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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 synchronization of convective lifecycles in an idealized microscopic model

How a cloud ensemble responds to external forcing is a puzzle in tropical convection research. Convectively coupled gravity waves (CCGWs) in a finite domain have controllable wavelengths, providing a ...

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This study presents a novel approach to understanding the synchronization of convective lifecycles in tropical convection through a microscale modeling framework. The methodological rigor and multiscale analysis contribute to its impact. The insights gained regarding the coupling of convective processes with gravity waves could enhance predictive capabilities in tropical meteorology, though further validation may be needed in more complex systems.

On a modified Cahn-Hilliard-Brinkman model with chemotaxis and nonlinear sensitivity

We consider an evolutionary PDE system coupling the Cahn-Hilliard equation with singular potential, mass source and transport effects, to a Brinkman-type relation for the macroscopic velocity field an...

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This article introduces a modified Cahn-Hilliard model that combines phase separation with chemotaxis effects, which is novel and relevant for understanding tumor growth dynamics. The methodology, particularly the focus on nonlinear sensitivity and weak solutions, showcases rigor. The application to tumor modeling highlights the potential impact on cancer research and therapies, making the findings exceptionally useful for future interdisciplinary explorations.

Internal shear layers generated by a vertically oscillating cylinder in unbounded and bounded rotating fluids

In previous works, we analysed the internal shear layers excited by a viscous forcing (longitudinal libration) in a spherical shell geometry (He et al., J. Fluid Mech. 939, A3, 2022; 974, A3, 2023). W...

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The article presents a detailed analysis of internal shear layers in rotating fluids, which is a novel extension of previous work. It combines theoretical modeling with analytical methods, contributing significant insights to fluid dynamics, particularly in understanding the transition between viscous and inviscid regimes. The robustness of its findings, especially regarding scaling behaviors and matching of solutions, adds considerable value to the field.

ManiSkill-ViTac 2025: Challenge on Manipulation Skill Learning With Vision and Tactile Sensing

This article introduces the ManiSkill-ViTac Challenge 2025, which focuses on learning contact-rich manipulation skills using both tactile and visual sensing. Expanding upon the 2024 challenge, ManiSki...

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This article presents a highly relevant challenge aimed at enhancing robotic manipulation skills through an innovative focus on tactile-vision integration. The novelty of combining tactile sensing with visual data processing can significantly impact robotics, pushing researchers towards developing more sophisticated manipulative capabilities. Its methodological rigor is underscored by the inclusion of standardized metrics for evaluation in both simulated and real-world environments, which adds to the robustness of the challenge.

Transformer Neural Processes -- Kernel Regression

Stochastic processes model various natural phenomena from disease transmission to stock prices, but simulating and quantifying their uncertainty can be computationally challenging. For example, modeli...

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The article presents a significant advancement in the efficiency of modeling stochastic processes using a novel architecture (TNP-KR) that directly addresses the computational challenges faced by traditional methods. By reducing the complexity of attention mechanisms in Neural Processes, it not only enhances performance but also extends applicability to larger datasets on consumer hardware, which is critical for practical applications. The robustness of the benchmarks across various tasks adds to its impact.

Intrinsic exceptional point -- a challenge in quantum theory

In spite of its unbroken ${\cal PT}-$ symmetry, the popular imaginary cubic oscillator Hamiltonian $H^{(IC)}=p^2+{\rm i}x^3$ does not satisfy all of the necessary postulates of quantum ...

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This article addresses a complex and nuanced aspect of quantum theory involving exceptional points, which is a burgeoning area of research. The focus on intrinsic exceptional points (IEPs) presents novel insights into the limitations of standard quantum mechanics, highlighting the unphysical nature of certain Hamiltonians. The methodological rigor in employing perturbation theory and analogies with established concepts like Kato's exceptional points strengthens its contributions. This work could inspire further research into alternative quantum systems and enhance understanding of symmetries in quantum mechanics.

