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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!

Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification

Deep learning has proven very promising for interpreting MRI in brain tumor diagnosis. However, deep learning models suffer from a scarcity of brain MRI datasets for effective training. Self-supervise...

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The study introduces a novel hybrid architecture that successfully combines CNN and Vision Transformers in the context of self-supervised learning, which is especially relevant given the challenges posed by limited dataset sizes in medical imaging. The dual-stage pre-training and fine-tuning approaches are methodologically sound and demonstrate superior performance over existing techniques. Additionally, the thorough evaluation across multiple datasets adds to its robustness.

Tensor-Based Foundations of Ordinary Least Squares and Neural Network Regression Models

This article introduces a novel approach to the mathematical development of Ordinary Least Squares and Neural Network regression models, diverging from traditional methods in current Machine Learning ...

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The article presents a unique mathematical perspective on Ordinary Least Squares and Neural Network regression using Tensor Analysis, a less commonly applied framework in this context. The introduction of new algorithms, particularly a streamlined Backpropagation Algorithm, enhances its applicability and potential impact on both theoretical and applied machine learning. The methodological rigor shown through detailed mathematical developments strengthens its contribution to the field, meriting a high relevance score.

Modelling Directed Networks with Reciprocity

Asymmetric relational data is increasingly prevalent across diverse fields, underscoring the need for directed network models to address the complex challenges posed by their unique structures. Unlike...

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The article offers a novel approach to directed network modeling, focusing on reciprocity, which is a less explored area compared to undirected models. The methodological rigor is evident in its analytical framework and the introduction of a model that accounts for covariates, which enhances its applicability to real-world data. The implications of the findings can influence both theoretical advancements and practical applications in network analysis.

Enhancement of Microwave to Optical Spin-Based Quantum Transduction via a Magnon Mode

We propose a new method for converting single microwave photons to single optical sideband photons based on spinful impurities in magnetic materials. This hybrid system is advantageous over previous p...

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This paper presents a novel approach to quantum transduction, addressing a significant limitation in current technologies. The proposed method's enhancement in speed and efficiency opens new avenues for quantum communication and computing, making it highly impactful in advancing quantum technologies. The identification of practical materials systems further enhances its applicability.

Omnidirectional Wireless Power Transfer for Millimetric Magnetoelectric Biomedical Implants

Miniature bioelectronic implants promise revolutionary therapies for cardiovascular and neurological disorders. Wireless power transfer (WPT) is a significant method for miniaturization, eliminating t...

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This article introduces a highly innovative omnidirectional wireless power transfer (WPT) system tailored for millimetric biomedical implants. The integration of magnetoelectric WPT with active echo sensing signifies a substantial advancement in ensuring efficient power delivery in challenging operational conditions, which is crucial for the future of wireless medical devices. The rigorous methodology and impressive experimental results highlight its practical applicability in the field.

Projective smooth representations in natural characteristic

We investigate under which circumstances there exists nonzero {\it{projective}} smooth \field[G]-modules, where \field is a field of characteristic $p$ and $G$ is a...

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The article addresses a significant gap in the understanding of projective smooth representations of locally pro-$p$ groups in characteristic $p$, specifically focusing on 'fair' groups. The proof of non-existence of non-trivial projective objects extends previous work and introduces an elementary approach with potential adaptability to other contexts. The discussion around the fairness condition in Chabauty spaces showcases both depth and broad applicability. However, while the findings are rigorous, their immediate applicability may be limited to a niche audience in representation theory.

Complete $\mathcal{O}(α_s^2)$ Corrections to the Leptonic Invariant Mass Spectrum in $b\to X_c l\barν_l$ Decay

In the determination of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cb}|$ from inclusive semileptonic $B$ -meson decays, moments of the leptonic invariant mass spectrum constitute...

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The article provides a robust analysis of $eta$-decay processes, enhancing the understanding of the leptonic invariant mass spectrum with complete $ ext{O}(α_s^2)$ corrections. The inclusion of the triple-charm channel offers valuable insights, addressing a gap in previous analyses, which bolsters its novelty. The methodology appears rigorously applied, promoting confidence in the results. The potential implications for precision measurements in particle physics and flavor physics further substantiate its relevance.

