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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!

The Igusa local zeta function of certain Thom-Sebastiani type functions

In this paper, we give an explicit formula of the Igusa local zeta function of a Thom-Sebastiani type sum of two separated-variable Newton non-critical polynomials. Data for the description are availa...

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The article presents an explicit formula for the Igusa local zeta function in the context of Thom-Sebastiani type functions, contributing significant theoretical advancements in algebraic geometry and number theory. The focus on Newton non-critical polynomials adds a layer of novelty and specificity, suggesting a strong methodological rigor. However, its applicability may be limited to specific problems within advanced mathematics, which may reduce its broader impact.

Global Controllability of the Kawahara Equation at Any Time

In this article, we prove that the nonlinear Kawahara equation on the periodic domain $\mathbb{T}$ (the unit circle in the plane) is globally approximately controllable in $H^s(\mathbb{T})$ for \(...

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The article presents a notable advance in the field of control theory by establishing global approximate controllability of the Kawahara equation, a significant nonlinear PDE. The application of geometric control theory to a specific equation such as the Kawahara underscores its potential for broader applications in the control of complex systems. The methodology is robust and well-supported, contributing both to theoretical understanding and practical implications of controllability in nonlinear PDEs.

Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications

Architecture recovery tools help software engineers obtain an overview of the structure of their software systems during all phases of the software development life cycle. This is especially important...

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This article addresses a critical need for effective architecture recovery in microservice applications, a growing area in software engineering. The multivocal literature review provides a comprehensive overview of existing tools and their comparative performance, which is novel and essential for practitioners. The established methodology and the high F1-scores for the tools suggest methodological rigor, and the implications of tool combinations indicate a strong potential for practical application in CI/CD environments.

Branching laws and a duality principle, Part I

For a semisimple Lie group $G$ satisfying the equal rank condition, the most basic family of unitary irreducible representations is the Discrete Series found by Harish-Chandra. In this paper, ...

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The article addresses an advanced topic in representation theory of Lie groups, specifically focusing on branching laws for Discrete Series representations. The incorporation of integral and differential operators indicates a robust methodological approach, enhancing the novelty and depth of analysis. Given the foundational nature of representation theory in various applications, including mathematical physics and geometry, this work is poised to inspire new research directions in these areas.

Benchmarking learned algorithms for computed tomography image reconstruction tasks

Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image reconstruction. However, t...

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This article provides a comprehensive benchmarking of learned algorithms for CT image reconstruction, addressing a significant gap in the field due to the lack of standardized datasets. Its methodological rigor, through the use of a well-defined dataset and performance metrics, makes it a valuable resource. The open-source toolbox promotes reproducibility and further research, enhancing its impact. However, the novelty is moderate since benchmarking itself is a common practice, though the use of a real-world dataset adds uniqueness.

Hypothetical Lorentz invariance violation and the muon content of extensive air showers

Extensive air showers (EAS), produced by cosmic rays in the atmosphere, serve as probes of particle interactions, providing access to energies and kinematical regimes beyond the reach of laboratory ex...

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This article presents a novel approach to explaining discrepancies observed in cosmic-ray physics, specifically the muon content of extensive air showers, by proposing a mechanism related to Lorentz invariance violation. Its methodological rigor, including the theoretical framework and potential experimental tests, enhances its impact. The implications for both particle physics and astrophysics could be significant, particularly in refining models of cosmic-ray interactions.

Distributed Bragg reflector-mediated excitation of InAs/InP quantum dots emitting in the telecom C-band

We demonstrate that optical excitation of InAs quantum dots (QDs) embedded directly in an InP matrix can be mediated via states in a quaternary compound constituting an InP/InGaAlAs bottom distributed...

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The study presents a novel approach to enhancing photon emission from InAs quantum dots through a distributed Bragg reflector, addressing crucial factors such as carrier transfer and relaxation time that affect photon coherence. Its findings hold significant implications for quantum optics and photonic device design, marking a robust advancement in the field. The experimental rigor and relevance to telecommunications applications are also strong points for this research.

SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs

We present SmolTulu-1.7b-Instruct, referenced in this report as SmolTulu-DPO-1130, an instruction-tuned language model that adapts AllenAI's Tulu 3 post-training pipeline to enhance Huggingface...

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The article presents a novel approach to optimizing the training of instruction-tuned language models by examining the interplay between learning rate and batch size. This is crucial for improving the capabilities of smaller models, driving advancements in their applicability. The empirical results showing state-of-the-art performance on specific reasoning tasks add strong methodological rigor to its claims. Its potential for practical application in developing efficient AI systems makes it highly impactful.

Grasping by parallel shape matching

Grasping is essential in robotic manipulation, yet challenging due to object and gripper diversity and real-world complexities. Traditional analytic approaches often have long optimization times, whil...

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This article presents a novel approach to robotic grasping via parallel shape matching, addressing significant limitations in traditional and data-driven methods. The incorporation of a GPU-based optimization strategy enhances efficiency, making it applicable for real-time robotic applications. The reported high success rate and low computation time suggest strong practical implications and usability in complex environments. The methodological rigor, demonstrated by the experimental validation, further supports a high relevance score.

ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature Enhancement

Medical image segmentation plays an important role in clinical decision making, treatment planning, and disease tracking. However, it still faces two major challenges. On the one hand, there is often ...

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The article addresses critical challenges in medical image segmentation, presenting innovative solutions that exhibit strong methodological rigor and practical applicability. The framework's ability to handle low contrast and ambiguous boundaries demonstrates high potential for real-world implementation in clinical settings. Its extensive experimental validation on diverse datasets further enhances its credibility and relevance.

SyncViolinist: Music-Oriented Violin Motion Generation Based on Bowing and Fingering

Automatically generating realistic musical performance motion can greatly enhance digital media production, often involving collaboration between professionals and musicians. However, capturing the in...

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The article introduces SyncViolinist, which represents a significant advancement in generating realistic violin performance motions from audio alone. The multi-stage framework overcomes existing limitations in the field, showcasing both novelty and methodological rigor, evidenced by the comparisons to state-of-the-art methods and validation from professional violinists. The practical applications in digital media and potential for further exploration in related audio-to-motion research enhance its relevance and impact.

Approximate Revenue from Finite Range Mechanisms

We consider an economic environment where a seller wants to sell an indivisible unit of good to a buyer. We show that revenue from any strategy-proof and individually rational mechanism defined on clo...

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This article presents a novel approach to revenue approximation in economic mechanisms, providing a significant theoretical contribution under conditions that reinforce its validity (strategy-proofness and individual rationality). By showing that mechanisms with finite range can approximate those with more complexity, the article simplifies the analysis of optimal mechanisms, which is a substantial contribution to mechanism design theory. Its systematic exploration of closed intervals in single crossing domains enhances its methodological rigor, and the results have practical implications for economic transactions involving indivisible goods.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts

Parameter-efficient transfer learning (PETL) has become a promising paradigm for adapting large-scale vision foundation models to downstream tasks. Typical methods primarily leverage the intrinsic low...

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The article introduces a novel method (ALoRE) that effectively enhances parameter-efficient transfer learning (PETL) in visual adaptation by utilizing a multi-branch paradigm and hypercomplex parameterization, addressing limitations in traditional single-branch structures. Its methodological rigor is evidenced by extensive experiments across multiple tasks, demonstrating significant improvements in accuracy and efficiency, which represents a substantial advancement in the field.

Phenomenology of orbital torque, pumping and mixing conductance in metallic bilayers

The conversion between spin and orbital currents is at the origin of the orbital torque and its Onsager reciprocal, the orbital pumping. Here, we propose a phenomenological model to describe the orbit...

