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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!

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation

Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotat...

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The article presents a novel approach to medical image segmentation, an important area in healthcare and diagnostics. By leveraging the Segment Anything Model (SAM) for pseudo labeling, the authors address a significant limitation in current methodologies — the dependence on large annotated datasets. The improvements in Dice scores indicate substantial performance gains, showcasing the potential of their method. The approach is methodologically sound and offers practical implications for situations with limited annotated data, making it a significant contribution to the field.

Hypergraph $p$ -Laplacian equations for data interpolation and semi-supervised learning

Hypergraph learning with $p$ -Laplacian regularization has attracted a lot of attention due to its flexibility in modeling higher-order relationships in data. This paper focuses on its fast num...

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The article presents a novel approach to hypergraph $p$-Laplacian equations which enhances existing methodologies in data interpolation and semi-supervised learning. It introduces a mathematically well-posed and computationally efficient alternative, addressing critical challenges in existing models, thus promising significant innovation. The rigorous numerical experiments showcase practical applicability, which strengthens its relevance for future research, although additional real-world applications could further enhance impact.

Eccentricity spectrum of join of central graphs and Eccentricity Wiener index of graphs

The eccentricity matrix of a simple connected graph is derived from its distance matrix by preserving the largest non-zero distance in each row and column, while the other entries are set to zero. Thi...

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The article offers a novel exploration of the eccentricity spectrum related to central graphs, exposing new relationships and bounds that contribute to graph theory. Its focus on specific graph operations and the introduction of new families of cospectral graphs enhance its relevance. However, it may primarily appeal to niche research areas within combinatorial graph theory, somewhat limiting its broader impact overall.

flashcurve: A machine-learning approach for the simple and fast generation of adaptive-binning light curves with Fermi-LAT data

Gamma rays measured by the Fermi-LAT satellite tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely vari...

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This article presents a novel deep-learning approach for generating adaptive-binning light curves, which offers significant improvements over existing methods in terms of speed and accuracy. The methodological rigor and applicability extend beyond gamma-ray astronomy, suggesting interdisciplinary value. The emphasis on multi-messenger physics also indicates potential transformational impact on the study of high-energy astrophysics.

GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking

Molecular docking is a major element in drug discovery and design. It enables the prediction of ligand-protein interactions by simulating the binding of small molecules to proteins. Despite the availa...

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The article presents a novel approach utilizing Graph Neural Networks for algorithm selection in molecular docking, addressing a critical issue in the field. Its methodological rigor, demonstrated through validation on a substantial dataset, underlines its potential impact. The research is likely to inspire future works in both algorithm development and applications in drug discovery.

Dynamics of micro and nanoscale systems in the weak-memory regime: A mathematical framework beyond the Markov approximation

The visible dynamics of small-scale systems are strongly affected by unobservable degrees of freedom, which can belong either to external environments or internal subsystems and almost inevitably indu...

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The article presents a novel mathematical framework that advances our understanding of non-Markovian dynamics in micro and nanoscale systems. Its rigorous approach to eliminating unobservable degrees of freedom provides significant contributions to the field. The robustness of the mathematical theorem and its applications to various models indicate high applicability and methodological rigor, essential for future research. However, its specialized nature might limit its immediate accessibility to broader audiences.

Dynamics of Micro and Nanoscale Systems in the Weak-Memory Regime

Memory effects are ubiquitous in small-scale systems. They emerge from interactions between accessible and inaccessible degrees of freedom and give rise to evolution equations that are non-local in ti...

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This article presents a novel theoretical framework for understanding memory effects in small-scale systems, addressing a critical gap in the current literature. The rigorous extension of the Markov approximation in contexts where time scales are comparable exhibits methodological rigor and applicability. By deriving explicit bounds and a convergent perturbation scheme, the authors offer practical tools that could inspire further exploration in theoretical and applied physics, enhancing its potential impact on the field.

Learning To Sample the Meta-Paths for Social Event Detection

Social media data is inherently rich, as it includes not only text content, but also users, geolocation, entities, temporal information, and their relationships. This data richness can be effectively ...

