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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!

Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search

Mixed-integer linear programs (MILPs) are extensively used to model practical problems such as planning and scheduling. A prominent method for solving MILPs is large neighborhood search (LNS), which i...

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This article presents a significant advancement in the field of Mixed-Integer Linear Programming (MILP) by integrating machine learning with large neighborhood search (LNS) methods. The introduction of the two-layer LNS approach represents a novel strategy for enhancing efficiency in solving large-scale MILPs, which is a common challenge in optimization. The rigorous computational experiments that demonstrate notable performance gains add to the methodological robustness of the research, highlighting its practical applicability and relevance to both academia and industry.

Quasi-normal modes of slowly-rotating Johannsen black holes

The detection of gravitational waves with ground-based laser interferometers has opened a new window to test and constrain General Relativity (GR) in the strong, dynamical, and non-linear regime. In t...

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This article presents a significant study on the quasi-normal modes of Johannsen black holes and relates it to gravitational wave observations, particularly GW170104. Its methodological rigor in deriving fitting formulas and the constraints placed on deformation parameters offers novel insights into black hole physics, potentially influencing future research in gravitational wave astronomy.

Diffraction patterns in attosecond photoionization time delay

Upon absorbing a photon, the ionized electron sails through the target force field in attoseconds to reach free space. This navigation probes details of the potential landscape that get imprinted into...

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The article presents a novel approach to studying diffraction patterns in the context of photoionization time delays, combining advanced theoretical frameworks with recent experimental capabilities. Its focus on attosecond temporal resolution highlights significant methodological rigor and relevance to ultrafast science. The implications for understanding molecular interactions at a fundamental level offer unique insight into quantum processes, enhancing its potential impact on future research in both fundamental and applied physics.

Particle-in-cell methods in edge plasma physics: the PICLS code

Over the past decades, multiple gyrokinetic codes have shown to be able to simulate turbulence and associated transport in the core of Tokamak devices. However, their application to the edge and scrap...

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The article presents a significant advancement in the simulation of edge plasma physics through the development of the PICLS code, which addresses critical challenges in the study of the scrape-off layer (SOL) in Tokamaks. Its methodological rigor is showcased by the extension into three dimensions, which enhances its applicability and reliability. The integration of full-f gyrokinetic models and comparisons with existing codes further validates its relevance. However, the degree of novelty is potentially limited as it builds upon existing methodologies rather than introducing fundamentally new concepts.

Generalized pentagram maps via Q-nets and refactorization mappings

We introduce a family of generalizations of the pentagram maps related to QQ-nets. A specific example is considered, and we find the map can be treated as a refactorization mapping in the Poi...

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This article presents a novel approach to generalizing pentagram maps and connects it to the broader context of $Q$-nets and Poisson-Lie groups, which contributes significantly to mathematical physics and integrable systems. The robustness of the methodological framework, along with the introduction of refactorization mappings, increases its utility. The treatment of spectral parameters and invariant Poisson brackets suggests a strong mathematical rigor. However, more practical applications and examples would enhance its relevance further.

Model-Editing-Based Jailbreak against Safety-aligned Large Language Models

Large Language Models (LLMs) have transformed numerous fields by enabling advanced natural language interactions but remain susceptible to critical vulnerabilities, particularly jailbreak attacks. Cur...

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The article introduces a novel approach (Targeted Model Editing) that significantly enhances the stealth and efficacy of jailbreak attacks on LLMs. It addresses a critical aspect of LLM security, which is the vulnerability to jailbreak techniques while maintaining the model's functionalities. The methodological rigor is evident in the detailed analysis and implementation results, making this work a key contribution to the field. Additionally, the implications of this research may prompt future considerations for stronger safety measures within AI models, underscoring its potential impact.

GN-FR:Generalizable Neural Radiance Fields for Flare Removal

Flare, an optical phenomenon resulting from unwanted scattering and reflections within a lens system, presents a significant challenge in imaging. The diverse patterns of flares, such as halos, streak...

