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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!

A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information

Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches require selecting ap...

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This article presents a novel, data-driven methodology for evaluating the effectiveness of various descriptors in translating complex and noisy molecular data into meaningful insights. The focus on the interplay between descriptor choice and noise management is particularly innovative and relevant in the field of molecular dynamics, where data quality is often compromised. Additionally, the use of advanced clustering methods to rank descriptors adds methodological rigor and potential for broader application in similar studies.

Forest-skein groups IV: dynamics

We study forest-skein (FS) groups using dynamics. A simple Ore FS category produces three FS groups analogous to Richard Thompson's groups. Reconstruction theorems of McCleary and Rubin apply to t...

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The article offers significant advancements in the study of forest-skein groups by employing dynamic methods, which are relatively novel in the context of group theory. The reconstruction theorems enhance the theoretical framework, adding rigor to the findings. The identification of finitely presented infinite simple groups with unique dynamical actions contributes new knowledge to the field, suggesting potential unexplored avenues in group dynamics and topology.

Horizons of some asymptotically stationary spacetimes

On a class of dynamical spacetimes which are asymptotic as $t\to\infty$ to a stationary spacetime containing a horizon $\mathcal{H}_0$ , we show the existence of a unique null hypersurf...

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The article presents novel findings regarding the behavior of null hypersurfaces in asymptotically stationary spacetimes, with significant implications for black hole physics. Its mathematical rigor and connection to established theories, such as the stable and unstable manifold theorems, enhances its credibility. Furthermore, it enriches understanding in gravitational theories and may inspire future research into black hole dynamics and cosmological models.

An $Ω$ -Result for the Counting of Geodesic Segments in the Hyperbolic Plane

Let $Γ$ be a cocompact Fuchsian group, and $l$ a fixed closed geodesic. We study the counting of those images of $l$ that have a distance from $l$ less than or equal to...

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The article presents a novel result regarding the asymptotic behavior of counting geodesics in the hyperbolic plane, a topic of significant interest in geometric group theory and mathematics generally. The use of an $Ω$-result is mathematically rigorous, providing substantial insight into the error term of the counting function. Its implications could extend to various areas of mathematics and related fields, particularly in understanding geometric structures and their properties.

Accurate and efficient localized basis sets for two-dimensional materials

First-principles density functional theory (DFT) codes which employ a localized basis offer advantages over those which use plane-wave bases, such as better scaling with system size and better suitabi...

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The article addresses a critical need for efficient computational methods in the study of two-dimensional materials, showcasing notable improvements in both accuracy and speed through the development of optimized localized basis sets for DFT calculations. Its methodological rigor is significant, as it benchmarks against an established code, providing a reliable comparison. The novelty lies in the integration of advanced polarization orbitals, enhancing flexibility in calculations. Such contributions could inspire further advancements in computational techniques for 2D materials, making it quite impactful in its field.

Probing false vacuum decay on a cold-atom gauge-theory quantum simulator

In the context of quantum electrodynamics, the decay of false vacuum leads to the production of electron-positron pair, a phenomenon known as the Schwinger effect. In practical experimental scenarios,...

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The article presents a significant advancement in the understanding of false vacuum decay and the Schwinger effect through an experimental setup using cold-atom quantum simulators. Its methodological rigor and the novelty of applying lattice gauge theory in this context make it highly relevant. The potential for experimental exploration of phenomena previously difficult to observe could greatly influence future research directions in both theoretical and experimental physics.

CHANG-ES XXXV: Cosmic Ray Transport and Magnetic Field Structure of NGC 3556 at 3 GHz

Radio halos of edge-on galaxies are crucial for investigating cosmic ray propagation and magnetic field structures in galactic environments. We present VLA C-configuration S-band (2--4 GHz) observatio...

