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Articles 1 - 30 of 809

Full-Text Articles in Physical Sciences and Mathematics

Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark May 2021

Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark

SMU Data Science Review

In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve ...


Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova May 2021

Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova

SMU Data Science Review

Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing ...


Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire May 2021

Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire

All Graduate Theses and Dissertations

The choice of academic major and, subsequently, an academic institution has a massive effect on a person’s career. It not only determines their career path but their earning potential, professional happiness, etc. [1] About 40% of people who are admitted to a college do not graduate within six years. Yet, very limited resources are available for students to help make those decisions, and each guidance counselor is responsible for roughly 400 to 900 students across the United States. A tool to help these decisions would benefit students, parents, and guidance counselors.

Various research studies have shown that personality traits ...


Improving Reader Motivation With Machine Learning, Tanner A. Bohn Apr 2021

Improving Reader Motivation With Machine Learning, Tanner A. Bohn

Electronic Thesis and Dissertation Repository

This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success.

There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the ...


A Hybrid Method For Auralizing Vibroacoustic Systems And Evaluating Audio Fidelity/Sound Quality Using Machine Learning, Andrew Jared Miller Apr 2021

A Hybrid Method For Auralizing Vibroacoustic Systems And Evaluating Audio Fidelity/Sound Quality Using Machine Learning, Andrew Jared Miller

Theses and Dissertations

Two separate methods are presented to aid in the creation and evaluation of acoustic simulations. The first is a hybrid method that allows separate low and high-frequency acoustic responses to be combined into a single broadband response suitable for auralization. The process consists of four steps: 1) creating separate low-frequency and high-frequency responses of the system of interest, 2) interpolating between the two responses to get a single broadband magnitude response, 3) adding amplitude modulation to the high-frequency portion of the response, and 4) calculating approximate phase information. An experimental setup is used to validate the hybrid method. Listening tests ...


Utilizing Graph Structure For Machine Learning, Stefan Dernbach Apr 2021

Utilizing Graph Structure For Machine Learning, Stefan Dernbach

Doctoral Dissertations

The information age has led to an explosion in the size and availability of data. This data often exhibits graph-structure that is either explicitly defined, as in the web of a social network, or is implicitly defined and can be determined by measuring similarity between objects. Utilizing this graph-structure allows for the design of machine learning algorithms that reflect not only the attributes of individual objects but their relationships to every other object in the domain as well. This thesis investigates three machine learning problems and proposes novel methods that leverage the graph-structure inherent in the tasks. Quantum walk neural ...


Applying Machine Learning To Neutron-Gamma Ray Discrimination From Scintillator Readout Using Wavelength Shifting Fibers, Michael K. Moore Cdt'21, M H. Scherrer, Sean Clement, Daniel S. Hawthorne, Daniel C. Ruiz, Chad C. Schools, Craig Smith, Brian Wade Apr 2021

Applying Machine Learning To Neutron-Gamma Ray Discrimination From Scintillator Readout Using Wavelength Shifting Fibers, Michael K. Moore Cdt'21, M H. Scherrer, Sean Clement, Daniel S. Hawthorne, Daniel C. Ruiz, Chad C. Schools, Craig Smith, Brian Wade

West Point Research Papers

Advances in machine learning have found wide applications including radiation detection. In this work, machine learning is applied to neutron-gamma ray discrimination of an organic liquid scintillator (OLS) readout using wavelength shifting (WLS) fibers. The objective of using WLS fiber is to enable the transfer of the light signal from the scintillation medium, with almost any active volume geometry, to a low-profile photomultiplier. This is a common practice in high-energy physics research and has proven to be very effective for such applications. The drawback of this approach is the light pulses carried to the photomultiplier through the WLS fibers do ...


Scite: The Next Generation Of Citations, Sean Rife, Domenic Rosati, Joshua M. Nicholson Mar 2021

Scite: The Next Generation Of Citations, Sean Rife, Domenic Rosati, Joshua M. Nicholson

Faculty & Staff Research and Creative Activity

Key points

  • While the importance of citation context has long been recognized, simple citation counts remain as a crude measure of importance.
  • Providing citation context should support the publication of careful science instead of headline‐grabbing and salami‐sliced non‐replicable studies.
  • Machine learning has enabled the extraction of citation context for the first time, and made the classification of citation types at scale possible.


