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Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment

Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are pri...

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The article presents a novel, training-free approach to addressing perception bias in large multimodal models (LMMs) during image quality assessment, which is a crucial advancement given the current limitations of these models in IQA tasks. The methodology is innovative and rigorously tested, contributing significantly to the understanding of how semantic biases impact quality perception. The practical implications are strong, especially with the availability of code for public use, promoting wider adoption and further research in the area.

Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models

Knowledge editing aims to efficiently and cost-effectively correct inaccuracies and update outdated information. Recently, there has been growing interest in extending knowledge editing from Large Lan...

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The article presents a novel approach to knowledge editing in Multimodal Large Language Models (MLLMs), which is a compelling and underexplored aspect of AI research. The proposed Fine-Grained Visual Knowledge Editing (FGVEdit) benchmark and the MSCKE framework address unique challenges in multimodal contexts with methodological rigor. The empirical demonstration of effectiveness through extensive experiments further supports its relevance. The focus on both visual and textual information integration adds significant applicability.

Automated 3D Physical Simulation of Open-world Scene with Gaussian Splatting

Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current m...

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This article presents a novel approach to 3D simulation that integrates multi-modal large language models with physics-based simulation, addressing a significant challenge in the field—efficiently simulating realistic object dynamics without extensive manual input. The innovative methodology of employing MLLMs for physical property perception and the introduction of probabilistic distribution estimation for material properties is a substantial technical advancement. The reported efficiency gains and enhanced realism make this research highly relevant for advancements in the field.

Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification

In this study, we explore the essential challenge of fast scene optimization for Gaussian Splatting. Through a thorough analysis of the geometry modeling process, we reveal that dense point clouds can...

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This article addresses a significant challenge in scene optimization, presenting a novel approach that enhances efficiency without sacrificing quality. The rigorous methodology and strong results suggest meaningful contributions to both theoretical and practical applications in the field. The provision of code for reproducibility further strengthens its impact and relevance across disciplines.

Visual Cue Enhancement and Dual Low-Rank Adaptation for Efficient Visual Instruction Fine-Tuning

Fine-tuning multimodal large language models (MLLMs) presents significant challenges, including a reliance on high-level visual features that limits fine-grained detail comprehension, and data conflic...

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This article presents a novel approach to fine-tuning multimodal large language models, which is of high relevance given the growing importance of MLLMs in various applications. The proposed methods (VCE and Dual-LoRA) address significant challenges in visual comprehension and task adaptability, showcasing methodological rigor and innovation. The experimental results on benchmarks emphasize the practical applicability and potential for broader impact within the field.

Off-policy estimation with adaptively collected data: the power of online learning

We consider estimation of a linear functional of the treatment effect using adaptively collected data. This task finds a variety of applications including the off-policy evaluation (\textsf{OPE}) in c...

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This article presents a significant advancement in the understanding of off-policy evaluation (OPE) and average treatment effect (ATE) estimation using adaptively collected data. The combination of establishing theoretical bounds for AIPW estimators with practical applications in online learning makes it novel and potentially impactful. The framework provided can greatly influence future research in both the theoretical and practical realms of causal inference and contextual bandits.

Joint Vision-Language Social Bias Removal for CLIP

Vision-Language (V-L) pre-trained models such as CLIP show prominent capabilities in various downstream tasks. Despite this promise, V-L models are notoriously limited by their inherent social biases....

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This article tackles a critical issue of social bias in Vision-Language models, specifically CLIP, which has substantial implications for their real-world applicability. The proposed method showcases novelty by addressing the unbalanced debiasing method that has not been rigorously evaluated in past studies. Furthermore, the introduction of a new evaluation protocol enhances methodological rigor, allowing for a more comprehensive understanding of bias removal's effectiveness, which is essential for advancing research in this area.

Cancer incidence estimation from mortality data: a validation study within a population-based cancer registry

We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio IMR, the IMR method. Using ...

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The study presents a robust methodological framework for cancer incidence estimation using mortality data, which is a critical area in epidemiological research. Its use of advanced statistical models and Bayesian methods enhances its rigor. The validation with actual cases demonstrates practical applicability and contributes to improving cancer epidemiology, making it a valuable resource for future studies. However, the specificity to the Granada region may limit generalizability.

Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model

The analysis of 3D medical images is crucial for modern healthcare, yet traditional task-specific models are becoming increasingly inadequate due to limited generalizability across diverse clinical sc...

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The article presents a novel approach in medical imaging through the implementation of a 2D-Enhanced 3D multimodal large language model (MLLM), addressing significant limitations in current models. Its methodological rigor is underscored by systematic experimentation on a large-scale benchmark, which demonstrates superior performance outcomes. The integration of 2D and 3D modalities to enhance clinical analysis showcases high applicability for real-world clinical settings, indicating substantial potential for impact on future research and practice in this area.

Multiplexed readout of ultrasensitive bolometers

Recently, ultrasensitive calorimeters have been proposed as a resource-efficient solution for multiplexed qubit readout in superconducting large-scale quantum processors. However, experiments demonstr...

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The article presents significant advancements in the design and operation of multiplexed SNS sensors, which are crucial for improving the efficiency of quantum processors. The novel implementation of frequency multiplexing and the demonstration of low cross talk in qubit readout are particularly impactful for both practical applications and further research developments in quantum computing technology.

FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning

To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolutional channels or layer...

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The FGP approach presents a novel integration of feature-based and gradient-based criteria for convolutional layer pruning, marking a significant advancement in the field of model optimization. The methodological rigor is evident given the experimental validation across multiple datasets and tasks, indicating robustness and applicability in varied scenarios. Additionally, the focus on improving computational efficiency while preserving model accuracy is highly relevant in the context of real-world deployment in resource-constrained environments.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning

Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and synchronization delay...

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The article presents a novel approach (PPLL) that effectively addresses a significant challenge in multi-GPU training of large-scale deep learning models by minimizing communication overhead and synchronization delays. The methodology is rigorously validated through comprehensive experiments, showing substantial improvements in training speed without compromising model performance. Its innovative use of local learning algorithms suggests a strong potential for broader implications in machine learning frameworks, which further enhances its relevance.

Chikungunya: The Silent Threat in the Shadows

Chikungunya virus (CHIKV) is one of the most relevant arboviruses affecting public health today. It belongs to the Togaviridae family and alphavirus genus, causing an arthritogenic disease known as Ch...

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The article addresses a significant public health issue with Chikungunya virus, focusing on its pathogenesis and the gaps in current therapeutic strategies. The review's comprehensive overview of host and vector interactions, along with viral genetic aspects, showcases its potential for influencing future research directions and developing preventive measures or treatments. The rigorous approach and the urgent nature of the topic add to the article's impact.

Lucia: A Temporal Computing Platform for Contextual Intelligence

The rapid evolution of artificial intelligence, especially through multi-modal large language models, has redefined user interactions, enabling responses that are contextually rich and human-like. As ...

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The article presents a novel platform, Lucia, which integrates contextual temporal memory with AI, pushing boundaries in human-computer interaction and cognitive enhancement. Its focus on wearability and real-time data processing shows methodological rigor and addresses a growing demand for personalized AI-driven solutions. The interdisciplinary nature of combining cognitive science with AI and wearable technology amplifies its potential impact on various fields.

User Experience Evaluation of Augmented Reality: A Systematic Literature Review

Due to technological development, Augmented Reality (AR) can be applied in different domains. However, innovative technologies refer to new interaction paradigms, thus creating a new experience for th...

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This systematic literature review provides a comprehensive summary of current research on user experience evaluation specifically in augmented reality (AR), a cutting-edge technology. Its systematic approach and identification of gaps, particularly in Training and Education, highlight its methodological rigor and novelty. The findings can guide future research and the development of standardized metrics for UX evaluation in AR, thus enhancing usability in practical applications.

Cross-Layer Encrypted Semantic Communication Framework for Panoramic Video Transmission

In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy c...

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The article introduces a novel framework for encrypted semantic communication that advances the field of video transmission technology by incorporating a dynamic cross-layer approach. This framework demonstrates significant improvements in transmission efficiency and adaptability, which are critical in modern communication systems, especially under constraint conditions. The use of deep learning techniques such as Deep JSCC further enhances the methodological rigor, making the findings highly relevant for future research in both theoretical and applied contexts.

Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness

Social graph-based fake news detection aims to identify news articles containing false information by utilizing social contexts, e.g., user information, tweets and comments. However, conventional meth...

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The article introduces a novel evaluation scheme that addresses a major limitation in the field of fake news detection by considering temporality, making the findings more applicable in practical contexts. The proposed method, DAWN, enhances existing techniques and exhibits rigorous empirical validation, indicating strong robustness and potential impact. This approach could lead to significant advancements in the design of algorithms for real-world applications, establishing it as a critical contribution.

SuPLE: Robot Learning with Lyapunov Rewards

The reward function is an essential component in robot learning. Reward directly affects the sample and computational complexity of learning, and the quality of a solution. The design of informative r...

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The article presents a novel approach to designing reward functions in robot learning using Lyapunov exponents, addressing a significant challenge in the field. Its empirical validation on classical benchmarks adds methodological rigor, and the focus on practical scenarios enhances its applicability.

Efficient Streaming Voice Steganalysis in Challenging Detection Scenarios

In recent years, there has been an increasing number of information hiding techniques based on network streaming media, focusing on how to covertly and efficiently embed secret information into real-t...

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The paper presents a novel framework specifically designed to tackle significant challenges in the field of voice steganalysis, an area poised to grow with increasing digital communication. The methodological rigor of introducing a Dual-View framework and the extensive experiments conducted suggest a robust contribution that could significantly enhance detection methods. Its applicability to real-time voice streams in network environments underlines its relevance to current security issues, making it impactful for future research.

DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs

Direct preference learning offers a promising and computation-efficient beyond supervised fine-tuning (SFT) for improving code generation in coding large language models (LMs). However, the scarcity o...

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This article presents a highly innovative approach to improving code generation in large language models through a novel direct preference learning framework that does not rely on externally annotated datasets. The methodological rigor displayed in using self-generated tests shows significant potential for scalability and applicability in the field of AI and natural language processing. Additionally, the empirical results indicate strong performance improvements, marking it as a substantial contribution to the ongoing advancements in code generation techniques.