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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Advising big data transfer over dedicated connections based on profiling optimization

Advising big data transfer over dedicated connections based ...

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作者： Yun, Daqing Wu, Chase Q. Rao, Nageswara S. V. Kettimuthu, Rajkumar Computer and Information Sciences Program Harrisburg University HarrisburgPA17101 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States

Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward;ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based data Transfer Advisor (Probdata) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. Probdata employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in Probdata and then detail its design and implementation. The advising procedure and performance benefits of FastProf and Probdata are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs. © 1993-2012 IEEE.

关键词： data transfer

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Topological data analysis of black hole images

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Physical Review D 2022年第2期106卷 023017-023017页

作者： Pierre Christian Chi-kwan Chan Anthony Hsu Feryal Özel Dimitrios Psaltis Iniyan Natarajan Physics Department Fairfield University 1073 N Benson Road Fairfield Connecticut 06824 USA Department of Astronomy University of Arizona 933 North Cherry Avenue Tucson Arizona 85721 USA Data Science Institute University of Arizona 1230 N. Cherry Avenue Tucson Arizona 85721 USA Program in Applied Mathematics University of Arizona 617 N. Santa Rita Tucson Arizona 85721 USA Department of Computer Science University of Arizona 1040 4th Street Tucson Arizona 85721 USA Wits Centre for Astrophysics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg 2050 South Africa South African Radio Astronomy Observatory Observatory 7925 Cape Town South Africa

Features such as photon rings, jets, or hot spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high-resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or the diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images in a form that is suitable for topological analysis.

关键词： Astronomical black holes

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database Management System Design with Time Series Modification for Child Growth and Malnutrition Monitoring in The Regency of Serdang Bedagai

Database Management System Design with Time Series Modificat...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Ika Nurlaila Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia BINUS Online Learning Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

A poor data management has led zero stunting strategy in Indonesia moved nowhere. Some justifications on the demography and numbers of affected children remain a matter of plain assumptions without a clear supporting data. We hence develop a database system which enables time series monitoring on the expected increment of children's physical growth. At this initial step, our database system will be implemented at Public Health Office (Dinas Kesehatan) of Serdang Bedagai, South Celebes. This is a modification from Connoly and Begg's principles. Our database was built through 3-steps design started with conceptual followed by logical and at final step was normalizations-enriched physical design. By implementing this proposed system, the assigned healthcare workers will be able record every bit of superficial growth increase. Information obtained in the study is of essence for the efficient and effective intervention formula to achieve national zero stunting goal.

关键词： Pediatrics Monitoring database systems Dictionaries Drugs Public healthcare

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Wind gust detection using physical sensors in quadcopters capstone project 2018

arXiv

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arXiv 2019年

作者： Gu, Suwen Lin, Menghao Computer Science Department Data Analytics Program Fordham University United States

We propose the use of basic inertial measurement units (IMU) which contain sensors such as accelerometers and gyroscopes already on-board drones to detect the speed and direction of wind gusts. The ability to quickly sense wind gusts has many applications, the most notable of which is in flight assistance of the drone, where it may adjust motor power parameter to compensate for such external factors or steer the drone toward a safer direction. To illustrate the feasibility of the approach, we conducted studies to assess how reliably wind speed and wind direction can be detected while a quad-copter drone is hovering and then in-motion, using off-the-shelf classifiers. Empirical results with real-life data, collected on a micro aerial vehicle (MAV) in a physical room with a consumer-grade fan, show that (1.1) wind speed can be detected with high accuracy after training, not only on the same drone, but also across different drones of the same class, while (1.2) wind direction can be detected with high accuracy after training on the same drone, but with limited generalizability to other drones. (1.3) We demonstrate how real-time detection of wind speed, using offline trained models, is feasible and can be done with high accuracy. (2.1) Finally, we find the reason behind the lower accuracy for wind direction detection during the analysis of drones in-motion. Copyright © 2019, The Authors. All rights reserved.

关键词： Wind

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Facilitating team-based data science: Lessons learned from the DSC-WAV project

arXiv

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arXiv 2021年

作者： Legacy, Chelsey Zieffler, Andrew Baumer, Benjamin S. Barr, Valerie Horton, Nicholas J. Department of Educational Psychology University of Minnesota MinneapolisMN55455 United States Program in Statistical & Data Sciences Smith College NorthamptonMA01063 United States Department of Computer Science Mount Holyoke College South HadleyMA01075 United States Department of Mathematics and Statistics Amherst College Amherst MA01002 United States

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students’ perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own *** Codes 97K80, 97P99 Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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Comparing the Accuracy of Multiple Commercial Wearable Devices: A Method

