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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!

DT-LSD: Deformable Transformer-based Line Segment Detection

Line segment detection is a fundamental low-level task in computer vision, and improvements in this task can impact more advanced methods that depend on it. Most new methods developed for line segment...

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The article presents a novel approach to line segment detection using deformable transformer-based models, which is a significant advancement over traditional CNN-based methods. Its introduction of Line Contrastive DeNoising (LCDN) not only enhances model training efficiency but also pushes the accuracy boundaries in this essential computer vision task. This combination of innovation in model architecture and methodological improvement suggests a strong potential for impact in the field.

MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification

We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillation techniques in a ...

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This article introduces a novel framework (MERLOT) that combines large language model distillation with Mixture-of-Experts architecture, addressing a critical challenge in encrypted traffic classification with a focus on scalability and efficiency. The methodological rigor is evident through comprehensive experimentation across multiple datasets, demonstrating superior performance compared to existing models. The applicability is broad as it tackles an urgent problem in cybersecurity, making it highly relevant for future research and practical applications.

The Alexander polynomial of twisted torus knots

Twisted torus knots are a generalization of torus knots, obtained by introducing additional full twists to adjacent strands of the torus knots. In this article, we present an explicit formula for the ...

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The article presents a novel formula for the Alexander polynomial of twisted torus knots, which is an important contribution to knot theory and could drive further investigation into related knot invariants. Utilizing Fox's free differential calculus adds methodological rigor, suggesting that the results are robust. Furthermore, the implications for knot genus and $L$-space knots highlight potential applications in understanding the topology of three-manifolds, which positions this work as influential for future studies in this area.

Multi-scale simulation of red blood cell trauma in large-scale high-shear flows after Norwood operation

Cardiovascular surgeries and mechanical circulatory support devices create non-physiological blood flow conditions that can be detrimental, especially for pediatric patients. A source of complications...

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The study presents a novel multi-scale numerical model that addresses a critical problem in pediatric cardiovascular surgeries. Its methodological rigor in using CFD and Lagrangian tracking provides robust insights into the hemolytic effects of surgical shunt designs, which is highly relevant for improving patient outcomes. The results offer substantial clinical implications and could inspire future research on blood flow dynamics and surgical design optimization.

Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection

Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data share the same label s...

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The article presents a novel approach to improving semi-supervised object detection in open-set scenarios, which is a significant challenge in current research. The introduction of a feature-level clustering method with contrastive loss and the optimization of logits-level uncertainties represent robust methodological advancements. The extensive experiments validating the proposed method further enhance its credibility and potential impact in the field.

NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection

Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without rel...

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The proposed NCAirFL scheme addresses a significant challenge in over-the-air federated learning by eliminating the need for channel state information, thus enhancing efficiency and practicality in real-world applications. The novelty lies in its approach of using non-coherent detection and error compensation, and its quantitative results indicating improved convergence rates suggest strong methodological rigor. The relevance of this work lies in addressing current technological limitations, making it potentially transformative for federated learning in resource-constrained environments.

From Signal Space To STP-CS

Under the assumption that a finite signal with different sampling lengths or different sampling frequencies is considered as equivalent, the signal space is considered as the quotient space of $\m...

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The article presents a novel approach to conceptualizing signal space through the lens of quotient spaces, which can lead to innovative insights in compressed sensing techniques. Its rigorous exploration of topological structures and the application to sensing matrix construction adds to its relevance. The combination of theoretical foundations and practical applications enhances its potential impact on the field.

Gracefulness of two nested cycles: a first approach

It is known that if a plane graph is graceful (resp. near-graceful), then its semidual is conservative (resp. near-conservative). In this work we prove that the semidual of a plane graph of size $...

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The article addresses specific properties of planar graphs related to gracefulness, which is a relatively niche topic. While it presents useful mathematical proofs, the applicability of its results outside of theoretical graph theory may be limited. However, it does contribute towards understanding the attributes of semidual graphs, which could foster further inquiry in this area. The methodological rigor appears sound, yet the novelty of the findings may not be substantial enough to draw broad interest across various related fields.

Gravitational entanglement witness through Einstein ring image

We investigate the interplay between quantum theory and gravity by exploring gravitational lensing and Einstein ring images in a weak gravitational field induced by a mass source in spatial quantum su...

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The article poses a novel exploration of the intersection between quantum mechanics and general relativity, specifically through the lens of gravitational lensing and Einstein ring images. Its emphasis on visualizing gravitational entanglement offers a fresh avenue of research that can significantly impact both quantum gravity studies and experimental methodologies in cosmology. The methodological rigor followed in analyzing distinct gravitational models adds robustness, while the introduction of specific metrics such as the which-path information indicator helps to bridge theory with observable phenomena.

Asymptotics in Wasserstein Distance for Empirical Measures of Markov Processes

In this paper we introduce some recent progresses on the convergence rate in Wasserstein distance for empirical measures of Markov processes. For diffusion processes on compact manifolds possibly with...

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This article presents significant advancements in understanding the convergence of empirical measures for Markov processes, particularly in terms of their Wasserstein distance. Its methodological rigor, particularly the establishment of a precise convergence rate and the use of renormalization limits, is noteworthy. Such findings can influence a range of applications in stochastic processes and probability theory, marking the work as both impactful and relevant.

