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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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Complexity of Mds Matrix Verification

SSRN

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SSRN 2024年

作者： Kesarwani, Abhishek Insight Centre for Data Analytics School of Computer Science and the Information Technology University of College Cork Ireland

This paper talk about a new method for validating a matrix’s MDS property that outperforms the naive method. The complexity of our method is approximately half that required by the naive approach. The method is also ... 详细信息

关键词： Matrix algebra

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Synthesis of Imidazole Coated Magnetic Particles as a Candidate Material for Polymer Electrolyte Membrane in Fuel Cells 2nd

Synthesis of Imidazole Coated Magnetic Particles as a Candid...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Atmaja, Lukman Wafiroh, Siti Nashrullah, Hubbi Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Chemistry Faculty of Sciences and Technology Universitas Airlangga Surabaya60115 Indonesia School of Pharmacy Institut Teknologi Bandung Bandung40132 Indonesia

ISBN: (纸本)9789819787111

The aim of this research is to create a novel conductive material that has the capability to efficiently initiate proton transfer in a fuel cell membrane by conjugating imidazole with magnetic particles (MagP). The alignment of heterocyclic molecules by applying MagP facilitated proton transfer. The imidazole coated magnetic particles (Imid-MagP) was synthesized through several steps including the consecutive synthesis of oleic coated MagP (Oleic-MagP) and epoxy coated MagP (Epoxy-MagP). Elemental analysis of Imid-MagP was carried and the particle size distribution and morphology of Imid-MagP were investigated. It was identified that Fe was the most abundant element in Imid-MagP (87.94 wt %), the particle size distribution of Imid-MagP was 134.16 nm, and the morphology of Imid-MagP exhibited a lump-like material. The FTIR spectra of Imid-MagP exhibited a shift of imidazole at 1550 cm−1, which refers to the N-H deformation band. The ion exchange capacity (IEC), an important property in fuel cells that indicates the amount of proton exchanged in the material, of Imid-MagP was 0.675 mmol.g−1. The Imid-MagP material demonstrated excellent properties for improving proton conductivity, which is very important in fuel cell applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Proton transfer

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TRDM: A Two-stage Real-time Discrimination Method for Spiral Weld Defects under Dynamic Distorted Imaging

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15420-15434页

作者： Cui, Wenqi Song, Kechen Zhang, Yu Jia, Xiujian Liu, Xiaozheng Yan, Yunhui Northeastern University School of Mechanical Engineering and Automation Liaoning Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Northeastern University Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry China

Non-destructive testing of weld based on radio-graphic images is crucial for quality control of spiral steel pipes. However, accurately discriminating defect images from high-resolution and low-contrast weld images remains a challenging task. Moreover, the real-time imaging of radiography in industrial settings introduces dynamic distorted interferences to weld images, which has received limited attention in existing research. To address these issues, we propose a two-stage real-time discrimination method (TRDM) consisting of two carefully designed networks: an extremely lightweight weld region segmentation network and a semantic reconstruction-based discrimination network. Our method aims to adaptively reduce interference from invalid regions in the weld images to better exploit defect features from a semantic perspective and reconstruct them as defect-free regions as much as possible. To overcome the scarcity of weld defect image data, we construct two publicly available industrial weld datasets, including an image dataset and a video dataset. TRDM achieves competitive performance with state-of-the-art techniques, demonstrating its effectiveness in discriminating weld images. Furthermore, we integrate the TRDM into developed detection software for defect assessment, validating its efficacy in real welding scenarios. © 2004-2012 IEEE.

关键词： Nondestructive examination

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Performance evaluation of predictive models for detecting MMR gene mutations associated with Lynch syndrome in cancer patients in a Chinese cohort in Taiwan

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International Journal of Cancer 2024年第12期155卷 2201-2210页

作者： Hung, Fei-Hung Peng, Hung-Pin Hung, Chen-Fang Hsieh, Ling-Ling Yang, An-Suei Wang, Yong Alison Health Data Analytics and Statistics Center Office of Data Science Taipei Medical University Biomedical Translation Research Center Academia Sinica Department of Research Koo Foundation Sun-Yat Sen Cancer Center Department of Internal Medicine Koo Foundation Sun-Yat Sen Cancer Center Genomics Research Center Academia Sinica School of Medicine National Yang Ming Chiao Tung University