Improvement In Automated Diagnosis Of Soft Tissues Tumors Using Machine Learning, El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan Mar 2021

Improvement In Automated Diagnosis Of Soft Tissues Tumors Using Machine Learning, El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan

Big Data Mining and Analytics

Soft Tissue Tumors (STT) are a form of sarcoma found in tissues that connect, support, and surround body structures. Because of their shallow frequency in the body and their great diversity, they appear to be heterogeneous when observed through Magnetic Resonance Imaging (MRI). They are easily confused with other diseases such as fibroadenoma mammae, lymphadenopathy, and struma nodosa, and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients. Researchers have proposed several machine learning models to classify tumors, but none have adequately addressed this misdiagnosis problem. Also, similar studies that have proposed models for ...


Mathematical Validation Of Proposed Machine Learning Classifier For Heterogeneous Traffic And Anomaly Detection, Azidine Guezzaz, Younes Asimi, Mourade Azrour, Ahmed Asimi Mar 2021

Mathematical Validation Of Proposed Machine Learning Classifier For Heterogeneous Traffic And Anomaly Detection, Azidine Guezzaz, Younes Asimi, Mourade Azrour, Ahmed Asimi

Big Data Mining and Analytics

The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes. The recognition of new elements is possible based on predefined classes. Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities. To overcome this problem, new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic. The main objective of ...


Identifying, Analyzing, And Using Discriminatory Variables For Classification Of Neutrino Signal And Background Noise In Multivariate Analysis In The Askaryan Radio Array Experiment, Jesse Osborn Mar 2021

Identifying, Analyzing, And Using Discriminatory Variables For Classification Of Neutrino Signal And Background Noise In Multivariate Analysis In The Askaryan Radio Array Experiment, Jesse Osborn

Honors Theses, University of Nebraska-Lincoln

The Askaryan Radio Array Experiment, located near the South Pole, works to pinpoint specific instances of neutrinos from outside the solar system interacting with nucleons inside the Antarctic ice, emitting radio waves. I have taken data from the ARA stations which is presumed to be background noise and compared it to simulated data meant to look like a neutrino signal. I developed a suite of variables for discrimination between the two data sets, using a computer algorithm to generate a single output variable which can be used to distinguish noise events from signal events. I maximized this discrimination process for ...


Creating A Multifarious Cyber Science Major, Raymond Blaine, Jean Blair, Christa Chewar, Rob Harrison, James J. Raftery, Edward Sobiesk Mar 2021

Creating A Multifarious Cyber Science Major, Raymond Blaine, Jean Blair, Christa Chewar, Rob Harrison, James J. Raftery, Edward Sobiesk

West Point Research Papers

Existing approaches to computing-based cyber undergraduate majors typically take one of two forms: a broad exploration of both technical and human aspects, or a deep technical exploration of a single discipline relevant to cybersecurity. This paper describes the creation of a third approach—a multifarious major, consistent with Cybersecurity Curricula 2017, the ABET Cybersecurity Program Criteria, and the National Security Agency Center for Academic Excellence—Cyber Operations criteria. Our novel curriculum relies on a 10-course common foundation extended by one of five possible concentrations, each of which is delivered through a disciplinary lens and specialized into a highly relevant computing ...


Feature Selection On Permissions, Intents And Apis For Android Malware Detection, Fred Guyton Jan 2021

Feature Selection On Permissions, Intents And Apis For Android Malware Detection, Fred Guyton

CCE Theses and Dissertations

Malicious applications pose an enormous security threat to mobile computing devices. Currently 85% of all smartphones run Android, Google’s open-source operating system, making that platform the primary threat vector for malware attacks. Android is a platform that hosts roughly 99% of known malware to date, and is the focus of most research efforts in mobile malware detection due to its open source nature. One of the main tools used in this effort is supervised machine learning. While a decade of work has made a lot of progress in detection accuracy, there is an obstacle that each stream of research ...


A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita Jan 2021

A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita

All Works

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine ...


Review Of Forecasting Univariate Time-Series Data With Application To Water-Energy Nexus Studies & Proposal Of Parallel Hybrid Sarima-Ann Model, Cory Sumner Yarrington Jan 2021

Review Of Forecasting Univariate Time-Series Data With Application To Water-Energy Nexus Studies & Proposal Of Parallel Hybrid Sarima-Ann Model, Cory Sumner Yarrington

Graduate Theses, Dissertations, and Problem Reports

The necessary materials for most human activities are water and energy. Integrated analysis to accurately forecast water and energy consumption enables the implementation of efficient short and long-term resource management planning as well as expanding policy and research possibilities for the supportive infrastructure. However, the integral relationship between water and energy (water-energy nexus) poses a difficult problem for modeling. The accessibility and physical overlay of data sets related to water-energy nexus is another main issue for a reliable water-energy consumption forecast. The framework of urban metabolism (UM) uses several types of data to build a global view and highlight issues ...