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Procedia computer science 2019年 157卷 567-572页

作者： Bens Pardamean Haryono Soeparno Bharuno Mahesworo Arif Budiarto James Baurley Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 BioRealm LLC USA

To maintain a good fitness level, performing a minimum daily physical activity is important. Many people think that they have already met the suggested guidelines. However, most of them were wrong when they quantify their activity level using a personal activity tracker device. This problem indicates the need for personal physical activity tracker or wearable device to maintain the fitness level. However, as wide-range wearable devices available today, it is difficult to find the most suitable one regarding the price and quality. This paper proposes a study to explore the association between the quality and the price of the devices. The quality is evaluated by comparing the accuracy of the heart rate sensor and step counter of several wearable devices with the ground truth measurements. The test result is then analyzed using a series of statistical methods including, a paired t-test, Pearson’s correlation test, and Bland-Altman plot. Both t-test and correlation coefficient is used to plot the association between quality and price.

关键词： Wearable Device Fitness Tracker Correlation Heart Rate Step Counting Bland-Altman

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An integrated system of mobile application and IoT solution for pond monitoring

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Ferdinan Darmalim Alam Ahmad Hidayat Tjeng Wawan Cenggoro Kartika Purwandari Sutristo Darmalim Bens Pardamean NusaNet Jakarta Indonesia 12710 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The current progress of information and communication technology has been critical to the development of the Internet of Things (IoT) in a aquaculture system. IoT solution in this system utilizes interconnectivity between devices to obtain fish pond monitoring data from sensing devices and then transmit the data to a remote server to be analyzed for decision-making in pond management. In this paper, we propose a smart pond IoT solution that provides a continuous and real-time pond monitoring. The system principally embeds five sensors that measure critical environmental parameters to determine water quality: temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), and salinity. The IoT solution also integrates the development of a mobile application on the android platform that allows remote monitoring capabilities for users to maintain efficient pond management. The application is designed with a friendly-user interface that helps the farmer to monitor sensor measurements, manage fish production cycle, record fish feeding schedule, and also monitor the health status of fish from multiple ponds.

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An artificial intelligence method for vessel detection in images of cardiac catheterization

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Informatics in Medicine Unlocked 2023年 39卷

作者： Chien, Ting-Ying Ting, Hsien-Wei Li, Hao-Wei Chang, Hsiao-Huang Department of Computer Science and Engineering Yuan Ze University 320 Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University 320 Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University 320 Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare 242 New Taipei City Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Veterans General Hospital Taipei 11217 Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan

Coronary artery disease (CAD) is one of the top ten leading causes of death in countries worldwide. There are several methods used by physicians to diagnose CAD. One common method is the use of X-ray angiogram (XRA) images. When employing a computer-aided automatic analysis method to locate blood vessels in XRA images, problems such as complex background noise, low contrast, and difficulty in separating blood vessels from the background are encountered, especially for blood vessels with a low contrast to the background noise. In this study, we used various image preprocessing methods to highlight blood vessels from complex backgrounds and different types of blood vessel, followed by the employment of four superpixel segmentation methods to partition an image into multiple segments, and finally the utilization of Weighted Voting to determine the best superpixel algorithm. A genetic algorithm was used to identify the best weights by which to determine the locations of blood vessels in images of cardiac catheterization. Our proposed method achieved an average F1-score of 73.4% in the analysis of five patients, which was better than the other superpixel algorithms tested. The results of this research will be helpful for future SYNTAX score analysis. © 2023

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Machine learning applications for therapeutic tasks with genomics data

arXiv

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arXiv 2021年

作者： Huang, Kexin Xiao, Cao Glass, Lucas M. Critchlow, Cathy W. Gibson, Greg Sun, Jimeng Health Data Science Program Harvard University Boston02120 United States Analytics Center of Excellence IQVIA CambridgeMA02139 United States Center for Observational Research Amgen Thousand OaksCA91320 United States Center for Integrative Genomics Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science Carle's Illinois College of Medicine University of Illinois at Urbana-Champaign Urbana61820 United States

The genome contains instructions for building the function and structure, and guiding the evolution of molecules and organisms. Recent high-throughput techniques allow the generation of a vast amount of genomics data. However, the path of transforming genomics data into tangible therapeutics is filled with obstacles. We observe that genomics data alone are insufficient but rather require investigation of its interplay with data such as compounds, proteins, electronic health records, images, texts, etc. To make sense of these complex data, machine learning techniques are often utilized for identifying patterns and drawing insights from data. In this review, we study twenty-two genomics applications of machine learning that can enable faster and more efficacious therapeutic development, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. Challenges remain, including technical issues such as learning under different contexts given specific low resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

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