Identifying Lynch syndrome significantly impacts cancer risk management, treatment, and prognosis. Validation of mutation risk predictive models for mismatch repair (MMR) genes is crucial for guiding genetic counseling and testing, particularly in the understudied Asian population. We evaluated the performance of four MMR mutation risk predictive models in a Chinese cohort of 604 patients with colorectal cancer (CRC), endometrial cancer (EC), or ovarian cancer (OC) in Taiwan. All patients underwent germline genetic testing and 36 (6.0%) carried a mutation in the MMR genes (MLH1, MSH2, MSH6, and PMS2). All models demonstrated good performance, with area under the receiver operating characteristic curves comparable to Western cohorts: PREMM₅ 0.80 (95% confidence interval [CI], 0.73–0.88), MMRPro 0.88 (95% CI, 0.82–0.94), MMRPredict 0.82 (95% CI, 0.74–0.90), and Myriad 0.76 (95% CI, 0.67–0.84). Notably, MMRPro exhibited exceptional performance across all subgroups regardless of family history (FH+ 0.88, FH- 0.83), cancer type (CRC 0.84, EC 0.85, OC 1.00), or sex (male 0.83, female 0.90). PREMM₅ and MMRPredict had good accuracy in the FH+ subgroup (0.85 and 0.82, respectively) and in CRC patients (0.76 and 0.82, respectively). Using the ratio of observed and predicted mutation rates, MMRPro and PREMM₅ had good overall fit, while MMRPredict and Myriad overestimated mutation rates. Risk threshold settings in different models led to different positive predictive values. We suggest a lower threshold (5%) for recommending genetic testing when using MMRPro, and a higher threshold (20%) when using PREMM₅ and MMRPredict. Our findings have important implications for personalized mutation risk assessment and counseling on genetic testing. © 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.

关键词： cancer risk management genetic counseling Lynch syndrome MMR mutation risk prediction

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Decentralised Content Creation in Digital Learning: A Blockchain Concept 5th

Decentralised Content Creation in Digital Learning: A Blockc...

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5th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Koul, Saroj Singh, Sudhanshu Verma, Rakesh Jindal Global Business School OP Jindal Global University NCR Delhi India Analytics and Data Science NITIE Mumbai India

ISBN: (纸本)9789811641763

In today's times, when the whole world is practising social distancing, the utility of in-person training is going through an existential dilemma. The learning ecosystem needs to rapidly evolve into fulfilling the global needs of learning and retooling to stay relevant and use new opportunities. The existing models of content creation and learning generally assume that a single faculty has to create all modules with topics and lessons both included. This article proposes a conceptual model for applying blockchain in decentralised content creation. Here, education is taken up as a developmental case. While much more content is now available online for learning than ever before, the quality of the content at topic and lesson levels is being observed to have a wide variation in quality resulting in inconsistent delivery of knowledge. The blocks are made of modular knowledge pieces consisting of lessons that can be appended to the initial module/topic creator (s) tightly integrated with a permissioned blockchain with nodes consisting of co-creators, reviewers, approvers and other interested parties. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge management

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Effects of Daily News Sentiment on Stock Price Forecasting

arXiv

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arXiv 2023年

作者： Srinivas, R. Satish Gedela, Rakesh Babu Sabu, Rohit Das, Avik Nath, Gourab Datla, Vaibhav Data Science Tiger Analytics Chenai India Data Science Genpact Hyderabad India Data Science Techno India University Kolkata India Department of Data Science Praxis Business School Bangalore India Data Science PWC Bangalore India

Predicting future prices of a stock is an arduous task to perform. However, incorporating additional elements can significantly improve our predictions, rather than relying solely on a stock's historical price data to forecast its future price. Studies have demonstrated that investor sentiment, which is impacted by daily news about the company, can have a significant impact on stock price swings. There are numerous sources from which we can get this information, but they are cluttered with a lot of noise, making it difficult to accurately extract the sentiments from them. Hence the focus of our research is to design an efficient system to capture the sentiments from the news about the NITY50 stocks and investigate how much the financial news sentiment of these stocks are affecting their prices over a period of time. This paper presents a robust data collection and preprocessing framework to create a news database for a timeline of around 3.7 years, consisting of almost half a million news articles. We also capture the stock price information for this timeline and create multiple time series data, that include the sentiment scores from various sections of the article, calculated using different sentiment libraries. Based on this, we fit several LSTM models to forecast the stock prices, with and without using the sentiment scores as features and compare their performances. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Software-Defined Number Formats for High-Speed Belief Propagation

