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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!

Magnetic tunneling between disc-shaped obstacles

In this paper we derive formulae for the semiclassical tunneling in the presence of a constant magnetic field in 2 dimensions. The `wells' in the problem are identical discs with Neumann boun...

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This article presents novel contributions to the understanding of semiclassical tunneling in magnetic fields, particularly by studying identical discs and providing a reduction method. The methodological rigor in deriving asymptotic formulas and effective operators like Harper's equation may provide significant insights, especially for researchers exploring the interplay between magnetic fields and quantum mechanics. However, while innovative, its niche application limits broader impact.

Automatic staff reconstruction within SIMSSA proect

The automatic analysis of scores has been a research topic of interest for the last few decades and still is since music databases that include musical scores are currently being created to make music...

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The article presents a novel approach to reconstructing staff lines in ancient music scores, which addresses a significant need in the field of musicology and digital music analysis. The demonstration of high performance in a specialized task suggests methodological rigor and potential applicability in similar contexts. Additionally, it contributes to the growing body of research using machine learning techniques for music information retrieval, which is a rapidly expanding area.

Higher Gaussian maps for special classes of curves

In this paper we study higher Gaussian (or Wahl) maps for the canonical bundle of certain smooth projective curves. More precisely, we determine the rank of higher Gaussian maps of the canonical bundl...

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The study of higher Gaussian maps for projective curves presents a novel approach to understanding their geometrical properties, particularly in the context of algebraic geometry. The article focuses on specific classes of curves, delving into canonical bundles, and provides mathematical rigor in determining the ranks of these maps, which has implications for both theory and applications in topology and algebraic geometry. This specificity enhances its relevance, making it a solid contribution to the field.

Instrumentation of Software Systems with OpenTelemetry for Software Visualization

As software systems grow in complexity, data and tools that provide valuable insights for easier program comprehension become increasingly important. OpenTelemetry has become a standard for the collec...

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The article presents a practical application of OpenTelemetry for software visualization, addressing a significant need in the field as software systems become increasingly complex. The methodological approach appears solid, with a focus on both application and unit test instrumentation, which extends its relevance. The use of a visualization tool like ExplorViz adds a layer of applicability, enhancing the practical value of the findings.

Volume-law entanglement fragmentation of quasiparticles

We study the entanglement entropy in quasiparticle states where certain unit patterns are excited repeatedly and sequentially in momentum space. We find that in the scaling limit, each unit pattern co...

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This article presents a significant advancement in understanding the entanglement behavior in quasiparticle states, introducing the concept of volume-law entanglement fragmentation. The numerical and analytical approaches add methodological rigor, which enhances its validity. The findings have the potential to inspire future research in both condensed matter physics and quantum information theory, particularly in areas involving many-body systems and quantum state manipulation.

An improvement of the estimates of the modulus of the Hankel determinants of second and third order for the class $\mathcal{S}$ of univalent functions

Using some properties of the Grunsky coefficients we improve earlier results for upper bounds of the Hankel determinants of the second and third order for the class $\mathcal{S}$ of univalent ...

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The article presents improvements on previous estimates of Hankel determinants in univalent function theory, which is a significant area in complex analysis. The novelty lies in leveraging Grunsky coefficients to enhance existing results. However, its impact may be limited to a specific mathematical niche without broader applications outside this area.

Convergence of Nonmonotone Proximal Gradient Methods under the Kurdyka-Lojasiewicz Property without a Global Lipschitz Assumption

We consider the composite minimization problem with the objective function being the sum of a continuously differentiable and a merely lower semicontinuous and extended-valued function. The proximal g...

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The article presents a significant advancement in the field of optimization by tackling the convergence issues of nonmonotone proximal gradient methods without requiring a global Lipschitz condition. The novelty of applying the Kurdyka-Lojasiewicz property broadens the applicability of these methods, potentially impacting computational algorithms in various fields. The methodological rigor in providing a clear comparison with monotone strategies strengthens its contributions to the existing literature.

Risk-Neutral Pricing Model of Uniswap Liquidity Providing Position: A Stopping Time Approach

In this paper, we introduce a novel pricing model for Uniswap V3, built upon stochastic processes and the Martingale Stopping Theorem. This model innovatively frames the valuation of positions within ...

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This paper presents a novel and innovative approach to pricing within the decentralized finance (DeFi) ecosystem, specifically Uniswap V3. By utilizing stochastic processes and the Martingale Stopping Theorem, the model provides a rigorous theoretical framework that enhances the understanding of liquidity provisioning. The focus on practical applicability ensures relevance for liquidity providers and highlights potential impacts on hedging strategies. The incorporation of Greek risk measures further deepens its utility and analytical depth, indicating a high potential for practical implementation in real-world scenarios.

Bound states in the continuum of infinite quality factor in finite unit cells

A theory based on the superposition principle is developed to uncover the basic physics of the wave behavior in a finite grating of N unit cells. The theory reveals that bound states in the continuum ...

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The article presents a novel theoretical framework exploring bound states in the continuum (BICs) with applications in photonics, which is significant due to its implications for high-quality resonant modes. The methodological rigor is apparent through the consideration of boundary conditions and the transition of BICs to quasi-BICs. The exploration of geometrical perturbations adds a practical aspect to the theory, indicating potential real-world applications, although it may benefit from experimental validation. Overall, the study is likely to influence future research into photonic structures.

Classification of threefold canonical thresholds

We show that the set $\mathcal{T}_{3, \mathrm{sm}}^{\mathrm{can}}$ of smooth threefold canonical thresholds coincides with $\mathcal{T}_{2, \mathrm{sm}}^{\mathrm{lc}}=\mathcal{HT}_{2}$ ...

