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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!

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.

CLIP Unreasonable Potential in Single-Shot Face Recognition

Face recognition is a core task in computer vision designed to identify and authenticate individuals by analyzing facial patterns and features. This field intersects with artificial intelligence image...

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The article demonstrates a novel approach by utilizing CLIP for single-shot face recognition, which addresses existing challenges in facial recognition systems, particularly the high false positive rates. Its methodological rigor in integrating NLP with CV signifies a step forward in multimodal AI applications. The potential to simplify training paradigms while enhancing model performance is particularly impactful for both academia and industry, making this research quite relevant for future investigations.

The Case for Inverse Semirings

A semiring generalises the notion of a ring, replacing the additive abelian group structure with that of a commutative monoid. In this paper, we study a notion positioned between a ring and a semiring...

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This paper presents a novel exploration into inverse semirings, introducing important theoretical advancements and computational examples. The authors not only fill a gap between existing algebraic structures but also provide significant fundamental results, showcasing their potential applications. The clear links drawn with existing theories in rings and idempotent semirings enhance its impact.

Defining Lyapunov functions as the solution of a performance estimation saddle point problem

In this paper, we reinterpret quadratic Lyapunov functions as solutions to a performance estimation saddle point problem. This allows us to automatically detect the existence of such a Lyapunov functi...

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The paper offers a novel reinterpretation of Lyapunov functions through a performance estimation saddle point problem, enhancing the methodology for detecting convergence in algorithms. The integration with DSP-CVXPY demonstrates methodological rigor and brings computational efficiency. Its implications for algorithm design and performance evaluation in control systems mark it as impactful in its field, while the clarity of applicability to complex algorithms indicates a strong potential for future research.

Behaviors of the Tate--Shafarevich group of elliptic curves under quadratic field extensions

Let $E$ be an elliptic curve defined over $\Bbb{Q}$ . We study the behavior of the Tate--Shafarevich group of $E$ under quadratic extensions $\Bbb{Q}(\sqrt{D})/\Bbb{Q}$ ....

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The article addresses a significant aspect of the study of elliptic curves and their associated Tate--Shafarevich groups, exploring their behavior under quadratic field extensions, which is an interesting and relevant area in number theory. The theoretical advancements and results regarding finiteness conditions could inspire further research in the field. The methodology appears rigorous and grounded in established theoretical frameworks, suggesting strong analytical depth.

Non-IID data in Federated Learning: A Systematic Review with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without...

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This article tackles a critical and timely issue in the field of Federated Learning—dealing with non-IID data. Its systematic review format offers a comprehensive taxonomy, relevant metrics, and methods, which is essential for advancing understanding of a major barrier in FL. The integration of theoretical and practical insights paves the way for future research, indicating high applicability and potential for impact.

A pursuit problem for squared Bessel processes

In this note, we are interested in the probability that two independent squared Bessel processes do not cross for a long time. We show that this probability has a power decay which is given by the fir...

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The paper addresses an interesting aspect of squared Bessel processes, specifically the non-crossing behavior of independent processes, which is relevant to both probability theory and stochastic processes. The use of hypergeometric functions provides a novel analytical approach, and the inclusion of Cramér's estimates enhances its applicability. However, the specialized nature of the topic may limit its broader impact.

An Effective Iterative Solution for Independent Vector Analysis with Convergence Guarantees

Independent vector analysis (IVA) is an attractive solution to address the problem of joint blind source separation (JBSS), that is, the simultaneous extraction of latent sources from several datasets...

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The article introduces an innovative methodological improvement to independent vector analysis (IVA) by presenting a new proximal alternating algorithm with provable convergence guarantees. This advancement addresses a crucial aspect of joint blind source separation (JBSS), which is significant within its field. The rigorous formulation and elaboration of the cost function also contribute to the article's robustness, making it a strong candidate for future research applications, particularly in complex data scenarios.