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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!

Fractality-induced Topology

Fractal geometries, characterized by self-similar patterns and non-integer dimensions, provide an intriguing platform for exploring topological phases of matter. In this work, we introduce a theoretic...

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The article explores a novel intersection between fractality and topology, presenting a theoretical framework that enhances our understanding of topological phases in non-traditional settings. Its methodological rigor in introducing isospectral reduction is noteworthy, and the implications for both theoretical and experimental research in fractal materials are substantial. The novelty of linking fractals to topological phases enriches the discourse in condensed matter physics and materials science, providing a fresh perspective that could inspire future investigations in the field.

First-Principles Insights into Metallic Doping Effects on Yttrium {10-10} Grain Boundary

Yttrium and its alloys are promising materials for high-tech applications, particularly in aerospace and nuclear reactors. The doping of metallic elements at grain boundaries can significantly influen...

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This article presents a novel and systematic approach using first-principles calculations to investigate the effects of metallic doping at the grain boundaries of yttrium alloys. The depth of analysis, including the decomposition of strengthening energy and the focus on segregation energy, adds significant methodological rigor and contributes to the understanding of material properties that are crucial for high-tech applications. This research may influence the design and optimization of Y-based alloys, thus having substantial implications for the aerospace and nuclear sectors.

Differential uniformity of polynomials of degree 10

We prove that polynomials of degree 10 over finite fields of even characteristic with some conditions on theirs coefficients have a differential uniformity greater than or equal to 6 over $\mathbb...

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The article addresses a significant problem in the area of finite fields and their applications in cryptography and coding theory. The result regarding the differential uniformity of degree 10 polynomials is novel and could have implications for the construction of cryptographic functions, making it especially relevant for those working on the security of cryptographic schemes. Its methodological rigor, focusing on polynomials in finite fields, enhances its credibility and applicability. The implications for future research in developing stronger cryptographic systems and understanding polynomial behavior are substantial.

Target Height Estimation Using a Single Acoustic Camera for Compensation in 2D Seabed Mosaicking

This letter proposes a novel approach for compensating target height data in 2D seabed mosaicking for low-visibility underwater perception. Acoustic cameras are effective sensors for sensing the marin...

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This article showcases a novel method that significantly enhances the capabilities of acoustic cameras in underwater environments, which are often limited by visibility issues. The integration of height information into 2D mosaicking represents a meaningful innovation, especially as it addresses collision avoidance in marine robotics, an area of increasing importance. The methodological rigor demonstrated through experimental validation strengthens its potential impact.

Nevanlinna Second Main Theorem and tautological inequality for parabolic manifolds

We obtain a Second Main Theorem type inequality for holomorphic maps $f : M \to X$ , where $M$ is a parabolic manifold and $X$ is smooth projective with dim $M$ $\le...

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The article presents significant advancements in the field of complex geometry, particularly in the context of parabolic manifolds and holomorphic mappings. The introduction of a Second Main Theorem type inequality is a novel contribution that could influence further research in this area. The methodology appears rigorous, and its applicability to logarithmic pairs suggests potential interdisciplinary connections. However, the impact may be somewhat niche given the specific focus on parabolic manifolds.

Double Splay Nematic Order in Confined Polar Fluids

In this study, we demonstrate that when a ferroelectric nematic is confined between two glass plates coated with ionic polymers, a modulated phase emerges in a narrow temperature range between the nem...

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The article presents novel experimental findings on double splay nematic order in confined polar fluids, which is significant for understanding the physical properties of ferroelectric materials. The methodological approach using optical microscopy is rigorous, providing compelling evidence for the phase transitions. The study's focus on the effects of ionic polymer coatings offers practical implications for material design, fostering potential applications in electro-optical devices. However, while impactful, the scope may be somewhat limited to niche applications within its field.

Cities beyond proximity

The concept of `proximity-based cities' has gained attention as a new urban organizational model. Most prominently, the 15-minute city contends that cities can function more effectively, equi...

