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arXiv 2024年

作者： Zarrinmehr, Saied Akleman, Ergun Chen, Jianer Computer Science and Engineering Department Texas A&M University College StationTX77831 United States Visual Computing & Computational Media Texas A&M University College StationTX77831 United States Computer Science and Engineering College of Engineering Texas A&M University College StationTX77831 United States

One of the theoretically intriguing problems in computer-aided geometric modeling comes from the stitching of the tensor product Bézier patches. When they share an extraordinary vertex, it is not possible to obtain continuity C1 or G1 along the edges emanating from that extraordinary vertex. Unfortunately, this stitching problem cannot be solved by using higher degree or rational polynomials. In this paper, we present a modified de Casteljau subdivision algorithm that can provide a solution to this problem. The main advantage of the modified subdivision is that the continuity C1 on a given boundary edge does not depend on the positions of the control points on other boundary edges. The modified subdivision allows us to obtain the desired C1 continuity along the edges emanating from the extraordinary vertices along with the desired G1 continuity in the extraordinary vertices (see Figures 2 and 3). Our modified de Casteljau subdivision, when combined with topological modeling, provides a framework for interactive real-time modeling of piecewise smooth manifold meshes with arbitrary topology. Based on this approach, we have extended an interactive modeling system to topologically update a smooth surface in real-time by opening or closing holes, creating handles, and combining and disconnecting surfaces (see Figure 1 for an example). The smooth surfaces resulting from any given orientable two-manifold polynomial mesh have the following properties: (1) These surfaces consist of hierarchically organized C2 continuous bicubic Bézier patches. (2) They provide visual smoothness by providing geometric G1 continuity in the extraordinary vertices and C1 continuity along the edges emanating from the extraordinary vertices. (3) They can be updated interactively in real time by directly evaluating the underlying Bézier patches. © 2024, CC BY.

关键词： Topology

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

arXiv

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arXiv 2024年

作者： Absur, Md. Nurul Saha, Sourya Nova, Sifat Nawrin Nasif, Kazi Fahim Ahmad Nasib, Md. Rahat Ul Department of Computer Science City University of New York New York United States Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia United States Samsung Austin Research Center AustinTX United States

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments. © 2024, CC BY-NC-ND.

关键词： Scalability

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Named Entity Recognition for Thai Historical Data

Named Entity Recognition for Thai Historical Data

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Nasith Laosen Kanjana Laosen Thummarat Paklao Department of Digital Technology Faculty of Science and Technology Phuket Rajabhat University Phuket Thailand Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), enabling various advanced NLP applications like information extraction, question answering, and text summarization. Thai NER presents unique challenges due to the absence of capitalization, explicit word boundaries, and sentence-ending punctuation. While NER on general domain Thai datasets has been explored, its application to historical text remains an underexplored area. Historical texts contain specialized terminology, necessitating domain-specific NER solutions for optimal performance. This study investigates Thai historical NER, utilizing historical textual data collected from the Wikipedia website. Our goal is to identify suitable word segmentation and NER methods. We evaluate the performance of the Attacut word segmentation algorithm against Deepcut and Newmm. Our findings demonstrate Attacut's superiority, achieving an F1-score of 0.9557. Furthermore, our proposed SBC model (Sentence Transformer + BiLSTM + CRF) outperforms pre-trained LLMs (BERT-th1, XLM-R, WangchanBERTa) in NER, achieving an average F1-score of 0.97. The overall performance of the Attacut algorithm and the SBC model highlights their suitability for developing advanced NLP applications within the Thai historical domain.

关键词： Terminology Software algorithms Text summarization Named entity recognition Transformers Question answering (information retrieval) Internet

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AceMiner: Accelerating Graph Pattern Matching using PIM with Optimized Cache System

AceMiner: Accelerating Graph Pattern Matching using PIM with...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Liang Yan Xiaoyang Lu Xiaoming Chen Sheng Xu Xingqi Zou Yinhe Han Xian-He Sun Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing Department of Compute Science Illinois Institute of Technology Chicago IL School of Computer and Information Anhui Normal University Wuhu

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Graph pattern matching (GPM), a critical algorithm for discovering specific patterns within complex structures, is becoming increasingly important in the data-driven world. GPM applications are memory-bound and can be accelerated by memory-centric computing systems, such as processing-in-memory (PIM). However, there are three primary challenges when it comes to accelerating GPM applications with PIM: (1) difficulty in utilizing locality, (2) heavy data movement, and (3) heavy comparison overhead due to pruning. To address these challenges, we propose AceMiner, a framework to accelerate GPM applications with a software and hardware co-design per-spective using PIM. In AceMiner, we embed hybridCache, a novel in-DRAM cache system with lower access latency and optimized replacement policy, to leverage the potential locality and reduce data movement in PIM. Additionally, we introduce a comparison unit to address the huge pruning overhead. Experimental results show that AceMiner outperforms the state-of-the-art, achieving speedups of 40.2% and 13.3% over NDMiner and DIMMining respectively, with less energy consumption and design overhead.