Weyl group of type $E_6$ and K3 surfaces

Adapting methods of previous papers by A. Sarti and the author, we construct K3 surfaces from invariants of the Weyl group of type $E_6$ . We study in details one of these surfaces, which turns...

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This article presents a novel approach by connecting the Weyl group of type $E_6$ with the study of K3 surfaces, which offers a fresh perspective on both algebraic geometry and representation theory. The detailed examination of the specific surface, along with its Picard number and elliptic fibration, provides valuable insights and could encourage further exploration in related geometrical contexts. The combination of advanced concepts suggests a strong applicability to both theoretical and applied aspects in these fields.

New insight into the orbital parameters of the gamma-ray binary HESS J0632+057

The gamma-ray binary HESS J0632+057 consists of a Be star and an undetected compact object in a $\sim$ 317 day orbit. The interpretation of the emission from this system is complicated by the l...

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This article presents significant advancements in understanding the orbital parameters of the gamma-ray binary HESS J0632+057, particularly by combining new observations with archival data to derive updated orbital solutions. The methodology appears rigorous, employing state-of-the-art techniques and addressing previously unresolved inconsistencies in the literature. The findings have potential implications for both theoretical models of binary interactions and observations of gamma-ray binaries. However, the necessity for further observations to refine the results suggests there is still work to be done in this area.

Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus

Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (ALT)}$ , which...

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This article presents a novel approach to enhancing the reasoning capabilities of LLMs, a significant challenge in the field of natural language processing. The introduction of a principled method for synthetic logic training and the creation of a dedicated corpus provides strong methodological rigor. The empirical results demonstrate substantial improvements across multiple benchmarks, indicating substantial applicability and potential for future research to build on these findings.

Small- $x$ evolution of dipole amplitude: forward--off-forward correspondence

We have shown that the small- $x$ evolution of the off-forward leading-log dipole scattering amplitudes, both pomeron and odderon, in the momentum space can be completely determined by the evol...

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The article presents a novel approach by linking off-forward and forward dipole scattering amplitudes with a strong theoretical underpinning. Its focus on small-$x$ evolution and its implications for high-energy scattering processes positions it as a significant contribution to the field of quantum chromodynamics (QCD) and hadronic physics. The findings could inspire future research on multi-particle interactions and high-energy phenomena, demonstrating methodological rigor and applicability.

Phase transition from Weyl to knotted semimetal using bi-circular laser

The Fermi surface topology of a triple non-hermitian (NH) Weyl semimetal (WSM) driven by bi-circularly polarized light is presented in this study. A NH WSM in particular has remarkable outlines. Bi-ci...

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The article presents a novel exploration of phase transitions between Weyl and knotted semimetals using bi-circular laser light, a topic that has not been previously discussed. The integration of non-hermitian physics in the context of laser fields provides a fresh perspective in the field of topological materials. The methodological rigor in studying the changes in Fermi surfaces and Berry curvature highlights its potential to guide future research and applications, particularly in quantum materials and photonic technologies.

Phase transition analogues in laser collisions with a dark-field setup

Laser pulse collisions are a promising tool for the investigation of light-by-light scattering phenomena induced by quantum vacuum fluctuations. Using the numerical code based on the vacuum emission p...

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This article introduces new insights into light-by-light scattering and phase transitions in laser pulse collisions, revealing a significant interplay between laser profiles and emission dynamics. The theoretical and numerical analysis is rigorous, representing a novel approach in exploring quantum vacuum fluctuations. Its practicality and implications for future experiments add to its relevance.

On some geometric interpretations of fractional-order operators

This discussion paper presents some parts of the work in progress. It is shown that G.W. Leibniz was the first who raised the question about geometric interpretation of fractional-order operators. Geo...

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The article demonstrates novelty by introducing geometric interpretations of fractional-order operators and derivative concepts that have historical roots, particularly connecting them to foundational figures like Leibniz. The methodological approach seems interesting and could inspire further discourse in fractional calculus. However, as it's a discussion of ongoing work, the completeness of the findings may limit immediate impact.

