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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!

ReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration

While recent works on blind face image restoration have successfully produced impressive high-quality (HQ) images with abundant details from low-quality (LQ) input images, the generated content may no...

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The article introduces a novel adaptation of the Latent Diffusion Model which has significant potential to improve face image restoration tasks by incorporating reference images. The methodology is innovative, and the introduction of a new dataset (FFHQ-Ref) provides valuable resources for future research. The approach appears rigorous, and the focus on discriminative features adds depth to the model's capability. However, the impact of the findings could depend on how well the model performs compared to existing methodologies in practical applications.

Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images

Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. ...

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This study addresses a critical problem in the aftermath of earthquakes by providing an innovative solution for enhancing crack detection systems through semi-synthetic image generation. The novelty lies in the combination of computer vision, deep learning, and the creative use of parametric meta-annotations to build an augmented dataset. The methodological rigor, shown through comparative evaluations that validate performance improvements, adds to its relevance. Overall, this research has strong implications for practical applications in disaster management and structural safety assessment, making it a significant contribution to the field.

First experiences with the LHCb heterogeneous software trigger

Since 2022, the LHCb detector has been taking both proton-proton and lead-ion data at the LHC collision rate using a fully software-based trigger. This has been implemented on GPUs at its first stage ...

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The article presents a novel implementation of a fully software-based trigger for the LHCb detector, utilizing GPUs and CPUs which marks a significant advancement in real-time data analysis in high-energy physics. The methodological rigor of being able to handle both proton-proton and lead-ion data expands its applicability. The early results shared indicate potential for immediate impacts on ongoing physics analyses and future research directions.

Synchronization and desynchronization in ensembles of mobile agents

Mechanisms of emergence and destruction are analyzed, as well as characteristics of synchronous and asynchronous modes of behavior of ensembles (swarms) of interacting mobile agents moving according t...

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The article addresses an advanced topic in the study of mobile agents, specifically exploring both synchronization and desynchronization which are crucial for understanding collective behavior in complex systems. The use of chaotic phase trajectories of Rossler and Lorenz systems provides a novel methodological approach, suggesting implications for real-world applications in robotics and swarm intelligence. The findings could inspire future research in optimizing the behavior of swarms and mobile agent systems, potentially impacting various technological developments.

Level Up or Game Over: Exploring How Dark Patterns Shape Mobile Games

This study explores the prevalence of dark patterns in mobile games that exploit players through temporal, monetary, social, and psychological means. Recognizing the ethical concerns and potential har...

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The study addresses a critical issue of ethical design in mobile gaming, providing quantitative data on the prevalence of dark patterns. The focus on both problematic and benign games enhances its relevance, especially given the growing importance of player welfare in game design. The methodological approach, analyzing user-generated data from a significant number of games, demonstrates rigor and depth. This article is positioned to inspire further research on ethical standards and practices in game development.

BMS3_3 fermionic localization

We consider the geometric action formulation for 3d pure gravity with vanishing cosmological constant. We use fermionic localization to compute the exact torus partition function for a constant repres...

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This article presents a novel approach by utilizing fermionic localization to examine the geometric action formulation of 3D pure gravity, particularly in the context of BMS$_3$ symmetry. Its focus on computing the torus partition function is significant for theoretical physics and will likely contribute to deeper understanding of quantum gravity and its mathematics. The method used indicates potential advancements in mathematical techniques applied to gravity theories, creating pathways for future research into related areas.

Mechanical State Estimation with a Polynomial-Chaos-Based Statistical Finite Element Method

The Statistical Finite Element Method (statFEM) offers a Bayesian framework for integrating computational models with observational data, thus providing improved predictions for structural health moni...

