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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!

RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models

In recent years, tremendous success has been witnessed in Retrieval-Augmented Generation (RAG), widely used to enhance Large Language Models (LLMs) in domain-specific, knowledge-intensive, and privacy...

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The article presents a novel black-box watermarking approach specifically designed for Retrieval-Augmented Generation systems, addressing a critical issue of Intellectual Property protection in Large Language Models. Its methodological rigor and effectiveness against various attacks underscore its relevance, making it significantly impactful for the field. The incorporation of multiple LLMs for watermark generation adds originality and robustness that could inspire further advancements in digital rights management for AI applications.

Deriving Coding-Specific Sub-Models from LLMs using Resource-Efficient Pruning

Large Language Models (LLMs) have demonstrated their exceptional performance in various complex code generation tasks. However, their broader adoption is limited by significant computational demands a...

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The article presents a novel approach to derive coding-specific sub-models from LLMs using unstructured pruning techniques, addressing the critical issue of resource limitations in deploying LLMs for code generation. Its focus on domain-specific calibration datasets and solid analytical evidence adds to the methodological rigor and expands the understanding of LLM interactions with various programming languages. The implications for accessibility and efficiency in machine learning applications in software development are also significant, making this work highly relevant for future research.

Online Prompt and Solver Selection for Program Synthesis

Large Language Models (LLMs) demonstrate impressive capabilities in the domain of program synthesis. This level of performance is not, however, universal across all tasks, all LLMs and all prompting s...

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The article tackles a significant problem in program synthesis, particularly the selection of the appropriate solver among various LLMs and symbolic solvers. Its use of an online learning approach via a multi-armed bandit algorithm is a notable contribution, providing a method to optimize solver selection dynamically. The experimental results demonstrate substantial improvements over existing methods, indicating a robust methodology and applicability in real-world scenarios. The novelty lies in integrating LLMs with traditional solvers, sharpening focus on a practical challenge in AI and software development.

Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions

Semantic segmentation for autonomous driving is an even more challenging task when faced with adverse driving conditions. Standard models trained on data recorded under ideal conditions show a deterio...

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The article presents a novel approach to semantic segmentation in autonomous driving, specifically targeting challenges posed by adverse weather conditions. This area is critically relevant given the increasing deployment of autonomous vehicles and the safety concerns associated with them. The proposed Progressive Semantic Segmentation (PSS) method shows methodological rigor and innovation by tackling catastrophic forgetting and enhancing model robustness across varying domains. Its extensive evaluation on different datasets demonstrates practical applicability and potential for real-world impact, thus scoring high on relevance for future research.

A Generalization of Seifert Geometry Based on the Siegel Upper Half-Space

Parallel to $\widetilde{\mathrm{SL}(2,\mathbb{R})}$ -geometry fibering over the hyperbolic plane, we construct a geometry fibering over the Siegel upper half-space $\mathrm{Sp}(2n,\mathbb{R...

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This article presents a significant advancement in the understanding of Seifert geometry by generalizing it to cases involving the Siegel upper half-space. The construction of a new geometric framework and the provision of a volume formula for these manifolds demonstrate novelty and depth in the mathematical rigor. The connection to previous geometries and the potential applications to manifolds make it impactful. However, the specific applicability of the findings might be limited to a more niche area of mathematics, dampening its broader influence.

Optimized Sampling for Non-Line-of-Sight Imaging Using Modified Fast Fourier Transforms

Non-line-of-Sight (NLOS) imaging systems collect light at a diffuse relay surface and input this measurement into computational algorithms that output a 3D volumetric reconstruction. These algorithms ...

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The article presents a significant methodological innovation by applying Non-Uniform Fast Fourier Transform (NUFFT) techniques to Non-Line-of-Sight imaging. This approach addresses critical challenges such as spatially non-uniform sampling and data readout speeds, which are essential for real-world applications. The preservation of computational efficiency while enhancing reconstruction quality represents a notable advancement in the field, suggesting the potential for substantial impact on both academic research and practical implementations in imaging systems.

Joint Communications and Sensing for 6G Satellite Networks: Use Cases and Challenges

Satellite Networks (SN) have traditionally been instrumental in providing two key services: communications and sensing. Communications satellites enable global connectivity, while sensing satellites f...

