Thesis Tide - Find relevant and interesting papers

By-Example Synthesis of Vector Textures

We propose a new method for synthesizing an arbitrarily sized novel vector texture given a single raster exemplar. Our method first segments the exemplar to extract the primary textons, and then clust...

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The proposed method for synthesizing vector textures offers a novel approach by leveraging a single raster exemplar, which is a unique contribution to the field of texture synthesis. The segmentation and clustering of textons based on visual similarity suggest methodological rigor, and the use of perceptual-based metrics for comparison indicates a thorough evaluation of the method's effectiveness. Its applicability spans various areas such as computer graphics, digital art, and perhaps even machine learning, making it relevant to a wide range of research.

Inflaton Self Resonance, Oscillons, and Gravitational Waves in Small Field Polynomial Inflation

In this work, we investigate the post-inflationary dynamics of a simple single-field model with a renormalizable inflaton potential featuring a near-inflection point at a field value $φ_0$ . Du...

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The article presents a comprehensive analysis of a unique single-field inflationary model featuring an intriguing mechanism due to the potential's concave shape. The use of lattice simulations to study the non-perturbative regime adds robustness, while the implications for primordial black holes and gravitational waves provide significant relevance for both theoretical and observational astrophysics. However, the complexity of the model may limit immediate applicability in broader contexts, hence a slightly lower score.

Learning to Help in Multi-Class Settings

Deploying complex machine learning models on resource-constrained devices is challenging due to limited computational power, memory, and model retrainability. To address these limitations, a hybrid sy...

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This article presents a novel advancement in the field of resource-efficient machine learning by extending an existing model to multi-class classification, which significantly broadens its applicability and potential impact. The development of a differentiable and convex surrogate loss function, alongside empirical validation in practical scenarios, enhances its methodological rigor and relevance.

Exoplanet Occurrence Rate with Age for FGK Stars in Kepler

We measure exoplanet occurrence rate as a function of isochrone and gyrochronology ages using confirmed and candidate planets identified in Q1-17 DR25 Kepler data. We employ Kepler's pipeline dete...

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This article presents a comprehensive analysis of exoplanet occurrence rates related to stellar ages, providing a methodologically rigorous approach to a critical aspect of exoplanet research. The use of Kepler data enhances its reliability, and while the findings indicate inconclusive trends, they open avenues for future studies. The exploration of the influence of stellar mass and metallicity adds depth, though the small sample size limits robustness. Overall, it offers valuable insights and raises pertinent questions for the field.

Nonreciprocal interactions induce frequency shifts in superradiant lasers

Superradiant lasers, which consist of incoherently driven atoms coupled to a lossy cavity, are a promising source of coherent light due to their stable frequency and superior narrow linewidth. We show...

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This article presents novel findings on how nonreciprocal interactions affect the performance of superradiant lasers, an area of significant interest in both fundamental and applied physics. The investigation of competing dipole interactions adds depth to the understanding of superradiant laser dynamics and opens pathways for improved designs. The methodological approach appears rigorous, contributing to the study's robustness and applicability to quantum technologies.

Theory of the kinetic helicity effect on turbulent diffusion of magnetic and scalar fields

Kinetic helicity is a fundamental characteristics of astrophysical turbulent flows. It is not only responsible for the generation of large-scale magnetic fields in the Sun, stars, and spiral galaxies,...

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The article provides a novel theoretical framework that enhances the understanding of the role of kinetic helicity in turbulent diffusion processes that are significant in astrophysical settings. Its methodological approach using path integrals adds rigor, making the findings applicable to real-world astrophysical phenomena like magnetic field generation. Although the focus is specialized, the implications for both magnetic and scalar field dynamics in turbulence suggest broad relevance in fluid dynamics and astrophysics.

Generation of reusable learning objects from digital medical collections: An analysis based on the MASMDOA framework

Learning Objects represent a widespread approach to structuring instructional materials in a large variety of educational contexts. The main aim of this work consists of analyzing from a qualitative p...

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This article presents a novel approach to generating reusable learning objects in the medical field, which is essential for enhancing educational practices in healthcare. Its focus on applying the MASMDOA framework to analyze a new tool (Clavy) adds methodological rigor. The implications for improving access to medical knowledge and catering to diverse learning needs are particularly relevant in today’s digital education landscape.

