Thesis Tide - Find relevant and interesting papers

Normalized solutions for NLS equations with potential on bounded domains: Ground states and multiplicity

We investigate normalized solutions for a class of nonlinear Schrödinger (NLS) equations with potential $V$ and inhomogeneous nonlinearity $g(|u|)u=|u|^{q-2}u+β|u|^{p-2}u$ on a bounded...

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This article presents a significant advance in the study of nonlinear Schrödinger equations, particularly regarding the existence of normalized solutions in the context of bounded domains. The paper's novelty lies in the ability to prove global minimizers without requiring star-shaped conditions for certain cases, which addresses an open problem in the field. The methodological rigor demonstrated through the handling of nonlinearity and potential variations strengthens its impact. Furthermore, the findings could influence research on similar equations and variational methods, thereby promising new directions for future studies.

Simulating Vibrational Dynamics on Bosonic Quantum Devices

Bosonic quantum devices, which utilize harmonic oscillator modes to encode information, are emerging as a promising alternative to conventional qubit-based quantum devices, especially for the simulati...

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The article presents a novel methodological advancement in the realm of digital quantum simulations tailored specifically for bosonic quantum devices. Its significance stems from the innovative decomposition of vibrational Hamiltonians and the introduction of an approach that enhances the efficiency of simulations on current quantum hardware. This has strong implications for both fundamental research and practical applications in quantum computing.

ROICtrl: Boosting Instance Control for Visual Generation

Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions ...

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This article presents a novel method (ROICtrl) that significantly enhances the ability of text-based visual generation models to accurately manage multiple instances. The introduction of ROI-Unpool alongside ROI-Align innovatively addresses long-standing limitations in previous methods. The methodological rigor is evident in addressing both efficiency and accuracy, making it highly applicable to real-world scenarios in computer vision. The potential for integration with existing models increases its relevance for advancing further research in this domain.

Structural Parameterization of Locating-Dominating Set and Test Cover

We investigate structural parameterizations of two identification problems: LOCATING-DOMINATING SET and TEST COVER. In the first problem, an input is a graph $G$ on $n$ vertices and an...

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The article presents significant advances in the structural parameterization of two important identification problems in graph theory and combinatorial optimization. By extending previous work and introducing a dynamic programming scheme, it offers novel insights and methodologies, which demonstrate methodological rigor and applicability to related problems. The exploration of parameters beyond treewidth adds depth and increases relevance.

Dark Matter Axion Detection with Neural Networks at Ultra-Low Signal-to-Noise Ratio

We present the first analysis of Dark Matter axion detection applying neural networks for the improvement of signal-to-noise ratio. The main sources of thermal noise from a typical read-out chain are ...

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This article presents a novel approach to axion detection by utilizing neural networks, showcasing significant advancements in improving the signal-to-noise ratio. The rigorous simulation of noise sources and the application of advanced modal methods emphasize methodological strength. The potential to reduce measurement times or improve sensitivity in dark matter research is particularly impactful, aligning with ongoing challenges in particle physics.

Higher Euler-Kronecker Constants of Number fields

The higher Euler-Kronecker constants of a number field $K$ are the coefficients in the Laurent series expansion of the logarithmic derivative of the Dedekind zeta function about $s=1$ ....

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This article presents novel results related to the arithmetic properties of higher Euler-Kronecker constants, which are linked to significant concepts in number theory related to the Dedekind zeta function. The focus on proving new formulas and establishing bounds enhances its methodological rigor and provides a solid base for further explorations in this area.

MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation

Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address t...

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This study presents a highly relevant and novel dataset that addresses a significant limitation in 3D content generation from text prompts. The extensive size and quality of the dataset, along with the innovative multi-stage annotation pipeline, suggest a strong methodological rigor. The inclusion of human metadata further enhances its applicability. The authors' demonstrations of superiority over existing datasets add to its potential impact in advancing techniques in computer vision and 3D rendering.

Gaussian fluctuations of non-reciprocal systems

Non-reciprocal systems can be thought of as disobeying Newtons third law - an action does not cause an equal and opposite reaction. In recent years there has been a dramatic rise in interest towards s...

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The article addresses a timely topic in physics—non-reciprocal systems—which are garnering increased attention due to their relevance in both fundamental and applied contexts. By exploring linear non-reciprocal models, the authors contribute to the theoretical understanding of these systems and their stability, potentially influencing future studies and applications. The discussion of Gaussian fluctuations and connection to real-world phenomena adds novelty and robustness to the findings, making it relevant for further exploration in various fields.

Evaluating Generative AI-Enhanced Content: A Conceptual Framework Using Qualitative, Quantitative, and Mixed-Methods Approaches

Generative AI (GenAI) has revolutionized content generation, offering transformative capabilities for improving language coherence, readability, and overall quality. This manuscript explores the appli...

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The article presents a well-structured conceptual framework that combines qualitative, quantitative, and mixed-methods approaches to evaluate the impact of generative AI on scientific writing. Its methodological rigor and applicability in high-stakes fields like healthcare enhance its relevance. Moreover, it addresses critical topics such as trust in AI technologies and the evaluation of AI-generated content, making it a significant contribution to the field.

Efficient Mathematical Programming Formulation and Algorithmic Framework for Optimal Camera Placement

Optimal camera placement plays a crucial role in applications such as surveillance, environmental monitoring, and infrastructure inspection. Even highly abstracted versions of this problem are NP-hard...

