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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!

Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing

Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level behaviors, while fine-...

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The article addresses a critical gap in testing methodologies for autonomous driving software, specifically within unit testing, which has been under-explored compared to system-level testing. The combination of industrial application (Autoware) and use of LLMs for test case generation adds novelty and relevance. The proposed approach, AwTest-LLM, aims to directly enhance test coverage and reliability of ADS, which is crucial for the safety-critical nature of this field. The methodology appears rigorous, with implications that could influence future software engineering practices in autonomous driving systems.

Lower bounds on the $\ell$ -rank of ideal class groups

For a prime number $\ell$ and an extension of number fields $K/F$ , we prove new lower bounds on the $\ell$ -rank of the ideal class group of $K$ based on prime ramificat...

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This article provides significant advancements in the understanding of ideal class groups in number theory, specifically through novel lower bounds based on prime ramification. The methodological rigor and the application of these results to specific extensions enhance its relevance. It also introduces practical implications in the study of arithmetic properties of number fields, thus inviting future research in related areas.

Quantitative analysis of vectorial torques in thin 3d Co ferromagnet using orbital-spin conversion

Recent findings in orbitronics pointed out large current-induced torques originating, in the current understanding, from incident orbital currents. These are generated by orbital Rashba-Edelstein effe...

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The article presents novel experimental insights into the orbital-spin conversion mechanisms affecting magnetic torques, which is significant for the field of spintronics. The rigor in methodology, particularly the use of second harmonic Hall techniques, indicates a robust framework for understanding orbital vs spin current dynamics. Additionally, the findings may help guide future research on materials engineering in thin films, contributing to the development of more efficient spintronic devices.

Detection of Vascular Leukoencephalopathy in CT Images

Artificial intelligence (AI) has seen a significant surge in popularity, particularly in its application to medicine. This study explores AI's role in diagnosing leukoencephalopathy, a small vesse...

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This article presents a novel application of AI in diagnosing leukoencephalopathy through convolutional neural networks, showcasing high accuracy and rigorous methodology. The inclusion of model interpretability with Grad-CAM adds value, making the findings relevant for both clinical practice and future research on AI in medical imaging.

A Tale of Two Models: Understanding Data Workers' Internal and External Representations of Complex Data

Data workers may have a a different mental model of their data that the one reified in code. Understanding the organization of their data is necessary for analyzing data, be it through scripting, visu...

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The article presents a novel exploration of the mismatch between data workers' mental models and the actual data model, highlighting a crucial aspect of data literacy and cognition in data analytics. The methodology, which includes interviews and reflexive data analysis, is relevant and thorough, addressing a gap in the existing literature on data representation. Moreover, the suggested design interventions could have practical implications for the development of more effective data analysis tools, enhancing their usability and facilitating better decision-making.

Optimization is Better than Generation: Optimizing Commit Message Leveraging Human-written Commit Message

Commit messages are crucial in software development, supporting maintenance tasks and communication among developers. While Large Language Models (LLMs) have advanced Commit Message Generation (CMG) u...

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The proposed Commit Message Optimization (CMO) approach represents a notable advancement in the area of commit message enhancement, particularly through the integration of human inputs and LLMs. Its novel methodology and demonstrated improvement over existing frameworks indicate a strong potential to improve software development communication and efficiency. The rigorous evaluation further supports its applicability across various software contexts.

More varieties of 4-d gauge theories: product representations

Recently, we used methods of arithmetic geometry to study the anomaly-free irreducible representations of an arbitrary gauge Lie algebra. Here we generalize to the case of products of irreducible repr...

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The article presents novel findings in the realm of gauge theories by using methods from arithmetic geometry, which is a distinctive approach that could pave the way for innovative perspectives on representation theory and its implications in theoretical physics. The methodological rigor is evident, and the results have significant implications for understanding anomalies in particle physics, making it relevant for both mathematics and physics communities.

Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes

Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic meanings and relatio...

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This article addresses a relevant and specific application of text embedding models in the nuanced domain of construction delay disputes, showcasing methodological rigor through empirical evaluation of multiple models. Its contributions to automatic text classification in a legal context provide novel insights that can influence future research in AI applications for legal document analysis and similar fields.

From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation

Deep reinforcement learning (RL) has shown remarkable success in complex domains, however, the inherent black box nature of deep neural network policies raises significant challenges in understanding ...

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The article presents a novel model-agnostic method for enhancing interpretability in deep reinforcement learning (RL), addressing a significant gap in existing research. The use of Shapley values to provide transparent representations is innovative and offers a comprehensive understanding of the decision-making process. The evaluation on various RL algorithms adds robustness to the claims, making it a valuable contribution to both theoretical and applied domains of RL.

Efficient Probabilistic Assessment of Power System Resilience Using the Polynomial Chaos Expansion Method with Enhanced Stability

Increasing frequency and intensity of extreme weather events motivates the assessment of power system resilience. The random nature of these events and the resulting failures mandates probabilistic re...

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The article presents a novel approach combining polynomial chaos expansion with Maximin-LHS for probabilistic assessment of power system resilience. Its methodological improvement enhances computational efficiency and repeatability, addressing a significant gap in the field. The findings have practical implications for developing adaptation measures against extreme weather, indicating substantial applicability and relevance for future research in this area.

Efficient Sampling of Temporal Networks with Preserved Causality Structure

In this paper, we extend the classical Color Refinement algorithm for static networks to temporal (undirected and directed) networks. This enables us to design an algorithm to sample synthetic network...

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The article presents a novel extension of the Color Refinement algorithm to temporal networks, addressing a significant gap in the handling of causality in temporal sampling. Its methodological rigor is strong, with both theoretical and empirical validation of the proposed algorithm. The potential for application in a range of real-world situations enhances its relevance, particularly given the increasing importance of temporal dynamics in network analysis.

So long Kolmogorov: the forward and backward turbulence cascades in a supernovae-driven, multiphase interstellar medium

The interstellar medium (ISM) of disk galaxies is turbulent, and yet the fundamental nature of ISM turbulence, the energy cascade, is not understood in detail. In this study, we use high-resolution si...

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This article presents novel findings on turbulence cascades in the interstellar medium, providing valuable insights into the complex dynamics and energy transfer in astrophysical contexts. The high-resolution simulations and the detailed analysis of both compressible and incompressible modes demonstrate methodological rigor and contribute significantly to the understanding of turbulence in astrophysics. The findings challenge existing paradigms (like the Kolmogorov theory), suggesting a deeper understanding of turbulent processes in galactic disks, which could inspire further investigation in related fields.

Quasi-projective manifolds uniformized by Carathéodory hyperbolic manifolds and hyperbolicity of their subvarieties

Let $M$ be a Carathéodory hyperbolic complex manifold. We show that $M$ supports a real-analytic bounded strictly plurisubharmonic function. If $M$ is also complete Kähler, we ...

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The article introduces significant results related to the interplay of hyperbolic geometry and complex manifold theory, specifically through the lens of Carathéodory hyperbolic structures. Its findings on the existence of plurisubharmonic functions and theoretical implications on subvarieties deepen the understanding of the structure of quasi-projective manifolds. The methodological rigor and applicability to established conjectures (like Lang's conjecture) add to its relevance and potential impact in the field, meriting a high score.

Chasing the Light: Shadowing, Collimation, and the Super-Eddington Growth of Infant Black Holes in JWST-Discovered AGNs

Observations with the James Webb Space Telescope (JWST) have uncovered a substantial population of high-redshift broad-line active galactic nuclei (AGNs) characterized by moderate luminosities, weak X...

