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arXiv 2021年

作者： Gao, Zeyu Shi, Jiangbo Zhang, Xianli Li, Yang Zhang, Haichuan Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'an710049 China

The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained classification of nuclei to two cross-category classification tasks, based on two high-resolution feature extractors (HRFEs) which are proposed for learning these two tasks. The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited for the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset. © 2021, CC BY.

关键词： Grading

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Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image

arXiv

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arXiv 2021年

作者： Gao, Zeyu Hong, Bangyang Zhang, Xianli Li, Yang Jia, Chang Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an Shaanxi710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an Shaanxi710049 China

Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the papillary architecture, yet some subtle differences, including cellular and cell-layer level patterns. However, the cellular and cell-layer level patterns almost cannot be captured by existing CNN-based models in large-size histopathological images, which brings obstacles to directly applying these models to such a fine-grained classification task. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histopathological images for the pRCC subtyping task by extracting finer features from instance patches (by cropping around segmented nuclei and assigning predicted grades). The proposed i-ViT takes top-K instances as input and aggregates them for capturing both the cellular and cell-layer level patterns by a position-embedding layer, a gradeembedding layer, and a multi-head multi-layer self-attention module. To evaluate the performance of the proposed framework, experienced pathologists are invited to selected 1162 regions of interest from 171 whole slide images of type 1 and type 2 pRCC. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin. © 2021, CC BY.

关键词： Formal languages

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Attentive fine-tuning of transformers for translation of low-resourced languages @LoResMT 2021

arXiv

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arXiv 2021年

作者： Puranik, Karthik Hande, Adeep Priyadharshini, Ruba Durairaj, Thenmozhi Sampath, Anbukkarasi Thamburaj, Kingston Pal Chakravarthi, Bharathi Raja Department of Computer Science Indian Institute of Information Technology Tiruchirappalli India ULTRA Arts and Science College Tamil Nadu Madurai India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Kongu Engineering College Tamil Nadu Erode India Sultan Idris Education University Tanjong Malim Perak Malaysia Insight SFI Research Centre for Data Analytics National University of Ireland Galway Ireland

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English → Marathi and English ↔ Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English→Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English↔Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English→Marathi, Irish→English, and English→Irish respectively. The codes for our systems are published. © 2021, CC BY.

关键词： Computational linguistics

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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年第Part1期332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

arXiv

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arXiv 2023年

作者： Zhang, Linglin Brown-Mulry, Beatrice Nalla, Vineela Hwang, InChan Gichoya, Judy Wawira Gastounioti, Aimilia Banerjee, Imon Seyyed-Kalantari, Laleh Woo, MinJae Trivedi, Hari School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Information Technology Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Computational Imaging Research Center Washington University in St. Louis School of Medicine 4525 Scott Avenue St. LouisMO63110 United States Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd ScottsdaleAZ85259 United States School of Computing and Augmented Intelligence Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging dataset (EMBED) containing mammograms from 115,931 patients imaged at Emory Healthcare between 2013-2020, with Breast Imaging Reporting and data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Multiple deep learning models were trained to distinguish between abnormal tissue patches and randomly selected normal tissue patches from screening mammograms. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics and investigated their associations with false negatives and false positives. We also performed multivariate logistic regression to control for confounding between subgroups. The top-performing model, ResNet152V2, achieved accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operating characteristics curve 0.975 (95%CI=0.972-0.978). Before controlling for confounding, nearly all subgroups showed statistically significant differences in model performance. However, after controlling for confounding, we found lower false negative risk associates with Other race (RR=0.828;p= .050), biopsy-proven benign lesions (RR=0.927;p= .011), and mass (RR=0.921;p= .010) or asymmetry (RR=0.854;p= .040);higher false negative risk associates with architectural distortion (RR=1.037;p Copyright © 2023, The Authors. All rights reserved.

关键词： Mammography

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Every atom-atom map can be explained by electron pushing diagrams

arXiv

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arXiv 2023年

作者： Flamm, Christoph Müller, Stefan Stadler, Peter F. Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 WienA-1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Chemical reactions can be understood as transformations of multigraphs (molecules) that preserve vertex labels (atoms) and degrees (sums of bonding and non-bonding electrons), thereby implying the atom-atom map of a reaction. The corresponding reaction mechanism is often described by an electron pushing diagram that explains the transformation by consecutive local relocations of invidudal edges (electron pairs). Here, we show that every degree-preserving map between multigraphs, and thus every atom-atom map, can be generated by cyclic electron pushing. Moreover, it is always possible to decompose such an explanation into electron pushing diagrams involving only four electron pairs. This in turn implies that every reaction can be decomposed into a sequence of elementary reactions that involve at most two educt molecules and two product molecules. Hence, the requirement of a mechanistic explantion in terms of electron pushing and small imaginary transition states does not impose a combinatorial constraint on the feasibility of hypothetical chemical *** Codes 05C92, 92E10 Copyright © 2023, The Authors. All rights reserved.

