检索结果-内蒙古大学图书馆

Human Reproduction 2023年第SUPPLEMENT_1期38卷

作者： Zikopoulos, A Grigoriadis, S Karagkouni, D Maziotis, E Tomara, P Kotsifaki, A Agapitou, K Pappas, A Hatzigeorgiou, A Pantos, K Sfakianoudis, K Simopoulou, M Department of Obstetrics and Gynecology Royal Cornwall Hospital Truro Treliske United Kingdom Department of Physiology Medical School National and Kapodistrian University of Athens Athens Greece DIANA Lab Department of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Centre for Human Reproduction Genesis Athens Clinic Athens Greece

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

学校读者我要写书评

暂无评论

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

关键词：

Online Handbook of Argumentation for AI: Volume 3

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Bengel, Lars Bezou-Vrakatseli, Elfia Blümel, Lydia Castagna, Federico D'Agostino, Giulia Odekerken, Daphne Patil, Minal Suresh Robinson, Jordan Wu, Hao Xydis, Andreas Artificial Intelligence Group University of Hagen Germany King’s College London United Kingdom Artificial Intelligence Group University of Hagen Germany School of Computer Science University of Lincoln United Kingdom Institute of Argumentation Linguistics and Semiotics Università della Svizzera Italiana Switzerland Department of Information and Computing Sciences Utrecht University Netherlands National Police Lab AI Netherlands Umeå Universitet Sweden Department of Electrical Engineering and Electronics University of Liverpool United Kingdom University of Aberdeen United Kingdom Department of Informatics King’s College London United Kingdom

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI. The handbook’s goals are to: 1. Encourage collaboration and knowledge discovery between members of the argumentation community. 2. Provide a platform for PhD students to have their work published in a citable peer-reviewed venue. 3. Present an indication of the scope and quality of PhD research involving argumentation for AI. © 2022, CC BY.

关键词： Handbooks

Heart rate detection with accelerometric sensors under the mattress

学校读者我要写书评

暂无评论

Heart rate detection with accelerometric sensors under the m...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Massimo Conti Carlo Aironi Simone Orcioni Ralf Seepold Maksym Gaiduk Natividad Martínez Madrid Dip. di Ingegneria dell’Informazione Università Politecnica delle Marche Ancona Italy Department of Computer Science HTWG Konstanz Germany Ubiquitous Computing Lab at HTWG Konstanz Germany The School of Informatics Reutlingen University Germany

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.

关键词： Sensors Electrocardiography Correlation Convolution Heart rate Monitoring Accelerometers

Crossmodal language grounding in an embodied neurocognitive model

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Heinrich, Stefan Yao, Yuan Hinz, Tobias Liu, Zhiyuan Hummel, Thomas Kerzel, Matthias Weber, Cornelius Wermter, Stefan Knowledge Technology Group Dept. of Informatics Universität Hamburg Germany Natural Language Processing Lab Dept. of Computer Science and Technology Tsinghua University China International Research Center for Neurointelligence University of Tokyo Japan

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterising the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilise the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children’s playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organise hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter

学校读者我要写书评

暂无评论

Research on Biomedical Engineering 2020年第2期36卷 107-119页

作者： dos Santos, Jucelino Cardoso Marciano Carrijo, Gilberto Arantes de Fátima dos Santos Cardoso, Cristiane Ferreira, Júlio César Sousa, Pedro Moises Patrocínio, Ana Cláudia Signal Processing Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 1N UberlândiaMGCEP 38400-000 Brazil Computer Vision Lab Informatics Nucleo Goiano Federal Institute of Education Science and Technology Campus Urutaí Rodovia Geraldo Silva Nascimento km 2.5 Zona Rural Bloco de Informática UrutaíGOCEP 75790-000 Brazil Biomedical Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 3N UberlândiaMGCEP 38400-000 Brazil

Purpose: Blood vessel segmentation is the most important step for detecting changes in retinal vascular structures in retinal images. While these images are widely used in clinical diagnosis, they are generally degraded by noise and are limited by low contrast. In this paper, we address the problem of improving fundus image quality for blood vessel detection. Methods: We used contrast limited adaptive histogram equalization (CLAHE) to improve contrast and the Wiener filter for noise reduction. A multilayer artificial neural network was used to optimize the values from CLAHE and the Wiener filter for blood vessel segmentation. Furthermore, several training and classification rounds were performed (3240, with 200 epochs each), using a combination of CLAHE and Wiener parameters and a fixed network configuration. Results: The proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively. Conclusion: The results were encouraging for almost all metrics and comparable to those of state-of-the-art blood vessel segmentation processes. Therefore, the parameter set effectively improved the fundus image quality for blood vessel segmentation, relative to the classification. These results are important since the more precise the segmentation step is, the greater the chances are of building a robust and specialized diagnostic system. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Image quality

Correction: Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

学校读者我要写书评

暂无评论

Journal of medical Internet research 2022年第12期24卷 e45081页

作者： Mladjan Jovanovic Goran Mitrov Eftim Zdravevski Petre Lameski Sara Colantonio Martin Kampel Hilda Tellioglu Francisco Florez-Revuelta Department of Computer Science Singidunum University Belgrade Serbia. Faculty of Computer Science and Engineering University Saints Cyril and Methodius Skopje North Macedonia. Signals & Images Lab Institute of Information Science and Technologies National Research Council of Italy Pisa Italy. Faculty of Informatics Vienna University of Technology Vienna Austria. Department of Computing Technology University of Alicante Alicante Spain.