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This article presents a novel approach to mechanical state estimation by integrating statistical methods with finite element analysis, which could significantly improve predictive accuracy in structural health monitoring. The use of polynomial chaos and KL expansion for uncertainty quantification is innovative and has potential applications in various engineering fields. The methodological rigor displayed through robust mathematical frameworks and sampling-free techniques enhances the article's impact. Moreover, the practical demonstrations in elastostatic problems support applicability, though the paper's reliance on specific modeling assumptions may limit its generalizability across other scenarios.

Linearization of Newton's second law

The geometric linearization of nonlinear differential equation is a robust method for the construction of analytic solutions. The method is related to the existence of Lie symmetries which can be used...

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The article introduces a novel geometric method for linearizing nonlinear differential equations, specifically focusing on Newton's second law, which has implications for analytical solution construction. Its connection to Lie symmetries adds a layer of sophistication to the methodology, which enhances its relevance. The findings can potentially influence the study of dynamical systems and control theory by presenting new ways to analyze particle motion under force applications.

SMIC: Semantic Multi-Item Compression based on CLIP dictionary

Semantic compression, a compression scheme where the distortion metric, typically MSE, is replaced with semantic fidelity metrics, tends to become more and more popular. Most recent semantic compressi...

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The article presents a novel approach to semantic compression, which leverages the CLIP model to enhance coding efficiency in image collections. It tackles inter-item redundancy effectively, which is a significant advantage over existing methods. The methodological rigor in employing a dictionary-based codec and the demonstration of improved compression rates while maintaining fidelity adds to its robustness. The focus on semantic transparency positions this work to inspire future research in multimedia compression and artificial intelligence applications.

Obliquely interacting solitary waves and wave wakes in free-surface flows

This paper investigates the weakly nonlinear isotropic bi-directional Benney--Luke (BL) equation, which is used to describe oceanic surface and internal waves in shallow water, with a particular focus...

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The research presents a significant advancement in understanding wave dynamics in fluid mechanics, specifically addressing soliton interactions in shallow water flows. The utilization of established theories like the Whitham modulation theory to derive new results demonstrates methodological rigor. The inclusion of analytical formulas and numerical validation strengthens the findings and their applicability to real-world scenarios. It also potentially enhances predictive capabilities in various applications, including coastal engineering and oceanography. However, while the contributions are strong, the niche focus may limit its broader impact across diverse areas.

Open Cluster Study Using GaiaGaia I:Membership and Cluster Properties

Star clusters are interesting laboratories to study star formation, single and binary stellar evolution, and stellar dynamics. We have used the exquisite data from GaiaGaia's data release 3 ...

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This study provides a novel, non-parametric method for identifying star cluster members using advanced Gaia data. The approach's versatility enables its application to various cluster types, which enhances its relevance in the field of stellar evolution and dynamics. The robust estimation of cluster properties through a rigorous method adds to the potential impact of this work on future research.

moonboot: An R Package Implementing m-out-of-n Bootstrap Methods

The m-out-of-n bootstrap is a possible workaround to compute confidence intervals for bootstrap inconsistent estimators, because it works under weaker conditions than the n-out-of-n bootstrap. It has ...

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The article introduces an innovative R package targeting a specific statistical method that expands upon existing bootstrap techniques. Its focus on the m-out-of-n bootstrap adds significant value in situations where traditional bootstrap methods are inconsistent. The theoretical groundwork combined with practical implementation highlights its potential utility for researchers needing robust statistical tools. The Monte Carlo simulations for evaluation add methodological rigor, ensuring that the findings are well-supported.

Fully independent response in disordered solids

Unlike in crystals, it is difficult to trace emergent material properties of amorphous solids to their underlying structure. Nevertheless, one can tune features of a disordered spring network, ranging...

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The article introduces a novel concept of 'fully independent response' in disordered solids, which has the potential to significantly enhance the understanding of material properties in conjunction with inverse design methodologies. The quantification and formalization of independent responses offer both theoretical insights and practical applicability, reflecting a high degree of methodological rigor. This innovation could catalyze further research into materials science and possibly result in new material designs based on these principles.

