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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!

Adaptive Phase 2/3 Design with Dose Optimization

FDA's Project Optimus initiative for oncology drug development emphasizes selecting a dose that optimizes both efficacy and safety. When an inferentially adaptive Phase 2/3 design with dose select...

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The article addresses a crucial issue in adaptive trial designs within the oncology field, particularly in relation to FDA's Project Optimus initiative. Its focus on refining methods for optimizing dose selection and controlling Type I error in Phase 2/3 trials demonstrates both novelty and practical applicability. Furthermore, the proposed methods could lead to more efficient trials, positively impacting drug development timelines and costs, which is of high relevance in an industry that often faces challenges with lengthy approvals and regulatory compliance.

A simplified method for the evaluation of the total resistance of a foiling yacht in upright condition

An extremely schematic model of the forces acting an a sailing yacht equipped with a system of foils is here presented and discussed. The role of the foils is to raise the hull from the water in order...

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The article presents a novel and simplified modeling approach for evaluating the performance of foiling yachts, which could have significant implications for yacht design and optimization in naval architecture. Its focus on computational fluid dynamics (CFD) and parametric studies adds robustness to the methodology, potentially advancing the state-of-the-art in yacht performance analysis. The insights into resistance reduction and efficiency gains are relevant for both practitioners and researchers in the field, suggesting new areas for exploration regarding foiling technology.

Zeta and L functions of Voevodsky motives

We associate an $L$ -function $L^{\mathrm{near}}(M,s)$ to any geometric motive over a global field $K$ in the sense of Voevodsky. This is a Dirichlet series which converges in s...

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This article presents a novel association of an L-function to geometric motives in the framework of Voevodsky's work, which is significant for both theoretical and practical implications. The methodological rigor and the engagement with recent advancements (like Ayoub's operations) enhance its potential impact. The distinction made with Serre's product suggests a significant contribution to the field's understanding and applications. However, its very specific mathematical focus may limit its wider applicability.

On Poincaré polynomials for plane curves with quasi-homogeneous singularities

We define a combinatorial object that can be associated with any conic-line arrangement with ordinary quasi-homogeneous singularities, which we call the combinatorial Poincaré polynomial. We prove a T...

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The article introduces a novel combinatorial object related to conic-line arrangements and brings a new perspective to the study of Poincaré polynomials for quasi-homogeneous singularities. Its focus on factorization in the context of free arrangements adds both depth and breadth to existing research, potentially influencing further studies in algebraic geometry. The methodological rigor appears solid, backed by a clear mathematical exposition. However, its applicability may be limited to more niche areas in geometry, which affects its broader impact.

Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

Time series forecasting always faces the challenge of concept drift, where data distributions evolve over time, leading to a decline in forecast model performance. Existing solutions are based on onli...

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The article introduces a novel framework, extsc{Proceed}, that addresses a critical issue in time series forecasting related to concept drift, which has significant implications in real-world applications. Its proactive approach showcases methodological rigor, and its extensive empirical validation enhances its reliability. Furthermore, the framework has potential for broader applicability across various forecasting scenarios, making it a promising advancement in the field.

Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy

Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared in the training sam...

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The article introduces a novel framework, S+NER, which addresses the important issue of Out-of-Entity errors in NER tasks. Its methodological rigor is highlighted by the use of pre-trained language models and contrastive learning, making it a significant advancement. The extensive experiments across five benchmarks further support its validity and potential impact on both academia and industry.

On the subgroup membership problem in bounded automata groups

We are interested in the generalised word problem (aka subgroup membership problem) for stabiliser subgroups of groups acting on rooted $d$ -regular trees. Stabilisers of infinite rays in the t...

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The paper provides significant insights into the subgroup membership problem for bounded automata groups, a topic relevant to algebra and theoretical computer science. Its findings regarding the constructability of ET0L languages and the implications for infinite rays add both novelty and mathematical rigor to the field. The approach can inspire future research in combinatorial group theory and other areas, even though it focuses on a specific type of group.

