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arXiv 2023年

作者： Robben, Michael Hajighasemi, Amir Nasr, Mohammad Sadegh Veerla, Jai Prakesh Alsup, Anne Marie Rout, Biraaj Shang, Helen H. Fowlds, Kelli Malidarreh, Parisa Boodaghi Koomey, Paul Saurav, Jillur Rahman Luber, Jacob M. Department of Computer Science and Engineering The University of Texas Arlington United States Multi-Interprofessional Center for Health Informatics University of Texas Arlington United States Department of Bioengineering The University of Texas Arlington United States Department of Internal Medicine Ronald Reagan University of California Los Angeles Medical Center United States Department of Biology The University of Texas Arlington United States

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight. © 2023, CC BY-NC-ND.

关键词： Pathology

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A CNN-based Stock Price Trend Prediction with Futures and Historical Price 1

A CNN-based Stock Price Trend Prediction with Futures and Hi...

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Frnda, Jaroslav Diaz, Vicente Garcia Lin, Jerry Chun-Wei Shandong University of Science and Technology School of Computer Science and Engineering China Brandon University Department of Mathematics and Computer Science Brandon Canada University of Zilina Department of Quantitative Methods and Economic Informatics Zilina Slovakia University of Oviedo School of Computer Engineering Oviedo Spain Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665404839

In this paper, focusing on the task of feature extraction using financial time series as well as trend prediction for prices, a new stock sequence array convolutional neural network model is presented. This model shows promising results in improving the accuracy in stock trading forecasts. The implemented model collects data from historical sources and futures (leading indicators) of stocks. The model then uses arrays as the input map for a Convolutional Neural Network (CNN) framework. Through in-depth experimental results using the Taiwanese stock market, we are able to show that the designed approach achieves strong results. Furthermore, where compared with state-of-the-art similar models, our results show promising performance among all compared approaches. © 2020 IEEE.

关键词： Forecasting

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Real-time Emotion Recognition in Smart Homes

Real-time Emotion Recognition in Smart Homes

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Kristián Fodor Zoltán Balogh György Molnár Department of Computer Science Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Slovakia Kándó Kálmán Faculty of Electrical Engineering Óbuda University Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary

To make machines emotionally more intelligent, it is necessary to choose the right measuring devices that are able to collect physiological biosignals, which can be used to recognize the emotional state of the user. These devices should not restrict the users in their everyday life, therefore commercial, non-invasive Internet of Things (IoT) devices must be used. Thanks to the interoperability of IoT devices, it is possible to send the obtained data to a server, where it can be used in the predict method of a trained machine learning algorithm. Based on the recognized emotion, it is possible to create different instructions for smart home elements.

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The sensitivity of language models and humans to Winograd schema perturbations 58

The sensitivity of language models and humans to Winograd sc...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Abdou, Mostafa Ravishankar, Vinit Barrett, Maria Belinkov, Yonatan Elliott, Desmond Søgaard, Anders Department of Computer Science University of Copenhagen Department of Informatics University of Oslo John A. Paulson School of Engineering and Applied Sciences Harvard University Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9781952148255

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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Interfacial magnetic characteristics of nearly compensated gadolinium iron garnet

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Physical Review Materials 2024年第4期8卷 044403-044403页

作者： Karthik Srinivasan Alexander J. Grutter Thomas E. Gage P. Quarterman Christy J. Kinane Andrew J. Caruana Guichuan Yu Javier Garcia-Barriocanal Sean Langridge Bethanie J. H. Stadler Department of Electrical and Computer Engineering University of Minnesota 200 Union Street SE Minneapolis Minnesota 55455 USA Department of Electrical and Computer Engineering Boise State University Boise Idaho 83725 USA NIST Center for Neutron Research National Institute of Standards and Technology Gaithersburg Maryland 20878 USA Center for Nanoscale Materials Argonne National Laboratory Lemont Illinois 60439 USA ISIS-Neutron and Muon Source STFC Rutherford Appleton Laboratory Didcot Oxfordshire OX11 0QX United Kingdom Characterization Facility University of Minnesota 100 Union Street SE Minneapolis Minnesota 55455 USA Institute of Informatics University of Minnesota 2231 6th Street SE Minneapolis Minnesota 55455 USA

