Miracle-dz/UniFAR-FTUs huggingface dataset:
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

Ziga, Kyle J. M.S, Purdue University, December 2018. Texture Synthesis and Photorealistic Re-rendering of Room Scene Images. Major Professor: Fengqing Zhu. In this thesis, we investigate methods for texture synthesis and texture re-rendering of indoor room scene images. The goal is to create a photorealistic redesign of interior spaces by replacing surface finishes with a new product based on a single room scene image. Specifically, we focus on automating this process to reduce manual input while enabling high-quality and easy-to-use experience. The most common method of rendering textures into a scene is called texture mapping. Texture mapping involves mapping pixels in a texture sample to vertices in an object model. Typically, a large texture sample is required to perform texture mapping properly. Given a small texture sample, texture synthesis creates a large sized texture that appears to have been made by the same underlying process. In the first part of this thesis, we present a method of texture synthesis that automatically determines a set of parameters to produce satisfactory results based on the texture types. The next challenge is to create a photorealistic re-rendering of the synthesized texture in the room scene image. 3D scene information such as geometry, lighting and reflectance is crucial to making the re-rendered image realistic. These properties contribute to the image formation process and must be estimated to create a scene-consistent modification. Knowing these parameters allows effects like highlights, shadows and inter-object reflections to be maintained during the re-rendering process. We detail methods for estimating these parameters from a single indoor image. Finally, we will show a web-based implementation of these methods using the WebGL library ThreeJS.
Stimulating Intrinsic Motivation in Millennial Students: A New Generation, a New Approach

There has been a fundamental change in health care pedagogy to address the demands and challenges posed by the present generation of millennial students. There is also a growing recognition of the role of intrinsic motivation as a catalyst in a positive learning experience. The term intrinsic motivation refers to energizing behavior that comes from within an individual and develops due to an inherent interest in the activity at hand. However, stimulating intrinsic motivation in the present generation of millennial health care students is a daunting task, considering their diverse and disparate nature. In addition, the inherent generational differences between educators and students, and an increasing emphasis on technological tools have resulted in a dichotomy in the educational environment leading to the development of a greater incidence of burnouts among students. Hence, numerous innovative techniques have been introduced in health care education to enhance the levels of intrinsic motivation in these students. Unfortunately, most of these approaches have only been moderately successful due to their limited ability to address the unique educational expectations of millennial students. The cumulative evidence suggests that specific approaches to stimulate intrinsic motivation should aim at nurturing the learning efforts of students, bridging the generational barriers between educators and students, and ameliorating the stress associated with health care education. Hence, the specific aim of this narrative review is to suggest empirically proven curricular strategies and institutional reforms to enhance intrinsic motivation in health care students belonging to the Millennial Generation.
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF COMMITTEE APPROVAL

Li, Jing Ph.D., Purdue University, May 2019. Fast and Robust UAV to UAV Detection and Tracking Algorithm. Major Professors: Charles A. Bouman, Juan P. Wachs. Unmanned Aerial Vehicle (UAV) technology is being increasingly used in a wide variety of applications ranging from remote sensing, to delivery, to security. As the number of UAVs increases, there is a growing need for UAV to UAV detection and tracking systems for both collision avoidance and coordination. Among possible solutions, autonamous “see-and-avoid” systems based on low-cost high-resolution video cameras offer the important advantages of light-weight and low power sensors. However, in order to be effective, camera based “see-and-avoid” systems will require sensitive, robust, and computationally efficient algorithms for UAV to UAV detect and tracking (U2U-D&T) from a moving camera. In this thesis, we propose a general architecture for a highly accurate and computationally efficient U2U-D&T algorithms for detecting UAVs from a camera mounted on a moving UAV platform. The thesis contains three studies of U2U-D&T algorithms. In the first study, we present a new approach to detect and track other UAVs from a single camera in our own UAV. Given the sequence of video frames, we estimate the background motion via perspective transformation model and then identify distinctive points in the background subtracted image to detect moving objects. We find spatio-temporal characteristics of each moving object through optical flow matching and then classify our targets which have very different motion compared with background. We also perform tracking based on Kalman filter to enforce the temporal consistency on our detection. The algorithm is tested on real videos from UAVs and results show that it is effective to detect and track small UAVs with limited computing resources.
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

Mahfuz, Rehana M.S., Purdue University, May 2020. Defending Against Adversarial Attacks with Denoising Autoencoders. Major Professor: Aly El Gamal. Gradient-based adversarial attacks on neural networks threaten extremely critical applications such as medical diagnosis and biometric authentication. These attacks use the gradient of the neural network to craft imperceptible perturbations to be added to the test data, in an attempt to decrease the accuracy of the network. We propose a defense to combat such attacks, which can be modified to reduce the training time of the network by as much as 71%, and can be further modified to reduce the training time of the defense by as much as 19%. Further, we address the threat of uncertain behavior on the part of the attacker, a threat previously overlooked in the literature that considers mostly white box scenarios. To combat uncertainty on the attacker’s part, we train our defense with an ensemble of attacks, each generated with a different attack algorithm, and using gradients of distinct architecture types. Finally, we discuss how we can prevent the attacker from breaking the defense by estimating the gradient of the defense transformation.
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

