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Milnor Invariants --From classical links to surface-links, and beyond--

This is an English translation of the expository article written by the author in Japanese for publication in {\em Sugaku}. The author will explain Milnor invariants from the viewpoint of his research...

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This article provides an accessible overview of Milnor invariants and their applications to classical and surface-links, making it potentially useful for both new learners and experienced researchers. However, being an expository piece, it lacks original research data or novel findings, which limits its immediate impact in advancing the field on its own. Its value lies in its ability to synthesize existing knowledge rather than to create new, transformative insights.

Pressure Dependence of Ultrafast Carrier Dynamics in Excitonic Insulator Ta $_2$ NiSe $_5$

An excitonic insulator (EI) phase is a consequence of collective many-body effects where an optical band gap is formed by the condensation of electron-hole pairs or excitons. We report pressure-depend...

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The article presents novel findings on the pressure-dependent dynamics of an excitonic insulator, specifically in Ta$_2$NiSe$_5$, which is a key material in the study of quantum phase transitions. The methodology is robust, utilizing advanced optical techniques, and the results highlight the material's transition phases, enhancing its significance in understanding excitonic behavior under external pressures. These insights could drive future research on excitonic insulators and their applications.

LISA test-mass charging. Particle flux modeling, Monte Carlo simulations and induced effects on the sensitivity of the observatory

Context. The LISA space observatory will explore the sub-Hz spectrum of gravitational wave emission from the Universe. The space environment, where will be immersed in, is responsible for charge accum...

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This article addresses a critical aspect of the LISA space observatory, specifically the effects of particle flux on the charging of test masses, which significantly impacts its sensitivity. The rigorous application of Monte Carlo simulations and particle flux modeling contributes robustly to the understanding of noise which is vital for the mission's success. Novel methodologies developed in this research can be applied to future space missions, enhancing their design and operational strategies.

Even More On Twisted $A_{2n}$ Class-S Theories

This paper is a continuation of our investigation into the Coulomb branches of twisted $A_{2n}$ of class-S. In arXiv:2411.17675, we found predictions for the contributions of twisted punctures...

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This paper builds on prior research to delve deeper into the intricate structure of the twisted $A_{2n}$ class-S theories, providing new insights and a rigorous methodological framework. The introduction of a new order-reversing map on nilpotent orbits adds a novel perspective to the study, enhancing its potential impact within theoretical physics, especially in relation to the understanding of Coulomb branches and S-duality. However, its audience may be somewhat niche, restricting broader interdisciplinary relevance.

Improved Parallel Derandomization via Finite Automata with Applications

One main genre of algorithmic derandomization comes from the construction of probability distributions with small support that fool a randomized algorithm. This is especially well-suited to paralleliz...

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This article presents a novel approach to derandomization via parallel algorithms using finite automata, addressing a significant challenge in algorithm design. The reduction in processor complexity and the clean separation of problem-specific optimizations from general problems demonstrate methodological rigor. Moreover, the applications to well-known problems like the Gale-Berlekamp Switching Game and approximate MAX-CUT suggests high applicability and potential influence on future research in randomized algorithms and parallel computing.

Privacy-preserving Robotic-based Multi-factor Authentication Scheme for Secure Automated Delivery System

Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile transport problem, in par...

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The article addresses critical security and privacy challenges in robotic delivery systems, which is a highly relevant and emerging area in logistics and automation. The proposed multi-factor authentication scheme is novel, particularly with the addition of an audio-visual fusion defender against machine learning attacks. The rigorous formal analysis and real-world implementation aspects demonstrate methodological rigor and applicability, potentially setting a foundation for future research in secure robotic applications.

Linearly scalable fast direct solver based on proxy surface method for two-dimensional elastic wave scattering by cavity

This paper proposes an $O(N)$ fast direct solver for two-dimensional elastic wave scattering problems. The proxy surface method is extended to elastodynamics to obtain shared coefficients for ...

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The paper presents a novel and efficient computational technique for elastic wave scattering, which is of high relevance in various fields such as geophysics and engineering. The method's linear scalability and high parallel efficiency offer significant advancements in solving large-scale problems, showcasing both methodological rigor and practical applicability in real-world scenarios. The numerical results provide strong supporting evidence for the performance claims made by the authors, thereby enhancing the method's credibility.

Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision

Integrating RGB and NIR stereo imaging provides complementary spectral information, potentially enhancing robotic 3D vision in challenging lighting conditions. However, existing datasets and imaging s...

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The article presents a novel approach to integrating RGB and NIR imaging with pixel alignment, addressing a significant gap in existing datasets and systems. This methodological innovation, along with the provision of a comprehensive dataset, indicates strong potential for advancing robotic vision, particularly in complex lighting. The robust experimental validation enhances the credibility of their proposed methods, making it highly relevant for the field.

SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations

Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most wid...

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The SPARC-X-API presents a significant advancement in the accessibility and usability of real-space DFT methods. Its design focuses on bridging the gap between advanced computational techniques and user-friendly interfaces, which is critical for accelerating research in this area. The use of established standards like ASE enhances its integration potential, making it relevant for a broad audience. The methodological rigor of including JSON schema for validation and supporting complex boundary conditions further substantiates its robustness.

Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart Grid

Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentralized machine learning ...

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This article presents a novel approach combining GANs and Transformers with a strong focus on privacy preservation in energy theft detection, which is increasingly important in today's energy management systems. The introduction of a mask-based method to combat privacy leakage is particularly innovative and addresses a significant gap in current methodologies. The methodological rigor and experimental validation further enhance its impact and utility in the field.