Generalized free energy and excess entropy production for active systems

We propose a generalized free energy potential for active systems, including both stochastic master equations and deterministic nonlinear chemical reaction networks. Our generalized free energy is def...

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This article presents a novel concept of generalized free energy for active systems, which broadens the understanding of non-equilibrium thermodynamics. The introduction of excess entropy production (EPR) in characterizing active systems adds depth to existing frameworks, suggesting potential new avenues for research. The methodological rigor is demonstrated through derivations and applications to various systems, enhancing its relevance. Its interdisciplinary connections to thermodynamics, statistical mechanics, and information theory signify a substantial impact on future research.

Pairing in Fission: Studies of Static Approach and Collective Inertias

Pairing plays a crucial role in the microscopic description of nuclear fission. Microscopic methods provide access to three quantities related to pairing, namely, the pairing gap ( $Δ$ ), the pa...

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This article addresses a significant aspect of nuclear physics related to fission, emphasizing the role of pairing in understanding the process. The study's focus on microscopic methods to quantify important pairing-related parameters reflects methodological rigor. Its potential implications for both theoretical advancements and practical applications in nuclear energy and safety lend it substantial relevance.

Assessing Personalized AI Mentoring with Large Language Models in the Computing Field

This paper provides an in-depth evaluation of three state-of-the-art Large Language Models (LLMs) for personalized career mentoring in the computing field, using three distinct student profiles that c...

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This article presents a novel approach to integrating AI-driven mentoring systems in computing education, leveraging the capabilities of leading LLMs to address diversity in student profiles. The methodological rigor includes both quantitative and qualitative evaluations, which strengthen the findings. The focus on personalized mentoring highlights current issues in the field and potential for further research in educational technology and AI applications.

Probing Lorentz violating effects on the exclusive $e^+e^-$ production in ultraperipheral heavy-ion collisions

The impact of Lorentz violating (LV) terms on the exclusive $e^+e^-$ production in ultraperipheral heavy-ion collisions at the Large Hadron Collider (LHC) is investigated, considering vectoria...

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The article addresses an important topic in high-energy physics, specifically the implications of Lorentz violation in particle interactions within heavy-ion collisions, which is a novel investigation. The methodological approach, which includes presenting both differential and total cross-sections, underscores good analytical rigor. The potential to improve current upper bounds on Lorentz violating terms makes it conceptually significant and could foster further studies in the field.

SwarmGPT-Primitive: A Language-Driven Choreographer for Drone Swarms Using Safe Motion Primitive Composition

Catalyzed by advancements in hardware and software, drone performances are increasingly making their mark in the entertainment industry. However, designing smooth and safe choreographies for drone swa...

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The proposed SwarmGPT-Primitive system presents a novel integration of language models with swarm robotics, addressing a significant gap in drone choreography design by simplifying the process for non-experts. This innovation, coupled with its rigorous safety assessment through optimization-based filters, enhances the practical applicability of drone swarms in real-world performances. Its clear focus on safety and usability holds strong implications for both entertainment and robotic applications, highlighting its interdisciplinary value.

Existence of solutions to a quasilinear nonlocal PDE

In this paper, we introduce a new class of quasilinear operators, which represents a nonlocal version of the operator studied by Stuart and Zhou [1], inspired by models in nonlinear optics. We will st...

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The article presents a novel class of quasilinear operators with nonlocal properties, which could broaden the landscape of existing mathematical theories in partial differential equations (PDEs). The exploration of existence results through variational methods indicates methodological rigor. The applicability of these findings to nonlinear optics further enhances the relevance, promising potential implications in real-world phenomena. Although it focuses primarily on theoretical implications, the clear analysis of nonexistence results provides additional depth.

Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution

Predicting the evolution of complex systems governed by partial differential equations (PDEs) remains challenging, especially for nonlinear, chaotic behaviors. This study introduces Koopman-inspired F...

