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arXiv 2019年

作者： Guarin, Diego L. Yunusova, Yana Taati, Babak Dusseldorp, Joseph R. Mohan, Suresh Tavares, Joana van Veen, Martinus M. Fortier, Emily Hadlock, Tessa A. Jowett, Nate KITE Toronto Rehabilitation Institute University Health Network TorontoON Canada Department of Otolaryngology/Head and Neck Surgery Massachusetts Eye and Ear Infirmary Harvard Medical School Boston United States Department of Speech Language Pathology University of Toronto TorontoON Canada Hurvitz Brain Sciences Program Sunnybrook Research Institute ON Canada Department of Computer Science University of Toronto TorontoON Canada Institute of Biomaterials and Biomedical Engineering University of Toronto TorontoON Canada Department of Plastic and Reconstructive Surgery Royal Australasian College of Surgeons University of Sydney Sydney Australia Faculty of Health Sciences Brasilia University Brasilia Brazil

Importance: Quantitative assessment of facial function is challenging, and subjective grading scales such as House-Brackmann, Sunnybrook, and eFACE have well-recognized limitations. Machine learning approaches to facial landmark localization carry great clinical potential as they enable high-throughput automated quantification of relevant facial metrics from photographs and videos. However, the translation from research settings to clinical application still requires important improvements. Objective: To develop a novel machine learning algorithm for fast and accurate localization of facial landmarks in photographs of facial palsy patients and utilize this technology as part of an automated computer-aided diagnosis system. Design, Setting, and Participants: Portrait photographs of eight expressions obtained from 200 facial palsy patients and 10 healthy participants were manually annotated, by localizing 68 facial landmarks in each photograph, by three trained clinicians using a custom graphical user interface. A novel machine learning model for automated facial landmark localization was trained using this disease-specific database. Algorithm accuracy was compared to manual markings and the output of a model trained using a larger database consisting only of healthy subjects. Main Outcomes and measurements: Root mean square error normalized by the inter-ocular distance (NRMSE) of facial landmark localization between prediction of machine learning algorithm and manually localized landmarks. Results: Publicly available algorithms for facial landmark localization provide poor localization accuracy when applied to photographs of patients compared to healthy controls (NRMSE, 8.56 ± 2.16 vs. 7.09 ± 2.34, p Copyright © 2019, The Authors. All rights reserved.

关键词： Photography

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Utilisation of down and upsample in pre-processing to enhance quality of kinect depth compression

Utilisation of down and upsample in pre-processing to enhanc...

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International Conference on Science in Information Technology (ICSITech)

作者： Christin Erniati Panjaitan Chung-An Shen Shanq-Jang Ruan Electrical Engineering Study Program Institut Teknologi Del (IT Del) Department of Electronic and Computer Engineering National Taiwan University of Science and Technology

This paper presents high performance kinect depth video compression with low computational cost. In the proposed scheme, down-sample is placed at the first part of pre-processing to reduce computational cost. Therefore, down-sampled image is refined in the middle of pre-processing part. At last stage of preprocessing, up-sample scheme is located in order to return into the original size then feed into H.264/AVC encoder. The proposed method can gain 3-4 dB and reduce computational time by 44 %-50 %.

关键词： Encoding Bit rate Video compression Computational efficiency Complexity theory Interpolation Rendering (computer graphics)

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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CWave: High-performance single-source any-angle path planning on a grid

CWave: High-performance single-source any-angle path plannin...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dmitry A. Sinyukov Taşkin Padir Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA Department of Electrical and Computer Engineering Northeastern University Boston MA

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. “Any-angle” attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A* on an 8-connected graph, the path can turn with 45°-increments only. The key idea of the presented algorithm is that it does not represent the grid as a graph and uses discrete geometric primitives to define the wave front. In its purest form, CWave requires for computation only integer arithmetics and multiplication by two, but can accumulate the distance error at turning points. A modified version of CWave with minimal usage of floating-point calculations is also developed. It allows to eliminate any accumulative errors which is proven mathematically and experimentally on several maps. The performance of the algorithm on three maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta* and Field A* adapted for single-source planning. The limitations of the current implementations of the algorithm as well as potential improvements are discussed.

关键词： Path planning Algorithm design and analysis Two dimensional displays Navigation Planning Mobile robots

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Correction to: Comparison of 12 surrogates to characterize CT radiation risk across a clinical population

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European radiology 2021年第10期31卷 8068页

作者： Francesco Ria Wanyi Fu Jocelyn Hoye W Paul Segars Anuj J Kapadia Ehsan Samei Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Medical Physics Graduate Program Departments of Radiology Physics Biomedical Engineering and Electrical and Computer Engineering Duke University 2424 Erwin Road Suite 302 Durham NC 27710 USA.