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The paper presents an innovative approach to integrate communications and sensing in satellite networks, which is a key advancement needed for the development of 6G technologies. The exploration of Joint Communications and Sensing (JCAS) is timely and essential given the increasing demand for satellite services. The identification of specific challenges also opens avenues for future research, showing methodological rigor and relevance. Its potential to optimize resource use reflects a significant impact on both theoretical understanding and practical application in satellite networks.

Scaffold-SLAM: Structured 3D Gaussians for Simultaneous Localization and Photorealistic Mapping

3D Gaussian Splatting (3DGS) has recently revolutionized novel view synthesis in the Simultaneous Localization and Mapping (SLAM). However, existing SLAM methods utilizing 3DGS have failed to provide ...

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The article presents an innovative approach to improving SLAM technology through the introduction of scaffold mechanisms and advanced Gaussian modeling. The methodology is robust, as it combines effective techniques to enhance rendering quality for multiple camera types, addressing a significant gap in existing research. The performance improvements demonstrated by quantitative metrics (like PSNR) provide solid evidence of its impact, indicating high applicability in real-world scenarios.

Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion

Late gadolinium enhancement MRI (LGE MRI) is the gold standard for the detection of myocardial scars for post myocardial infarction (MI). LGE MRI requires the injection of a contrast agent, which carr...

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The article introduces a novel approach that addresses significant limitations associated with the traditional use of contrast agents in MRI scans, making it highly relevant for clinical applications. The methodological rigor demonstrated through the combination of motion and texture analysis to achieve segmentation is impressive and innovative. It holds potential for broad adoption in both clinical practice and future research within cardiovascular imaging.

Automated Analysis of Logically Constrained Rewrite Systems using crest

We present crest, a tool for automatically proving (non-)confluence and termination of logically constrained rewrite systems. We compare crest to other tools for logically constrained rewriting. Exten...

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The article introduces 'crest', a novel tool designed to automate the analysis of logically constrained rewrite systems. Its focus on proving non-confluence and termination addresses significant challenges in this area, potentially advancing both theoretical understanding and practical applications. The comprehensive experimental validation presents a strong methodological rigor, enhancing its credibility and relevance for researchers and practitioners in computer science. However, further clarification on the specific methodologies and comparisons with existing tools could strengthen its overall impact.

Is Your Autonomous Vehicle Safe? Understanding the Threat of Electromagnetic Signal Injection Attacks on Traffic Scene Perception

Autonomous vehicles rely on camera-based perception systems to comprehend their driving environment and make crucial decisions, thereby ensuring vehicles to steer safely. However, a significant threat...

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The article addresses a critical security threat in autonomous vehicles, a rapidly advancing field. Its focus on Electromagnetic Signal Injection Attacks (ESIA) is novel, as it highlights an often-overlooked aspect of AI perception vulnerabilities. The development of a simulation method for ESIA and the creation of a simulated attack dataset add significant methodological rigor to the study. This work’s potential to inspire further research on AI robustness and security in autonomous systems and its applicability to real-world challenges support a high relevance score.

FOCUS: Towards Universal Foreground Segmentation

Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to...

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The article presents a significant advancement by introducing a common framework for foreground segmentation, addressing a gap in previous methods that focused on individual tasks. Its innovative approach, which integrates multi-scale semantic networks and contrastive learning, showcases methodological rigor. The comprehensive experimental validation across diverse datasets further enhances its credibility and applicability, making the findings relevant for both theory and practice.

Almost Optimal Synthesis of Reversible Function in Qudit Model

Quantum oracles are widely adopted in problems, like query oracle in Grover's algorithm, cipher in quantum cryptanalytic and data encoder in quantum machine learning. Notably, the bit-flip oracle,...

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The article presents significant advancements in the synthesis of reversible functions within the qudit model, a largely underexplored area compared to qubit systems. The proposed methods demonstrate asymptotic optimality, showcasing both novelty and practical applicability in quantum computing. The rigorous approach to both algorithm development and gate synthesis positions this work as a leading contribution that could inspire further research and applications in quantum computation.