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The article presents a detailed observational study with robust data analysis, focusing on cosmic ray transport and magnetic field structures in a specific galaxy. Its findings contribute valuable insights into galactic magnetic fields and cosmic ray propagation, crucial for understanding the dynamics of galaxies. The methodology, including Rotation Measure Synthesis and a detailed cosmic-ray transport model, demonstrates strong methodological rigor. The implications for cosmic ray behavior and star formation feedback are of particular interest, making this study relevant for both astrophysics and cosmology. The novelty lies in the examination of a specific galaxy's radio halo, potentially inspiring further research in similar areas.

Stream-Based Active Learning for Process Monitoring

Statistical process monitoring (SPM) methods are essential tools in quality management to check the stability of industrial processes, i.e., to dynamically classify the process state as in control (IC...

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The article presents a novel approach to an important problem in statistical process monitoring through stream-based active learning, which is a promising direction given the challenges of class imbalance and dynamic state recognition. Its combination of theoretical advancement and practical application enhances its impact. The methodological rigor, including validation through simulations and a real-world case study, increases confidence in the results, making it relevant for industry practices.

Models of Mira variables of the Large Magellanic Cloud

Consistent stellar evolution and nonlinear radial stellar pulsation calculations were carried out for models of asymptotic giant branch stars with initial masses $1.5M_\odot\le M_\mathrm{ZAMS}\le ...

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The article presents a novel approach to modeling Mira variables, combining stellar evolution theories with nonlinear pulsation dynamics. The findings on the period-luminosity relation and the analysis of oscillation modes contribute significantly to our understanding of these stars, which are crucial for distance measurement in astronomy. The methodological rigor and potential applicability in observational astronomy enhance the article's relevance.

Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition

Skeleton-based action recognition has achieved remarkable performance with the development of graph convolutional networks (GCNs). However, most of these methods tend to construct complex topology lea...

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This article presents a novel approach that addresses critical limitations in current graph convolutional networks by incorporating topological symmetry and deformable temporal convolutions. The methodological rigor is high, as it effectively combines these concepts leading to improved performance benchmarks on multiple established datasets. Its potential for advancing the field of action recognition is significant, suggesting this research could inspire further studies in related areas.

Emulating a computing grid in a local environment for feature evaluation

The necessity for complex calculations in high-energy physics and large-scale data analysis has led to the development of computing grids, such as the ALICE computing grid at CERN. These grids outperf...

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The article presents a novel approach to emulating computing grids in local environments, which can significantly enhance the evaluation of features without disturbing operational systems. This methodological innovation is particularly relevant for high-energy physics and large-scale data analysis, offering a practical solution to a common challenge faced by researchers in this field. The rigor of the proposed solution and its applicability to significant operational systems support a strong relevance score.

Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework

Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes - also referred to as target-private unknown classes - are prese...

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The proposed RRDA framework represents a significant advancement in Source-free Open-set Domain Adaptation (SF-OSDA) by addressing key challenges such as distribution shifts and the effective learning of features for unknown classes. The novelty lies in its two-step approach that enhances classification capabilities and generalization while maintaining privacy considerations, making it highly relevant in current data-sensitive research contexts. The extensive experimental validation further supports its robustness and potential applicability.

Power Efficient Cooperative Communication within IIoT Subnetworks: Relay or RIS?

The forthcoming sixth-generation (6G) industrial Internet-of-Things (IIoT) subnetworks are expected to support ultra-fast control communication cycles for numerous IoT devices. However, meeting the st...

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The study presents novel communication protocols tailored for the challenging environment of IIoT subnetworks, addressing critical issues of power efficiency, low latency, and reliability. The methodological rigor, including the use of SPCA and comprehensive simulation results, supports the validity of the proposed solutions. The work stands out due to its comparative analysis of two distinct technologies—relay and reconfigurable intelligent surfaces—which is highly relevant in the ongoing evolution of 6G technologies, making it a significant contribution to the field.

Light Cone Distribution Amplitude for the $Λ$ Baryon from Lattice QCD

We calculate the leading-twist light-cone distribution amplitudes of the light $Λ$ baryon using lattice methods within the framework of large momentum effective theory. Our numerical computati...