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The article presents a novel mathematical framework for addressing a complex and important problem in camera placement, employing rigorous methodologies such as integer programming and adaptive sampling that enhance its contribution to the field. The results demonstrate substantial improvements over baseline methods, indicating practical applicability and efficiency, which could influence design choices in various applications involving surveillance and monitoring. The emphasis on theoretical bounds further strengthens its impact by providing foundational insights for future research.

Multi-Label Bayesian Active Learning with Inter-Label Relationships

The primary challenge of multi-label active learning, differing it from multi-class active learning, lies in assessing the informativeness of an indefinite number of labels while also accounting for t...

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The article presents a novel approach to multi-label active learning that addresses significant challenges such as label correlations and imbalanced data distributions. The methodological rigor, evident from extensive experiments and performance comparisons with established methods, enhances the contribution of this research. Its applicability to real-world scenarios and the holistic assessment of uncertainty through improved label relationship modeling mark it as particularly impactful in advancing the field.

Changing topological type of compression bodies through cone manifolds

Generically, small deformations of cone manifold holonomy groups have wildly uncontrolled global geometry. We give a short concrete example showing that it is possible to deform complete hyperbolic me...

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The paper presents a novel approach to understanding the deformation of hyperbolic metrics through cone manifolds, revealing a significant interaction between topological properties and geometric structures. The example provided appears illustrative and relatable, indicating methodological rigor. This contributes meaningfully to the field of low-dimensional topology and could inspire future investigations into geometrical deformation methods.

Signal Detection in Colored Noise Using the Condition Number of $F$ -Matrices

Signal detection in colored noise with an unknown covariance matrix has numerous applications across various scientific and engineering disciplines. The analysis focuses on the square of the condition...

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The article introduces a novel statistical method for signal detection in colored noise, addressing a significant open problem in the field. Its focus on the condition number of covariance matrices and the derivation of cumulative distribution functions through advanced techniques in random matrix theory demonstrates methodological rigor and innovation. The potential applications across various scientific and engineering domains highlight its broad relevance.

$C^{k}$ -regular extremal black holes in maximally-symmetric spacetime and the third law of black hole thermodynamics

In this work we extend the proof of Ryan Unger and Christoph Kehle's work, "Gravitational collapse to extremal black holes and the third law of black hole thermodynamics", to construct e...

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The paper provides a significant extension to existing work on black hole thermodynamics, particularly by addressing the controversial third law in a new context involving new spacetime geometries. Its methodological rigor, as it builds on previous research while developing a deeper understanding of black hole formation, adds to its novelty and potential impact in the field.

Spatio-temporal Causal Learning for Streamflow Forecasting

Streamflow plays an essential role in the sustainable planning and management of national water resources. Traditional hydrologic modeling approaches simulate streamflow by establishing connections ac...

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The article presents a novel application of spatio-temporal graph neural networks for streamflow forecasting, integrating causal learning with domain knowledge which signifies methodological innovation. The real-world validation enhances its applicability and robustness, suggesting a strong potential for impact in hydrologic modeling and water resource management. Its interdisciplinary approach may inspire future research in related fields.

Stealthy Multi-Task Adversarial Attacks

Deep Neural Networks exhibit inherent vulnerabilities to adversarial attacks, which can significantly compromise their outputs and reliability. While existing research primarily focuses on attacking s...

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The paper makes a novel contribution to the understanding of adversarial attacks in multi-task settings, addressing a significant gap in existing research. Its method not only identifies specific vulnerabilities but also provides a framework that enhances the robustness of non-targeted tasks, which is crucial for real-world applications. The combination of methodological rigor, practical implications, and the introduction of an automated approach for loss function tuning adds substantial value to the field.

State Anxiety Biomarker Discovery: Electrooculography and Electrodermal Activity in Stress Monitoring

Anxiety has become a significant health concern affecting mental and physical well-being, with state anxiety, a transient emotional response, linked to adverse cardiovascular and long-term health outc...

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This article introduces innovative biomarkers for monitoring state anxiety via non-invasive methods, demonstrating methodological rigor through the use of comprehensive datasets and SHAP for model interpretation. Its potential to enhance personalized health monitoring and intervention strategies is a significant advancement in the field of mental health, suggesting strong applicability and potential impact on future research directions.

Demonstration of the light collection stability of a PEN-based wavelength shifting reflector in a tonne scale liquid argon detector

Liquid argon detectors rely on wavelength shifters for efficient detection of scintillation light. The current standard is tetraphenyl butadiene (TPB), but it is challenging to instrument on a large s...

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The article presents a novel and practical alternative to the current standard in wavelength shifting materials for liquid argon detectors, which is essential for advancements in the field of particle physics. The rigorous large-scale measurement and stability testing of PEN over a significant timeframe demonstrate methodological rigor and the potential for substantial impact on future detector designs and applications. The findings could simplify the instrumentation process on a large scale, which is of high relevance to the field.

Automated Test Transfer Across Android Apps Using Large Language Models

The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user int...

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This article presents a novel application of Large Language Models (LLMs) in automating the transfer of UI tests across mobile applications, significantly improving the efficiency of the testing process. The high success rate and reduction of manual effort demonstrate methodological rigor and strong applicability in real-world scenarios, making it a substantial contribution to the field.

Neural Networks Use Distance Metrics

We present empirical evidence that neural networks with ReLU and Absolute Value activations learn distance-based representations. We independently manipulate both distance and intensity properties of ...

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This article presents novel empirical findings that challenge conventional views on neural network activations, specifically advancing our understanding of how these models process distance metrics versus intensity. The methodological rigor in independently manipulating both properties adds to the robustness of the findings, suggesting the potential for significant insights into neural network interpretations that could influence future model design and theoretical frameworks.