Thesis Tide - Find relevant and interesting papers

Nuclear level density of ${}^{128}$ Te from (\mathrm{p},\mathrm{p}'γ) scattering and complementary photonuclear data

We have extracted the nuclear level density of ${}^{128}$ Te from a (\mathrm{p},\mathrm{p} 'γ) scattering experiment using the large-volume \labr\ and \cebr\ detectors from ELI-NP...

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This article presents novel experimental data on the nuclear level density of ${}^{128}$Te, utilizing a direct measurement method that reduces dependence on conventional model assumptions. The integration of $( ext{p}, ext{p}' ext{γ})$ scattering with photonuclear data enhances the robustness of the results. The divergence observed from the Skyrme force model may inspire new theoretical approaches in nuclear physics, marking a significant advance in our understanding of nuclear structure.

A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining

This paper introduces an intelligent baggage item recommendation system to optimize packing for air travelers by providing tailored suggestions based on specific travel needs and destinations. Using F...

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The article presents a novel approach to personalized recommendations in a practical domain (air travel) using advanced techniques (FastText and ARM). The methodological rigor is commendable, as it combines various components to create a comprehensive system for optimizing packing. The potential for improving user experience and operational efficiency in travel makes it highly relevant. However, the implementation context might limit broader applicability, reducing its overall impact slightly.

Grey system model on time scales

The Grey System Theory (GST) is a powerful mathematical framework employed for modeling systems with uncertain or incomplete information. This paper proposes an integration of the GST with time scales...

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The integration of Grey System Theory with time scales represents a novel contribution to existing modeling techniques, particularly beneficial for complex systems with periodic and continuous interactions. Its methodological rigor and potential applicability in diverse scenarios enhance its relevance.

Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization

This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization ta...

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The research presents a novel approach to UAV path planning by incorporating formation dynamics into the optimization process, utilizing a unique combination of teaching-learning-based optimization with enhancements like mutation and elite strategy. Its applications in UAV swarms can significantly advance the field of autonomous flight, enabling safer and more efficient operations in complex environments. The rigorous testing and simulation results further bolster its methodological rigor, making it highly relevant.

Cosmological Solutions in Scalar-Tensor theory via the Eisenhart-Duval lift

We implement the Einsenhart-Duval lift in scalar-tensor gravity as a means to construct integrable cosmological models and analytic cosmological solutions. Specifically, we employ a geometric criterio...

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The article presents a novel application of the Eisenhart-Duval lift to construct integrable solutions in scalar-tensor gravity, which is an innovative approach that could significantly impact the field of theoretical cosmology. The use of geometric criteria to simplify complex field equations enhances its methodological rigor, making it a potentially influential piece of work. The focus on modified theories of gravity, particularly their analytic solutions, highlights its relevance to current astrophysical problems and may inspire further research in both theoretical frameworks and observational studies of the universe.

Optimizing hypergraph product codes with random walks, simulated annealing and reinforcement learning

Hypergraph products are quantum low-density parity-check (LDPC) codes constructed from two classical LDPC codes. Although their dimension and distance depend only on the parameters of the underlying c...

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This article presents novel optimization techniques for hypergraph product codes in the context of quantum coding theory, which is a cutting-edge area in quantum information science. The application of Reinforcement Learning and Simulated Annealing represents innovative methodological approaches aimed at overcoming established limitations in code performance. The results, demonstrating improvements over existing state-of-the-art techniques, highlight the practical relevance and potential for substantial impacts in quantum communication, making it a valuable contribution to the field.

YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks

Multimodal AI Agents are AI models that have the capability of interactively and cooperatively assisting human users to solve day-to-day tasks. Augmented Reality (AR) head worn devices can uniquely im...

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The proposed YETI multimodal AI agent demonstrates substantial novelty by shifting from reactive to proactive interaction in AR tasks, which can significantly enhance user experience and engagement. The methodological rigor is evident as it implements interpretable signals like SSIM for scene understanding and alignment, providing a solid foundation for its proactive intervention capabilities. This contributes to both theoretical and practical advancements in multimodal AI, augmented reality, and human-computer interaction.

Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information

Understanding users' product preferences is essential to the efficacy of a recommendation system. Precision marketing leverages users' historical data to discern these preferences and recommen...

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The article introduces Style4Rec, a novel transformer-based recommendation system that incorporates style and shopping cart data, addressing a gap in current methodologies. Its significant performance improvements over existing benchmarks indicate a solid methodological framework and practical applicability, signaling potential for widespread adoption in e-commerce platforms. The focus on user preferences through style and current data is particularly timely and relevant.

Magnetically induced deformation of isotropic magneto-active elastomers and its relation to the magnetorheological effect

Can isotropic Magneto-Active Elastomers (MAEs) undergo giant magnetically induced deformations and exhibit huge magnetorheological effects simultaneously? In this experimental and theoretical study, w...

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This study presents a novel approach in understanding the deformation behaviors of Magneto-Active Elastomers (MAEs) under magnetic fields, bridging the gap between mechanical properties and microstructural evolution. The combination of experimental observations with a theoretical model enhances its impact. The findings could lead to improved applications of MAEs in various fields, particularly in soft robotics and adaptive materials.

PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks w...

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The article presents a novel approach to tackling the challenges of multi-modal class-incremental learning (MMCIL) with a focus on missing modalities, which is an underexplored area. The introduction of modality-specific prompts and the analytical linear solution to the MMCIL problem offers a rigorous and innovative methodology that addresses a significant gap in current research. The positive experimental results on various datasets further support its potential impact.

RIS-Aided Fluid Antenna Array-Mounted UAV Networks

This paper investigates reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) downlink networks with fluid antennas (FA), where RIS enables non-line-of-sight (NLoS) transmiss...

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The article presents a novel approach by integrating reconfigurable intelligent surfaces (RIS) with dynamically adjustable fluid antennas (FA) for UAV networks, which represents a significant advancement in the field of wireless communication. The methodological rigor is supported by advanced optimization techniques and robust simulation results that indicate superior performance metrics compared to existing methods. This work demonstrates both theoretical significance and practical applicability, suggesting potential for real-world deployment in enhancing UAV communication networks. However, further empirical validation in diverse operational environments may be needed to fully assess its practicality.

Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals

This paper studies the brave new idea for Multimedia community, and proposes a novel framework to convert dreams into coherent video narratives using fMRI data. Essentially, dreams have intrigued huma...

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This article presents a groundbreaking approach to decoding dreams using fMRI technology, which could have significant implications for both neuroscience and multimedia storytelling. Its novelty lies in integrating subjective dream experiences with objective neurophysiological data, offering a new avenue for understanding the visual representations of dreams. The methodological rigor in applying advanced techniques in fMRI analysis and language modeling enhances its potential impact.

ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset

Effective chart summary can significantly reduce the time and effort decision makers spend interpreting charts, enabling precise and efficient communication of data insights. Previous studies have fac...

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The article introduces a novel approach to mitigating hallucinations in automatic chart summary generation, which is a significant challenge in data visualization and interpretation. The methodological rigor is evident in the implementation of multiple agents and self-consistency tests, enhancing the credibility of the findings. Moreover, the availability of a benchmark dataset promotes further research and application, making this work highly relevant and impactful.

Visualization of quantum interferences in heavy-ion elastic scattering

We investigate various interference effects in elastic scattering of the $α+ {}^{40}\text{Ca}$ system at $E_{\rm lab}=29$ MeV. To this end, we use an optical potential model and decomp...

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The article presents a novel approach to visualizing quantum interference effects in nuclear scattering, combining theoretical modeling with visual data interpretation. Its methodological rigor in decomposing scattering amplitudes into fundamental components enhances understanding of complex quantum phenomena, making it relevant for physicists in nuclear and quantum fields. Moreover, the insights gained can inform future experiments and theoretical models, thus its high score for potential impact.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos

Large Reconstruction Models (LRMs) have recently become a popular method for creating 3D foundational models. Training 3D reconstruction models with 2D visual data traditionally requires prior knowled...