Methods For Developing A Machine Learning Framework For Precise 3d Domain Boundary Prediction At Base-Level Resolution, Spiro C. Stilianoudakis Jan 2021

Methods For Developing A Machine Learning Framework For Precise 3d Domain Boundary Prediction At Base-Level Resolution, Spiro C. Stilianoudakis

Theses and Dissertations

High-throughput chromosome conformation capture technology (Hi-C) has revealed extensive DNA looping and folding into discrete 3D domains. These include Topologically Associating Domains (TADs) and chromatin loops, the 3D domains critical for cellular processes like gene regulation and cell differentiation. The relatively low resolution of Hi-C data (regions of several kilobases in size) prevents precise mapping of domain boundaries by conventional TAD/loop-callers. However, high resolution genomic annotations associated with boundaries, such as CTCF and members of cohesin complex, suggest a computational approach for precise location of domain boundaries.

We developed preciseTAD, an optimized machine learning framework that leverages a random ...


Improving A Wireless Localization System Via Machine Learning Techniques And Security Protocols, Zachary Yorio Dec 2020

Improving A Wireless Localization System Via Machine Learning Techniques And Security Protocols, Zachary Yorio

Masters Theses, 2020-current

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and ...


Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren Dec 2020

Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren

Doctoral Dissertations

Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have ...


An Assessment Of The Hydrological Trends Using Synergistic Approaches Of Remote Sensing And Model Evaluations Over Global Arid And Semi-Arid Regions, Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy Manikandan, Thomas Piechota, Daniele Struppa Dec 2020

An Assessment Of The Hydrological Trends Using Synergistic Approaches Of Remote Sensing And Model Evaluations Over Global Arid And Semi-Arid Regions, Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy Manikandan, Thomas Piechota, Daniele Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an ...


Deep Q Learning Applied To Stock Trading, Agnibh Dasgupta Dec 2020

Deep Q Learning Applied To Stock Trading, Agnibh Dasgupta

All Graduate Theses and Dissertations

Developing a strategy for stock trading is a vital task for investors. However, it is challenging to obtain an optimal strategy, given the complex and dynamic nature of the stock market. This thesis aims to explore the applications of Reinforcement Learning with the goal of maximizing returns from market investment, keeping in mind the human aspect of trading by utilizing stock prices represented as candlestick graphs. Furthermore, the algorithm studies public interest patterns in form of graphs extracted from Google Trends to make predictions. Deep Q learning has been used to train an agent based on fused images of stock ...


Deep Neural Network For Complex Open-Water Wetland Mapping Using High-Resolution Worldview-3 And Airborne Lidar Data, Vitor S. Martins, Amy L. Kaleita, Brian K. Gelder, Gustavo W. Nagel, Daniel A. Maciel Dec 2020

Deep Neural Network For Complex Open-Water Wetland Mapping Using High-Resolution Worldview-3 And Airborne Lidar Data, Vitor S. Martins, Amy L. Kaleita, Brian K. Gelder, Gustavo W. Nagel, Daniel A. Maciel

Agricultural and Biosystems Engineering Publications

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several ...


Acquisition, Processing, And Analysis Of Video, Audio And Meteorological Data In Multi-Sensor Electronic Beehive Monitoring, Sarbajit Mukherjee Dec 2020

Acquisition, Processing, And Analysis Of Video, Audio And Meteorological Data In Multi-Sensor Electronic Beehive Monitoring, Sarbajit Mukherjee

All Graduate Theses and Dissertations

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures.

Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on ...


In The Margins: Reconsidering The Range And Contribution Of Diazotrophs In Nearshore Environments, Corday R. Selden Dec 2020

In The Margins: Reconsidering The Range And Contribution Of Diazotrophs In Nearshore Environments, Corday R. Selden

OEAS Theses and Dissertations

Dinitrogen (N2) fixation enables primary production and, consequently, carbon dioxide drawdown in nitrogen (N) limited marine systems, exerting a powerful influence over the coupled carbon and N cycles. Our understanding of the environmental factors regulating its distribution and magnitude are largely based on the range and sensitivity of one genus, Trichodesmium. However, recent work suggests that the niche preferences of distinct diazotrophic (N2 fixing) clades differ due to their metabolic and ecological diversity, hampering efforts to close the N budget and model N2 fixation accurately. Here, I explore the range of N2 fixation across physico-chemical ...