Inverse Faraday effect in 3d, 4d, and 5d transition metals

Using first-principles calculations, we systematically investigate the spin contributions to the inverse Faraday effect (IFE) in transition metals. The IFE is primarily driven by spin-orbit coupling (...

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The article presents a novel approach to understanding the inverse Faraday effect in transition metals through comprehensive first-principles calculations. The exploration of spin contributions, especially in elements with smaller magnetic moments, adds depth to existing literature. The findings related to the tuning of IFE by manipulating Fermi levels could prove instrumental for practical applications in spintronics and related fields, thus inspiring future research directions. The clarity of the methodology and implications of the results strengthen its overall contribution.

On corona of Konig-Egervary graphs

Let $α(G)$ denote the cardinality of a maximum independent set and $μ(G)$ be the size of a maximum matching of a graph $G=\left( V,E\right)$ . If $α(G)+μ(G)=\left\vert V\ri...

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The article presents a novel characterization of coronas of König-Egerváry graphs, which extends previous knowledge in graph theory. The focus on $k$-König-Egerváry graphs adds a layer of specificity and relevance to the study of independent sets and matchings. The methodology appears robust, and the implications of the findings could inspire further research into related graph families. However, the specialized nature may limit its immediate application compared to broader studies in the field.

Non-unique water and contrast agent solutions in dual-energy CT

The goal of this work is to study occurrences of non-unique solutions in dual-energy CT (DECT) for objects containing water and a contrast agent. Previous studies of the Jacobian of nonlinear systems ...

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This article addresses a significant gap in understanding non-unique solutions in dual-energy CT, which has implications for accurate diagnostic imaging. The novel approach of leveraging simulations and analyzing the Jacobian determinant offers a robust methodological framework. The findings could enhance the reliability of DECT mechanisms and improve clinical applications by identifying potential discrepancies in material mapping. The rigorous simulation and identification of solution sets contribute to the methodological rigor of the study, making it impactful for future research in the area.

Indices and residues: from Poincaré-Hopf to Baum-Bott, and Marco Brunella

In this expository article, we study and discuss invariants of vector fields and holomorphic foliations that intertwine the theories of complex analytic singular varieties and singular holomorphic fol...

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The article presents an interdisciplinary analysis that connects the Poincaré-Hopf theorem and Baum-Bott formula with applications to complex analytic singular varieties and holomorphic foliations. Its exploration of invariants crucially broadens understanding in complex geometry and algebraic topology. Its methodological rigor and adaptability to various mathematical contexts enhance its potential to inspire future research.

The Game-Theoretic Symbiosis of Trust and AI in Networked Systems

This chapter explores the symbiotic relationship between Artificial Intelligence (AI) and trust in networked systems, focusing on how these two elements reinforce each other in strategic cybersecurity...

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The article presents a novel and comprehensive analysis of the interplay between AI and trust within highly relevant cybersecurity frameworks. Its game-theoretic approach adds significant methodological rigor and provides an innovative perspective that could lead to practical applications and governance models. The exploration of trust dynamics enhances the relevance of this research in both academia and industry, positioning it as a potential reference point for future studies and implementations.

An Abstract Domain for Heap Commutativity

Commutativity of program code (the equivalence of two code fragments composed in alternate orders) is of ongoing interest in many settings such as program verification, scalable concurrency, and secur...

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The article presents a novel abstract domain specifically designed for analyzing code commutativity in heap-manipulating programs. This is a significant advancement in the static analysis of concurrent programs, as it bridges a gap in existing literature. The methodology appears rigorous, having been mechanized in Coq, which enhances its credibility and applicability. The potential implications for program verification, concurrency, and security analysis add to the article's impact.

Independence of multipliers in several variables complex dynamics

We establish the independence of multipliers for polynomial endomorphisms of $\mathbb C^n$ and endomorphisms of $\mathbb P^n.$ This allows us to extend results about the bifurcation me...