关键词： Energy consumption Random access memory computer architecture Minimization Software Hardware Pattern matching

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kexin Sun Zhineng Chen Gongwei Wang Jun Liu Xiongjun Ye Yu-Gang Jiang Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science & Shanghai Fudan University Shanghai Qi Zhi Institute Peking University People's Hospital Department of Urology National Cancer Center & National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College

The cost of pathological examination makes virtual restaining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual restaining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple postprocessing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bidirectional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

关键词：

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A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

arXiv

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arXiv 2023年

作者： Ye, Junjie Chen, Xuanting Xu, Nuo Zu, Can Shao, Zekai Liu, Shichun Cui, Yuhan Zhou, Zeyang Gong, Chao Shen, Yang Zhou, Jie Chen, Siming Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China School of Data Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities. However, despite the abundance of research on the difference in capabilities between GPT series models and fine-tuned models, there has been limited attention given to the evolution of GPT series models’ capabilities over time. To conduct a comprehensive analysis of the capabilities of GPT series models, we select six representative models, comprising two GPT-3 series models (i.e., davinci and text-davinci-001) and four GPT-3.5 series models (i.e., code-davinci-002, text-davinci-002, text-davinci-003, and gpt-3.5-turbo). We evaluate their performance on nine natural language understanding (NLU) tasks using 21 datasets. In particular, we compare the performance and robustness of different models for each task under zero-shot and few-shot scenarios. Our extensive experiments reveal that the overall ability of GPT series models on NLU tasks does not increase gradually as the models evolve, especially with the introduction of the RLHF training strategy. While this strategy enhances the models’ ability to generate human-like responses, it also compromises their ability to solve some tasks. Furthermore, our findings indicate that there is still room for improvement in areas such as model robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent

arXiv

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arXiv 2024年

作者： Kushnir, Samuel Leng, Jiaqi Peng, Yuxiang Fan, Lei Wu, Xiaodi Department of Computer Science University of Maryland United States Department of Mathematics University of Maryland United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Mathematics Simons Institute for the Theory of Computing University of California Berkeley United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States

We develop an open-source, end-to-end software (named QHDOPT), which can solve nonlinear optimization problems using the quantum Hamiltonian descent (QHD) algorithm. QHDOPT offers an accessible interface and automatically maps tasks to various supported quantum backends (i.e., quantum hardware machines). These features enable users, even those without prior knowledge or experience in quantum computing, to utilize the power of existing quantum devices for nonlinear and nonconvex optimization tasks. In its intermediate compilation layer, QHDOPT employs SimuQ, an efficient interface for Hamiltonian-oriented programming, to facilitate multiple algorithmic specifications and ensure compatible cross-hardware deployment. The detailed documentation of QHDOPT is available at https://***/jiaqileng/QHDOPT. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Software Development Based on User centered Design for Rural Self Help Group

Software Development Based on User Centered Design for Rural...

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Emerging Technology (INCET), International Conference for

作者： Pappala Kumar Aditya Naga Sushanth Kumar Vuppala Kalla Likhit Sai Eswar Ramesh Guntha Sruthy Anand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Rural empowerment is possible only by empowering the whole community; in this context, women's empowerment is pivotal to achieving rural upliftment. There are several studies on women's empowerment and the role of digital tools in empowering rural communities. In this work, our focus is on designing and developing the software to empower the rural SHG community. Our work also presents details of some of the existing work and introduces a user-friendly software solution to streamline the management and administration of one of the rural Self-Help Groups (SHGs) in India, which works in several hierarchies such as clusters, groups, and users. We adopted a user-centric design of the software solution as it has to accommodate users with diverse digital literacy levels. We used Google Flutter and *** framework to develop this software.

关键词： Economics Software design Navigation Scalability User centered design Programming Software

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CWT IoT Device for Detecting Rare Events of Orchid Disease

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IEEE Internet of Things Journal 2024年第12期11卷 22830-22842页

作者： Chen, Claire Y. T. Lin, Yi-Bing Chen, Wen-Liang Wu, Kuan-Chieh Lin, Yun-Wei Sun, Edward W. Liu, Chun-You Montpellier Business School Montpellier34090 France Department Of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan China Medical University Taichung 404 China Miin Wu School Of Computing National Cheng Kung University Tainan70101 Taiwan The Research Center For Information Technology Innovation Academia Sinica Taipei115 Taiwan College Of Biological Science And Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College Of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Talence33405 France Department Of Biomedical Informatics China Medical University Taichung404 Taiwan

Existing non-image-based solutions for detecting Phalaenopsis orchid diseases rely on time-domain analysis of environmental conditions. These solutions are suitable for loosely controlled open farm fields. Conversely, the diseases are infrequent events in Internet of Things (IoT)-based smart orchid greenhouses, where environmental conditions are well-controlled. In tightly controlled settings, standard time domain methods fall short in detecting subtle changes in environmental conditions during rare orchid disease events. We observed that the influence of the time series of temperature and humidity on orchid fungus within the well-controlled greenhouse is not readily apparent, and the time-frequency coefficients of temperature and humidity must be used to detect orchid disease in this rare event case. In order to address this challenge, we propose OrchidTalk, an IoT-based deep learning platform, which employs a time-frequency domain analysis using the continuous wavelet transform (CWT) filter, for the purpose of detecting rare events of orchid diseases. Through a nontrivial process, we select the Gaussian wavelet with four derivatives to serve as the mother wavelet of the CWT in the data extraction phase, and invent the CWT IoT device to translate the time-domain IoT raw data to the time-frequency domain IoT data. The resulting CWT features are used as inputs to the Orchid-3D model (3-D ConvLSTM), which significantly outperforms the previously proposed Orchid-1D (CNN and LSTM) and Orchid-2D (2-D ConvLSTM) models. For the rare event detection of 0.2% of sick orchid plants, OrchidTalk achieves a recall of 0.902, precision of 0.92, accuracy of 0.99, and an F1-score of 0.911. To the best of our knowledge, this represents the highest prediction performance achieved for a well-controlled orchid greenhouse. © 2014 IEEE.

关键词： Time domain analysis

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Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading 39

Like an Ophthalmologist: Dynamic Selection Driven Multi-View...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

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