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The article introduces a novel Learning To Sample framework that addresses the critical issue of meta-path selection in heterogeneous information networks, which is a significant problem in social event detection. Its focus on automation and efficiency could greatly enhance model performance and reduce human bias in meta-path selection, thereby contributing to the advancement of the field. The methodological rigor and potential applicability to various contexts in social media analytics are noteworthy, although further empirical validation would strengthen its impact.

The direct measurement of gravitational potential decay rate at cosmological scales II -- Improved dark energy constraint from $z\le1.4$

The gravitational potential decay rate (DR) is caused by the cosmic acceleration of the universe, providing a direct probe into the existence of dark energy (DE). We present measurements of DR and exp...

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This article provides a significant advancement in understanding dark energy by introducing a new approach to measure the gravitational potential decay rate at higher redshifts. The methodology appears rigorous, and the findings contribute to the ongoing discourse on dark energy models, especially concerning their constraints. The consideration of systematic errors further enhances the study's credibility and relevance. However, while it extends existing measurements, the overall advancement may feel incremental rather than revolutionary, hence the score below 9.

Semiparametric quantile functional regression analysis of adolescent physical activity distributions in the presence of missing data

In the age of digital healthcare, passively collected physical activity profiles from wearable sensors are a preeminent tool for evaluating health outcomes. In order to fully leverage the vast amounts...

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This paper provides a novel approach by integrating quantile functional regression with spline mixed models to create a robust framework for analyzing complex physical activity data collected from wearables, especially under the challenge of missing data. The methodological innovation is significant, as it directly confronts biases typically encountered in this type of research. The practical applications of this framework in adolescent health studies also enhance its relevance, ensuring it reaches beyond mere academic interest into impactful real-world applications.

Bose-Einstein condensation in a rigidly rotating relativistic boson gas

We study the Bose-Einstein condensation (BEC) of a free Bose gas under rigid rotation. The aim is to explore the impact of rotation on the thermodynamic quantities associated with BEC, including the B...

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This article offers significant advancements in our understanding of Bose-Einstein condensation in rotating relativistic boson gases, which is a novel exploration of how rotation modifies thermodynamic properties. The analytical approach and derivation of critical parameters provide a rigorous foundation for future research. Its findings regarding the nature of phase transitions also hold implications for both theoretical predictions and potential applications.

Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

The capabilities and limitations of Large Language Models have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture. On the one hand, LLMs demonstrate a g...

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The article presents a novel exploration of how pretraining data influences reasoning in large language models (LLMs). It employs a rigorous methodology by contrasting the data reliance for reasoning versus factual questions, shedding light on the procedural knowledge that underlies LLM reasoning. By addressing the challenges in measuring generalization strategies, it opens avenues for enhancing the design and application of LLMs, making it highly impactful. However, its focus is somewhat narrow, which limits its broader applicability.

Ryll-Wojtaszczyk Formulas for bihomogeneous polynomials on the sphere

We investigate projection constants for spaces of bihomogeneous harmonic and bihomogeneous polynomials on the unit sphere in finite-dimensional complex Hilbert spaces. Using averaging techniques, we d...

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This article presents a significant extension of classical results regarding bihomogeneous polynomials, demonstrating both novelty and methodological rigor through the use of averaging techniques. The connection made with weighted L1-norms of Jacobi polynomials adds depth and applicability to the results. Furthermore, the practical expressions for computation and the asymptotic estimates could foster further studies in polynomial approximation, enhancing its relevance in both theoretical and computational settings.

Complex Frequency Fingerprint

In this work, we present a novel method called the complex frequency fingerprint (CFF) to detect the complex frequency Green's function, $G(ω\in\mathbb{C})$ , in a driven-dissipative system...

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The introduction of the complex frequency fingerprint (CFF) method marks significant novelty in the analysis of non-Hermitian systems, particularly with its application to the non-Hermitian skin effect (NHSE). This could potentially lead to new experimental techniques and theoretical understanding, offering a deeper characterization of dissipative systems. The methodological rigor in applying the CFF to measure the complex frequency density of states adds to its robustness, although further experimental validation would enhance its credibility.

