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Edge-Edge Correlations without Edge-States: $η$ -clustering State as Ground State of the Extended Attractive SU(3) Hubbard Chain

We explore the phase diagram of the extended attractive SU( $3$ ) Hubbard chain with two-body hopping and nearest-neighbor attraction at half-filling. In the large on-site attraction limit, we i...

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The study introduces a novel perspective on edge-edge correlations in a complex quantum system, contributing significantly to the understanding of phase transitions in higher-dimensional quantum devices. The identification of different phases and the innovative approach using boundary off-diagonal long-range order (bODLRO) is a potentially impactful discovery in condensed matter physics. The use of DMRG solidifies the methodological rigor of the research, although it may have limitations when extrapolating to larger systems or different interactions.

Effective SAM Combination for Open-Vocabulary Semantic Segmentation

Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes. Traditional methods address this by sequentially connecting a powerful mask prop...

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The paper presents a novel methodology (ESC-Net) that significantly improves both the efficiency and accuracy of open-vocabulary semantic segmentation, addressing key limitations of existing two-stage approaches. The application of SAM and image-text correlations for prompt generation is innovative, and the obtained results on benchmark datasets highlight the potential impact on real-world applications. The methodological rigor is underscored by comprehensive ablation studies, ensuring the reliability of the findings.

Glassy Relaxation Dynamics in the Two-Dimensional Heavy Fermion Antiferromagnet CeSiI

The recent discovery of the van der Waals (vdW) layered heavy fermion antiferromagnetic metal CeSiI offers promising potential for achieving accessible quantum criticality in the two-dimensional (2D) ...

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This article presents a novel investigation of the two-dimensional heavy fermion antiferromagnet CeSiI, which could significantly advance the understanding of quantum criticality and magnetic interactions in low-dimensional systems. The methodological approach is rigorous, utilizing magnetotransport measurements to explore thickness-dependent phenomena. The identification of glassy relaxation dynamics introduces a new perspective on magnetic phenomena, potentially opening avenues for further research in both theoretical and experimental contexts.

MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts

Molecule discovery is a pivotal research field, impacting everything from the medicines we take to the materials we use. Recently, Large Language Models (LLMs) have been widely adopted in molecule und...

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This article presents a novel framework for improving the alignment between molecular structures and their corresponding text, addressing a significant gap in the application of LLMs within chemical informatics. The methodological rigor, by leveraging both teacher-student learning paradigms and fine-grained contextual analysis, showcases innovation. Its strong empirical results, achieving state-of-the-art performance, suggest high applicability in both experimental and practical scenarios.

Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models

This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-related tweets. We e...

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This article presents a novel approach by integrating large language models with social media analysis, specifically targeting HPV vaccine skepticism. Its rigorous experimental evaluation across different LLMs and methodologies provides substantial insights into stance detection, making it highly relevant in both the domains of health communication and AI application. Additionally, the potential for intervention in public health narratives offers significant interdisciplinary value.

Higher order Schrödinger equations on hyperbolic spaces

We study the following higher order Schrödinger equation on hyperbolic space $\mathbb{H}^n$ : $P_m u +a(x) u = |u|^{q - 2}u,$ where $P_m$ is the $2m$ order GJMS operator...

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The article presents a novel approach to higher order Schrödinger equations by exploring those defined on hyperbolic space, thus contributing significant new insights into the mathematical theory behind quantum mechanics in non-Euclidean geometries. The development of a new concentration compactness principle adds to its methodological rigor, which is critical for future research in both mathematical and physical contexts. Furthermore, the implications for potential functions broadens the scope of applications.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning

Graph Prompt Learning (GPL) represents an innovative approach in graph representation learning, enabling task-specific adaptations by fine-tuning prompts without altering the underlying pre-trained mo...

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The paper addresses a largely unexplored area in an emerging field by evaluating privacy risks in Graph Prompt Learning (GPL). The rigorous approach to quantifying privacy vulnerabilities, especially through both Attribute Inference and Link Inference attacks, showcases a comprehensive understanding of the topic. Additionally, the identification of effective defense mechanisms is a valuable contribution that can influence future research in both privacy preservation and graph learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data

Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs with Federated Learn...

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This paper tackles a timely and nuanced topic by addressing the limitations of current MLLM fine-tuning methodologies, particularly in the context of federated learning and multimodal heterogeneous data. The introduction of a comprehensive benchmark and a novel framework demonstrates both methodological rigor and practical relevance. The extensive experimental evaluations further enhance the credibility of the findings, which have the potential to significantly impact the fields of AI and privacy-focused machine learning. Its applicability across various domains where privacy and diverse data types are crucial makes it highly valuable for future research developments.

VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving

This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that employ neural rende...

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VisionPAD introduces a novel approach to self-supervised learning, which is critical for advancing vision-based algorithms in autonomous driving. The method's emphasis on efficiency, motion cues learning, and geometric perception is highly relevant given the increasing complexity of real-world driving conditions. Additionally, its strong empirical validation on significant datasets suggests both robustness and potential for adoption in existing systems.

Any-to-3D Generation via Hybrid Diffusion Supervision

Recent progress in 3D object generation has been fueled by the strong priors offered by diffusion models. However, existing models are tailored to specific tasks, accommodating only one modality at a ...

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This study presents a novel framework (XBind) that significantly advances the field of 3D object generation by enabling cross-modal generation, which is a substantial improvement over existing unidirectional methods. The integration of multiple modalities and the introduction of the MS Loss function showcase methodological rigor and innovation, with the potential to inspire further research in this area. The claims of extensive experiments add credibility to the results, suggesting strong applicability in diverse scenarios.

