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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!

The existence and stability of viscosity solutions to perturbed contact Hamilton-Jacobi equations

We consider a contact Hamiltonian $H(x,p,u)$ with certain dependence on the contact variable $u$ . If $u_{-}$ is a viscosity solution of the contact Hamilton-Jacobi equation \...

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This article addresses an important aspect of viscosity solutions in the context of contact Hamilton-Jacobi equations, which are central to optimal control theory and differential equations. The novelty of the study lies in its treatment of perturbations and their effects on stability and existence of solutions. This makes the work impactful for both theoretical advancements and practical applications. The proofs of existence and stability are methodologically rigorous, adding credibility to the findings. However, the specificity of the topic may limit broader applicability outside of specialized mathematics fields.

GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction

The surging demand for batteries requires advanced battery management systems, where battery capacity modelling is a key functionality. In this paper, we aim to achieve accurate battery capacity predi...

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The article presents a novel approach (GiNet) that enhances battery capacity predictions significantly, demonstrating both methodological rigor and impressive accuracy improvements over existing models. Its ability to integrate sequential and contextual learning represents a substantial advancement in battery management systems, making it highly relevant for both immediate applications and future research in this domain.

A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field

In the medical field, accurate diagnosis of lung cancer is crucial for treatment. Traditional manual analysis methods have significant limitations in terms of accuracy and efficiency. To address this ...

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This article presents a novel application of deep learning through a pre-trained MobileNetV2 model specifically designed for lung tumor classification. The high accuracy of 99.6% and improvements in feature extraction indicate significant methodological rigor and potential for clinical application. Its discussion of market implications points to broader economic relevance, enhancing its impact.

IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression Segmentation

3D Referring Expression Segmentation (3D-RES) aims to segment point cloud scenes based on a given expression. However, existing 3D-RES approaches face two major challenges: feature ambiguity and inten...

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The IPDN presents significant innovations in addressing critical challenges in 3D Referring Expression Segmentation, particularly through the integration of a Multi-view Semantic Embedding module and a Prompt-Aware Decoder. These contributions exhibit strong methodological rigor and are grounded in a rich experimental framework that demonstrates a clear advancement over existing techniques. The potential for enhancing model interpretability and performance in real-time applications marks this work as impactful, especially as 3D vision continues to grow in relevance for various sectors.

ByteFS: System Support for (CXL-based) Memory-Semantic Solid-State Drives

Unlike non-volatile memory that resides on the processor memory bus, memory-semantic solid-state drives (SSDs) support both byte and block access granularity via PCIe or CXL interconnects. They provid...

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The article presents a novel file system, ByteFS, specifically designed to leverage the unique characteristics of memory-semantic SSDs, an emerging technology in storage systems. Its focus on dual byte/block access granularity is significant as it addresses performance and efficiency challenges in modern storage. The rigorous implementation and evaluation demonstrate a substantial performance improvement over existing systems, indicating robustness and applicability. Moreover, the implications for software system design could influence future research in this area, ensuring its relevance.

On a reaction-diffusion virus model with general boundary conditions in heterogeneous environments

To describe the propagation of West Nile virus and/or Zika virus, in this paper, we propose and study a time-periodic reaction-diffusion model with general boundary conditions in heterogeneous environ...

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The article proposes an innovative reaction-diffusion model tailored to understanding the epidemiology of significant viruses like West Nile and Zika. The mathematical rigor displayed in establishing existence and uniqueness of solutions under general boundary conditions adds robustness to the work. This model could influence future research on disease spread in heterogeneous environments, making it particularly relevant in the context of modelling disease dynamics amidst environmental changes.

PICOSEC Micromegas Precise-timing Detectors: Development towards Large-Area and Integration

PICOSEC Micromegas (MM) is a precise timing gaseous detector based on a Cherenkov radiator coupled with a semi-transparent photocathode and an MM amplifying structure. The detector conceprt was succes...

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The article presents a significant advancement in precise timing detectors, showcasing innovative development towards scalable large-area designs with demonstrated performance in time resolution. The research addresses critical needs in particle physics, especially concerning high-energy physics experiments. The methodological rigour in experimental validation further strengthens its impact in the field.

