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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!

On the double critical Maxwell equations

In this paper, we focus on (no)existence and asymptotic behavior of solutions for the double critical Maxwell equation involving with the Hardy, Hardy-Sobolev, Sobolev critical exponents. The existenc...

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This paper presents significant results on the existence and non-existence of solutions to the double critical Maxwell equations, utilizing advanced mathematical tools and methodologies. The contribution is robust, addressing a specific open problem in the field and providing thorough analysis with implications for both theoretical understanding and practical applications in physics and engineering. The novelty lies in the detailed examination of critical exponents, furthering knowledge in critical phenomena and related mathematical analysis, which could influence future research directions.

Simulating squirmers with smoothed particle dynamics

Microswimmers play an important role in shaping the world around us. The squirmer is a simple model for microswimmer whose cilia oscillations on its spherical surface induce an effective slip velocity...

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The study presents a novel application of smoothed particle dynamics (SPD) to effectively simulate squirmers, addressing a significant gap in fluid-solid coupling methodologies. Its detailed validation of the model across different microswimmer types and conditions enhances its credibility and applicability. Moreover, it combines fundamental physics with computational innovation, indicating a strong potential to influence future research in both theoretical and applied contexts.

Topology-Aware Popularity Debiasing via Simplicial Complexes

Recommender systems (RS) play a critical role in delivering personalized content across various online platforms, leveraging collaborative filtering (CF) as a key technique to generate recommendations...

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This article presents a novel approach to address a significant problem in recommender systems—the popularity bias in recommendations—using a well-defined methodology that integrates simplicial complexes into GNNs. The rigorous experimentation on real-world datasets and the potential of the methodology to improve long-tail item recommendations reflect its robustness and applicability. Furthermore, the proposed TSP framework is designed for easy integration, making it highly relevant for existing systems and inspiring future research in the field.

UPdec-Webb: A Dataset for Coaddition of JWST NIRCam Images

We present the application of the image coaddition algorithm, Up-sampling and PSF Deconvolution Coaddition (UPDC), for stacking multiple exposure images captured by the James Webb Space Telescope (JWS...

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The article presents a novel image coaddition algorithm that enhances the quality of data from the JWST, a premier space telescope. The innovative methodology (UPDC) addresses well-known issues, such as point spread function effects, indicating strong methodological rigor. The demonstration of superior results compared to existing techniques like the Drizzle algorithm adds to its impact, especially with new discoveries highlighted. The accessibility of the dataset can further influence the broader community's research in astrophysics. Overall, it advances observational capabilities in an important research area.

GraCo -- A Graph Composer for Integrated Circuits

Designing integrated circuits involves substantial complexity, posing challenges in revealing its potential applications - from custom digital cells to analog circuits. Despite extensive research over...

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GraCo introduces a novel approach to circuit design using reinforcement learning, which represents a significant advancement in automating integrated circuit synthesis. The focus on computational efficiency and the introduction of prior design knowledge adds depth to the methodology. Its comparisons with a baseline provide empirical evidence of its performance, making it a robust contribution to the field.

A Hierarchical Poisson Generator for Universal Graphs under Limited Resources

Graph generation is one of the most challenging tasks in recent years, and its core is to learn the ground truth distribution hiding in the training data. However, training data may not be available d...

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The proposed method addresses a critical gap in graph generation under resource limitations, utilizing a novel hierarchical approach that combines theoretical grounding with practical experimentation. Its innovative use of Poisson distribution and degree mixing enhances its appeal in scenarios where training data is sparse or unavailable. The robustness of the results across multiple datasets signals significant applicability.

A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity

We introduce, for the first time, a cohomology-based Gromov-Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups, focusing on loops, voids, and higher-dimen...