Mining double-line spectroscopic candidates in the LAMOST medium-resolution spectroscopic survey using human-AI hybrid method

We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multi-systems, specifically focusing on double-line spectrosc...

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This article combines traditional astrobiological methods with machine learning, presenting a novel approach to identify double-line spectroscopic binaries in large datasets. Its methodological rigor and significant improvements in detection efficiency are particularly noteworthy, making it relevant for both current and future astronomical research.

LIBER: Lifelong User Behavior Modeling Based on Large Language Models

CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While leveraging semantic in...

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The article presents a novel framework (LIBER) for integrating large language models into recommender systems, targeting specific shortcomings in current models related to user behavior dynamics. The approach is methodologically sound, as evidenced by its successful deployment in a real application (Huawei's music service) with measurable performance improvements. Such empirical validation enhances its relevance and utility for both academia and industry in the field of recommendation systems.

Diffusiophoretic transport of colloids in porous media

Understanding how colloids move in crowded environments is key for gaining control over their transport in applications such as drug delivery, filtration, contaminant/microplastic remediation and agri...

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This article presents a novel approach by integrating chemical gradients with traditional models of colloid transport in porous media, showing significant deviations in transport behavior. The use of microfluidic experiments, numerical simulations, and theoretical modeling demonstrates methodological rigor and the potential for broader implications across various applications. Its focus on diffusiophoretic effects in real-world scenarios is particularly pertinent and timely.

Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods

This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis...

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The article presents novel insights into the limitations of Graph Neural Networks (GNNs) in link prediction tasks, specifically addressing how the aggregation method affects performance. The rigorous experiments provide an evidence-based evaluation that can inform future research in GNNs and link prediction techniques. Additionally, the findings could contribute to the advancement of more effective algorithms, enhancing the article's relevance and impact.

Uncorrectable-error-injection based reliable and secure quantum communication

Quantum networks aim to communicate distant quantum devices, such as quantum computers. In this context, a critical requirement is the secure and reliable transmission of arbitrary quantum states. Qua...

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This article presents a novel approach to improving the reliability and security of quantum communication networks, addressing practical challenges through the integration of quantum error correction techniques. The proposal is methodologically rigorous and responds directly to real-world limitations, making the research significant for future developments in quantum communication. Its potential for scalability and efficiency positions it as a critical contribution to the field.

The Moon-forming Impact as a Constraint for the Inner Solar System's Formation

The solar system planets are benchmarks for the planet formation theory. Yet two paradigms coexist for the four terrestrial planets: the prolonged collisional growth among planetesimals lasting $&...

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The article addresses a fundamental question in planetary formation, specifically the mechanisms by which terrestrial planets form, with a focus on the origin of the Moon. It employs robust modeling and simulation techniques to critique the pebble accretion scenario, offering compelling evidence for the chaotic collisional growth model. This direct connection to planetary formation theories enhances its relevance and potential impact on future research, particularly in providing constraints for longstanding debates in planetary science.

Understanding LLM Embeddings for Regression

With the rise of large language models (LLMs) for flexibly processing information as strings, a natural application is regression, specifically by preprocessing string representations into LLM embeddi...

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This article presents a novel approach to applying LLM embeddings in regression tasks, contributing to the understanding of their effectiveness compared to traditional methods. The insights regarding the preservation of Lipschitz continuity and the quantification of model effects add depth to the conversation around the utility of LLMs beyond standard applications.

High-Bandwidth, Low-Computational Approach: Estimator-Based Control for Hybrid Flying Capacitor Multilevel Converters Using Multi-Cost Gradient Descent and State Feedforward

This paper presents an estimator-based control framework for hybrid flying capacitor multilevel (FCML) converters, achieving high-bandwidth control and reduced computational complexity. Utilizing a hy...

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The article introduces a novel estimator-based control framework that significantly improves the performance of hybrid flying capacitor multilevel converters while reducing computational requirements. Its methodological rigor and the integration of advanced techniques like multi-cost gradient descent and state feedforward add value, making it a potentially influential work in the field. The practical applications, particularly in critical sectors like data centers and electric aviation, enhance its relevance.

The CUSUM Test with Observation-Adjusted Control Limits in Parameters Change Detection for the Extremely Heavy-Tailed Distributions Sequences

In this paper, we propose an new the CUSUM sequential test (control chart, stopping time) with the observation-adjusted control limits (CUSUM-OAL) for monitoring quickly and adaptively the change in d...

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The novelty of introducing a CUSUM test with observation-adjusted control limits specifically for extremely heavy-tailed distributions is significant, especially given the challenges associated with monitoring such distributions. The paper demonstrates methodological rigor through theoretical results and numerical simulations, enhancing its applicability in fields requiring change detection in non-standard distributions. This makes it highly relevant for both practical applications and theoretical developments.

Starkiller: subtracting stars and other sources from IFU spectroscopic data through forward modeling

We present starkiller, an open-source Python package for forward-modeling flux retrieval from integral field unit spectrograph (IFU) datacubes. Starkiller simultaneously provides stellar spectral clas...

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The article presents a novel open-source tool, Starkiller, which enhances the analysis of IFU spectroscopic data through effective forward modeling and synthetic difference imaging. Its capability to separate different sources within dense stellar fields is a significant methodological advancement, promoting greater accuracy in astronomical observations. The robustness of the approach and its broad applicability to various types of astronomical objects (e.g., comets, asteroids, nebulae) increase its potential impact on the field.