EgoHand: Ego-centric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMUs

Recent advanced Virtual Reality (VR) headsets, such as the Apple Vision Pro, employ bottom-facing cameras to detect hand gestures and inputs, which offers users significant convenience in VR interacti...

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The article presents a novel approach to hand pose estimation and gesture recognition using millimeter-wave radar and inertial measurement units (IMUs), which represents a significant advancement over traditional camera-based systems. The integration of privacy protection features is highly relevant in the context of growing concerns about data security in VR applications. The method's demonstrated accuracy and robustness across various contexts significantly enhances its applicability, indicating strong potential for real-world deployment. The use of advanced machine learning techniques, particularly the proposed Transformer architecture, adds to the methodological rigor of the research.

Towards Real-World Validation of a Physics-Based Ship Motion Prediction Model

The maritime industry aims towards a sustainable future, which requires significant improvements in operational efficiency. Current approaches focus on minimising fuel consumption and emissions throug...

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The study presents a novel physics-based model for ship motion prediction that directly addresses a significant gap in real-world validation, which is crucial for the maritime industry's transition to more autonomous and efficient operations. Its methodological rigor in comparing predictions with real-time voyage data enhances its credibility and applicability, particularly in settings aimed at reducing fuel consumption and emissions. This article has the potential to influence both academics and industry practitioners significantly, making it highly relevant for future research and developments in maritime technology.

Detecting Free Group Automorphisms via Virtual Homology Representations

Let $F_n= F\langle x_1,...,x_n\rangle$ denote the free group of rank $n\ge 2$ and let $\mathrm{End}(F_n)$ be the endomorphism monoid of $F_n$ . We show that automorphism...

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The article presents novel mathematical insights by linking free group automorphisms to homological properties, enhancing our understanding of algebraic topology and group theory. The methods employed are rigorous and the results contribute to existing theories regarding homotopy equivalences. The applicability of the findings to complex structures, such as punctured surfaces, indicates potential for broader implications in topology. However, the specificity of the topic may limit its immediate relevance to more general mathematical fields.

Enhancing LLMs for Governance with Human Oversight: Evaluating and Aligning LLMs on Expert Classification of Climate Misinformation for Detecting False or Misleading Claims about Climate Change

Climate misinformation is a problem that has the potential to be substantially aggravated by the development of Large Language Models (LLMs). In this study we evaluate the potential for LLMs to be par...

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The article addresses a critical current issue—climate misinformation—and its impact on public understanding. It effectively evaluates the performance of both proprietary and open-source LLMs, highlighting the importance of human oversight and expert involvement in improving AI models for governance tasks. The findings are novel and provide valuable insights into the potential application of LLMs in broader contexts beyond climate issues, which could inspire future research in AI ethics, misinformation detection, and cross-domain applications.

Data-Driven Analysis of Droplet Morphology in Inkjet Systems: Toward Generating Stable Single-Drop Regimes

The growing demand for new microelectronic devices and pharmaceutical advancements has heightened interest in inkjet printing as a means of high-precision manufacturing technique. This study leverages...

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This article presents a robust data-driven approach to optimize inkjet systems, which is crucial for microelectronics and pharmaceuticals. The methodical analysis of droplet generation and the provision of an openly published dataset enhance its novelty and practical applicability. The detailed methodology offers a solid framework for future research. However, the impact may be limited by the specificity of the system examined, thus a score of 8.5 reflects its significant, yet somewhat specialized, contribution to the field.

On the semi-classical limit for the Landau-Fermi-Dirac equation

We study sequences of solutions to the inhomogeneous Landau-Fermi-Dirac equation with Coulomb potential in which the quantum parameter converges to zero. Our main result establishes the compactness of...

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The article addresses a significant theoretical advancement in the semi-classical limit of the Landau-Fermi-Dirac equation, contributing to the understanding of the interplay between classical and quantum mechanics. The methodological rigor, through the establishment of compactness and convergence of sequences of solutions, indicates a solid approach to a complex problem. This work could potentially inspire further research into quantum-to-classical transitions and related areas in mathematical physics.