Persistent breather and dynamical symmetry in a unitary Fermi gas

SO(2,1) dynamical symmetry makes a remarkable prediction that the breathing oscillation of a scale invariant quantum gas in an isotropic harmonic trap is isentropic and can persist indefinitely. In 2D...

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This article presents a significant advance in the understanding of many-body quantum systems, particularly with its experimental realization of a long-lived breathing mode in a unitary Fermi gas that displays SO(2,1) dynamical symmetry. The methodological rigor and novel findings, including insights into the nature of quantum anomalies in different dimensions, render it particularly impactful. This discovery not only enriches foundational knowledge in quantum gas dynamics but also has implications for future research into non-equilibrium dynamics and related theoretical frameworks.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?

Over the past few decades, Artificial Intelligence(AI) has progressed from the initial machine learning stage to the deep learning stage, and now to the stage of foundational models. Foundational mode...

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The article effectively addresses a significant transition in AI towards foundational models, centering on the crucial role of bidirectional encoders like BERT. It provides a comprehensive analysis of their performance in downstream applications, highlighting methodological rigor through performance comparisons on established benchmarks such as SQuAD and GLUE. Moreover, its exploration of the evolution and improvements of these models may spark new research avenues in the modeling of language processing tasks, although it could benefit from broader interdisciplinary perspectives.

Kinetic simulations underestimate the effects of waves during magnetic reconnection

Collisionless plasma systems are often studied using fully kinetic simulations, where protons and electrons are treated as particles. Due to their computational expense, it is necessary to reduce the ...

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This article presents novel insights into the often neglected effects of electron-scale waves in kinetic simulations of magnetic reconnection, demonstrating significant implications for understanding momentum balance in collisionless plasma systems. The critical evaluation of numerical parameters is methodologically rigorous and highlights the potential pitfalls in current simulation practices, making it highly relevant for future research.

A Real-World Benchmark for Evaluating Fine-Grained Issue Solving Capabilities of Large Language Models

Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user experience. The process of resolving real-world issues encompasses task...

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The article presents a highly relevant and innovative benchmark for evaluating the fine-grained issue-solving capabilities of large language models (LLMs) in software engineering contexts. It addresses significant gaps in existing benchmarks, offering methodological rigor through the systematic construction of FAUN-Eval and its dual evaluation approach combining both LLM outputs and manual checks. The inclusion of real-world GitHub issues enhances its applicability and ensures practical relevance. The findings on model performance variations provide valuable insights for researchers and practitioners in the field, making the benchmark both impactful and useful for advancing future research in LLMs and software development.

Neural Finite-State Machines for Surgical Phase Recognition

Surgical phase recognition is essential for analyzing procedure-specific surgical videos. While recent transformer-based architectures have advanced sequence processing capabilities, they struggle wit...

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The article introduces a novel approach, the Neural Finite-State Machine, that enhances surgical phase recognition by leveraging principles from classical models while integrating them into modern deep learning frameworks. Its methodological rigor and the promising improvements in performance metrics suggest a significant impact on the field. Additionally, the demonstration of generalizability beyond surgical contexts indicates potential interdisciplinary applications.

Topological Momentum Skyrmions in Mie Scattering Fields

Topological quasiparticles such as skyrmions and merons have recently attracted enormous attentions in the form of diverse optical degrees of freedom. However, these structures have not been explored ...

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This article presents a significant advancement in the understanding of topological quasiparticles, linking them to optical fields, a relatively unexplored area. The methodology involving multipole Mie scattering is innovative, and the analysis of topological stability enhances robustness. These factors combined suggest high impact and relevance.

On the small cap decoupling for the moment curve in $\mathbb{R}^3$

This paper proves sharp small cap decoupling estimates for the moment curve $\mathcal{M}^n=\{(t,t^2,\ldots,t^n):0\leq t\leq 1\}$ in the remaining small cap parameter ranges for $\mathbb{R}...

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This article presents significant advancements in the understanding of small cap decoupling estimates for moment curves, a relevant topic in harmonic analysis and geometric measure theory. Its methodological rigor and the application to higher dimensions (specifically in \\mathbb{R}^3) add novelty and depth to existing research, making it beneficial for both theoretical exploration and practical applications. However, its specific focus limits broader interdisciplinary impact, which reduces its overall relevance score slightly.

AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions

In software maintenance, bug reproduction is essential for effective fault localization and repair. Manually writing reproduction scripts is a time-consuming task with high requirements for developers...

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This article presents a novel approach to an important problem in software maintenance, addressing automation in bug reproduction through an agent-based framework, which showcases methodological rigor and practical applicability. The performance improvement reported compared to existing baselines demonstrates significant potential for real-world impact.

Diffeomorphic Latent Neural Operator Learning for Data-Efficient Predictions of Solutions to Partial Differential Equations

A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be qui...

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The article presents a novel approach to training neural operators for PDE solutions, emphasizing the use of diffeomorphic mappings which enhances generalization across spatial domains. This method addresses significant limitations in data requirements for training, making it highly relevant for practical applications in engineering and medical data analysis. The rigorous methodological framework, along with promising numerical experimentation, indicates a strong potential for impactful contributions to both theoretical and applied fields.

FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they ...

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FASIONAD presents a novel dual-system framework that integrates fast and slow cognitive processes for autonomous driving. Its approach tackles both routine tasks and complex scenarios effectively, which is critical for improving the robustness of autonomous vehicles. The introduction of a new benchmark further solidifies its utility in advancing research in this area. The method's innovative nature and applicability to real-world driving scenarios underscore its potential impact.