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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!

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness

Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challenging to meet in practi...

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The article addresses a critical gap in multi-agent reinforcement learning (MARL) by introducing the CoDe framework, which accounts for real-world delays in communication. Its innovative approach to intent representation and dual alignment of intent and timeliness is both novel and applicable, potentially influencing future research on collaboration in distributed systems. The rigorous experimental validation adds to its methodological soundness and practical implications.

First observation of reactor antineutrinos by coherent scattering

Neutrinos are elementary particles that interact only very weakly with matter. Neutrino experiments are therefore usually big, with masses on the multi-ton scale. The thresholdless interaction of cohe...

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This article presents groundbreaking results from the Conus+ experiment, marking a significant advancement in neutrino detection methodologies. The use of low-energy nuclear reactor neutrinos and semiconductor detectors with unprecedented sensitivity showcases methodological innovation that could inspire substantial progress in neutrino physics. Additionally, the results may provide constraints on various theoretical models, highlighting their potential for influencing future research directions in physics beyond the Standard Model.

Discovering Hidden Visual Concepts Beyond Linguistic Input in Infant Learning

Infants develop complex visual understanding rapidly, even preceding of the acquisition of linguistic inputs. As computer vision seeks to replicate the human vision system, understanding infant visual...

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This article presents a novel interdisciplinary approach that merges cognitive science with computer vision, providing insights into both infant learning processes and advancements in artificial intelligence. The innovative introduction of a training-free framework to uncover hidden visual concept neurons signifies a significant methodological advancement. The paper not only explores fundamental aspects of cognitive development in infants but also has practical implications for improving machine learning models by informing them about human-like perception. The robust comparison with existing models lends credibility and relevance to its findings, enhancing its potential influence on future research.

Design and Control of a Bipedal Robotic Character

Legged robots have achieved impressive feats in dynamic locomotion in challenging unstructured terrain. However, in entertainment applications, the design and control of these robots face additional c...

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The article showcases a novel approach to integrating artistic expression into bipedal robots, addressing a significant gap in the intersection of robotics and entertainment. The use of reinforcement learning for control is methodologically rigorous, and the focus on creating an intuitive operator interface enhances its applicability. The potential for creating believable robotic characters can greatly advance human-robot engagement in various applications, though it may require more extensive validation across diverse settings to fully establish its robustness in real-world scenarios.

Roots of polynomial sequences in root-sparse regions

Given a family $(q_k)_k$ of polynomials, we call an open set $U$ root-sparse if the number of zeros of $q_k$ is locally uniformly bounded on $U$ . We study the interplay...

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This article presents a novel perspective on the relationship between the roots of polynomials and their derivatives in specific mathematical contexts, which is a relatively underexplored area. Its methodological rigor is strong, particularly in the application of weak* convergence and its implications in polynomial dynamics, suggesting a high potential for further investigations and applications. The theoretical implications could inspire future research in various mathematical domains, particularly in polynomial dynamics and potential theory.

Local predictors of explosive synchronization with ordinal methods

We propose to use the ordinal pattern transition (OPT) entropy measured at sentinel central nodes as a potential predictor of explosive transitions to synchronization in networks of various dynamical ...

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The study introduces a novel entropy measure (OPT) that improves on traditional early warning signals and demonstrates its applicability across different complex systems. The use of diverse case studies strengthens the findings, suggesting broad applicability and potential for future research advancements. However, the impact may be limited to specific domains of dynamical systems and networks.

A study on the 1- $Γ$ inverse of tensors via the M-Product

In this paper, we will study the issue about the 1- $Γ$ inverse, where $Γ\in\{†, D, *\}$ , via the M-product. The aim of the current study is threefold. Firstly, the definition and chara...

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The article presents a focused study on a specific type of tensor inverse, which is a niche but technically significant aspect of tensor analysis. The introduction of equivalent conditions and algorithms indicates methodological rigor and potential applicability for computational applications. However, the specialized nature may limit broader interdisciplinary impact.