Rudraksh: A compact and lightweight post-quantum key-encapsulation mechanism

Resource-constrained devices such as wireless sensors and Internet of Things (IoT) devices have become ubiquitous in our digital ecosystem. These devices generate and handle a major part of our digita...

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The article presents a novel lightweight post-quantum key-encapsulation mechanism, Rudraksh, specifically designed for resource-constrained devices such as IoT and wireless sensors. The rigorous analysis of various design elements and the comparative performance metrics against existing schemes, notably Kyber, demonstrate significant advancements in efficiency and security, which are critical given the rise of quantum computing threats. The methodological rigor and the practical implications of deploying this KEM in real-world applications contribute to its high relevance.

Exploring the role of hydrodynamic interactions in spherically-confined drying colloidal suspensions

We study the distribution of colloidal particles confined in drying spherical droplets using both dynamic density functional theory (DDFT) and particle-based simulations. In particular, we focus on th...

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The article presents a fresh perspective on the interplay between hydrodynamic interactions and particle distribution in drying colloidal systems, which is pertinent for understanding fundamental processes in colloidal science and material engineering. The use of both DDFT and particle-based simulations introduces robust methodological rigor, while the focus on nonequilibrium dynamics adds novelty. The findings regarding the limitations of DDFT in capturing complex interactions raise critical questions for future studies, making this work relevant for ongoing research.

Inference for generalized additive mixed models via penalized marginal likelihood

Existing methods for fitting generalized additive mixed models to longitudinal repeated measures data rely on Laplace-approximate marginal likelihood for estimation of variance components and smoothin...

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This article presents a significant advancement in the methodology for fitting generalized additive mixed models, particularly in improving the accuracy of variance component and smoothing parameter estimates. The critique of existing methods and the introduction of an innovative adaptive quadrature approach demonstrate strong novelty and potential for applicability in real-world data scenarios. Its implications for bias reduction and enhanced statistical properties underscore its relevance.

PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments

Considerable efforts have been made to improve monocular depth estimation under ideal conditions. However, in challenging environments, monocular depth estimation still faces difficulties. In this pap...

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The article introduces a novel framework (PromptMono) that incorporates visual prompts and a new GCPA module for monocular depth estimation in challenging environments, addressing a significant limitation in the field. The self-supervised learning aspect is particularly relevant today given the computational advantages and data efficiency it offers. The use of established datasets like Oxford Robotcar and nuScenes enhances the credibility of the experimental validation.

Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models

Existing zero-shot temporal action detection (ZSTAD) methods predominantly use fully supervised or unsupervised strategies to recognize unseen activities. However, these training-based methods are pro...

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The article presents a novel approach to zero-shot temporal action detection that significantly reduces computational costs and removes the dependency on training data. This is particularly impactful as it addresses key limitations in existing methodologies, increasing practical applicability in real-world scenarios. The introduction of a training-free strategy and a test-time adaptation method showcases methodological rigor and innovation, paving the way for future research in both action detection and vision-language applications.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction

Accurate prediction of mobile traffic, \textit{i.e.,} network traffic from cellular base stations, is crucial for optimizing network performance and supporting urban development. However, the non-stat...

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The article presents a novel framework (NPDiff) that shifts the focus from conventional denoising approaches to understanding and utilizing noise in mobile traffic predictions. This innovative perspective, backed by experimental evidence of substantial performance improvement, indicates a high potential for impact. The methodological rigor and application of diffusion models add to its relevance in the field of predictive analytics.

Delay-Doppler Multiplexing With Global Filtering

This paper proposes a novel modulation technique called globally filtered orthogonal time frequency space (GF-OTFS) which integrates single-carrier frequency division multiple access (SC-FDMA)-based d...

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The proposed GF-OTFS technique demonstrates strong novelty by merging SC-FDMA with UFMC to address Doppler interference, which is significant for communications in dynamic environments. Its robust mathematical formulation and performance evaluation against current state-of-the-art methods emphasize methodological rigor and applicability to real-world challenges. Additionally, improvements in spectral containment and mitigation of inter-Doppler interference provide a practical advancement in the field of wireless communication techniques, particularly for mobile communications.