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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!

Deep Learning VLBI Image Reconstruction with Closure Invariants

Interferometric closure invariants, constructed from triangular loops of mixed Fourier components, capture calibration-independent information on source morphology. While a complete set of closure inv...

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The article presents a novel application of deep learning to improve VLBI imaging, which addresses a significant gap in existing methodologies by focusing on closure invariants. The approach shows strong experimental results and high fidelity in image reconstruction, indicating both methodological rigor and potential for practical advancement. The independence from calibration and tunable parameters enhances its applicability, increasing relevance across the field.

Wavespeed selection of travelling wave solutions of a two-component reaction-diffusion model of cell invasion

We consider a two-component reaction-diffusion system that has previously been developed to model invasion of cells into a resident cell population. This system is a generalisation of the well-studied...

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This article presents a novel analysis of a two-component reaction-diffusion system that extends previous models, making a significant contribution to the understanding of cell invasion dynamics. Its methodological rigor is evident in the explicit calculations of wave solutions and the asymptotic analysis. The insights into initial conditions and their influence on travelling wave solutions are particularly valuable for the field, potentially guiding future experimental designs and theoretical models.

Two-dimensional superconductivity in new niobium dichalcogenides-based bulk superlattices

Transition metal dichalcogenides exhibit many unexpected properties including two-dimensional (2D) superconductivity as the interlayer coupling being weakened upon either layer-number reduction or che...

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The study reports novel findings on 2D superconductivity within new niobium dichalcogenide-based superlattices, showcasing both unique materials and properties. The combination of novel material synthesis with clear superconducting characterization demonstrates methodological rigor. Furthermore, the work establishes a new platform that may encourage explorations of other 2D superconductors, enhancing its impact on the field.

Modular $\mathbb{Z}_2$ -Crossed Tambara-Yamagami-like Categories for Even Groups

We explicitly construct nondegenerate braided $\mathbb{Z}_2$ -crossed tensor categories of the form $\operatorname{Vect}_Γ\oplus\operatorname{Vect}_{Γ/2Γ}$ . They are $\mathbb{Z}_2&#...

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This article introduces novel nondegenerate braided $ extbf{Z}_2$-crossed tensor categories, which expand upon existing structures, making it quite impactful within the field of category theory and modular tensor categories. The construction's connection to physical concepts and potential applications in quantum algebra enhances its relevance, showcasing both theoretical innovation and practicality. However, the work might require further exploration and validation in various contexts to fully ascertain its utility, impacting the final score.

Chiral swimmer with a regular arbitrary active patch

We investigate the low Reynolds number hydrodynamics of a spherical swimmer with a predominantly hydrophobic surface, except for a hydrophilic active patch. This active patch covers a portion of the s...

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This article presents a novel investigation into the hydrodynamics of chiral swimmers with designated active patches, contributing significantly to the field of microfluidics and active matter. The analytical calculation of swimming dynamics under varying configurations showcases methodological rigor and presents valuable findings with potential applications in synthetic biology and medicine. The findings not only advance theoretical understanding but also have practical implications in designing active particles for drug delivery and other biomedical applications.

Detection of the lowest mass ratio contact binary in the universe: TYC 3801-1529-1

This paper presents the first analysis of the contact binary TYC 3801-1529-1. We observed four sets of multiple bands complete light curves and one set of radial velocity curve of the primary componen...

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This paper presents groundbreaking findings regarding TYC 3801-1529-1, which is identified as the lowest mass-ratio contact binary discovered to date. The novelty of the discovery, combined with the rigorous observational methodology and the implications for theoretical models of binary evolution, marks it as a significant contribution to the field. Furthermore, the detailed analysis of the light curves and discussion on the potential implications for future research on contact binary mergers enhances its relevance.

Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods

Large language model unlearning aims to remove harmful information that LLMs have learnt to prevent their use for malicious purposes. LLMU and RMU have been proposed as two methods for LLM unlearning,...

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The paper provides a thorough evaluation of two unlearning methods for large language models, which is highly relevant given the increasing focus on ethical AI and the need to mitigate harmful content in AI systems. The experimental design appears rigorous, comparing both methods across different benchmarks. This contribution is significant because it not only evaluates current methods but also identifies their limitations, which could influence future developments in unlearning techniques.

Sorted Consecutive Occurrence Queries in Substrings

The string indexing problem is a fundamental computational problem with numerous applications, including information retrieval and bioinformatics. It aims to efficiently solve the pattern matching pro...

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This paper provides a significant advancement in solving string indexing and querying problems, which are foundational in computational fields. It introduces generalized models to address consecutive occurrences in substrings, improving query efficiency. The methodological rigor is apparent in the proposed data structures with reduced query times, making the findings applicable in practical scenarios, especially in information retrieval and bioinformatics. The connection to geometric problems broadens its impact, suggesting interdisciplinary relevance.

Homogenized $\textit{C. elegans}$ Neural Activity and Connectivity Data

There is renewed interest in modeling and understanding the nervous system of the nematode $\textit{Caenorhabditis elegans}$ ( $\textit{C. elegans}$ ). This is particularly interesting a...

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The study presents a significant contribution to the field of neurobiology by providing a consolidated dataset that combines neural activity and connectome data from multiple experiments and different lab protocols. Its methodological rigor in standardizing diverse datasets enhances accessibility for other researchers, potentially catalyzing further studies in neural modeling. The dataset also bridges the structure-function gap, which is a crucial area of interest in neuroscience, thus holding promise for advancing research and applications.

Revealing Structure and Symmetry of Nonlinearity in Natural and Engineering Flows

Energy transfer across scales is fundamental in fluid dynamics, linking large-scale flow motions to small-scale turbulent structures in engineering and natural environments. Triadic interactions among...