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This article presents a novel interpretation of high-redshift AGNs using advanced geometrical models, addressing critical challenges in our understanding of black hole growth. The interplay between super-Eddington accretion and anisotropic radiation provides significant insights that could reshape existing theories. Its methodology is robust, employing observational data from JWST, which itself is cutting-edge instrumentation, enhancing the study's credibility and relevance.

Greening the Grid: Electricity Market Clearing with Consumer-Based Carbon Cost

To enhance decarbonization efforts in electric power systems, we propose a novel electricity market clearing model that internalizes the allocation of emissions from generations to loads and allows fo...

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The proposed model offers a novel approach by integrating consumer-side carbon costs into electricity market clearing, advancing decarbonization strategies, and potentially reshaping consumer behavior. The methodological rigor is demonstrated through case studies, which strengthen its applicability and relevance in practical scenarios. The study’s innovative framework could inspire further research on market dynamics and environmental policies, making it impactful for both academia and industry.

The functional graph of $f(X)=(cX^q+aX)(X^{q}-X)^{n-1}$ over quadratic extensions of finite fields

Let $\mathbb{F}_q$ be the finite field with $q=p^s$ elements, where $p$ is an odd prime and $s$ a positive integer. In this paper, we define the function $f(X)=(cX^...

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This article presents a novel approach to studying the dynamics of a specific function over finite fields, contributing valuable insights into cycle lengths and structures of functional graphs. Its focus on quadratic extensions is particularly relevant given their significant applications in number theory and algebra. The rigorous exploration of cycles and tree structures adds depth to the field, encouraging further research into related dynamical systems over finite fields.

Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random

We study the problem of PAC learning $γ$ -margin halfspaces with Massart noise. We propose a simple proper learning algorithm, the Perspectron, that has sample complexity $\widetilde{O}((εγ...

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The paper presents a significant advancement in the PAC learning framework for γ-margin halfspaces under Massart noise. The proposed algorithm, Perspectron, not only simplifies the learning process but also demonstrates a drastic improvement in sample complexity, addressing a long-standing gap in the literature. The rigorous approach and extension to generalized linear models under challenging conditions lend substantial methodological strength, which could influence future research directions significantly.

Logarithmic Non-Abelian Hodge Theory for curves in prime characteristic

For a curve C and a reductive group G in prime characteristic, we relate the de Rham moduli of logarithmic G-connections on C to the Dolbeault moduli of logarithmic G-Higgs bundles on the Frobenius tw...

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The article presents a significant advancement in the field of algebraic geometry and Hodge theory by extending the Non-Abelian Hodge Theorem to logarithmic settings in prime characteristic. Its novelty in establishing the Log-p-NAHT and deriving the isomorphism between different moduli structures indicates a strong potential for influencing subsequent research. The theoretical rigor and implications for cohomology establish its relevance. However, its specialized nature may limit immediate applicability outside specific research groups in algebraic geometry and representation theory.

Coded Deep Learning: Framework and Algorithm

The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hindering training in resource-limited settings. To allevia...

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This article presents a novel approach to deep learning that integrates coding theory, which is innovative and addresses significant challenges in model complexity and training resources. The proposed methods show promising empirical results and potential for efficiency improvements, making it particularly valuable for research and applications where computational resources are constrained. However, further validation in diverse scenarios would strengthen its impact.

Holonomy, Zeta Functions, and Cohomological Structures in Foliated Manifolds with Stratified Boundaries

This paper explores the interplay between holonomy, Ihara zeta functions, and cohomological structures within the framework of ratified F-completions of foliated manifolds. We develop a novel formalis...

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This article presents a significant advancement in the understanding of the relationship between holonomy, zeta functions, and cohomological structures in the context of foliated manifolds. It introduces a novel mathematical formalism (the Gamma-set) and provides a conjecture that could lead to new insights in both topology and geometry. The methodological rigor and the potential applications to spectral graph theory and tiling enhance its relevance and could inspire future research in multiple domains.