Bias Free Sentiment Analysis

This paper introduces the Semantic Propagation Graph Neural Network (SProp GNN), a machine learning sentiment analysis (SA) architecture that relies exclusively on syntactic structures and word-level ...

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The article presents a novel approach to sentiment analysis that addresses significant issues of bias and explainability in natural language processing, which are critical in both academic and practical applications. The introduction of the SProp GNN model, along with performance comparisons to both lexicon-based and transformer models, indicates methodological rigor and strong applicability. Its potential to improve fairness in sentiment analysis could inspire further research on bias reduction in AI models and enhance understanding in social sciences.

Recent advances in space sailing missions and technology: review of the 6th International Symposium on Space Sailing (ISSS 2023)

The 6th International Symposium on Space Sailing (ISSS 2023) took place on June 5-9, 2023 at the New York City College of Technology, the City University of New York. Since its inauguration in Herrsch...

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The review of the ISSS 2023 provides a comprehensive overview of advancements in space sailing, an innovative domain with significant implications for future space missions. The article captures detailed discussions on new concepts, technologies, and results presented at an international forum, which enhances its relevance. Its synthesis of diverse research activities can influence future project designs in space exploration.

The Challenges of Modeling Astrophysical Reacting Flows

Stellar evolution is driven by the changing composition of a star from nuclear reactions. At the late stages of evolution and during explosive events, the timescale can be short and drive strong hydro...

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This article addresses a significant challenge in the field of astrophysics, particularly in the modeling of high-energy stellar events. The exploration of standard approaches and their limitations indicates a critical evaluation of existing methodologies, which is essential for advancing the field. The proposed improvements in efficiency and accuracy could significantly enhance simulation fidelity, making this research both novel and methodologically rigorous.

First observations of a geomagnetic superstorm with a sub-L1 monitor

Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north-south component of the interplanetary...

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The article presents crucial observations that enhance our understanding of geomagnetic superstorms and their forecasting, an area that is currently under-explored. The novelty lies in employing sub-L1 monitoring to improve prediction timing and accuracy, thus addressing a significant gap in space weather forecasting methodologies. The methodological rigor is evident in the detailed analysis of the CMEs and the use of both ACE and STEREO-A data to derive conclusions about geomagnetic indices. Its implications for future space weather missions are profound, potentially shaping new research directions and technological developments in the field.

UMGAD: Unsupervised Multiplex Graph Anomaly Detection

Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task is widely ...

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The research addresses significant challenges in the field of graph anomaly detection, specifically in addressing multi-relational data and unsupervised settings. The introduction of a new threshold selection strategy marks a novel contribution. The use of advanced techniques like graph-masked autoencoder and contrastive learning showcases methodological rigor. However, to attain a perfect score, further exploration of the applicability of the method across broader datasets and real-world scenarios would be beneficial.

Optothermally Induced Active and Chiral Motion of the Colloidal Structures

Artificial soft matter systems have appeared as important tools to harness mechanical motion for microscale manipulation. Typically, this motion is driven either by the external fields or by mutual in...

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The study presents a novel mechanism for controlling colloidal motion using optothermal interactions, which is a significant advancement over traditional methods reliant on chemical environments. The methodology combines optical, thermal, and particle interaction principles, which showcases methodological rigor and innovation. Additionally, the experimental validation of simulated results strengthens the reliability of the findings. The implications for directed self-assembly and microfluidic manipulation indicate potential for wide applicability and interdisciplinary collaboration with optics, fluid dynamics, and materials science.

Step meandering: The balance between the potential well and the Ehrlich-Schwoebel barrier

This study presents a comprehensive and innovative exploration of how the surface potential energy landscape influences meander formation. Using the Vicinal Cellular Automaton model, which distinguish...

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The article presents a novel approach to understanding surface pattern dynamics through the Vicinal Cellular Automaton model, which is a significant advancement for the field of surface physics and material science. Its exploration into the interplay between potential energy landscapes and meandering is both innovative and methodologically rigorous. The findings could have broad implications for designing materials with specific surface properties.