On the ascent of almost and quasi-atomicity to monoid semidomains

A commutative monoid is atomic if every non-invertible element factors into irreducibles (also called atoms), while an integral (semi)domain is atomic if its multiplicative monoid is atomic. Notions w...

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This article investigates the ascent properties of almost atomicity and quasi-atomicity in commutative structures, contributing novel insights into their relationship with polynomial extensions and monoid domains. The clarity of the conditions established and the explicit examples provided enhance the methodological rigor and applicability. Furthermore, it addresses ongoing discussions in the field and builds upon prior research, showing a significant advancement in understanding these concepts. The depth and specificity of the results are likely to inspire further exploration in related areas.

Error Floor of Spinal Codes under ML Decoding

Spinal codes is a new family of capacity-achieving rateless codes that has been shown to achieve better rate performance compared to Raptor codes, Strider codes, and rateless Low-Density Parity-Check ...

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The article presents a thorough analysis of Spinal codes, highlighting their performance limitations and providing significant new insights into the error floor phenomenon. This level of understanding is crucial for advancing the design and application of these codes in practical scenarios. The combination of analytical expressions and numerical results showcases methodological rigor and offers valuable contributions to the field of coding theory, especially in identifying SNR thresholds which could influence future research directions in error correction and communication systems.

Intelligent Sailing Model for Open Sea Navigation

Autonomous vessels potentially enhance safety and reliability of seaborne trade. To facilitate the development of autonomous vessels, high-fidelity simulations are required to model realistic interact...

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The article introduces the first intelligent sailing model (ISM) specifically designed to simulate rule-compliant vessels, filling a significant gap in maritime traffic simulation for autonomous vessels. Its rigorous evaluation across numerous critical traffic scenarios demonstrates methodological robustness and practical applicability, which are crucial for future research in autonomous navigation. The model's potential to standardize simulations makes it highly impactful for further studies and developments in the field.

TreeKV: Smooth Key-Value Cache Compression with Tree Structures

Efficient key-value (KV) cache compression is critical for scaling transformer-based Large Language Models (LLMs) in long sequences and resource-limited settings. Existing methods evict tokens based o...

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TreeKV shows significant novelty in addressing key challenges in caching for Large Language Models, which is crucial for the efficiency and scalability of these models. The method's innovative use of a tree structure and its performance in both generation and prefilling tasks displays methodological rigor and broad applicability. The results demonstrate clear improvements over existing methods, indicating its potential to substantially influence future research on cache compression approaches in LLMs.

Anisotropic diffusion of high-energy cosmic rays in magnetohydrodynamic turbulence

The origin of cosmic rays (CRs) and how they propagate remain unclear. Studying the propagation of CRs in magnetohydrodynamic (MHD) turbulence can help to comprehend many open issues related to CR ori...

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The article presents a novel approach to understanding cosmic ray propagation through the lens of magnetohydrodynamic turbulence, utilizing advanced numerical simulations. This combination of factors addresses significant gaps in the comprehension of cosmic ray origins and diffusion mechanisms. Its methodological rigor is enhanced by the consideration of multiple parameters and outcomes, indicating robust findings relevant to complex astrophysical processes. The implications for both theoretical and observational astrophysics establish it as a foundational piece for future research in related areas.

SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs

The increasing threat of SMS spam, driven by evolving adversarial techniques and concept drift, calls for more robust and adaptive detection methods. In this paper, we evaluate the potential of large ...

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This paper addresses a crucial and timely issue in cybersecurity—SMS spam detection—by leveraging cutting-edge large language models (LLMs). The comparison of various methodologies (zero-shot, few-shot, fine-tuning) is methodologically rigorous and provides clear insights into optimal approaches. The high identified accuracy (98.6%) and attention to adversarial resilience and concept drift exhibit both novelty and applicability. Its findings could drive further exploration into using LLMs across various fields, enhancing detection systems in real-world scenarios.

Harnessing the Power of Vibration Motors to Develop Miniature Untethered Robotic Fishes

Miniature underwater robots play a crucial role in the exploration and development of marine resources, particularly in confined spaces and high-pressure deep-sea environments. This study presents the...