[This corrects the article DOI: 10.2196/36553.].

关键词：

Hardware acceleration of sparse and irregular tensor computations of ML models: A survey and insights

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Dave, Shail Baghdadi, Riyadh Nowatzki, Tony Avancha, Sasikanth Shrivastava, Aviral Li, Baoxin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States School of Computer Science University of California Los AngelesCA90095 United States Parallel Computing Lab at Intel Labs Bangalore India

Machine learning (ML) models are widely used in many important domains. For efficiently processing these computational- and memory-intensive applications, tensors of these over-parameterized models are compressed by leveraging sparsity, size reduction, and quantization of tensors. Unstructured sparsity and tensors with varying dimensions yield irregular computation, communication, and memory access patterns;processing them on hardware accelerators in a conventional manner does not inherently leverage acceleration opportunities. This paper provides a comprehensive survey on the efficient execution of sparse and irregular tensor computations of ML models on hardware accelerators. In particular, it discusses enhancement modules in the architecture design and the software support;categorizes different hardware designs and acceleration techniques and analyzes them in terms of hardware and execution costs;analyzes achievable accelerations for recent DNNs;highlights further opportunities in terms of hardware/software/model co-design optimizations (inter/intra-module). The takeaways from this paper include: understanding the key challenges in accelerating sparse, irregular-shaped, and quantized tensors;understanding enhancements in accelerator systems for supporting their efficient computations;analyzing trade-offs in opting for a specific design choice for encoding, storing, extracting, communicating, computing, and load-balancing the non-zeros;understanding how structured sparsity can improve storage efficiency and balance computations;understanding how to compile and map models with sparse tensors on the accelerators;understanding recent design trends for efficient accelerations and further opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

Editorial special issue on assumptions about the concept of childhood and the roles of children in design

学校读者我要写书评

暂无评论

International Journal of Child-computer Interaction 2019年第Mar.期19卷 89-92页

作者： Bekker, Tilde Barendregt, Wolmet Skovbjerg, Helle Marie Landoni, Monica Nicol, Emma Rubegni, Elisa Department of Industrial Design Eindhoven University of Technology Netherlands Department of Applied IT University of Gothenburg Sweden LAB for Play and Design Designskolen Kolding Denmark Faculty of Informatics Università della Svizzera italiana USI Switzerland Department of Computer and Information Sciences University of Strathclyde United Kingdom School of Computer Science University of Lincoln United Kingdom

The topic of this Special Issue of Child-computer Interaction (CCI) is Assumptions about the Concept of Childhood and the Roles of Children in Design. The special issue builds on a workshop held at the Interaction Design and Children conference (IDC) in Manchester in 2016 [1,2] called Roles and Values of Children in Design. Lately, many researchers in the field have argued that they are concerned with values, assumptions and children\'s roles. Yarosh et al. [3] for example wrote an influential overview paper that examined the values held by the Interaction Design and Children (IDC) community. However, as we clearly experienced during the workshop, and we will show in this editorial, we all have different interpretations of these words, and use different definitions in different contexts, often without being aware that there may be other definitions. Indeed, Yarosh et al.\'s overview provided an extensive analysis of papers published at the IDC conference, but at the same time the paper also used the word value and values in a variety of ways: including values of the IDC community, values related to technological design and values related to the role of children in design. In CCI some researchers are concerned with investigating children\'s values, some want to provide value through their designs, while others may want to make children reflect on their own values through a particular design. This confusion about what we mean with values makes it troublesome to determine whether previously described approaches, models and methodologies are relevant for a particular study. Therefore, it becomes harder to move forward as a field. In this editorial we aim to distinguish between the different interpretations of \'value\' and \'values\' in the CCI community by providing a framework for approaches to working with value and values. We relate this to assumptions about children and the roles they can play in design. The papers included in this special issue shed some new light

关键词： Infant workshops idiopathic dilated cardiomyopathy Workshops Child childhood

The role of metadata in reproducible computational research

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Leipzig, Jeremy Nüst, Daniel Hoyt, Charles Tapley Soiland-Reyes, Stian Ram, Karthik Greenberg, Jane Metadata Research Center Drexel University College of Computing and Informatics PhiladelphiaPA United States Institute for Geoinformatics University of Münster Münster Germany Laboratory of Systems Pharmacology Harvard Medical School Boston United States eScience Lab Department of Computer Science The University of Manchester Manchester United Kingdom INDE Lab Informatics Institute University of Amsterdam Amsterdam Netherlands Berkeley Institute for Data Science University of California Berkeley United States

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, RCR can significantly accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting RCR. Metadata provides context and provenance to raw data and methods and is essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described the relationship between metadata and RCR. This article employs a functional content analysis to identify metadata standards that support RCR functions across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our article provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work. © 2020, CC BY.

关键词： Pipelines