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The GN-FR article addresses a critical challenge in imaging through an innovative multi-view approach to flare removal, which is new to its field. The use of unsupervised learning in a Neural Radiance Fields context indicates strong methodological rigor and novelty. By providing a new dataset and methodology tailored for complex flare patterns, it significantly contributes to future advancements in image processing. This work is likely to inspire more research into related imaging challenges, particularly those involving artifacts caused by lens effects.

Adaptive2^2: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling

Advertising systems often face the multi-domain challenge, where data distributions vary significantly across scenarios. Existing domain adaptation methods primarily focus on building domain-adaptive ...

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Adaptive$^2$ presents a novel approach to domain adaptation in advertising systems, addressing a crucial gap in current methodologies by emphasizing adaptive domain mining rather than relying on hand-crafted domains. Its introduction of a self-supervised learning mechanism and a shared&specific network architecture demonstrates methodological rigor and innovation. The extensive validation through public benchmarks coupled with a real-world application indicates both its efficacy and practical value, making it a highly relevant piece of research that could inspire significant advancements in this field.

SAFIRE: Segment Any Forged Image Region

Most techniques approach the problem of image forgery localization as a binary segmentation task, training neural networks to label original areas as 0 and forged areas as 1. In contrast, we tackle th...

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SAFIRE presents a novel perspective on image forgery detection by shifting from binary segmentation to multi-source partitioning. This methodological innovation enhances the robustness of forgery localization tasks and could inspire further exploration of source-based approaches in image analysis. Its ability to focus on uniform characteristics rather than solely on forgery signatures suggests potential for broader applications in image forensics and security. The experimental validation of superior performance reinforces its relevance within the field.

DocSum: Domain-Adaptive Pre-training for Document Abstractive Summarization

Abstractive summarization has made significant strides in condensing and rephrasing large volumes of text into coherent summaries. However, summarizing administrative documents presents unique challen...

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DocSum addresses a critical gap in the field of document abstractive summarization by focusing on administrative documents, which present unique challenges often overlooked by existing models. Its methodological rigor in using domain-adaptive pre-training and fine-tuning with question-answer pairs greatly enhances its applicability across various sectors. This novel approach shows significant potential to improve practical outcomes, making it highly relevant and impactful for future research and applications in document summarization.

Semantic Scene Completion Based 3D Traversability Estimation for Off-Road Terrains

Off-road environments present significant challenges for autonomous ground vehicles due to the absence of structured roads and the presence of complex obstacles, such as uneven terrain, vegetation, an...

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The article introduces a novel approach (ORDformer) that addresses a significant challenge in autonomous vehicle navigation in off-road scenarios, combining multiple data modalities for improved performance. The methodological rigor is demonstrated through comprehensive experimentation and the introduction of a well-annotated dataset (RELLIS-OCC), which could facilitate future research. The potential for real-world applications in robotics and automotive industries enhances its relevance.

Magneto: Combining Small and Large Language Models for Schema Matching

Recent advances in language models opened new opportunities to address complex schema matching tasks. Schema matching approaches have been proposed that demonstrate the usefulness of language models, ...

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The article presents a novel approach for schema matching by integrating the strengths of small and large language models, addressing their individual limitations. It introduces a self-supervised method for data generation and establishes a new benchmark for testing schema matching algorithms, enhancing its impact and applicability. Empirical validation across diverse datasets confirms its efficacy, making it likely to inspire further research and development in language model applications.

Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification

Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet f...

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The paper presents a novel architecture (GNNMoE) that addresses critical challenges in graph neural networks, specifically regarding heterophilous data and scalability. Its conceptual innovations, such as the integration of fine-grained message-passing with a mixture-of-experts framework, represent a significant advancement in the field. The thorough experimental validation across various graph types strengthens the robustness of the findings. The focus on adaptability and computational efficiency also denotes a pragmatic approach relevant for real-world applications, enhancing future research prospects.