Automated external cervical resorption segmentation in cone-beam CT using local texture features

External cervical resorption (ECR) is a resorptive process affecting teeth. While in some patients, active resorption ceases and gets replaced by osseous tissue, in other cases, the resorption progres...

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The article presents a novel automated approach for segmenting external cervical resorption in cone-beam CT images. Its focus on combining local texture features and binary classification is a significant advancement over traditional methods, promising improved accuracy and efficiency in diagnosis. The evaluation on longitudinal datasets enhances its methodological rigor, suggesting high applicability in clinical settings, alongside its potential for developing biomarkers for treatment decision-making.

Nonextensive aspects of gluon distribution and the implications to QCD phenomenology

This study presents new insights into gluon transverse momentum distributions through nonextensive statistical mechanics, addressing their implications for QCD phenomenology. The saturation physics an...

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The article offers a novel framework by applying nonextensive statistical mechanics to gluon distributions, which is a fresh angle within quantum chromodynamics (QCD). The methodology appears sound, addressing significant implications for various collision systems and enhancing our understanding of saturation physics. Such insights could stimulate further research in this area, making it impactful for the field.

Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs

This paper presents an approach for generating high-quality, same-language subtitles for Estonian TV content. We fine-tune the Whisper model on human-generated Estonian subtitles and enhance it with i...

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The article presents a novel approach to improving subtitle generation using state-of-the-art techniques like semi-supervised learning and large language models. The combination of pseudo-labeling and LLM-based post-editing stands out, particularly for a language like Estonian, which may have limited resources compared to more commonly spoken languages. The methodology appears rigorous, and the findings suggest practical implications for real-time subtitle applications, increasing its relevance to both industry and academia.

Principles and Metrics of Extreme Learning Machines Using a Highly Nonlinear Fiber

Optical computing offers potential for ultra high-speed and low latency computation by leveraging the intrinsic properties of light. Here, we explore the use of highly nonlinear optical fibers (HNLFs)...

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The paper presents a novel approach to optical computing using Extreme Learning Machines in highly nonlinear optical fibers, introducing new metrics and providing experimental evidence. The investigation tackles fundamental aspects of dimensionality and system characteristics, which is critical for enhancing computational efficiency in optical systems. Its implications for improving accuracy and performance in classification tasks, particularly with the MNIST dataset, indicate significant practical applications. However, the reliance on a single dataset and the specific context of optical systems slightly limit its broader applicability.

The Intraday Bitcoin Response to Tether Minting and Burning Events: Asymmetry, Investor Sentiment, And "Whale Alerts" On Twitter

Tether Limited has the sole authority to create (mint) and destroy (burn) Tether stablecoins (USDT). This paper investigates Bitcoin's response to USDT supply change events between 2014 and 2021 a...

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The research addresses a specific dynamic relationship between Bitcoin and Tether minting/burning events, providing both empirical analysis and theoretical implications. The identification of the asymmetry in Bitcoin's price responses is novel and contributes to the understanding of cryptocurrency market behaviors. The methodological rigor appears solid as it encompasses a variety of temporal windows and contextualizes the findings with investor sentiment and social media indicators. This highlights both the economic and social facets of cryptocurrency trading, making it highly relevant.

Near-threshold dipole strength in {^{10}}Be with isoscalar character

Isoscalar dipole transitions are a distinctive fingerprint of cluster structures. A {1^-} resonance at 7.27(10) MeV, located just below the α-emission threshold, has been observed in the deuteron inel...

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The article presents novel findings on the dipole strength in {^{10}}Be, identifying a significant resonance that enhances understanding of cluster structures in nuclear physics. The combination of experimental and theoretical methods enriches the study's methodological rigor. It reveals important insights into the α cluster structure, which is crucial for nuclear structure physics.

Implementation of phase gates using single photons

Quantum computing algorithms using the quantum Fourier transform require repeated use of a phase shift gate. In the case of qubits using optical photons for operation, this gate can be implemented usi...

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The article addresses a significant aspect of quantum computing by focusing on the implementation of phase gates with single photons, which is essential for advancing quantum algorithms. It presents a novel approach that may have implications for the practical realization of quantum systems. The methodology appears rigorous, but further detail on experimental setups could reinforce its applicability. Additionally, the focus on optical photons adds a layer of complexity and potential for innovation in quantum information processing.