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This article presents a novel methodology combining cohomology and Gromov-Hausdorff metrics, which is a significant advancement in quantifying molecular similarity. The introduction of simplicial complexes and the focus on topological invariants such as loops and voids offer a new perspective for clustering molecular structures, potentially transforming data analysis in this field. The demonstrated application to OIHP structures further supports its relevance and practicality.

CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face Recognition

An important aspect of deploying face recognition (FR) algorithms in real-world applications is their ability to learn new face identities from a continuous data stream. However, the online training o...

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The article presents a novel framework, CLFace, to address significant challenges in the field of continual learning and face recognition, including catastrophic forgetting and storage constraints. The methodological rigor, demonstrated through experimental validation on benchmark datasets, supports its practical applicability in real-world scenarios, thereby enhancing both the theoretical and practical landscape of face recognition technology.

Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient

This paper studies the application of the DDPG algorithm in trajectory-tracking tasks and proposes a trajectorytracking control method combined with Frenet coordinate system. By converting the vehicle...

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The paper presents a novel approach to trajectory tracking in autonomous driving using the DDPG algorithm and Frenet coordinates, which enhances the precision and feasibility of the method in complex environments. The integration of the Actor-Critic framework with a robust experimental validation indicates methodological rigor and potential applicability. However, the existing focus primarily on vehicle trajectories may limit interdisciplinary applications.

Reinforcement Learning for Jointly Optimal Coding and Control over a Communication Channel

We develop rigorous approximation and near optimality results for the optimal control of a system which is connected to a controller over a finite rate noiseless channel. While structural results on t...

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This article presents a significant advancement in the integration of reinforcement learning with coding and control in communication systems, addressing practical implementation challenges that have hindered progress in the field. The focus on finite rate noiseless channels is particularly relevant, given the increasing demand for efficient communication in modern applications. The rigorous approximation and near optimality results emphasize methodological rigor, which adds to the article's credibility and potential for impact. However, further empirical validation may be needed to fully assess its applicability in real-world scenarios.

When Online Algorithms Influence the Environment: A Dynamical Systems Analysis of the Unintended Consequences

We analyze the effect that online algorithms have on the environment that they are learning. As a motivation, consider recommendation systems that use online algorithms to learn optimal product recomm...

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The article addresses a significant gap in the understanding of how online algorithms, specifically recommendation systems, can impact user preferences in an evolving environment. This analysis introduces a novel dynamical systems framework and applies it effectively to a common issue in machine learning. The meticulous approach of analyzing the dynamic interplay between the learning algorithm and user preferences showcases methodological rigor and relevance in addressing unintended consequences of popular technologies.

A 2x2 quantum dot array in silicon with fully tuneable pairwise interdot coupling

Recent advances in semiconductor spin qubits have achieved linear arrays exceeding ten qubits. Moving to two-dimensional (2D) qubit arrays is a critical next step to advance towards fault-tolerant imp...

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The paper presents a significant advancement in the realm of semiconductor spin qubits by successfully demonstrating a fully tunable 2D quantum dot array, which addresses previous fabrication challenges. The ability to achieve and control interdot couplings in a scalable format is novel and positions the research at the forefront of quantum computing technology. Methodological rigor is apparent through the use of advanced modeling and experimental validation at low temperatures, enhancing its relevance. The implications for fault-tolerant quantum computing efforts further solidify its potential impact on the field.

Exploring applications of topological data analysis in stock index movement prediction

Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representat...

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The article addresses the promising application of Topological Data Analysis (TDA) in financial prediction, a novel approach within financial analytics, which is increasingly focused on complex data patterns. The methodological rigor is apparent through the systematic exploration of multiple point cloud construction methods and the evaluation of various machine learning models, enhancing its significance. This study contributes applicable insights for practitioners aiming to improve stock movement predictions, bridging practical finance with advanced data analysis techniques.

Some Inequalities for Riesz Potential on Homogeneous Variable Exponent Herz-Morrey-Hardy Spaces

In harmonic analysis, studies of inequalities of Riesz potential in various function spaces have a very important place. Variable exponent Morrey type spaces and the examines of the boundedness of suc...