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The article presents a novel perspective on urban design by proposing a shift from proximity-based models to value-based considerations in city planning. Its emphasis on local identity and resilience highlights important social dimensions, which are often overlooked. The rigorous examination of bottom-up versus top-down strategies underscores the complexities of urban governance, making the findings highly applicable and insightful for future research in urban studies.

Learning from Label Proportions and Covariate-shifted Instances

In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only an aggregate label ...

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The article addresses a significant challenge in machine learning related to the use of label proportions and covariate-shifted instances, which are pertinent in real-world applications where supervision is often limited. The novel approach of integrating fully supervised data with bag-labels to enhance predictive performance demonstrates methodological rigor. The theoretical foundations and empirical benchmarks provided suggest both robustness and practical applicability, making this work influential for future studies in this area.

Silanization Strategies for Tailoring Peptide Functionalization on Silicon Surfaces: Implications for Enhancing Stem Cell Adhesion

Biomaterial surface engineering and integrating cell-adhesive ligands are crucial in biological research and biotechnological applications. The interplay between cells and their microenvironment, infl...

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This article presents a novel methodology for peptide functionalization on silicon surfaces, which can significantly enhance stem cell adhesion—a crucial factor in tissue engineering and regenerative medicine. The comparative analysis of different silanes and the exploration of their effects on cellular behavior adds a valuable contribution to the field. The rigorous experimental design and the challenge to established methods (like APTES) enhance the article's relevance, suggesting potential for broad applicability in biomaterials research.

Accelerating UMAP for Large-Scale Datasets Through Spectral Coarsening

This paper introduces an innovative approach to dramatically accelerate UMAP using spectral data compression.The proposed method significantly reduces the size of the dataset, preserving its essential...

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The article presents a novel approach to enhancing the speed and efficiency of UMAP, a widely-used dimensionality reduction technique. The use of spectral data compression is particularly innovative and could significantly impact the community by enabling the processing of larger datasets. The methodological rigor is evident through empirical validation on real-world datasets. Its implications for both theoretical and practical applications position it as a substantial contribution to the field.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression

Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machine learning problems...

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The proposed Graph-aware Logistic Regression (GLR) model presents a novel approach to node classification that addresses the limitations of existing GNN models, particularly in terms of generalization across various datasets. The emphasis on efficiency and scalability, combined with rigorous experimental validation, enhances its practical applicability and may influence future research into alternative non-neural methods. The balance of simplicity and effectiveness is particularly noteworthy, contributing to its relevance.

Attributed Graph Clustering in Collaborative Settings

Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structu...

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The article presents a novel collaborative framework for attributed graph clustering that addresses key issues of data isolation in unsupervised learning scenarios. The methodological rigor is underscored by the thorough experimentation with public datasets and the comparison with centralized methods, highlighting its practical applicability. Additionally, the focus on collaboration among different participants expands the traditional understanding of graph clustering, positioning this research as a significant contribution with potential implications for multiple domains.

HW/SW Implementation of MiRitH on Embedded Platforms

Multi-Party Computation in the Head (MPCitH) algorithms are appealing candidates in the additional US NIST standardization rounds for Post-Quantum Cryptography (PQC) with respect to key sizes and math...

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The article presents a novel approach to implementing an emerging post-quantum cryptography algorithm, MiRitH, specifically tailored for embedded systems, which is particularly relevant given current concerns over quantum computing threats to cryptography. The exploration of design space and optimization for limited-resource environments highlights the practical applicability of the research, contributing valuable insights to the field of secure embedded systems.

The $D^* D π$ and $B^* B π$ couplings from Dyson-Schwinger equations framework

Employing a unified Dyson-Schwinger/Bethe-Salpeter equations approach, we calculate the strong decay couplings $D^* D π$ and $B^* B π$ within the so-called impulse-approximation in the...

