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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!

Subspace and auxiliary space preconditioners for high-order interior penalty discretizations in $H(\mathrm{div})$

In this paper, we construct and analyze preconditioners for the interior penalty discontinuous Galerkin discretization posed in the space $H(\mathrm{div})$ . These discretizations are used as o...

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The article presents innovative preconditioning techniques for high-order discretizations in the $H( ext{div})$ space, which is crucial for addressing the Stokes problem in computational fluid dynamics. The robustness of these preconditioners against various parameters and the thorough numerical analysis demonstrate methodological rigor and significant practical applicability. The focus on divergence-free pressure-robust discretizations is highly relevant, as it addresses longstanding challenges in numerical simulations involving incompressible flows.

Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification

This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of location encoding, whic...

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This article presents a novel methodological contribution by integrating spatial point pattern statistics into GeoAI models for terrain feature classification, which could significantly influence both current practices and future research directions. The rigorous investigation of both first-order and second-order effects of spatial patterns adds depth to the methodology, potentially setting a new standard for how spatial contexts are considered in GeoAI. Its implications for enhancing model performance make it relevant across various applications, particularly in environmental and urban studies.

Union of Finitely Generated Congruences on Ground Term Algebra

We show that for any ground term equation systems $E$ and $F$ , (1) the union of the generated congruences by $E$ and $F$ is a congruence on the ground term algebra if a...

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This article presents novel insights into the structure of congruences in ground term algebra, specifically addressing conditions for the union of finitely generated congruences. The mathematical rigor and the clear computability aspects introduced, such as the decidability in square time, enhance its applicability significantly. Moreover, the implications for computational decision problems in algebra add to its relevance in theoretical computer science.

A distorted-wave approach to the elastic scattering of twisted electrons

The elastic scattering of spinless vortex electrons on realistic target atoms has been investigated. In particular, expressions are derived in different approximations for the elastic angular-differen...

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This article presents a significant methodological advancement by integrating distorted-wave approaches into the analysis of twisted electron scattering, which may pave the way for more accurate models in Quantum Mechanics and Atomic Physics. The added focus on vortex electron interactions with various atomic targets enhances its applicability to practical experimental setups, thus demonstrating high relevance. The study reveals critical insights about the limits of traditional models, indicating potential improvements for future research directions.

Privacy-Preserving Power Flow Analysis via Secure Multi-Party Computation

Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal s...

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The article presents a novel application of secure multi-party computation (SMPC) to address privacy concerns in smart grid power flow analysis, a crucial topic in the era of increasing reliance on smart meters. The methodological rigor is evident in the security analysis within a universal composability framework and the provision of performance benchmarks across various parameters. The implications of this work are significant, as it potentially enhances user acceptance and legal compliance for smart meter data usage by addressing privacy issues directly. Its interdisciplinary approach, connecting cryptography with energy systems, enhances its innovative value in both fields.

Emergence in graphs with near-extreme constraints

We prove the existence of an infinite number of distinct phases in the Strauss model of graphs with edge and triangle constraints.

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The article presents a significant advancement in the understanding of the Strauss model of graphs, particularly under specific constraints. The demonstration of an infinite number of distinct phases signifies a noteworthy theoretical contribution that could impact various applications in graph theory and statistical physics. The novelty and rigor in the approach enhance its potential for influencing future research directions.

Deep operator network models for predicting post-burn contraction

Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding an...

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This article presents a novel application of deep learning in the context of predicting post-burn wounds, showcasing significant improvements in computational efficiency while maintaining high predictive accuracy. The integration of cutting-edge machine learning techniques with traditional finite element methods addresses a crucial gap in the field of burn treatment and could substantially impact clinical practices.

Swift: A Multi-FPGA Framework for Scaling Up Accelerated Graph Analytics

Graph analytics are vital in fields such as social networks, biomedical research, and graph neural networks (GNNs). However, traditional CPUs and GPUs struggle with the memory bottlenecks caused by la...

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The proposed Swift framework demonstrates significant advancements in graph analytics processing through innovative use of multi-FPGA setups and a novel decoupled asynchronous model. Its approach addresses key limitations in traditional hardware, revealing potential for substantial contributions to high-performance computing and energy efficiency in large-scale graph analytics.

Reducibility among NP-Hard graph problems and boundary classes

Many NP-hard graph problems become easy for some classes of graphs, such as coloring is easy for bipartite graphs, but NP-hard in general. So we can ask question like when does a hard problem become e...

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The article presents a novel methodology for understanding the relationships between different NP-hard graph problems, which could significantly advance theoretical computer science. Its focus on boundary classes provides a fresh perspective on longstanding questions regarding problem reducibility, potentially paving new research pathways. The interplay between various NP-hard problems is crucial for theoretical developments in this field, making it highly relevant.

Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference

Jet suppression and modification is a hallmark feature of heavy-ion collisions. This can be attributed to an accumulated set of effects, including radiative and elastic energy loss and reabsorption of...

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This article employs Bayesian inference in a data-driven analysis to investigate jet quenching phenomena, a central aspect of high-energy heavy-ion collisions. Its methodological rigor, along with the novel insight into the universality of quenching weights and the unexpected findings regarding color dependence, indicates significant contributions to both theoretical and experimental frameworks in high-energy physics. The focus on multi-parton interactions adds depth to current understanding, making it relevant for future research.

An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains

Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fie...