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This article presents a novel phenomenological model that advances the understanding of orbital torque in metallic bilayers, which is a critical aspect of spintronics. The integration of orbital current dynamics with spin interactions suggests significant implications for future research, particularly in the design of advanced magnetic materials and devices. The methodological rigor and theoretical framework provided could serve as a foundation for experimental validation and further theoretical exploration.

Induced eccentricity splitting in disordered optical microspheres for machine learning enabled wavemeter

Accurate measurement of light wavelength is critical for applications in spectroscopy, optical communication, and semiconductor manufacturing, ensuring precision and consistency of sensing, high-speed...

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The article presents a novel approach to reconstructive wavemeters that integrates advanced optical engineering with machine learning techniques, showcasing high potential for precision measurements. The use of disordered optical microspheres enhances the uniqueness of the wavelength patterns, making the proposed device not only innovative but also highly applicable in various fields requiring accurate wavelength determination. Its affordability and compactness make it a practical alternative to existing methods, pushing the boundaries of current wavemeter technology and influencing future research directions in this area.

Edge-graceful usual fan graphs

A graph $G$ with $p$ vertices and $q$ edges is said to be edge-graceful if its edges can be labeled from $1$ through $q$ , in such a way that the labels induced ...

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The article tackles a specific and novel problem regarding edge-gracefulness in fan graphs, which adds to existing literature and builds on established theorems. The application of computational methods to derive results is a strong aspect. However, the overall impact may be limited due to the narrow focus on fan graphs, which could restrict its wider applicability.

A first measurement of galaxy merger rate increasing in dynamically colder protoclusters at cosmic noon

The process of galaxy cluster formation likely leaves an imprint on the properties of its individual member galaxies. Understanding this process is essential for uncovering the evolutionary connection...

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This article presents novel measurements of galaxy merger rates in protoclusters, connecting dynamical states with galaxy evolutionary processes. The use of spectroscopic data and HST imaging enhances methodological rigor and offers new insights into the relationship between cluster dynamics and galaxy formation, which is crucial for theoretical modeling in cosmology.

Broken time-reversal symmetry detected by tunneling spectroscopy of superconducting Pd-doped CaAgP

The appearance of broken time-reversal symmetry (TRS) in superconducting states is an intriguing issue in solid-state physics because of the incompatibility of the spontaneous magnetic field and the M...

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The article addresses a significant issue in the understanding of superconductivity, specifically the broken time-reversal symmetry (TRS) in a novel Pd-doped CaAgP system. The combination of tunneling spectroscopy with an analysis of magnetic field response is methodologically rigorous and innovative. The potential implications for the field of condensed matter physics, particularly in the study of unconventional superconductors and topological materials, highlight its high relevance. Its findings may inspire further research on TRS in related materials and superconducting states.

Maker-Breaker on Galton-Watson trees

We consider the following combinatorial two-player game: On the random tree arising from a branching process, each round one player (Breaker) deletes an edge and by that removes the descendant and all...

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The article presents a novel approach to analyzing a game theory scenario in the context of random structures, specifically Galton-Watson trees. This combination of game theory and probability theory is innovative and could yield significant insights into competitive processes in random environments. Additionally, the exploration of different information regimes enhances the applicability of the findings. However, it's worth noting that the specific results may require further empirical validation to substantiate broader applications in the cited fields.

First Principles based High-precision Modelling and Identification of Piezoelectric Fast Steering Mirror

We establish a high-precision composite model for a piezoelectric fast steering mirror (PFSM) using a Hammerstein structure. A novel asymmetric Bouc-Wen model is proposed to describe the nonlinear rat...

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The article presents a novel composite model for piezoelectric fast steering mirrors, which is crucial in fields requiring precision motion control. The use of a Hammerstein structure and a new asymmetric Bouc-Wen model offers significant advancements in modeling nonlinear hysteresis. The method is robust as it combines first principles with experimental validation. This rigor in methodology enhances its potential for adoption in various applications.