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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Durand R, José Ribamar Junior, Geraldo Braz Da Silva, Italo Francyles Santos Da Costa Oliveira, Rui Miguel Gil Applied Computing Group Federal University of Maranhão Vila Bacanga São Luís65080-805 Brazil Graduate Program in Adult Health - PPGSAD Federal University of Maranhão Vila Bacanga São Luís65080-805 Brazil

Penile cancer, although rare, has an increasing mortality rate in Brazil, highlighting the need for effective diagnostic methods. Artificial Intelligence (AI) in histopathological analysis can speed up and objectify diagnosis, but designing an ideal architecture is challenging. In this study, we propose a neural architecture search (NAS) methodology for detecting penile cancer in digital histopathology images. We explored different configurations of stem blocks and the inclusion of attention mechanisms, highlighting specific preferences depending on the magnification of the images. The results showed that the NAS methodology enabled the discovery of more accurate and optimized architectures for this task, surpassing conventional models. The proposed models achieve 89.5% and 88.5% F1-Score for 40X and 100X magnification, respectively. © 2025 The Author(s).

关键词： Lung cancer

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An Efficient Hardware Design for the VVC Prediction Refinement with Optical Flow

IEEE Design and Test

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IEEE Design and Test 2025年

作者： Perleberg, Murilo Munoz, Marcello Maass, Denis Afonso, Vladimir Agostini, Luciano Porto, Marcelo Federal University of Pelotas Video Technology Research Group - Graduate Program in Computing Pelotas Brazil Video Technology Research Group - Sul-rio-grandense Federal Institute Pelotas Brazil

The Prediction Refinement with Optical Flow (PROF) is incorporated in the Affine Prediction of the Versatile Video Coding (VVC) standard. While the Affine Prediction allows the representation of complex motions in the scene, the PROF enhances video encoding efficiency by generating a virtual rotation on each subblock of the current block. Due to the intensive computational processing, this work presents a dedicated hardware design for the PROF and its integration with the Affine Motion Estimation Reconstructor. The synthesis results show that the proposed PROF Core design can process 120 frames per second of 4K Ultra-High-Definition resolution videos while dissipating 2.2mW. © 2013 IEEE.

关键词： Image coding

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Energy-Efficient Hardware Architecture for VVC TZS Using Approximate Subtractors

Energy-Efficient Hardware Architecture for VVC TZS Using App...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Rafael Ferreira Luciano Agostini Cláudio M. Diniz Bruno Zatt Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas RS Brazil Microelectronics Group (GME) Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper investigates the impact of approximate subtractors in the hardware architecture of the Sum of Absolute Differences (SAD) computation within the Test Zone Search (TZS) algorithm, commonly used in current video encoders like the Versatile Video Coding (VVC). Four state-of-the-art imprecise subtractors $(AppS, AXSC1, AXSC2, AXSC3)$ were analyzed across various video resolutions to assess their influence on computational effort, energy consumption, and coding efficiency. The results show that subtractors like $AppS_{4}$ and $AXCS1_{4}$ provide significant reductions in energy consumption with minimal impact on video coding quality. These findings are especially relevant for low-power devices and embedded systems, where energy efficiency is critical. The use of imprecise subtractors offers a promising tradeoff between computational efficiency and energy savings, making them a viable solution for high-performance video encoders.

关键词： Video coding Energy consumption Embedded systems Energy conservation Energy resolution Computer architecture Energy efficiency Hardware Computational efficiency Power dissipation

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Diffusion models applied to skin and oral cancer classification

arXiv

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arXiv 2025年

作者： Uliana, José J.M. Krohling, Renato A. Labcin - Nature-inspired Computing Lab Federal University of Espírito Santo Vitória Brazil Labcin - Nature-inspired Computing Lab Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo Vitória Brazil

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset. © 2025, CC BY.

关键词： Lung cancer

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A Parallel JPEG Pleno Baseline Block-Based Profile Light Field Encoder Using OpenMP

A Parallel JPEG Pleno Baseline Block-Based Profile Light Fie...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Ismael Seidel André Filipe da Silva Fernandes José Luis Güntzel Dept. of Computer Science and Statistics (INE) Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

Light Fields (LFs) are a plenoptic image modality that provides more information on light rays, making them an excellent representation for immersive media. To compress such a modality, the Joint Photographic Experts Group (JPEG) committee created the JPEG Pleno Part 2 standard with two profiles. This work focuses on the reference encoder implementation for the Baseline Block-Based Profile (BBBP), called JPEG Pleno Model (JPLM). Our main contribution lies in the proposal and analysis of a parallel implementation of JPLM using OpenMP. We show that it is possible to accelerate encoding from nearly 2 to 10 times when using 2 to 16 threads, with a memory overhead ranging from 15% up to 78%, depending on the LF size. Moreover, the speedup comes with no cost in terms of coding efficiency, i.e., the LFs encoded with the proposed parallel version are bit-exact matches to ones encoded with the sequential version.

关键词： Costs Instruction sets Transform coding Transforms Encoding Light fields Software Decoding Proposals Standards

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U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation

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Molecular Cell 2025年第10期85卷 1968-1981.e7页

作者： Kim, GyeungYun Carroll, Christine L. Wakefield, Zachary Peters Tuncay, Mustafa Fiszbein, Ana Biology Department Boston University Boston 02215 United States Graduate Program in Molecular Biology Cell Biology & Biochemistry Boston University Boston 02215 United States Graduate Program in Cell and Molecular Biology Boston University Boston 02215 United States Bioinformatics Program Faculty of Computing and Data Science Boston University Boston 02215 United States Center for Computing & Data Sciences Boston University Boston 02215 United States

Emerging evidence indicates that splicing factors mediate the close link between transcription and splicing. However, the mechanisms underlying this coupling remain unclear. U1 small nuclear ribonucleoprotein particle (U1 snRNP) not only initiates splicing but also plays a crucial role in preventing premature cleavage and polyadenylation, facilitating long-distance transcriptional elongation. Here, we show that U1 snRNP regulates alternative promoter activity in human cells by inhibiting premature polyadenylation. In genes carrying premature polyadenylation sites between two promoters, U1 snRNP inhibition with antisense oligonucleotides leads to a significant decrease in downstream promoter activity. Conversely, restoring U1 snRNP activity or inhibiting premature polyadenylation rescues downstream promoter activity. Mechanistically, U1 snRNP inhibition correlates with reduced chromatin accessibility, decreased RNA polymerase II serine 5 phosphorylation, and increased promoter-proximal pause at downstream promoters. Our findings support a model in which U1 snRNP favors productive elongation from upstream promoters, triggering downstream promoter activation by destabilizing nucleosomes and promoting promoter escape. © 2025 The Author(s)

关键词： alternative promoters dowsntream promoters promoter-proximal pause splicing factors splicing transcription transcription cooperativity transcription initiation transcription interfierence transcription start sites U1 snRNP

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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HistAttentionNAS: A CNN built via NAS for Penile Cancer Diagnosis using Histopathological Images

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Procedia Computer Science 2025年 256卷 764-771页

作者： José Ribamar Durand R. Geraldo Braz Junior Italo Francyles Santos da Silva Rui Miguel Gil da Costa Oliveira Applied Computing Group - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil Graduate Program in Adult Health - PPGSAD - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil

关键词： Penile Cancer Detection Histopathologic Classification Neural Architecture Search

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

关键词： Mobile edge computing

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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