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This article addresses a significant challenge in the machine learning field, specifically related to Named Entity Recognition in low-resource domains. Its focus on experimental validation of data augmentation techniques is both timely and relevant, given the growing need for effective NER solutions in specialized sectors where annotated data is scarce. The rigorous examination of various models and datasets enhances methodological rigor and applicability, promising to inspire future research in both data augmentation and NER optimization.

The importance of the clustering model to detect new types of intrusion in data traffic

In the current digital age, the volume of data generated by various cyber activities has become enormous and is constantly increasing. The data may contain valuable insights that can be harnessed to i...

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This article presents a noteworthy application of clustering models in cyber security, particularly in intrusion detection. The use of K-means alongside traditional attack classification methods indicates methodological rigor and a focus on addressing a pressing issue in data traffic analysis. The identification of new attack types through clustering is innovative and reflects the growing importance of adaptive security measures. While the exploratory aspect is strong, the applicability of results to a broader set of real-world scenarios remains to be further validated, which slightly tempers the score.

Superconductivity in Ternary Germanite TaAl $_{x}$ Ge $_{2-x}$ with a C40 chiral structure

We report a new family of chiral intermetallic superconductors TaAl $_{x}$ Ge $_{2-x}$ . The mother compound TaGe $_2$ has a C40-type chiral hexagonal crystal structure with a pair ...

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This article introduces a novel family of superconductors with interesting chiral crystal structures, contributing to the understanding of superconductivity in ternary compounds. Its innovative approach in synthesizing and characterizing these materials adds valuable data to the field.

A Random-Effects Approach to Linear Mixed Model Analysis of Incomplete Longitudinal Data

We propose a random-effects approach to missing values for linear mixed model (LMM) analysis. The method converts a LMM with missing covariates to another LMM without missing covariates. The standard ...

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This article presents a novel approach to managing incomplete longitudinal data using a random-effects model, a pertinent issue in statistics. The comparative evaluation against established methods like MICE adds robustness, indicating methodological rigor. The theoretical insights and empirical validation further enhance its impact, making it a valuable contribution to the field of statistical analysis of longitudinal data.

New dimensional bounds for a branched transport problem

We consider a branched transport problem with weakly imposed boundary conditions. This problem arises as a reduced model for pattern formation in type-I superconductors. For this model, it is conjectu...

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The article presents a new theoretical advancement in understanding branched transport problems, particularly as it relates to pattern formation in superconductors. The proof of non-integer dimensional bounds in a simplified setting is significant and could have wider implications for the field. The focus on a rigorous mathematical approach adds methodological rigor, but the simplification to 2D could limit some applicability. Overall, its contributions are noteworthy and may inspire further research in both mathematics and material science.

Static impurity in a mesoscopic system of SU( $N$ ) fermionic matter-waves

We investigate the effects of a static impurity, modeled by a localized barrier, in a one-dimensional mesoscopic system comprised of strongly correlated repulsive SU( $N$ )-symmetric fermions. F...

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The article presents a novel investigation into the effects of static impurities on SU(N) fermionic matter-waves within mesoscopic systems. The inclusion of factors such as interaction strength and gauge fields contributes to its methodological rigor. The non-monotonic behavior of persistent currents adds a deeper understanding of quantum fluctuations and fractionalization, which are critical in current quantum physics research. Additionally, the implications for quantum technology applications enhance its relevance.

Interpreting seasonal and interannual Hadley cell descending edge migrations via the cell-mean Rossby number

The poleward extent of Earth's zonal-mean Hadley cells varies across seasons and years, which would be nice to capture in a simple theory. A plausible candidate, from Hill et al. (2022), combines ...

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The article presents a novel approach for understanding Hadley cell dynamics, utilizing a simplified theory based on the cell-wide Rossby number. Its empirical testing against reanalysis data enhances its methodological rigor and relevance, particularly in the context of climate change effects. This aspect contributes significantly to advancing research in atmospheric dynamics and climate variability.

On Intersecting Conformal Defects

We study the physics of 2 and 3 mutually intersecting conformal defects forming wedges and corners in general dimension. For 2 defects we derive the beta-function of the edge interactions for infinite...

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This article presents an innovative approach to understanding the physics of conformal defects by investigating the intersection of multiple defects in various dimensions. The novelty of deriving beta-functions for edge interactions and exploring the implications of intersection angles adds significant depth to the understanding of conformal field theories. The methodological rigor is evidenced by the systematic study of complex geometries and the connection to existing literature on anomalous dimensions. However, while the findings are substantial, the niche focus on high-dimensional defects may limit broader applicability in mainstream physics fields.

Combining missing data imputation and internal validation in clinical risk prediction models

Methods to handle missing data have been extensively explored in the context of estimation and descriptive studies, with multiple imputation being the most widely used method in clinical research. How...

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The article presents a novel approach by integrating deterministic imputation with internal validation specifically for clinical risk prediction models. The tutorial aspect enhances its applicability for practitioners. Its methodological rigor, supported by extensive simulation studies, provides valuable insights that can improve prediction accuracy in clinical settings, marking it as impactful for future research in the field.

Hot and cool: Characterising the companions of red supergiant stars in binary systems

In this article we study the nature of the recently identified populations of hot companions to red supergiant stars (RSGs). To this end, we compile the literature on the most well characterised syste...

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This article addresses a relatively novel area of research by examining hot companions of red supergiant stars and utilizing advanced observational techniques like Hubble Space Telescope spectra. Its thorough compilation of existing systems adds significant value to the current understanding of these stellar phenomena. The study not only contributes essential data but also suggests avenues for future research in both stellar evolution and binary systems, which enhances its impact.