This is a experimental project. Feel free to send feedback!

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!

Topological cliques in sparse expanders

In the paper, we focus on embedding clique immersions and subdivisions within sparse expanders, and we derive the following main results: (1) For any 0< η< 1/2, there exists $K>...

Useful Fields:

This article presents significant advancements in the understanding of clique immersions and subdivisions in sparse expander graphs. The results are mathematically rigorous and build upon established conjectures and previous work in the field. The novel contributions to the characterization of specific graph classes (like $(n,d,λ)$-graphs) regarding their growth and structure make it relevant for both theoretical and algorithmic applications. However, its impact may be somewhat limited to a narrower audience given its complexity and specialized focus.

Circuit Quantisation from First Principles

Superconducting circuit quantisation conventionally starts from classical Euler-Lagrange circuit equations-of-motion. Invoking the correspondence principle yields a canonically quantised circuit descr...

Useful Fields:

This article presents a novel approach to superconducting circuit quantization by beginning with a microscopic fermionic Hamiltonian, diverging from conventional mean field theory. The methodological rigor is strong as it proposes a new framework that deepens the fundamental understanding of circuit dynamics. Additionally, the concept of not assuming a spontaneously broken symmetry enhances its relevance, particularly for advanced quantum technologies, potentially influencing future experimental and theoretical work in superconductivity and circuit design.

BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?

Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm for text retrieval,...

Useful Fields:

The article presents a novel investigation into the capabilities of dense retrieval systems concerning Boolean logic, highlighting a critical gap in current methodologies. The introduction of the BoolQuestions dataset and experimental results establishes strong evidence for the claims made. The proposed contrastive continual training method as a baseline for future research adds value and demonstrates methodological rigor. Overall, the article addresses a significant aspect of natural language processing that has been overlooked, which could lead to substantial advancements in the field.

On sensitivities regarding shape and topology optimization as derivatives on Wasserstein spaces

In this paper, we apply the framework of optimal transport to the formulation of optimal design problems. By considering the Wasserstein space as a set of design variables, we associate each probabili...

Useful Fields:

This paper introduces a novel approach by integrating optimal transport with shape and topology optimization, thereby expanding the theoretical framework in a significant way. The application of Wasserstein spaces in design problems is both innovative and relevant, suggesting potential new methodologies in engineering and applied mathematics. Its methodological rigor is underscored by the linking of probability measures to shape configurations, offering a unique perspective on optimization techniques that could inspire substantial future research.

Deep Learning VLBI Image Reconstruction with Closure Invariants

Interferometric closure invariants, constructed from triangular loops of mixed Fourier components, capture calibration-independent information on source morphology. While a complete set of closure inv...

Useful Fields:

The article presents a novel application of deep learning to improve VLBI imaging, which addresses a significant gap in existing methodologies by focusing on closure invariants. The approach shows strong experimental results and high fidelity in image reconstruction, indicating both methodological rigor and potential for practical advancement. The independence from calibration and tunable parameters enhances its applicability, increasing relevance across the field.

Wavespeed selection of travelling wave solutions of a two-component reaction-diffusion model of cell invasion

We consider a two-component reaction-diffusion system that has previously been developed to model invasion of cells into a resident cell population. This system is a generalisation of the well-studied...

Useful Fields:

This article presents a novel analysis of a two-component reaction-diffusion system that extends previous models, making a significant contribution to the understanding of cell invasion dynamics. Its methodological rigor is evident in the explicit calculations of wave solutions and the asymptotic analysis. The insights into initial conditions and their influence on travelling wave solutions are particularly valuable for the field, potentially guiding future experimental designs and theoretical models.

Two-dimensional superconductivity in new niobium dichalcogenides-based bulk superlattices

Transition metal dichalcogenides exhibit many unexpected properties including two-dimensional (2D) superconductivity as the interlayer coupling being weakened upon either layer-number reduction or che...

Useful Fields:

The study reports novel findings on 2D superconductivity within new niobium dichalcogenide-based superlattices, showcasing both unique materials and properties. The combination of novel material synthesis with clear superconducting characterization demonstrates methodological rigor. Furthermore, the work establishes a new platform that may encourage explorations of other 2D superconductors, enhancing its impact on the field.

Modular $\mathbb{Z}_2$ -Crossed Tambara-Yamagami-like Categories for Even Groups

We explicitly construct nondegenerate braided $\mathbb{Z}_2$ -crossed tensor categories of the form $\operatorname{Vect}_Γ\oplus\operatorname{Vect}_{Γ/2Γ}$ . They are $\mathbb{Z}_2&#...

Useful Fields:

This article introduces novel nondegenerate braided $ extbf{Z}_2$-crossed tensor categories, which expand upon existing structures, making it quite impactful within the field of category theory and modular tensor categories. The construction's connection to physical concepts and potential applications in quantum algebra enhances its relevance, showcasing both theoretical innovation and practicality. However, the work might require further exploration and validation in various contexts to fully ascertain its utility, impacting the final score.

Chiral swimmer with a regular arbitrary active patch

We investigate the low Reynolds number hydrodynamics of a spherical swimmer with a predominantly hydrophobic surface, except for a hydrophilic active patch. This active patch covers a portion of the s...

Useful Fields:

This article presents a novel investigation into the hydrodynamics of chiral swimmers with designated active patches, contributing significantly to the field of microfluidics and active matter. The analytical calculation of swimming dynamics under varying configurations showcases methodological rigor and presents valuable findings with potential applications in synthetic biology and medicine. The findings not only advance theoretical understanding but also have practical implications in designing active particles for drug delivery and other biomedical applications.

STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk

Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and ne...

Useful Fields:

The paper presents a novel socio-technical framework (STRisk) for assessing hacking breach risks, integrating social media aspects into traditional predictive models. The methodological rigor, including a comprehensive analysis of a large dataset and effective use of machine learning, enhances its impact. The significant improvement in prediction accuracy demonstrates practical applicability, making this research valuable for cybersecurity and risk assessment fields.