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Molahosseini, Amir Sabbagh Lee, Junkyu Vandierendonck, Hans School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Institute for Analytics and Data Science University of Essex United Kingdom

This paper presents the design and implementation of Software-Defined Floating-Point (SDF) number formats for high-speed implementation of the Belief Propagation (BP) algorithm. SDF formats are designed specifically to meet the numeric needs of the computation and are more compact representations of the data. They reduce memory footprint and memory bandwidth requirements without sacrificing accuracy, given that BP for loopy graphs inherently involves algorithmic errors. This paper designs several SDF formats for sum-product BP applications by careful analysis of the computation. Our theoretical analysis leads to the design of 16-bit (half-precision) and 8-bit (mini-precision) widths. We moreover present highly efficient software implementation of the proposed SDF formats which is centered around conversion to hardware-supported single-precision arithmetic hardware. Our solution demonstrates negligible conversion overhead on commercially available CPUs. For Ising grids with sizes from 100×100 to 500×500, the 16- and 8-bit SDF formats along with our conversion module produce equivalent accuracy to double-precision floating-point format but with 2.86× speedups on average on an Intel Xeon processor. Particularly, increasing the grid size results in higher speed-up. For example, the proposed half-precision format with 3-bit exponent and 13-bit mantissa achieved the minimum and maximum speedups of 1.30× and 1.39× over single-precision, and 2.55× and 3.40× over double-precision, by increasing grid size from 100×100 to 500×500. © 2013 IEEE.

关键词： Digital arithmetic

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Neutrosophic Mean Estimators Using Extreme Indeterminate Observations in Sample Surveys

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Neutrosophic Sets and Systems 2025年 80卷 86-117页

作者： Yadav, Vinay Kumar Majhi, Deepak Alkhathami, Alia A. Prasad, Shakti Department of Data Sciences and Analytics School of Social Sciences M S Ramaiah University of Applied Sciences Bengaluru560054 India PG Department of Mathematics Veer Kunwar Singh University Bihar Ara802301 India Department of Basic Science College of Science and Theoretical Studies Saudi Electronic University Riyadh11673 Saudi Arabia Department of Mathematics National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

In classical statistics, research typically relies on precise data to estimate the population mean, especially when auxiliary information is available. However, in the presence of outliers, conventional statistical approaches that depend on accurate data and auxiliary information encounter challenges. The primary objective is to attain the most accurate population mean estimates while minimizing the mean square error. Neutrosophic statistics, a more attractive framework than classical statistics, deals with data characterized by imprecision and uncertainty. In this current article, we adapt Särndal’s strategy and introduce neutrosophic mean estimators, applying them to meteorological data, specifically stratified dew point data. In these proposed estimators, the incorporation of auxiliary information and the application of robust techniques address issues that arise due to outliers and imprecise observations. These factors can otherwise undermine the effectiveness of neutrosophic estimation methods. The article also suggests combining auxiliary information with extremely indeterminate neutrosophic observations, utilizing robust regression methods (Huber-M, Hampel-M, and Tukey-M), as well as the quantile regression technique. These approaches enhance the neutrosophic mean estimation process. The outcomes, which include the utilization of dew point data, showcase the superior performance of the proposed estimators compared to adapted estimators in a neutrosophic context. Ultimately, this study provides valuable insights by taking an initial step in defining and utilizing the concept of neutrosophic indeterminate extreme observations. © 2025, University of New Mexico. All rights reserved.

关键词： Mean square error

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Belton, Niamh Hagos, Misgina Tsighe Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning Ireland University College Dublin School of Medicine Ireland University College Dublin School of Computer Science Ireland University College Dublin Insight Centre for Data Analytics Dublin Ireland

ISBN: (纸本)9798350302493

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose 'Few Shot anOMaly dEtection' (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on 'few' examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, 'Stop Loss', improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available;https://***/niamhbelton/FewSOME. © 2023 IEEE.

关键词： Anomaly detection

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