关键词： Atoms

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Artificial Intelligence and Statistical Techniques in Short-Term Load Forecasting: A Review

arXiv

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arXiv 2021年

作者： Nassif, Ali Bou Soudan, Bassel Azzeh, Mohammad Attilli, Imtinan AlMulla, Omar Department of Computer Engineering University of Sharjah Sharjah27272 United Arab Emirates Data Analytics and Cybersecurity Research Centre RISE University of Sharjah Sharjah27272 United Arab Emirates Department of Computer Engineering University of Sharjah Sharjah27272 United Arab Emirates Sustainable Energy & Power Systems Research Centre RISE University of Sharjah Sharjah27272 United Arab Emirates Department of Data Science Princess Sumaya University for Technology Amman11941 Jordan Department of Electrical Engineering University of Sharjah Sharjah27272 United Arab Emirates Distribution Control Centre Dubai Water and Electricity Authority Dubai United Arab Emirates

Electrical utilities depend on short-term demand forecasting to proactively adjust production and distribution in anticipation of major variations. This systematic review analyzes 240 works published in scholarly journals between 2000 and 2019 that focus on applying Artificial Intelligence (AI), statistical, and hybrid models to short-term load forecasting (STLF). This work represents the most comprehensive review of works on this subject to date. A complete analysis of the literature is conducted to identify the most popular and accurate techniques as well as existing gaps. The findings show that although Artificial Neural Networks (ANN) continue to be the most commonly used standalone technique, researchers have been exceedingly opting for hybrid combinations of different techniques to leverage the combined advantages of individual methods. The review demonstrates that it is commonly possible with these hybrid combinations to achieve prediction accuracy exceeding 99%. The most successful duration for short-term forecasting has been identified as prediction for a duration of one day at an hourly interval. The review has identified a deficiency in access to datasets needed for training of the models. A significant gap has been identified in researching regions other than Asia, Europe, North America, and Australia. Copyright © 2021, The Authors. All rights reserved.

关键词： Neural networks

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Flexible and scalable field service with condition base maintenance models for simulating large scale scenarios 17

Flexible and scalable field service with condition base main...

引用

17th International Industrial Simulation Conference, ISC 2019

作者： Castañé, Gabriel G. Simonis, Helmut Brown, Kenneth N. Ozturk, Cemalettin Lin, Yiqing Antunes, Mark Insight centre for data analytics Department of Computer Science University College Cork Cork Ireland United Technologies Research Center East HartfordCT United States United Technologies Corporation WinipegMB Canada

ISBN: (纸本)9789492859075

Field service planning and scheduling research focuses on providing solutions to scenarios in which personnel travel between multiple locations carrying out different tasks specific to the field where they work. However, studying"what-if" activities in these scenarios is challenging for researchers, who have to deal with the impact on client service agreements, preferences of technicians when tasks are assigned, or unexpected faulty units. Therefore, simulation techniques are widely used to experiment and understand some critical aspects of workforce scheduling. Unfortunately, to mimic reality a high number of parameters are required within simulation models, raising scalability issues when the number of technicians, activities, and units increase over certain limits. This work1 presents a flexible simulation model to address field service planning and scheduling in large scale scenarios. The evaluation proves that the model executes in reasonable time for environments that consist of hundreds of thousands of locations in which technicians must perform diverse numbers of activities. © 2019 EUROSIS-ETI.

关键词： computer software

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The role of usability on e-learning user interactions and satisfaction: a literature review

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Journal of Systems and Information Technology 2019年第3期21卷 368-394页

作者： Gunesekera, Asela Indunil Bao, Yukun Kibelloh, Mboni Center for Modern Information Managment Huazhong University of Science and Technology Wuhan China Center for Big Data Analytics Jiangxi University of Engineering Xinyu China Faculty of Arts and Social Science Tumaini University Makumira – Dar es Salaam College Dar es Salaam Tanzania United Republic of Department of Computer Science General Sir John Kotelawala Defence University Ratmalana Sri Lanka

Purpose: The purpose of this study is to review the effect of usability factors on e-learning user relationships, namely, student–student interaction (SSI), student–instructor interaction (SII) and student–content interaction (SCI), in the existing e-learning literature. Further, this study intended to identify whether usability contributes to the satisfaction of e-learners. Design/methodology/approach: This study has undertaken a systematic review using the PRISMA methodology to filter the literature in the domain of e-learning with respect to usability concerns using six databases. An analytical framework has been formulated to evaluate the literature against different dimensions of interactions and usability. Findings: Results reveal that while SSI has grabbed 71.4 per cent research attention with respect to usability factors of e-learning systems, SCI has been given the least focus, i.e. 26.6 per cent. According to the results, e-learning systems’ usability issues influence the user relationships and affect the user satisfaction, which will lead to lack of user continuity. Practical implications: The findings of this review will provide insights to instructional designers to construct more satisfied learning content for the users. The analysis framework of this study will encourage researchers to drive future research in e-learning along with the concern of usability. Originality/value: This research emphasizes on the importance of SCI to focus future e-learning research on a different angle, in addition to SSI and SII. The analysis framework of this study will provide different dimensions, specifically for the empirical research in the domain of e-learning. © 2019, Emerald Publishing Limited.

关键词： Students

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The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community

arXiv

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arXiv 2021年

作者： Gothard, Kelly Dewhurst, David Rushing Minot, Joshua R. Adams, Jane Lydia Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Charles River Analytics 625 Mount Auburn Street CambridgeMA02138 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of 'incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language use on Reddit, a global online message board, we contextualize the incel community's online expressions of misogyny and real-world acts of violence perpetrated against women. After assembling around three million comments from incel-themed Reddit channels, we analyze the temporal dynamics of a data driven rank ordering of the glossary of phrases belonging to an emergent incel lexicon. Our study reveals the generation and normalization of an extensive coded misogynist vocabulary in service of the group's identity. Copyright © 2021, The Authors. All rights reserved.

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