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The article presents a novel approach to designing miniature untethered robotic fishes using ERM vibration motors, which enhances their maneuverability and minimization of mechanical components. The use of bio-inspired mechanisms adds an innovative dimension, and the experimental results suggest practical applications. However, further exploration of real-world testing and deployments would strengthen its impact.

Collective inference of the truth of propositions from crowd probability judgments

Every day, we judge the probability of propositions. When we communicate graded confidence (e.g. "I am 90% sure"), we enable others to gauge how much weight to attach to our judgment. Ideall...

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This article presents a novel approach to enhancing collective inference through the integration of cognitive science and crowdsourcing methods. The methodological rigor, evidenced by a large online experiment and the introduction of advanced algorithms for calibration, creates a strong foundation for this research. The potential application of these findings to combat disinformation in the digital age and improve decision-making in social networks significantly enhances its relevance.

CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving

In autonomous driving, traditional Computer Vision (CV) agents often struggle in unfamiliar situations due to biases in the training data. Deep Reinforcement Learning (DRL) agents address this by lear...

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The article presents a novel approach that integrates Curriculum Learning with Deep Reinforcement Learning for enhancing the adaptability and safety of autonomous driving agents. The use of a Proximal Policy Optimization agent in combination with a Variational Autoencoder represents a significant methodological advance, addressing key challenges like generalization and agent transparency. This innovative combination has potential implications for improving the robustness of autonomous systems in unpredictable environments.

Theoretical study of the Spectroscopic measurements of Kerr non-linear resonators with four-body interaction

Quantum annealing provides a promising way to solve combinational optimization problems where the solutions correspond to the ground state of the Ising Hamiltonian. We can implement quantum annealing ...

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This article presents a novel theoretical approach to exploring spectroscopic measurements in Kerr non-linear resonators, particularly with four-body interactions that are not widely studied. The integration of these interactions into quantum annealing presents a significant advancement in the field. The methodological rigor is underscored by both analytical and numerical validations, enhancing the credibility of the findings. Moreover, the implications for practical quantum annealing systems could inspire future experimental investigations, making it a potentially impactful study.

On the crossing profile of rectilinear drawings of $K_n$

We introduce the \textit{crossing profile} of a drawing of a graph. This is a sequence of integers whose $(k+1)^{\text{th}}$ entry counts the number of edges in the drawing which are involved ...

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This article presents a novel concept—the crossing profile of a graph drawing—which extends existing knowledge of edge crossings in graph theory. The investigation into complete graphs ($K_n$) adds depth to the understanding of geometric properties in graph drawings. The results concerning the asymptotic behavior of crossing profiles also provide potentially new directions for research in combinatorial optimization and discrete geometry. Methodological rigor is assured through elementary proofs and geometric constructions, appealing to a wide audience within this field.

Multiple Populations of the Large Magellanic Cloud Globular Cluster NGC 2257: No Major Environmental Effect on the Formation of Multiple Populations of the Old Globular Clusters in Large Magellanic Cloud

How the environment of the host galaxy affects the formation of multiple populations (MPs) in globular clusters (GCs) is one of the outstanding questions in the near-field cosmology. To understand the...

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This article contributes significant insights into the formation of multiple populations in globular clusters, particularly within the Large Magellanic Cloud, providing important data that suggests environmental factors may not strongly influence this process. Its methodological rigor (including a careful analysis of photometric data and a collaborative approach) enhances its validity and impacts its relevance. The findings also broaden understanding of galactic evolution and the characteristics of globular clusters, which could influence future research in stellar evolution and galaxy formation.

The Rapid ASKAP Continuum Survey (RACS) VI: The RACS-high 1655.5 MHz images and catalogue

We have conducted a widefield, wideband, snapshot survey using the Australian SKA Pathfinder (ASKAP) referred to as the Rapid ASKAP Continuum Survey (RACS). RACS covers $\approx$ 90% of the sk...

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This article presents a significant advancement in radio astronomy through the release of a comprehensive catalogue of radio sources derived from wideband observations. The large-scale coverage (90% of the sky) and significant number of sources (over 2.6 million) contribute to its high impact in the field. The careful calibration, reliability assessment, and accessibility of data enhance its methodological rigor and applicability for future research, particularly in cosmology and astrophysics.