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The study addresses a significant niche in harmonic analysis by focusing on inequalities concerning Riesz potentials within the context of variable exponent Herz-Morrey-Hardy spaces, which signifies novelty in approach and methodology. The findings may enhance the understanding of operator boundedness in complex function spaces, making it impactful for further research.

Sparse Zero Correlation Zone Arrays for Training Design in Spatial Modulation Systems

This paper presents a novel training matrix design for spatial modulation (SM) systems, by introducing a new class of two-dimensional (2D) arrays called sparse zero correlation zone (SZCZ) arrays. An ...

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The article presents a highly novel approach to training matrix design in spatial modulation systems, with significant implications for improving channel estimation in complex environments. The introduction of SZCZ arrays and their unique properties offers a fresh perspective and potential for further research in both theoretical and practical aspects of communication systems. The methodological rigor, as evidenced by simulations and comparisons to existing methods, enhances its applicability and relevance.

Inequalities on six points in a $\mathrm{CAT}(0)$ space

We establish a family of inequalities that hold true on any $6$ points in any $\mathrm{CAT}(0)$ space. We prove that the validity of these inequalities does not follow from any propert...

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The article presents a novel contribution to the study of geometric properties in CAT(0) spaces by establishing a specific set of inequalities that hold for six points. This indicates a progression in the understanding of distance geometry within these spaces, which is notable since the findings fall outside the established framework involving 5-point subsets. However, while the results are significant, the applicability of these inequalities and their potential use in broader contexts remains to be explored fully, limiting immediate influence.

Iterative decoding of short BCH codes and its post-processing

Effective iterative decoding of short BCH codes faces two primary challenges: identifying an appropriate parity-check matrix and accelerating decoder convergence. To address these issues, we propose a...

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The article presents a novel approach to improving the iterative decoding of short BCH codes, effectively addressing critical challenges in the field. The method proposed for deriving an optimized parity-check matrix and the integration of neural networks for post-processing indicate significant methodological rigor and innovation. The balance between performance, latency, and complexity achieved through extensive simulations suggests practical applicability, enhancing its relevance for both theoretical and applied research.

Large deviations at the origin of random walk in random environment

We consider a random walk in an i.i.d. random environment on Zd and study properties of its large deviation rate function at the origin. It was proved by Comets, Gantert and Zeitouni in dimension d = ...

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This article addresses a fundamental question in the theory of random walks in random environments by providing new insights into large deviation principles, particularly at the origin. The results extend previous work in a significant way, indicating robustness in modeling complex phenomena and potentially influencing future studies related to random walks. The mention of periodic environments adds novelty and increases applicability, facilitating connections to various mathematical disciplines.

Next-Generation Phishing: How LLM Agents Empower Cyber Attackers

The escalating threat of phishing emails has become increasingly sophisticated with the rise of Large Language Models (LLMs). As attackers exploit LLMs to craft more convincing and evasive phishing em...

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This article presents a significant exploration of how LLMs are transforming the phishing landscape, demonstrating both the risks and defenses associated with this technology. The methodological rigor in evaluating existing phishing detectors against LLM-generated threats is commendable, and the findings highlight urgent weaknesses in current cybersecurity measures. The interdisciplinary implications of this research, particularly in both offensive and defensive cyber capabilities, make it highly relevant.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network

Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large number of diverse l...

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This article presents a novel approach to medical image segmentation that addresses significant issues related to the annotation burden and error propagation in existing methods. The proposed Object Estimation Guided Correspondence Flow Network enhances the capability of existing self-supervised frameworks by effectively handling inter-slice variation and discontinuities, which is a common challenge in 3D medical imaging. The demonstration of improved generalizability across different datasets also indicates practical applicability, making the findings valuable for both academic and clinical research. The methodological rigor is supported by empirical evidence across varied conditions.