Reports on spin Hall magnetoresistance, magnonic spin currents from thermal gradients, and spin transfer-torque magnetic random-access memory using compensated ferrimagnets largely discuss bulk magnetization but lack consideration of depth profiles or interfacial characteristics. Here, magnetic and structural characterization of profiles and interfaces was performed for nearly compensated gadolinium iron garnet (GdIG) thin films. X-ray diffraction and reciprocal space maps show that sputter deposited GdIG on Si is polycrystalline with the desired cubic garnet phase, and GdIG on gadolinium gallium garnet (GGG) is epitaxial with <0.06% compressive strain. Temperature-dependent magnetometry confirms the compensation temperatures of GGG/GdIG and Si/GdIG to be 285 and 260 K, respectively, both near room temperature. Interestingly, these measurements suggest the presence of unsaturated rare-earth moments, which result in a characteristic hysteresis between heating and cooling sequences in the magnetization-temperature curves at zero field. Depth-profile measurements from polarized neutron reflectometry (PNR) indicate up to 91% volume fraction in GdIG on Si. At the interface, PNR reveals a region containing magnetized Fe-doped GGG, a low-density GdIG at the GGG/GdIG interface, and a thin magnetically dead layer at the Si/GdIG interface. Cross-sectional transmission electron microscopy and energy dispersive x-ray spectroscopy confirm the assessment of PNR. The magnetic characteristics of interfacial regions are attributed to intermixing of Fe-Ga at the GGG/GdIG interface and the presence of amorphous Fe-Si at the Si/GdIG interface.

关键词： Ferrimagnetism Magnetic insulators Rare-earth magnetic materials Magnetization measurements Neutron reflectometry Scanning transmission electron microscopy Sputtering

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Nonlinear consensus+innovations under correlated heavy-tailed noises: Mean square convergence rate and asymptotics

arXiv

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arXiv 2022年

作者： Vukovic, Manojlo Jakovetic, Dusan Bajovic, Dragana Kar, Soummya University of Novi Sad Faculty of Technical Sciences Department of Fundamental Sciences Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Serbia University of Novi Sad Faculty of Technical Sciences Department of Power Electronic and Communication Engineering Serbia Department of Electrical and Computer Engineering Carnegie Mellon University United States

We consider distributed recursive estimation of consensus+innovations type in the presence of heavy-tailed sensing and communication noises. We allow that the sensing and communication noises are mutually correlated while independent identically distributed (i.i.d.) in time, and that they may both have infinite moments of order higher than one (hence having infinite variances). Such heavy-tailed, infinite-variance noises are highly relevant in practice and are shown to occur, e.g., in dense internet of things (IoT) deployments. We develop a consensus+innovations distributed estimator that employs a general nonlinearity in both consensus and innovations steps to combat the noise. We establish the estimator’s almost sure convergence, asymptotic normality, and mean squared error (MSE) convergence. Moreover, we establish and explicitly quantify for the estimator a sublinear MSE convergence rate. We then quantify through analytical examples the effects of the nonlinearity choices and the noises correlation on the system performance. Finally, numerical examples corroborate our findings and verify that the proposed method works in the simultaneous heavy-tail communication-sensing noise setting, while existing methods fail under the same noise *** Codes 93E10, 93E35, 60G35, 94A13, 62M05 Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Effective Image Representation using Double Colour Histogram for Content-Based Image Retrieval

Informatica (Slovenia)

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Informatica (Slovenia) 2021年第7期45卷 97-105页

作者： Martey, Ezekiel Mensah Lei, Hang Li, Xiaoyu Appiah, Obed School of Information & Software Engineering University of Electronic Science and Technology of China China Department of Computer Science and Informatics University of Energy and Natural Resource Ghana

Image representation is critical to the successful realisation of Content-Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik, Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Image representation

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An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering

arXiv

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arXiv 2023年

作者： Hu, Nan Wu, Yike Qi, Guilin Min, Dehai Chen, Jiaoyan Pan, Jeff Z. Ali, Zafar School of Computer Science and Engineering Southeast University 2 Dongda Rd Jiangsu Nanjing211189 China Department of Computer Science The University of Manchester Oxford Rd ManchesterM13 9PL United Kingdom School of Informatics The University of Edinburgh 10 Crichton St EdinburghEH8 9AB United Kingdom

Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT, which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Framework for standardizing digital health in resource-constrained settings: a case study of Uganda's digital health communication infrastructure

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Oxford open digital health 2024年第2期2卷 oqae018页

作者： Andrew Alunyu Egwar Mercy Rebekah Amiyo Josephine Nabukenya Department of Computer Engineering and Informatics Busitema University Tororo Uganda. Department of Information Systems Makerere University Kampala Uganda.

Uganda has several digital health interventions, but most are not interoperable, failing the goal of digitizing the healthcare environment. One major reason for this failure is that the implementations are not based on a common set of standards formally agreed upon by the country's health ministry. Therefore, Uganda and other countries that have fallen behind in standardizing their health systems need to implement and fast-track the formulation of contextual digital health standards. This study followed a design science approach to develop a framework for standardizing the digital health communication infrastructure. The design went through three cycles of design and improvement. Results show that 17 digital health stakeholders in Uganda who evaluated the framework agreed that they are suitable to guide standardization activities in Uganda. This study argues that other countries with digital health challenges similar to those in Uganda can adopt the framework.

关键词： ICT network communication infrastructure digital health eHealth interoperability standardization framework standards

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