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This article addresses a significant problem in algebraic geometry, particularly pertaining to the classification of threefold canonical thresholds. The connection established between smooth threefold canonical thresholds and two-dimensional hypersurface log canonical thresholds highlights a novel relationship that can stimulate further exploration in the study of thresholds in various dimensions. The mathematical rigor in proving the classification of canonical thresholds adds to its methodological strength, making the findings robust and potentially influential in the field.

RedPajama: an Open Dataset for Training Large Language Models

Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filter...

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The paper presents an important contribution to the field of large language models by addressing critical issues related to dataset transparency and quality curation. The release of the RedPajama datasets encourages reproducibility and broadens access to training materials, which is vital for advancing open-source developments. Its comprehensive analysis of dataset quality and potential applications in real-world language models enhances its impact, providing both a solid foundation for future research and practical benefits for the community.

Shape modes and jet formation on ultrasound-driven wall-attached bubbles

Understanding bubble behaviour under ultrasound excitation is key for applications like industrial cleaning and biomedical treatments. Our previous work demonstrated that ultrasound-induced shape inst...

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This article presents significant advancements in understanding bubble dynamics under ultrasound excitation, a topic with substantial practical applications in fields like biomedical engineering and industrial cleaning. The novel identification of distinct shape response regimes and their implications for targeted drug delivery injects fresh insight into the understanding of microbubble behavior, indicating high methodological rigor through dual imaging techniques and theoretical validation. The interdisciplinary relevance enhances its impact, paving the way for future research on microbubble-utilized technologies in medicine and materials science.

Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies...

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The study introduces a novel approach combining AlphaFold 3 and topological deep learning to tackle the challenge of viral evolution, which is highly relevant in the context of rapid responses to emerging infectious diseases. The methodology displays significant innovation and rigor, especially in the application of TDL and TDA models for complex biological predictions, indicating strong potential for advancing viral research and therapeutic strategies.

Variability in Resistive Memories

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography,...

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The article presents a thorough review of a crucial aspect of resistive memories—cycle-to-cycle variability. Its focus on modeling techniques is particularly valuable, as accurate representation is essential for practical applications in electronics. The discussion of various modeling approaches enhances the article's relevance, indicating a comprehensive understanding of the issue. The novelty lies in bringing together current advancements and providing a multidisciplinary perspective that could inspire future research directions.

Transition-path sampling for Run-and-Tumble particles

We elaborate and validate a generalization of the renowned transition-path-sampling algorithm for a paradigmatic model of active particles, namely the Run-and-Tumble particles. Notwithstanding the non...

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This article presents a novel adaptation of the transition-path-sampling algorithm specifically for Run-and-Tumble particles, which represent a significant category of active particles. The method's ability to handle non-equilibrium dynamics and adapt to the lack of microscopical reversibility enhances its applicability and paves the way for richer analyses of non-equilibrium processes. The validation of the approach and practical application to rare transition pathways demonstrate methodological rigor and potential for profound implications in both theoretical and experimental contexts in the field.

Left-truncated discrete lifespans: The AFiD enterprise panel

Our model for the lifespan of an enterprise is the geometric distribution. We do not formulate a model for enterprise foundation, but assume that foundations and lifespans are independent. We aim to f...

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This article presents a novel approach to modeling enterprise lifespans using geometric distribution while accounting for left truncation and censoring. The use of real data from 1.4 million enterprises enhances the article's methodological rigor and applicability. However, the focus on a specific dataset and geographical context may limit its broader impact.

Different PCA approaches for vector functional time series with applications to resistive switching processes

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. As the data are by nature curves, functional principal component...

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The article presents novel methodologies for vector PCA tailored to functional time series, addressing a critical challenge in modeling memristor technology. The methodological rigor in comparing univariate and multivariate PCA approaches is commendable, and the application to resistive switching adds practical relevance. However, the impact may be somewhat limited to specialized areas within time series analysis and memristor research.

Brief Announcement: Parallel Construction of Bumped Ribbon Retrieval

A retrieval data structure stores a static function f : S -> {0,1}^r . For all x in S, it returns the r-bit value f(x), while for other inputs it may return an arbitrary result. The structure canno...

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The article presents a notable advancement in the field of retrieval data structures by providing a parallel construction method that enhances the performance of a novel structure known as Bumped Ribbon Retrieval (BuRR). The claimed speedup and minimal space overhead are significant, indicating practical applicability and potential for widespread use. However, the lack of detailed implementation information may limit its immediate impact on the community, affecting reproducibility and practical utility.

Ultra-Sparse Memory Network

It is widely acknowledged that the performance of Transformer models is exponentially related to their number of parameters and computational complexity. While approaches like Mixture of Experts (MoE)...

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The article presents a novel architecture (UltraMem) that addresses critical challenges in Transformer models related to parameter count and computational complexity. Its emphasis on ultra-sparse memory layers and demonstrated improvements in inference speed and efficiency marks a significant advancement in model architecture, which can influence future research in model optimization and efficiency. The methodological rigor is evident in the experiments involving large-scale memory slots and a solid comparison with existing models, making the findings highly relevant and impactful.

Quantum memory circuit for ion channel dynamics in the nervous system

The opening or closing mechanism of a voltage-gated ion channel is triggered by the potential difference crossing the cell membrane in the nervous system. Based on this picture, we model the ion chann...

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This article presents a novel approach to understanding ion channel dynamics through quantum memory, incorporating advanced theoretical modeling that connects quantum mechanics with neurobiology. Its implications for both fields are substantial, offering insights into neural signaling mechanisms and opening avenues for future investigations into bio-inspired quantum systems. The methodological rigor in deriving the quantum Langevin equation supports the robustness of the findings, enhancing its relevance and potential influence.