Active galactic nucleus time-variability analysis and its caveats

In this study, we demonstrate some of the caveats in common statistical methods used for analysing astronomical variability timescales. We consider these issues specifically in the context of active g...

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The article presents novel insights into the statistical analysis of AGN variability, identifying previously overlooked issues that could significantly impact the interpretation of light-curve data. The combination of thorough literature review and original simulations enhances its methodological rigor. This work is likely to lead to improvements in future analyses of astronomical variability, marking it as an important contribution to the field.

Long-term radio variability of active galactic nuclei at 37 GHz

We present the results of analysing the long-term radio variability of active galactic nuclei at 37 GHz using data of 123 sources observed in the Aalto University Metsähovi Radio Observatory. Our aim ...

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The study presents a comprehensive analysis of long-term radio variability in active galactic nuclei, employing a robust methodological framework through the use of periodograms to estimate power spectral densities. The focus on characteristic timescales and the relationship to jet structures introduces novel insights that can advance the understanding of active galactic nuclei variability. Moreover, the extended observational dataset enhances the credibility of the findings. The implications of this research could drive future studies that explore variability patterns and their astrophysical causes more deeply.

Hadronic particle acceleration in the supernova remnant SN 1006 as traced by Fermi-LAT observations

The supernova remnant SN 1006 is a source of high-energy particles detected at radio, X-rays, and tera-electronvolt gamma rays. It was also announced as a source of gamma rays by Fermi-LAT but only th...

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This article presents significant advancements in understanding particle acceleration in supernova remnants through comprehensive Fermi-LAT observational data. Its methodological rigor, particularly the multi-wavelength approach and the analysis of spectral indices, adds substantial value. The findings could lead to a deeper understanding of cosmic ray sources and the physical processes in supernova remnants, making it vital for both theoretical and observational astrophysics.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection

Due to the scarcity and unpredictable nature of defect samples, industrial anomaly detection (IAD) predominantly employs unsupervised learning. However, all unsupervised IAD methods face a common chal...

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The article presents a novel approach (RAAD) to improve industrial anomaly detection by addressing bias in normal samples. The proposed method not only enhances the sensitivity of models toward defects but also optimizes computational efficiency. Its systematic framework and practical validation on multiple datasets underline strong methodological rigor, offering significant applicability in real-world scenarios.

Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics

Textured meshes significantly enhance the realism and detail of objects by mapping intricate texture details onto the geometric structure of 3D models. This advancement is valuable across various appl...

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The study presents a distinct advance in the domain of 3D graphics by integrating texture and geometry for saliency detection, which has been a gap in existing research. The novel dataset and methodology employed in a VR environment contribute to the robustness of the findings and their applicability across various domains. This work is likely to inspire future studies in both computer graphics and human-computer interaction, making it impactful and relevant.

From communities to interpretable network and word embedding: an unified approach

Modelling information from complex systems such as humans social interaction or words co-occurrences in our languages can help to understand how these systems are organized and function. Such systems ...

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The proposed framework represents a significant advancement in both the graphs and NLP fields by addressing the limitations of existing embeddings regarding interpretability and computational efficiency. The methodological rigor is strong, particularly with the introduction of the Lower Dimension Bipartite Framework. Its ability to create interpretable embeddings has the potential to influence various applications, particularly in machine learning and social network analysis.

Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations

Multigrid methods are asymptotically optimal algorithms ideal for large-scale simulations. But, they require making numerous algorithmic choices that significantly influence their efficiency. Unlike r...

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The article presents a novel approach to enhance the efficiency of multigrid methods for large-scale simulations using genetic programming, which is both innovative and provides methodological rigor through numerical experiments. Its applicability in laser beam welding simulations makes it particularly relevant for practical engineering problems, demonstrating potential advancements in computational techniques. However, the integration of evolutionary algorithms with existing frameworks may require further exploration and validation in other contexts to fully gauge its generalizability.