A Correction to this paper has been published: https://***/10.1007/s00330-021-07903-z

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Load balancing factor using greedy algorithm in the routing protocol for improving internet access

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IOP Conference Series: Materials Science and engineering 2018年第1期420卷

作者： Ady Satria Poltak Sihombing Sawaluddin Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology University of Sumatera Utara

Load Balancing is very well applied to the distribution of internet access at any point of the Wi-Fi area so that the use of limited devices can be optimal and on target. The proper use of the Greedy Algorithms when applied to an access point device is very capable in resolving excessive loading in a single resource to take the best choice at every stage in an optimum process. Access point device is also a success factor in running the optimization of the distribution of Wi-Fi access on the Access Point is strongly influenced by the parameters set. In this research, user access factor in one very high Wi-Fi area also influences the possibility of incoming access failure. For the application of the results of this study can be utilized on all access point devices that have limits, but for speed internet access remains at the capacity provided bandwidth.

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A novel aromatic polyurea for high energy density capacitors

A novel aromatic polyurea for high energy density capacitors

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2017 IEEE Conference on Electrical Insulation and Dielectric Phenomenon, CEIDP 2017

作者： Li, Zongze Treich, Gregory M. Scheirey, Sydney K. Sotzing, Gregory A. Cao, Yang Department of Electrical and Computer Engineering University of Connecticut 97 North Eagleville Road StorrsCT06269-3136 United States Polymer Program University of Connecticut 97 North Eagleville Road StorrsCT06269-3136 United States

ISBN: (纸本)9781538611944

Dielectric film capacitors with high energy density represent some of the most pervasive devices used in electrical and electronic systems. With the rapid growing demands in electrification, new polymer film dielectrics with high dielectric constant and high breakdown strength are needed. A toluene diisocyanate (TDI) based organic polymer with a flexible diaminoether backbone (EDR148™) was synthesized and fabricated into ∼10 μm films by a blade cast method. Systematic dielectric spectroscopy study was performed to characterize the complex dielectric spectra over a broad range of frequencies and temperatures to probe global, segmental, and local relaxations. Furthermore, high field characterizations were conducted by using a D-E polarization loop tester and a specially designed pre-breakdown conduction measurement system for the fundamental understanding of the relationship between molecular structure and high filed polarization and breakdown. TDI-EDR148 exhibited a high dielectric constant of > 6 with a low loss factor of ∼1.5% and a high breakdown strength of >600 MV/m. These properties represent a high energy density of ∼12 J/cc for this aromatic polyuria film. The result was compared with a previously studied high K polymer to correlate and reveal mechanism leading to the design of polymers with high dipolar energy density. © 2017 IEEE.

关键词： Polymer films

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Development and Validation of Instruments Adoption FinTech services in Indonesia (Perspective of Trust and Risk)

Development and Validation of Instruments Adoption FinTech s...

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International Conference on Sustainable Information engineering and Technology (SIET)

作者： Erick Fernando Surjandy Meyliana Derist Touriano Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Technology Department Faculty of computer science and engineering Adiwangsa Jambi University Jambi Indonesia

ISBN: (纸本)9781538674086;9781538674079

This study explores the influence of trust and risk in the use of FinTech in Indonesian society. This exploration is based on findings from previous studies that suggest that trust and risk are important aspects in considering the use of FinTech. The focus of this research is to develop and validate the research instruments that will be used next. The basic theory of model development uses the theory of Technology Acceptance Model (TAM) developed by Davis. The models and instruments developed will be through pilot studies involving 133 communities as respondents. This Study is quantitative research and data obtained were analyzed using smart pls v3.0. This analysis is conducted to ensure the level of reliability and validity of the instrument. The results of this analysis find 31 instruments that are stated reliable and validity. So it can be used for collecting data from a survey in accordance with the research.

关键词： Instruments Reliability Information systems Business Adaptation models Economics Transportation

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Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

Driving in the Matrix: Can virtual worlds replace human-gene...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Johnson-Roberson, Matthew Barto, Charles Mehta, Rounak Sridhar, Sharath Nittur Rosaen, Karl Vasudevan, Ram Department of Naval Architecture and Marine Engineering University of Michigan Ann ArborMI48109 United States Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Robotics Program University of Michigan Ann ArborMI48109 United States Mechanical Engineering University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781509046331

Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers. © 2017 IEEE.

关键词： Object detection

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Correction: A Novel Narrative E-Writing Intervention for Parents of Children With Chronic Life-Threatening Illnesses: Protocol for a Pilot, Open-Label Randomized Controlled Trial

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JMIR research protocols 2020年第8期9卷 e22286页

作者： Andy Hau Yan Ho Oindrila Dutta Geraldine Tan-Ho Toh Hsiang Benny Tan Xinyi Casuarine Low Sashikumar Ganapathy Josip Car Ringo Moon-Ho Ho Chun Yan Miao Psychology Program School of Social Sciences Nanyang Technological University Singapore Singapore. Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore. Palliative Care Centre for Excellence in Research and Education Singapore Singapore. School of Computer Science and Engineering Nanyang Technological University Singapore Singapore. Club Rainbow Singapore Singapore. Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore.

[This corrects the article DOI: 10.2196/17561.].

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