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This article presents a novel approach that combines Koopman theory with advanced neural network techniques to address the significant challenge of predicting unstable flame front evolution. The methodological rigor in using kFNO and kCNN represents a creative application of machine learning to solve traditional PDE problems, indicating high potential for advancing research in both computational fluid dynamics and predictive modeling. The improved accuracy and framework provided could inspire future interdisciplinary research across various complex systems.

Fermions hard scattering from superstrings

We study high-energy scattering at fixed angle in the planar limit of non-Abelian gauge field theories from type IIB superstring theory scattering amplitudes. Firstly, we consider four-glueball scatte...

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The study presents a sophisticated and original approach to high-energy scattering processes through the lens of type IIB superstring theory, offering novel mathematical insights and connections between gauge field theories and string theory dynamics. Its focus on four-glueball and four-dilatino scattering is particularly relevant to theoretical physics and string theory. The methodology appears robust, involving explicit calculations and derivations that enhance the paper's overall impact in advancing our understanding of non-Abelian gauge theories in higher dimensions, potentially influencing further explorations in related fields.

From Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes

We focus on the classification problem with a separable dataset, one of the most important and classical problems from machine learning. The standard approach to this task is logistic regression with ...

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This article presents novel insights into the behavior of gradient descent in logistic regression, specifically regarding the use of large step sizes. Its comparative analysis with the perceptron algorithm and the implications for convergence rates and iteration complexity are significant contributions to optimization theory in machine learning, addressing a gap in understanding. The methodology appears rigorous, potentially offering a strong foundation for future work in optimization techniques. However, the applicability of findings might be limited to specific datasets and scenarios, which slightly lowers its relevance score.

On the injective dimension of unit Cartier and Frobenius modules

Let $R$ be a regular $F$ -finite ring of prime characteristic $p$ . We prove that the injective dimension of every unit Frobenius module $M$ in the category of unit Frobe...

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The article addresses a significant aspect of structural properties in the category of unit Frobenius and Cartier modules over regular and noetherian $F$-finite rings. The results presented expand the existing understanding of injective dimensions in a clear and rigorous manner, providing new bounds that can influence future research in algebraic geometry and representation theory. The findings could lead to further explorations of module categories and their applications in various mathematical fields.

Commercial CMOS Process for Quantum Computing: Quantum Dots and Charge Sensing in a 22 nm Fully Depleted Silicon-on-Insulator Process

Confining electrons or holes in quantum dots formed in the channel of industry-standard fully depleted silicon-on-insulator CMOS structures is a promising approach to scalable qubit architectures. In ...

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This article provides significant insights into the application of commercial CMOS processes for quantum dot-based qubits, showcasing novel measurement results that enhance our understanding of charge sensing in qubits. The integration of industry-standard fabrication techniques in quantum computing is an important advancement, establishing a practical pathway toward scalable and efficient quantum architectures.

PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion

Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances...

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PointCFormer presents innovative methods combining relation-based local feature extraction and a transformer framework, addressing significant limitations in existing point cloud completion techniques. The dual advantages of enhanced global structure retention and local detail capture suggest robust methodological advancements. It shows promise for practical applications, and its state-of-the-art performance in benchmarks indicates high relevance.

Ill-Posed Configurations in Random and Experimental Data Points Collection

Ill-posed configurations, such as collinear or coplanar point arrangements, are a persistent challenge in computational geometry, complicating tasks as in triangulation and convex hull construction. T...

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The article addresses a critical challenge in computational geometry and offers a probabilistic approach to understanding ill-posed configurations in both random and experimental data. Its methodological rigor in analyzing geometric constraints and its practical implications for real-world data collection make it a valuable contribution, particularly in a field that often struggles with such degeneracies. The emphasis on the systematic biases in physical systems adds significant depth and relevance to its findings, potentially guiding future research in data sampling protocols.