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The proposed UVRM model presents a significant advancement in the field of 3D reconstruction by removing the dependency on pose information, which has historically hampered scalability and usability. Its innovative use of transformer networks and score distillation sampling represents a methodological leap that offers potential for improved accuracy and efficiency in 3D modeling. Its applicability to unposed videos opens new avenues in both theoretical and practical aspects of the field, making it impactful for future research explorations.

Local Existence of a Classical Solution for Quasi-Linear Hyperbolic Systems

In this paper, we study quasi-linear hyperbolic systems. Our goal in this paper is to provide a new proof of local existence of a classical solution for the system. Most difficult point is to prove th...

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The article presents a new proof for the local existence of classical solutions in quasi-linear hyperbolic systems, which addresses a significant challenge in the field of partial differential equations (PDEs). The application of the Arzela-Ascoli theorem signifies a methodological advancement, potentially offering implications for further research in the existence theory of solutions. However, the novelty may be limited to specialists familiar with these systems, and broader impacts could depend on the extensions of this work to more complex scenarios.

Rational Tuning of LLM Cascades via Probabilistic Modeling

Understanding the reliability of large language models (LLMs) has recently garnered significant attention. Given LLMs' propensity to hallucinate, as well as their high sensitivity to prompt design...

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The article introduces a novel probabilistic model to enhance the operational tuning of large language model (LLM) cascades, addressing a prominent issue of reliability and performance in complex LLM setups. The methodological rigor is notable, as it not only proposes a new framework but also demonstrates clear empirical advantages over existing methods like grid search. The findings suggest significant implications for improving LLM systems, making this research highly impactful and applicable in both academic and practical scenarios.

Optical conductivity of the topologically-nontrivial MXenes, Mo $_2$ HfC $_2$ O $_2$ and W $_2$ HfC $_2$ O $_2$ : first-principles calculation and effective model analysis

The optical conductivity and the relevant electronic excitation processes are investigated in topologically-nontrivial MXenes, Mo $_2$ HfC $_2$ O $_2$ and W $_2$ HfC$_2...

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The article addresses a highly relevant topic in the field of material science by exploring the optical properties of topologically nontrivial MXenes, a relatively novel class of materials. The use of first-principles calculations adds methodological rigor, and the insights into the electronic structures related to parity inversion can inspire further studies in condensed matter physics and materials science. The combination of effective model analysis and numerical calculations demonstrates a strong methodological framework, enhancing its potential impact on future research.

A High-Power Clock Laser Spectrally Tailored for High-Fidelity Quantum State Engineering

Highly frequency-stable lasers are a ubiquitous tool for optical frequency metrology, precision interferometry, and quantum information science. While making a universally applicable laser is unrealis...

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The article presents significant advancements in laser technology specifically tailored for quantum applications, showcasing high levels of power, stability, and precision in quantum state engineering. Its novel approach to minimizing frequency noise and achieving high fidelity in quantum operations positions it as a crucial development in quantum information science. The methodology employed for noise characterization also offers broader applicability, ensuring its potential for influencing future research across multiple domains in optics and quantum technologies.

Monochromatic graph decompositions and monochromatic piercing inspired by anti-Ramsey colorings

Anti-Ramsey theory was initiated in 1975 by Erdős, Simonovits and Sós, inspiring hundreds of publications since then. The present work is the third and last piece of our trilogy in which we introduce ...

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This article presents a significant advancement in anti-Ramsey theory, particularly with the introduction of the generalized functions for graph colorings. The novelty lies in developing methods that yield asymptotically tight results, which could impact the understanding and application of colorings in graph theory. The methodological rigor, as supported by previous works, enhances its potential relevance and impact on future studies in related fields.