature. However, while robust, the applicability may remain somewhat limited to specialized theoretical frameworks, which slightly reduces its broader impact.