Eliminating Ratio Bias for Gradient-based Simulated Parameter Estimation

This article addresses the challenge of parameter calibration in stochastic models where the likelihood function is not analytically available. We propose a gradient-based simulated parameter estimati...

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The article introduces a novel gradient-based simulated parameter estimation framework that addresses a significant gap in the calibration of stochastic models. Its theoretical contributions regarding strong convergence and asymptotic normality provide a solid foundation for further research, while its practical applicability to neural networks indicates widespread relevance across fields. The proposed algorithm's ability to optimize accuracy and computational efficiency strengthens its impact.

Revisiting the activity-rotation relation for evolved stars

The magnetic dynamo mechanism of giant stars remains an open question, which can be explored by investigating their activity-rotation relations with multiple proxies. By using the data from the LAMOST...

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The article presents a comprehensive investigation into the activity-rotation relations of evolved stars, employing a variety of data which enhances methodological rigor. The findings suggest fundamental understanding of stellar activity that bridges gaps between different stellar classifications, indicating significance and novelty in its contributions to current astrophysical theories on magnetic dynamo mechanisms in stars. This could yield substantial implications for stellar evolution models and our understanding of stellar populations.

HapKnob -- A Motorized Shape-changing Haptic Knob Interface

The absence of physical interfaces creates challenges when interacting with touchscreen technology. This study aims to investigate an innovative haptic solution for interacting with graphical user int...

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The article presents a novel motorized haptic knob interface that addresses a significant challenge in touchscreen technology interaction. The innovative design of HapKnob, with its multiple shape configurations and force feedback capabilities, offers a fresh approach that pushes the boundaries of user interface design. Its potential applicability in environments with limited visual feedback increases its relevance and could inspire further research into multi-sensory interfaces.

MemoryFormer: Minimize Transformer Computation by Removing Fully-Connected Layers

In order to reduce the computational complexity of large language models, great efforts have been made to to improve the efficiency of transformer models such as linear attention and flash-attention. ...

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MemoryFormer presents a novel architectural design that significantly reduces computational complexity without sacrificing performance. Its innovative approach to replacing fully-connected layers is highly relevant in the context of large language models, where efficiency is paramount. The empirical validation across various benchmarks strengthens its impact, suggesting a solid methodological foundation and potential for widespread adoption.

BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for compa...

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This article tackles critical issues regarding the quality and usability of AI benchmarks, which are fundamental for the reliable assessment of AI models in important applications. Its comprehensive framework and practical checklist for benchmark developers represent significant advancements in the field. The implications for standardization and quality assurance are profound, which can lead to improved practices across many areas involved in AI.

Data Watermarking for Sequential Recommender Systems

In the era of large foundation models, data has become a crucial component for building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyrig...

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This article addresses a novel approach to data watermarking specifically for sequential recommender systems, which is highly relevant given the increasing concern for data copyright in AI applications. The methodology presented is rigorous, with systematic definitions of the watermarking problems and extensive experimental validation. Furthermore, its practical applicability to high-performance AI systems makes this work stand out.

Angle measurement method of electronic speckle interferometry based on Michelson interferometer

{This paper proposes an angle measurement method based on Electronic Speckle Pattern Interferometry (ESPI) using a Michelson interferometer. By leveraging different principles within the same device, ...

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The article presents a novel angle measurement method that effectively combines Electronic Speckle Pattern Interferometry with a Michelson interferometer, showcasing innovation in measurement accuracy and robustness. The rigorous experimental validation adds to its methodological strength, while its potential for real-time applications further solidifies its relevance in precision measurement technologies.

Empirical color correction to MIST and PARSEC isochrones on Gaia BR-RP and G-RP with benchmark open clusters

Recent literature reports a color deviation between observed Gaia color-magnitude diagrams (CMDs) and theoretical model isochrone predictions, particularly in the very low-mass regime. To assess its i...

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This article presents a significant advancement in the accuracy of isochrone fitting by addressing observed discrepancies in color-magnitude diagrams, particularly for low-mass stars. The empirical color correction functions developed have direct implications for age estimation in stellar clusters, enhancing the consistency of results across different methods. The methodological rigor shown in the benchmarking and application across multiple clusters adds robustness to the findings, making it a valuable contribution to the field of astrophysics.

Training Bilingual LMs with Data Constraints in the Targeted Language

Large language models are trained on massive scrapes of the web, as required by current scaling laws. Most progress is made for English, given its abundance of high-quality pretraining data. For most ...

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This article addresses a significant challenge in the field of natural language processing (NLP) regarding the performance disparity between languages due to data availability. It innovatively explores the use of auxiliary languages to enhance model performance in low-data scenarios, indicating strong methodological rigor and potential for real-world applications. The findings could influence future research on multilingual models and data utilization strategies.

Disco Intelligent Omni-Surfaces: 360-degree Fully-Passive Jamming Attacks

Intelligent omni-surfaces (IOSs) with 360-degree electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk ...

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The article introduces a novel concept of a Disco Intelligent Omni-Surface-based fully-passive jamming attack, highlighting its potential risks for wireless systems and security. The major strengths of this work include its methodological innovation in constructing passively jamming systems and its robust analysis of the jamming effect on channel performance. The applicability to physical layer security makes it particularly relevant in current discussions about secure communication. However, further exploration on practical implementations and real-world scenarios could enhance its overall impact.