Characterization of subordinate symmetric Markov processes

In this paper, we consider subordinate symmetric Markov processes which correspond to non-killing Dirichlet forms enjoying heat kernel estimates on a metric measure space with the volume doubling prop...

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This article introduces a novel perspective on subordinate symmetric Markov processes by providing new estimates on the jump kernel and clarifying its properties in comparison to diffusion processes. Its methodological rigor in utilizing Dirichlet forms enhances the robustness of the findings. The implications for both subordinate and non-subordinate processes suggest sizable applications, potentially influencing future research in stochastic processes and related areas.

Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning

In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are ran...

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This article introduces a novel approach to instance-dependent partial label learning by creating class-wise embeddings, addressing a significant gap in the current understanding of label noise in machine learning. The proposed method promises methodological rigor, as evidenced by extensive experimental comparisons with established methods across multiple datasets. Furthermore, the public availability of the code allows for reproducibility and potential further development by other researchers, enhancing its impact. However, the generalizability of the findings could be better established through additional real-world applications beyond the benchmark datasets used.

Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning

Entity alignment aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. Howev...

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The study introduces a novel dual-space embedding method that addresses the complex structure of knowledge graphs, showcasing methodological rigor and innovative approach through contrastive learning. Its results significantly advance the entity alignment field, making it highly relevant for both theoretical and practical applications in AI and data representation.

Asymptotic methods for confined fluids

The thermodynamics and microstructure of confined fluids with small particle number are best described using the canonical ensemble. However, practical calculations can usually only be performed in th...

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The article presents a novel asymptotic method for transforming observables from a commonly used grand-canonical ensemble to a more accurate canonical ensemble framework, addressing a significant gap in the treatment of confined fluids. This transformation can enhance the accuracy of thermodynamic predictions in practical applications, indicating a strong potential for advancing theoretical understanding and methodologies in the field. The methodological rigor provided through the integration of asymptotic analysis with classical density functional theory also supports the reliability of the findings, further bolstering its relevance. However, while innovative, the practical implications may somewhat limit broader applicability beyond specific model systems.

Quantum Security Analysis of the Key-Alternating Ciphers

We study the security of key-alternating ciphers (KAC), a generalization of Even-Mansour ciphers over multiple rounds, which serve as abstractions for many block cipher constructions, particularly AES...

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The paper addresses a significant gap in the understanding of key-alternating ciphers' security in the context of quantum computing, a leading-edge area of cryptography. Its methodological rigor and introduction of a quantum key-recovery attack represent a novel contribution, offering a deeper insight into the vulnerabilities of widely used cipher schemes like AES. This could have a substantial impact on future research direction in cryptography and secure communication.

Ultralong-range Rydberg molecules of Hg atoms

Ultralong-range Rydberg molecules, composed of an excited Rydberg atom and a ground-state atom, are characterized by large bond lengths, dipole moments, sensitivity to external fields, and an unusual ...

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The article presents a novel approach to Rydberg molecules using mercury atoms, which adds complexity due to the multivalence of Hg compared to previously studied alkali metals. The theoretical framework is robust and addresses uncharted territories, potentially inspiring further research in molecular physics and quantum mechanics. The discussions on spin entanglement and metastable states open new avenues for experimental exploration and applications in quantum technologies, enhancing its impact on the field.

Talking Like One of Us: Effects of Using Regional Language in a Humanoid Social Robot

Social robots are becoming more and more perceptible in public service settings. For engaging people in a natural environment a smooth social interaction as well as acceptance by the users are importa...

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The study presents a novel investigation into the impact of regional language usage on human-robot interaction, a critical area in social robotics. The use of a humanoid robot and the cultural implications of language variety provide both practical insights and theoretical contributions, highlighting the intersection of linguistics, technology, and social science. The methodological rigor is demonstrated through the specific use of a standardized measurement tool (RoSAS) and a clear experimental design, although the sample size is relatively small.