On Coordinated Drone-Courier Logistics for Intra-city Express Services

Problem definition: Drones, despite being acknowledged as a transformative force in the city logistics sector, are unable to execute the \textit{last-meter delivery} (unloading goods directly to custo...

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This article presents a novel approach by integrating drone and courier logistics, addressing a significant issue of last-mile delivery constraints. The methodological rigor through the use of an integrated optimization model and queueing theory enhances its applicability to real-world logistics, particularly in urban contexts. The emphasis on sustainability and network efficiency is timely given current urban logistics challenges, making it likely to influence future studies in this domain.

Automation of Electroweak Corrections

This dissertation addresses a topic that I have worked on over the past decade: the automation of next-to-leading order electroweak corrections in the Standard Model of particle physics. After introdu...

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The dissertation provides significant advances in automating electroweak corrections, which is crucial for high-precision tests of the Standard Model and for interpreting experimental results from colliders like the LHC. Its methodological rigor in presenting frameworks and addressing complex details signifies a novel contribution to computational particle physics, enhancing both theoretical predictions and practical applications. Additionally, it outlines future research directions, indicating potential for further development and interest in the field.

Dexterous Manipulation of Deformable Objects via Pneumatic Gripping: Lifting by One End

Manipulating deformable objects in robotic cells is often costly and not widely accessible. However, the use of localized pneumatic gripping systems can enhance accessibility. Current methods that use...

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The article presents a novel approach for lifting deformable objects using pneumatic grippers, addressing a significant challenge in robotic manipulation. The methodology is well-defined, tested on various materials, and demonstrates practical applications in industrial settings. The potential integration of a vision system also indicates future research directions, enhancing its relevance for ongoing work in robotics and automation.

An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes

The new era of large-scale data collection and analysis presents an opportunity for diagnosing and understanding the causes of health inequities. In this study, we describe a framework for systematica...

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This article presents a novel framework that integrates causal inference with large-scale data analysis to address health inequities, specifically investigating racial and ethnic disparities in ICU outcomes. Its methodological rigor and comprehensive analysis of both direct and indirect effects on health disparities demonstrate significant advancement in understanding health equity issues. The development of the IICE Radar as a monitoring tool adds practical applicability, enhancing its impact. The findings challenge existing paradigms and reveal unexpected protective effects, which stimulate further exploration into access to healthcare and its implications for health outcomes.

Skin formation in evaporating colloidal droplets

When a droplet containing a concentrated suspension evaporates in a dry environment, a layer often forms at the interface accumulating non-volatile material. Such a "skin layer" experiences ...

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The article presents a novel theoretical framework for understanding skin layer formation in evaporating colloidal droplets, addressing a key interdisciplinary challenge that combines theoretical physics, materials science, and fluid dynamics. The integration of deterministic macroscopic models with microscopic interactions represents significant methodological rigor. Additionally, the implications for predicting mechanical stability during evaporation processes have broad applicability in various fields, enhancing the relevance and potential impact of the findings.

HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure correction

Recent advancements in image translation for enhancing mixed-exposure images have demonstrated the transformative potential of deep learning algorithms. However, addressing extreme exposure variations...

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HipyrNet presents a novel framework that addresses a significant problem in mixed-exposure image enhancement using deep learning. Its methodological rigor is highlighted by the integration of a HyperNetwork, which dynamically adapts to varying exposure conditions—an innovative approach that could inspire further research and developments. The extensive experimental validation and performance improvement over existing methods make it a valuable contribution to the field.

Double Majorana Vortex Flat Bands in the Topological Dirac Superconductor

Vortex lines, known as topological defects, are cable of trapping Majorana modes in superconducting topological materials. Previous studies have primarily focused on topological bands with conventiona...

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This article presents significant advancements in understanding Majorana modes in topological Dirac superconductors, focusing on a novel mechanism of vortex states. The exploration of vortex lines in these materials is timely and may open avenues for future experimental confirmations and applications in quantum computing. The methodological rigor in studying unconventional pairing and its implications for high-energy physics adds to its impact.