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The article presents a novel method (Triadic Orthogonal Decomposition) that significantly enhances understanding of energy transfer in fluid dynamics—a critical issue in both natural and engineering contexts. The methodological rigor demonstrated through its application on real datasets (unsteady cylinder wake and wind turbine wake) underpins its credibility. Its potential for improving modeling accuracy and energy budget assessments in turbulent flow scenarios makes it highly relevant for future research.

STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk

Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and ne...

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The paper presents a novel socio-technical framework (STRisk) for assessing hacking breach risks, integrating social media aspects into traditional predictive models. The methodological rigor, including a comprehensive analysis of a large dataset and effective use of machine learning, enhances its impact. The significant improvement in prediction accuracy demonstrates practical applicability, making this research valuable for cybersecurity and risk assessment fields.

Machine Learning for Arbitrary Single-Qubit Rotations on an Embedded Device

Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies...

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The article addresses a significant challenge in quantum computing — single-qubit gate synthesis — and harnesses machine learning to advance this area. The methodological rigor is apparent in the multi-stage approach combining simulations with hardware validation, which enhances its applicability in practical scenarios. The potential for broader applicability across different quantum architectures adds to its novelty and relevance. However, the impact may be limited by the current technological stage of quantum computing. Overall, it is likely to inspire future research in both quantum computation and machine learning applications therein.

Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or cost...

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The article addresses a significant gap in the field of computer vision by proposing an innovative unsupervised method for homography estimation that is applicable to multimodal image pairs. This is an important advancement, given the challenges of obtaining ground-truth data in supervised learning. The methodology is robust with its novel alternating optimization approach and the use of Barlow Twins loss, which enhances its potential impact. Demonstrating superiority over existing methods adds to the article's relevance, supporting both theoretical and practical advancements in image processing.

Study of Group III-V Waveguides on Sapphire Platform for Photonic Integrated Circuits

Photonic integrated circuits (PICs) have been acknowledged as the promising platforms for the applications in data communication, Lidar in autonomous driving vehicles, innovative sensor technology, et...

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The article presents a novel approach to integrating photonic and electronic components using a sapphire platform, which could significantly enhance the development of Photonic Integrated Circuits (PICs). The rigorous study of waveguide performance, coupled with low-loss calculations across multiple wavelengths, adds methodological strength. It addresses a critical aspect of PIC technology—reducing losses in waveguides—which is fundamental for the practical application of these integrated systems in communication and sensing technologies. Its implications for high-performance, low-cost solutions substantiate its relevance and potential impact on future research directions.

Polarization of an electron scattered by static potentials

We study the polarization of an electron scattered by different static potentials. The initial state of the electron is chosen as a wavepacket to construct the definite orbital angular momentum, and t...

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The article presents a novel approach to studying electron polarization through the use of wave packets and various potential types. The alignment with recent experimental results enhances its relevance, suggesting the work could facilitate experimental validation and further exploration of quantum scattering phenomena. The methodological rigor demonstrated in the numerical calculations contributes significantly to the understanding of polarization in quantum scattering processes.

LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression

Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression...

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The article presents an innovative approach to learned image compression by integrating a large multi-modal model for generating captions and compressing them in a unified framework. This is a novel contribution that not only improves performance substantially (41.58% LPIPS BD-rate improvement) but also has potential implications for enhancing the perceptual quality of images. The use of semantic information in compression demonstrates strong methodological rigor and creativity, thus making a significant advancement in the field.

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models

Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of th...

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The article explores a timely and relevant topic in the intersection of AI and creative writing, showcasing novel insights into writer authenticity amid co-writing processes. The method of semi-structured interviews coupled with reader surveys provides a robust basis for findings, enhancing its impact. It offers practical implications for developing AI tools that support rather than overshadow personal voice, fostering future research into personalized AI applications in writing. The high degree of nuance adds valuable discourse to the ethics and aesthetics of AI-assisted creativity, which can influence both writers and technologists.

Two-terminal transport in biased lattices: transition from ballistic to diffusive current

We analyze quantum transport of charged fermionic particles in the tight-binding lattice connecting two particle reservoirs (the leads). If the lead chemical potentials are different they create an el...

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The study provides an insightful examination of the quantum transport characteristics in biased lattices, presenting a significant transition point from ballistic to diffusive transport. This not only adds depth to the existing literature on quantum transport phenomena but also delivers a framework for evaluating the impact of lattice configuration on transport behaviors, making it relevant for both theoretical and experimental research. The nuanced analysis of the role of decoherence and relaxation processes enhances its methodological rigor and applicability.

Classification of the limit shape for 1+1-dimensional FPP

We introduce a simplified model of planar first passage percolation where weights along vertical edges are deterministic. We show that the limit shape has a flat edge in the vertical direction if and ...

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This article provides significant advancements in understanding 1+1-dimensional first passage percolation models, demonstrating a novel approach to the limit shape characterization and deterministic weights, which is crucial for future theoretical explorations. The rigorous bounding of time constants adds methodological depth, enhancing the paper's impact on the field.

Probably Approximately Precision and Recall Learning

Precision and Recall are foundational metrics in machine learning where both accurate predictions and comprehensive coverage are essential, such as in recommender systems and multi-label learning. In ...

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This article presents a novel framework for considering Precision and Recall metrics in machine learning, particularly in situations with one-sided feedback. The introduction of graph-based hypotheses is an innovative approach that appears to have a rigorous statistical foundation. The results could significantly impact recommender systems and other areas where labeled data is limited and could inspire further research into learning from incomplete information.