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A Topic-level Self-Correctional Approach to Mitigate Hallucinations in MLLMs

Aligning the behaviors of Multimodal Large Language Models (MLLMs) with human preferences is crucial for developing robust and trustworthy AI systems. While recent attempts have employed human experts...

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The article presents a novel self-correctional approach (TPO) that addresses a significant challenge in MLLMs, namely, hallucinations. The methodology is innovative and demonstrates strong empirical results that could greatly improve the reliability of AI outputs, making it highly relevant for current AI research. The inclusion of robust experimental data, combined with the promise of open-source resources (code, model, and data), increases its applicability and potential for impact within the field.

Stationary and transient correlations in driven electrolytes

Particle-particle correlation functions in ionic systems control many of their macroscopic properties. In this work, we use stochastic density functional theory to compute these correlations, and then...

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This article presents significant advancements in understanding the correlation functions in driven electrolytes, utilizing stochastic density functional theory. The novelty lies in the analysis of long-range correlation behaviors during external perturbations, which can reshape theoretical frameworks in statistical mechanics and ionic systems. The methodology is robust, and the findings have potential implications for macroscopic transport properties, though future experimental validation is essential.

Emergence of ferroelectric topological insulator as verified by quantum Hall effect of surface states in (Sn,Pb,In)Te films

Emergent phenomena arising from nontrivial band structures based on topology and symmetry have been attracting keen interest in contemporary condensed-matter physics. Materials such as SnTe and PbTe a...

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The article presents groundbreaking findings on the relationship between ferroelectricity and topological insulator properties in thin films. Its novel insights into quantum Hall states and the manipulations of Fermi levels are pivotal for advancing understanding in condensed matter physics. The demonstrated implications on functional properties suggest substantial interdisciplinary applications in future research.

Exactly solvable conformal field theories

We review 2d CFT in the bootstrap approach, and sketch the known exactly solvable CFTs with no extended chiral symmetry: Liouville theory, (generalized) minimal models, limits thereof, and loop CFTs, ...

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The article tackles the foundational aspects of two-dimensional conformal field theories (CFT) through an analysis of known exactly solvable models, contributing significantly to both theoretical understanding and practical application in statistical and quantum field theory. The use of the bootstrap approach and exploration of analytic expressions for structure constants demonstrates methodological rigor and potential for future research that seeks to expand upon these models.

HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluator

AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face...

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This article introduces a novel framework (HEIE) that tackles significant issues in the evaluation of AIGC images, particularly focusing on explainability and localization of defects using cutting-edge MLLM approaches. The integration of CoT and hierarchical methodologies displays strong methodological rigor and novelty, making it highly applicable to both academia and industry in image processing and AI-generated content. The introduction of a new dataset further strengthens its impact as it provides essential resources for future research.

MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans

Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and int...

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The article presents a novel and high-quality public dataset crucial for advancing machine learning applications in medical imaging, specifically for bone quantification in preclinical studies. The organized challenge highlights collaborative innovation and provides a strong framework for future research, while the empirical accuracy of the developed solutions adds to its methodological rigor. The open-access nature of the data and models significantly enhances applicability and potential for broader impact.

Resurfaced CsPbBr3 Nanocrystals Enable Free Radical Thermal Polymerization of Efficient Ultrafast Polyvinyl Styrene Nanocomposite Scintillators

Lead halide perovskite nanocrystals (LHP-NCs) embedded in a plastic matrix are highly promising for a variety of photonic technologies and are quickly gaining attention as ultrafast, radiation-resista...

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This article presents a significant advancement in the field of photonic materials and radiation detection by overcoming the thermal instability of lead halide perovskite nanocrystals. The novelty of introducing CsPbBr3 nanocrystals passivated with fluorinated ligands is a key innovation, potentially leading to new applications in scintillation technologies. The methodological rigor is demonstrated through quantitative measurements of scintillation light yields and resilience to gamma radiation, providing solid empirical evidence to support the findings. These elements emphasize the potential to inspire further research on nanostructured materials in photonics and radiation applications.

Adsorption of molecular hydrogen on honeycomb ZnO monolayers: A quantum density-functional theory perspective

We investigate the adsorption of molecular hydrogen on pristine zinc oxide (ZnO) platelets. The volumetric and gravimetric hydrogen storage capacities of the ZnO monolayers are evaluated in a broad ra...

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The study presents a novel exploration of hydrogen adsorption on ZnO monolayers, which is significant for energy storage applications, particularly in hydrogen fuel technologies. The use of QLDFT offers a rigorous approach to evaluating thermodynamic properties, enhancing the reliability of the results. Furthermore, the comparative analysis with graphene broadens the applicability of findings, drawing attention to the unique properties of ZnO, which could inform future materials science research.

Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting

Industry 5.0 introduces new challenges for Long-term Time Series Forecasting (LTSF), characterized by high-dimensional, high-resolution data and high-stakes application scenarios. Against this backdro...

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This article addresses the pressing need for interpretable and efficient models in Long-term Time Series Forecasting (LTSF), a critical area in industries facing massive data inflows. The novelty of the proposed DiPE-Linear model, which effectively balances complexity with interpretability and performance, offers significant advancements over traditional methods. The methodological rigor shown by evaluating the model on various datasets enhances its credibility and applicability.

Photonic Spin Hall Effect in a Four-Level Coherent Control Scheme within Cavity QED

This paper investigates the manipulation of the photonic spin Hall effect (PSHE) using a four-level closed coherent control coupling scheme in cavity quantum electrodynamics (QED). The atomic system i...

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This article presents novel findings on the photonic spin Hall effect (PSHE) within a coherent control scheme in cavity QED, showcasing the potential for tunable optical control. The methodological rigor is high due to a thorough exploration of atomic configurations and their impact on PSHE, along with experimental implications. The findings could significantly influence the design of quantum optical systems.