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The article tackles a novel problem in the realm of complex dynamics by establishing the independence of multipliers in multi-variable settings, which can significantly enhance our understanding of polynomial endomorphisms. The independence of multipliers is a critical factor in dynamics, influencing bifurcation theories and stability assessments. The extension of previous results to higher dimensions (n >= 3) indicates the article's importance in advancing existing frameworks and suggesting new avenues for research. Methodologically, the proof involves important concepts such as irreducibility, enhancing the rigor and depth of the study, which adds to its impact.

Linking emitted drops to collective bursting bubbles across a wide range of bubble size distributions

Bubbles entrained by breaking waves rise to the ocean surface, where they cluster before bursting and release droplets into the atmosphere. The ejected drops and dry aerosol particles, left behind aft...

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The article presents a novel experimental framework for linking bubble dynamics to aerosol emission, addressing significant gaps in our understanding of sea spray processes. The methodology is rigorous, providing comprehensive measurements across a wide range of bubble sizes and demonstrating the utility of individual bursting scaling laws in predicting drop production. This integration enhances the applicability of existing models, potentially influencing both experimental and theoretical approaches in related domains.

A new Input Convex Neural Network with application to options pricing

We introduce a new class of neural networks designed to be convex functions of their inputs, leveraging the principle that any convex function can be represented as the supremum of the affine function...

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This article presents a novel approach by introducing a new class of Input Convex Neural Networks (ICNNs) specifically for options pricing, an area that increasingly relies on advanced computational techniques. The methodological rigor is highlighted by theoretical convergence bounds and numerical demonstrations, showcasing both the innovation and its practical application. The incorporation of a \'scrambling\' phase to enhance training further adds to its significance. Overall, this work has the potential to significantly impact financial modeling and machine learning methodologies.

Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification

Protein structures represent the key to deciphering biological functions. The more detailed form of similarity among these proteins is sometimes overlooked by the conventional structural comparison me...

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This article presents a novel integration of secondary structure elements into the Triangular Spatial Relationship method for protein classification, showcasing significant improvements in accuracy. The methodological innovation, along with the substantial performance gains demonstrated on large datasets, indicates a robust approach that could have widespread applicability in bioinformatics and structural biology. Its potential to enhance protein classification and understanding of biological functions is highly relevant and impactful.

Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation

Emotion recognition has significant potential in healthcare and affect-sensitive systems such as brain-computer interfaces (BCIs). However, challenges such as the high cost of labeled data and variabi...

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The article addresses significant challenges in EEG-based emotion recognition through innovative methods that enhance model transferability across datasets. The introduction of new techniques for data selection and test-time augmentation demonstrate methodological rigor and applicability, particularly in practical healthcare settings. Its experimental validation on established datasets further strengthens its relevance.

GenAI Assistance for Deep Reinforcement Learning-based VNF Placement and SFC Provisioning in 5G Cores

Virtualization technology, Network Function Virtualization (NFV), gives flexibility to communication and 5G core network technologies for dynamic and efficient resource allocation while reducing the c...

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This paper presents a novel hybrid approach combining Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) for optimizing Service Function Chain (SFC) provisioning in 5G networks, which addresses a crucial need in network function virtualization. The use of AI techniques adds significant value and showcases methodological rigor with practical applications. The emphasis on ultra-reliable low-latency communication (URLLC) further enhances relevance to modern telecommunications needs. This integration of generative models with RL algorithms represents a significant advance and can inspire future research in similar domains.

Maximal Jet Energy of Gamma-Ray Bursts through the Blandford-Znajek Mechanism

Gamma-ray bursts (GRBs) are among the most energetic events in the universe, driven by relativistic jets launched from black holes (BHs) formed during the collapse of massive stars or after the merger...

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This article presents a novel unified model for understanding the jet energy of Gamma-ray bursts (GRBs) through the Blandford-Znajek mechanism, which addresses significant questions in astrophysics regarding the constraints imposed by both thin and magnetically arrested disk models. Its methodologically robust approach synthesizes existing theories with new predictive curves, enhancing our understanding of jet dynamics in black hole systems. Additionally, its implications for both long and short GRBs broaden the scope of future observational and theoretical research in this area.