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This article presents a rigorous numerical study utilizing lattice QCD methods, which are of significant importance for understanding baryonic physics. The methodology—applying large momentum effective theory to compute light-cone distribution amplitudes (LCDAs)—is a novel contribution. Furthermore, the implications for weak decays offer potential connections to phenomenology, which enhances the article's relevance. The work seems methodologically sound, though some aspects of systematic uncertainties could be clearer to improve practical applicability.

Quantum advantage from soft decoders

In the last years, Regev's reduction has been used as a quantum algorithmic tool for providing a quantum advantage for variants of the decoding problem. Following this line of work, the authors of...

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The article presents novel advancements in quantum decoding algorithms, specifically focusing on the Optimal Polynomial Interpolation problem and its implications for lattice-based cryptography. The methodological rigor in providing a generic reduction adds substantial value. Its applicability to practical quantum computing scenarios and significant theoretical contributions notably enhance its relevance.

The corotational stability postulate: positive incremental Cauchy stress moduli for diagonal, homogeneous deformations in isotropic nonlinear elasticity

In isotropic nonlinear elasticity the corotational stability postulate (CSP) is the requirement that \begin{equation*} \langle\frac{\mathrm{D}^{\circ}}{\mathrm{D} t}[σ] , D \rangle > 0 \quad \for...

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The article proposes a new postulate, the corotational stability postulate (CSP), which adds a significant layer of understanding to the field of isotropic nonlinear elasticity. Its focus on stability conditions in terms of Cauchy stress moduli presents a fresh perspective on material behavior under deformation. The mathematical rigor and derivation of implications for various modulus types underscore the methodological strength. The clarity of the results shows potential applications in understanding material response, which may stimulate further investigations into stability postulates in nonlinear elasticity and related areas.

Symplectic Geometry, Poisson Geometry, and Beyond

Symplectic and Poisson geometry emerged as a tool to understand the mathematical structure behind classical mechanics. However, due to its huge development over the past century, it has become an inde...

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This article presents a comprehensive overview of symplectic and Poisson geometry, emphasizing their foundational role in classical mechanics while also highlighting their relevance to modern mathematical contexts. The novel approach of linking geometric structures to practical applications in physics and Lie theory adds significant value. The methodological rigor in detailing essential objects and techniques also enhances its utility for both researchers and students in the field.

Whisper Finetuning on Nepali Language

Despite the growing advancements in Automatic Speech Recognition (ASR) models, the development of robust models for underrepresented languages, such as Nepali, remains a challenge. This research focus...

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This article presents a significant advancement in the field of Automatic Speech Recognition (ASR) for the underrepresented Nepali language. The focus on fine-tuning established models like OpenAI's Whisper demonstrates innovative methodology, and the robust dataset creation process indicates a high degree of rigor. Additionally, the substantial improvements in Word Error Rate (WER) achieved signify practical applicability and potential for widespread impact.

Tactile interaction with social robots influences attitudes and behaviour

Tactile interaction plays an essential role in human-to-human interaction. People gain comfort and support from tactile interactions with others and touch is an important predictor for trust. While to...

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The article presents novel findings on the significance of tactile interaction with social robots, especially in regulating stress and influencing risk-taking behavior, which is an underexplored area in Human-Robot Interaction (HRI). The methodological rigor, with two studies differentiating between social and non-social interactions, adds credibility and depth to the research. The implications for emotional support and trust in interactions with robots make it particularly relevant for future research in HRI and robotics.

Asymptotic robustness of entanglement in noisy quantum networks and graph connectivity

Quantum networks are promising venues for quantum information processing. This motivates the study of the entanglement properties of the particular multipartite quantum states that underpin these stru...

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This article presents novel insights into the entanglement properties of quantum networks under noise, a topic of significant interest in quantum information science. The rigorous exploration of graph connectivity parameters in relation to GME demonstrates methodological rigor and contributes to a deeper understanding of network behavior. The findings have potential implications for future designs of quantum networks and robustness assessments, which enhances the article's impact.