A universal Euler system for GSp(4)

In our earlier work with Christopher Skinner (J. Eur. Math. Soc 24 (2022), no. 2; DOI 10.4171/JEMS/1124; Arxiv 1706.00201), we constructed Euler systems for the 4-dimensional spin Galois representatio...

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This article presents significant advances in the understanding of Euler systems in the context of GSp(4) and their dependence on local test data. The discovery of a 'universal' Euler class is both novel and potentially impactful for further research into Galois representations and automorphic forms. The methodological rigor in establishing the multiplicity-one result reinforces the findings, making the article highly relevant for specialists in the field.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment

Most modern No-Reference Image-Quality Assessment (NR-IQA) metrics are based on neural networks vulnerable to adversarial attacks. Attacks on such metrics lead to incorrect image/video quality predict...

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The article addresses a significant issue in image quality assessment by proposing a theoretically robust no-reference IQA metric, which is novel and relevant given the vulnerability of current methods to adversarial attacks. Its methodological rigor is evident through the comparative performance metrics presented (SROCC and PLCC), enhancing its impact on future research and applications in the field.

Measuring the expansion velocities of broad-line Ic supernovae: An investigation of neglected sources of error in two popular methods

The velocities of Ic-BL supernovae can be determined using two techniques (spline fitting and template fitting), sometimes resulting in different velocities for the same event. This work compares and ...

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The research addresses significant sources of error in widely used methodologies for measuring Ic-BL supernova velocities. By rigorously quantifying the discrepancies and suggesting best practices, it has the potential to improve the reliability of velocity measurements, thereby advancing the study of supernovae and their implications in astrophysics. Its direct comparative analysis of methods also offers a novel perspective that could prompt further methodological refinement in the field.

Locomotion Mode Transitions: Tackling System- and User-Specific Variability in Lower-Limb Exoskeletons

Accurate detection of locomotion transitions, such as walk to sit, walk to stair ascent, and descent, is crucial to effectively control robotic assistive devices, such as lower-limb exoskeletons, as e...

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This article presents innovative approaches to improve the accuracy of locomotion mode transitions in lower-limb exoskeletons, a field that is crucial for enhancing the functionality and user experience of assistive technologies. The use of adaptive methods (Statistics-Based and Bayesian Optimization) represents a significant advancement in addressing user-specific variability, which is a major challenge. The experimental results indicate a substantial improvement in transition detection accuracy, highlighting the practical implications of the research. Its implications for personalized healthcare and rehabilitation technologies further elevate its relevance, making it a potentially transformative contribution to the field.

$ΔT$ -noise in Multiterminal Hybrid Systems

The study of charge current fluctuations (noise) can give useful insights into the properties of nanoscale systems. In this work, the peculiar properties of noise in multiterminal hybrid normal-superc...

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The study explores a novel aspect of charge current fluctuations in nanoscale multiterminal hybrid systems, focusing on thermal out-of-equilibrium conditions (ΔT-noise). Its methodological rigor using the Landauer-Büttiker approach and detailed analysis of thermal and electrical biases provides significant insights that could influence future research in this area. The identification of contributions to noise that differ under various conditions offers new theoretical frameworks, enhancing our understanding of noise in superconducting systems which can have broader applications in quantum computing and nanoelectronics.

Large Language Models for Combinatorial Optimization of Design Structure Matrix

Combinatorial optimization (CO) is essential for improving efficiency and performance in engineering applications. As complexity increases with larger problem sizes and more intricate dependencies, id...

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This article presents a novel application of Large Language Models (LLMs) in the domain of combinatorial optimization, which is a significant advancement given the increasing complexity of engineering problems. The integration of network topology and domain knowledge with LLMs marks a departure from traditional methods, which could inspire new research avenues. The experimental results indicate methodological rigor and practical applicability, showcasing not only better performance than existing benchmarks but also the potential for real-world impact.