Three-body scattering hypervolume of two-component fermions in three dimensions

We study the zero-energy collision of three fermions, two of which are in the spin-down ( $\downarrow$ ) state and one of which is in the spin-up ( $\uparrow$ ) state. Assuming that the tw...

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This paper presents a novel approach to analyzing three-body scattering hypervolumes in fermionic systems, focusing on a specific scenario with two spin-down and one spin-up fermion. The derivation of asymptotic expansions enhances our understanding of many-body quantum interactions and offers a foundational parameter, $D$, that could have significant implications for astrophysical, nuclear, and condensed matter physics. Its methodological rigor, especially in terms of weak interactions, adds to its robustness.

Super-Localized Orthogonal Decomposition Method for Heterogeneous Linear Elasticity

We present the Super-Localized Orthogonal Decomposition (SLOD) method for the numerical homogenization of linear elasticity problems with multiscale microstructures modeled by a heterogeneous coeffici...

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The SLOD method introduces a novel approach to numerical homogenization in linear elasticity, showcasing improved sparsity and computational efficiency. Its ability to handle heterogeneous microstructures without periodicity assumptions represents a significant advancement in the field. The method's rigorous numerical analysis and scalable implementation further enhance its applicability and potential impact. This article is likely to influence future research on multiscale modeling techniques and the development of computational algorithms in elasticity.

On the Calculation of Pressure Derivatives in Mean-Field Thermal Field Theories

Accurate determination of higher-order pressure derivatives with respect to temperature $T$ and chemical potential $μ$ is essential for analyzing critical phenomena, transport properti...

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This article presents a novel and systematic approach to deriving higher-order pressure derivatives, addressing critical limitations in traditional numerical methods. The methodology is robust, particularly for applications in complex mean-field thermal field theories, making it not only relevant but potentially transformative for researchers working in areas related to critical phenomena and phase transitions. The rigorous mathematical foundation and practical illustration further support its broad applicability and reliability.

Rashba effect modulation in two-dimensional A2B2Te6 (A = Sb, Bi; B = Si, Ge) materials via charge transfer

Designing two-dimensional (2D) Rashba semiconductors, exploring the underlying mechanism of Rashba effect, and further proposing efficient and controllable approaches are crucial for the development o...

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This article presents a novel theoretical framework for manipulating the Rashba effect in 2D materials, which is significant for advancing spintronics technologies. The methodological rigor of first-principles calculations enhances its credibility, and the exploration of charge transfer mechanisms opens new avenues for research in both fundamental physics and applied materials science. The proposed strategies for modulation could lead to diverse applications in electronic devices, making it highly relevant for current and future research.

The{N/D}-Conjecture for Nonresonant Hyperplane Arrangements

This paper studies the Bernstein--Sato polynomials $b_{f}$ of homogeneous polynomials $f$ of degree $d$ with $n$ variables. It is open to know when $n\over d$ i...

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This article addresses a significant conjecture within the field of algebraic geometry, particularly regarding hyperplane arrangements and their implications for monodromy conjectures. The use of Bernstein-Sato polynomials introduces a novel aspect that could inspire further studies on roots and properties of these polynomials. The methodology appears robust and builds on established results, enhancing its credibility and potential impact.

Global well-posedness of the defocusing nonlinear wave equation outside of a ball with radial data for 3<p<5

We continue the study of the Dirichlet boundary value problem of nonlinear wave equation with radial data in the exterior $Ω= \mathbb{R}^3\backslash \bar{B}(0,1)$ . We combine the distorted Fou...

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The article introduces novel results regarding global well-posedness for a class of nonlinear wave equations, which is important in the analysis of partial differential equations. The method of proof is methodologically rigorous and extends previous results, indicating a progression in the understanding of nonlinearity in wave equations. Furthermore, the combination of techniques enhances its potential for influencing further research in this area.