Walls Have Ears: Eavesdropping User Behaviors Via Graphics-Interrupt-Based Side Channel, Haoyu Ma, Jianwen Tian, Debin Gao, Jia Chunfu Dec 2020

Walls Have Ears: Eavesdropping User Behaviors Via Graphics-Interrupt-Based Side Channel, Haoyu Ma, Jianwen Tian, Debin Gao, Jia Chunfu

Research Collection School Of Computing and Information Systems

Graphics Processing Units (GPUs) are now playing a vital role in many devices and systems including computing devices, data centers, and clouds, making them the next target of side-channel attacks. Unlike those targeting CPUs, existing side-channel attacks on GPUs exploited vulnerabilities exposed by application interfaces like OpenGL and CUDA, which can be easily mitigated with software patches. In this paper, we investigate the lower-level and native interface between GPUs and CPUs, i.e., the graphics interrupts, and evaluate the side channel they expose. Being an intrinsic profile in the communication between a GPU and a CPU, the pattern of graphics ...


Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas Dec 2020

Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (k- NN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of ...


Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert Ross, John Kelleher Dec 2020

Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert Ross, John Kelleher

Conference papers

Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the ...


Detecting Hacker Threats: Performance Of Word And Sentence Embedding Models In Identifying Hacker Communications, Susan Mckeever, Brian Keegan, Andrei Quieroz Dec 2020

Detecting Hacker Threats: Performance Of Word And Sentence Embedding Models In Identifying Hacker Communications, Susan Mckeever, Brian Keegan, Andrei Quieroz

Conference papers

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on deep/dark web and even surface web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we ...


A Deep Learning Diagnostic Platform For Diffuse Large B-Cell Lymphoma With High Accuracy Across Multiple Hospitals, Dongguang Li, Jacob R. Bledsoe, Yu Zeng, Wei Liu, Yiguo Hu, Ke Bi, Aibin Liang, Shaoguang Li Nov 2020

A Deep Learning Diagnostic Platform For Diffuse Large B-Cell Lymphoma With High Accuracy Across Multiple Hospitals, Dongguang Li, Jacob R. Bledsoe, Yu Zeng, Wei Liu, Yiguo Hu, Ke Bi, Aibin Liang, Shaoguang Li

Open Access Publications by UMMS Authors

Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide treatment options, but this requirement is difficult to meet. Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slides, diagnostic accuracy has not reached a clinically usable level. Establishment of an AI model often demands big datasets and an ability to handle large variations in sample preparation and image collection. Here, we establish a highly accurate deep learning platform, consisting of multiple convolutional ...


Activity Recognition With Smartphone Sensors, Xing Su, Hanghang Tong, Ping Ji Nov 2020

Activity Recognition With Smartphone Sensors, Xing Su, Hanghang Tong, Ping Ji

Tsinghua Science and Technology

The ubiquity of smartphones together with their ever-growing computing, networking, and sensing powers have been changing the landscape of people’s daily life. Among others, activity recoginition, which takes the raw sensor reading as inputs and predicts a user’s motion activity, has become an active research area in recent years. It is the core building block in many high-impact applications, ranging from health and fitness monitoring, personal biometric signature, urban computing, assistive technology, and elder-care, to indoor localization and navigation, etc. This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones’ sensors. We start ...


Patterns Of Chromatin-Modifications Discriminate Different Genomic Features In Arabidopsis, Anuj Srivastava, Xiaoyu Zhang, Sal Lamarca, Liming Cai, Russell L. Malmberg Nov 2020

Patterns Of Chromatin-Modifications Discriminate Different Genomic Features In Arabidopsis, Anuj Srivastava, Xiaoyu Zhang, Sal Lamarca, Liming Cai, Russell L. Malmberg

Tsinghua Science and Technology

Dynamic regulation and packaging of genetic information is achieved by the organization of DNA into chromatin. Nucleosomal core histones, which form the basic repeating unit of chromatin, are subject to various post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitinylation. These modifications have effects on chromatin structure and, along with DNA methylation, regulate gene transcription. The goal of this study was to determine if patterns in modifications were related to different categories of genomic features, and, if so, if the patterns had predictive value. In this study, we used publically available data (ChIP-chip) for different types of histone modifications (methylation ...