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This article provides significant insights into strong decay couplings using advanced theoretical frameworks (Dyson-Schwinger equations), which adds depth to existing knowledge. The novelty of presenting a new estimation for the $B^* B π$ couplings is particularly valuable, and the consistency with experimental and lattice data enhances its impact. However, the specificity of the calculations may limit broader applicability beyond particle physics.

A note on contractive semi-groups on a 1:1 junction for scalar conservation laws and Hamilton-Jacobi equations

We show that any continuous semi-group on $L^1$ which is (i) $L^1-$ contractive, (ii) satisfies the conservation law $\partial_t ρ+\partial_x(H(x,ρ))=0$ in $\mathbb{R}_+\tim...

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This article presents a novel theoretical analysis of contractive semi-groups within mathematical frameworks of scalar conservation laws and Hamilton-Jacobi equations, specifically at junctions. The methodological rigor is evident as it establishes a substantial connection between the properties of semi-groups and mathematical constructs that are key in certain applications. Its findings not only contribute to existing mathematical theories but may inspire further research into junction problems and numerical methods in applied contexts.

Aperture correction for Beamforming in radiometric detection of ultra-high energy cosmic rays

For high-energy cosmic-ray physics, it is imperative to determine the mass and energy of the cosmic ray that initiated the air shower in the atmosphere. This information can be extracted from the long...

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The article introduces a novel approach to beamforming in the context of radiometric detection of cosmic rays, addressing a key limitation in the resolution related to finite aperture effects. The presented methods for correcting these effects and the detailed investigation into their impact suggest methodological rigor and practical applications that could enhance future research in high-energy cosmic-ray physics and related fields. The focus on expanding detection capabilities and improving data fidelity in challenging conditions (like thunderstorms) further adds to the article's relevance.

Mirror Descent Algorithms for Risk Budgeting Portfolios

This paper introduces and examines numerical approximation schemes for computing risk budgeting portfolios associated to positive homogeneous and sub-additive risk measures. We employ Mirror Descent a...

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This article presents a novel application of Mirror Descent algorithms in the context of risk budgeting portfolios, which is a relevant and emerging area in financial mathematics. It not only introduces theoretical advancements concerning convergence and non-asymptotic rates but also provides rigorous numerical analyses that demonstrate practical applications. The combination of theoretical and empirical approaches enhances its impact, making it a substantial contribution to the field.

On sharp anisotropic Hardy inequalities

Recently, Yanyan Li and Xukai Yan showed the following interesting Hardy inequalities with anisotropic weights: Let $n\geq 2$ , $p \geq 1$ , pα> 1-n, $p(α+ β)> -n$...

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The paper addresses a specific and important topic within the field of functional inequalities, focusing on anisotropic Hardy inequalities that have practical implications in various areas such as partial differential equations and mathematical physics. The determination of best constants is a significant contribution that enhances the understanding and application of these inequalities. The methodology appears rigorous and could influence future research exploring anisotropic behavior in differential equations and related areas.

Enhancing Blind Source Separation with Dissociative Principal Component Analysis

Sparse principal component analysis (sPCA) enhances the interpretability of principal components (PCs) by imposing sparsity constraints on loading vectors (LVs). However, when used as a precursor to i...

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The article presents a novel approach in blind source separation that integrates dissociative principal component analysis (DPCA) with established techniques to overcome the limitations of sparse PCA. The introduction of adaptive algorithms improves efficacy significantly across diverse applications, suggesting strong methodological rigor. The potential impacts on various imaging techniques indicate high applicability, which enhances its relevance.

Characterisation of SiC radiation detector technologies with synchrotron X-rays

To cope with environments with high levels of radiation, non-silicon semiconductors such as silicon carbide detectors are being proposed for instrumentation. 4H-SiC diodes for radiation detection ha...

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The article presents novel characterisation methodologies for SiC radiation detectors, focusing on their potential applications in radiation-heavy environments such as medical instrumentation. The combination of advanced materials and synchrotron X-ray technology underlines the methodological rigor and innovation in the research. However, while the findings are promising, their practical implications may require further exploration, which slightly lowers the score.