Experimental entanglement swapping through single-photon $χ^{(2)}$ nonlinearity

In photonic quantum information processing, quantum operations using nonlinear photon-photon interactions are vital for implementing two-qubit gates and enabling faithful entanglement swapping. Howeve...

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The article presents a significant advancement in entanglement swapping through experimentally demonstrating the use of single-photon $χ^{(2)}$ nonlinearity. This is a novel approach that addresses the challenges faced in all-photonic quantum operations, offering a method that could lead to improved quantum communication systems. The high signal-to-noise ratio achieved indicates robustness and reliability in the methodology, further enhancing its applicability. The results could lead to impactful developments in quantum computing and communication technologies, thus justifying a high relevance score.

APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents

We present APT, an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex and creative structures within the Minecraft environment. Unlike previous ap...

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The article introduces a novel framework, APT, which significantly advances the integration of large language models into autonomous construction tasks within a dynamic environment like Minecraft. Its focus on combining spatial reasoning, memory, and multimodal inputs presents a fresh and impactful contribution to the fields of AI, robotics, and game design. The rigorous benchmarking and A/B testing add methodological rigor, enhancing its applicability and potential for future advancements in AI-driven automation and creativity.

Semantic Data Augmentation for Long-tailed Facial Expression Recognition

Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expression...

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The article presents a novel approach to data augmentation specifically tailored for long-tailed distribution challenges in facial expression recognition, addressing a significant barrier in the field. The methodology combines Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), which shows methodological rigor and potential for broader applications beyond just facial expression recognition. This innovative solution is relevant both practically and theoretically, leading to high applicability in multiple domains.

LHPF: Look back the History and Plan for the Future in Autonomous Driving

Decision-making and planning in autonomous driving critically reflect the safety of the system, making effective planning imperative. Current imitation learning-based planning algorithms often merge h...

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The article presents a novel approach to integrating historical planning information in autonomous driving, which addresses significant shortcomings in current planning algorithms related to continuity and error accumulation. The system's innovative use of a historical intention aggregation module is particularly valuable. Its empirical validation against existing methods strengthens its contribution, making it applicable to real-world scenarios in autonomous driving. However, further exploration of the long-term implications and broader applicability could enhance future research directions.

Adaptive Model Hierarchies for Multi-Query Scenarios

In this contribution we present an abstract framework for adaptive model hierarchies together with several instances of hierarchies for specific applications. The hierarchy is particularly useful when...

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The framework introduces a novel approach to adaptive model hierarchies, which presents useful implications for optimization problems and Monte Carlo methods. The integration of these hierarchies into multi-query scenarios showcases a fresh perspective that could optimize computational efficiency. However, the abstract only hints at applications without detailing specific experimental results or case studies, which limits the immediate impact.

DGNN-YOLO: Dynamic Graph Neural Networks with YOLO11 for Small Object Detection and Tracking in Traffic Surveillance

Accurate detection and tracking of small objects such as pedestrians, cyclists, and motorbikes are critical for traffic surveillance systems, which are crucial in improving road safety and decision-ma...

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The introduction of DGNN-YOLO represents a significant advancement in the challenge of detecting and tracking small objects in dynamic and occluded environments. The combination of dynamic graph neural networks with the YOLO framework showcases novelty and methodological rigor. It operates effectively in complex traffic conditions, which is a critical area for intelligent transportation systems, thus addressing real-world problems. Extensive experimental validation adds to the credibility of the findings, enhancing the article's potential impact on both research and practical applications.

Weakly acyclic diagrams: A data structure for infinite-state symbolic verification

Ordered binary decision diagrams (OBDDs) are a fundamental data structure for the manipulation of Boolean functions, with strong applications to finite-state symbolic model checking. OBDDs allow for e...

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The introduction of weakly acyclic diagrams (WADs) represents a significant advancement in the field of symbolic verification, particularly for infinite-state systems where traditional methods may falter. By generalizing OBDDs and demonstrating their efficacy in handling infinite languages, the article not only addresses critical limitations in the current methodologies but also opens avenues for more robust verification tools. The methodological rigor in demonstrating the theory of WADs suggests that this work can influence future research in verification techniques and related computational models.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors

We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training ...

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This article presents a novel framework that leverages single-image priors to estimate depth and normal maps from video without the need for large annotated datasets, addressing a significant limitation in the field of computer vision and video analysis. Its methodological rigor demonstrates a sound understanding of optical flow and temporal consistency, potentially leading to significant advancements in real-time applications. The zero-shot training capability notably enhances its applicability in scenarios where data scarcity is a concern, making it highly impactful for future research and development.

DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model

Sign language translation (SLT) is challenging, as it involves converting sign language videos into natural language. Previous studies have prioritized accuracy over diversity. However, diversity is c...

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The article presents a highly innovative approach to sign language translation, addressing a critical gap in existing methods by emphasizing diversity alongside accuracy. The use of a diffusion model and the introduction of a Guidance Fusion Module demonstrate methodological rigor and potential for wide applicability in related technologies. The combination of high performance on datasets and the potential for real-world impact in inclusivity for sign language users further enhances its significance.

Probing vector gravitational atom with eccentric intermediate mass-ratio inspirals

Ultralight bosons, proposed as candidates for dark matter, are predicted by various new physics models. In the presence of bosons with suitable masses, superradiant (SR) instability can naturally tran...

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The study introduces a novel investigation into the dynamics of gravitational atoms formed by ultralight bosons, which is relevant for understanding dark matter candidates. The methodological rigor in employing a perturbative model to analyze gravitational interactions adds to the credibility of the findings. The implications for future gravitational wave detections create a strong potential for application in astrophysics.