Liu, Ke M.S., Purdue University, December 2018. Pattern Exploration from Citizen Geospatial Data. Major Professor: Jie Shan. Due to the advances in location-acquisition techniques, citizen geospatial data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study society and citizens through exploring patterns from geospatial data. In this thesis, we investigate patterns of population and human sentiments using GPS trajectory data and geo-tagged tweets. Kernel density estimation and emerging hot spot analysis are first used to demonstrate population distribution across space and time. Then a flow extraction model is proposed based on density difference for human movement detection and visualization. Case studies with volleyball game in West Lafayette and traffics in Puerto Rico verify the effectiveness of this method. Flow maps are capable of tracking clustering behaviors and direction maps drawn upon the orientation of vectors can precisely identify location of events. This thesis also analyzes patterns of human sentiments. Polarity of tweets is represented by a numeric value based on linguistics rules. Sentiments of four US college cities are analyzed according to its distribution on citizen, time, and space. The research result suggests that social media can be used to understand patterns of public sentiment and well-being.
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF COMMITTEE APPROVAL

Pathak, Durvesh. M.S.E.C. E. , Purdue University, December 2018. Compressed Convolutional Neural Network for Autonomous Systems. Major Professor: Mohamed El-Sharkawy. The word “Perception” seems to be intuitive and maybe the most straightforward problem for the human brain because as a child we have been trained to classify images, detect objects, but for computers, it can be a daunting task. Giving intuition and reasoning to a computer which has mere capabilities to accept commands and process those commands is a big challenge. However, recent leaps in hardware development, sophisticated software frameworks, and mathematical techniques have made it a little less daunting if not easy. There are various applications built around to the concept of “Perception”. These applications require substantial computational resources, expensive hardware, and some sophisticated software frameworks. Building an application for perception for the embedded system is an entirely different ballgame. Embedded system is a culmination of hardware, software and peripherals developed for specific tasks with imposed constraints on memory and power. Therefore, the applications developed should keep in mind the memory and power constraints imposed due to the nature of these systems. Before 2012, the problems related to “Perception” such as classification, object detection were solved using algorithms with manually engineered features. However, in recent years, instead of manually engineering the features, these features are learned through learning algorithms. The game-changing architecture of Convolution Neural Networks proposed in 2012 by Alex K [1], provided a tremendous momentum in the direction of pushing Neural networks for perception. This thesis is an attempt to develop a convolution neural network architecture for embedded systems, i.e. an
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF DISSERTATION APPROVAL

Li, Huang, Ph.D., Purdue University, August 2019. Visual Analytics and Interactive Machine Learning for Human Brain Data. Major Professor: Shiaofen Fang. This study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. In this study, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, Spherical Volume Rendering. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases. For interactive machine learning, nowadays machine learning algorithms usually require a large volume of data to train the algorithm-specific models, with little or no user feedback during the model building process. Such a big data based automatic learning strategy is sometimes unrealistic for applications where data collection or
Reviving the Transcriptome Studies: An Insight Into the Emergence of Single-Molecule Transcriptome Sequencing

Advances in transcriptomics have provided an exceptional opportunity to study functional implications of the genetic variability. Technologies such as RNA-Seq have emerged as state-of-the-art techniques for transcriptome analysis that take advantage of high-throughput next-generation sequencing. However, similar to their predecessors, these approaches continue to impose major challenges on full-length transcript structure identification, primarily due to inherent limitations of read length. With the development of single-molecule sequencing (SMS) from PacBio, a growing number of studies on the transcriptome of different organisms have been reported. SMS has emerged as advantageous for comprehensive genome annotation including identification of novel genes/isoforms, long non-coding RNAs and fusion transcripts. This approach can be used across a broad spectrum of species to better interpret the coding information of the genome, and facilitate the biological function study. We provide an overview of SMS platform and its diverse applications in various biological studies, and our perspective on the challenges associated with the transcriptome studies.
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks

Deep learning-based techniques have achieved state-of-the-art performance on a wide variety of recognition and classification tasks. However, these networks are typically computationally expensive to train, requiring weeks of computation on many GPUs; as a result, many users outsource the training procedure to the cloud or rely on pre-trained models that are then fine-tuned for a specific task. In this paper, we show that the outsourced training introduces new security risks: an adversary can create a maliciously trained network (a backdoored neural network, or a BadNet) that has the state-of-the-art performance on the user’s training and validation samples but behaves badly on specific attacker-chosen inputs. We first explore the properties of BadNets in a toy example, by creating a backdoored handwritten digit classifier. Next, we demonstrate backdoors in a more realistic scenario by creating a U.S. street sign classifier that identifies stop signs as speed limits when a special sticker is added to the stop sign; we then show in addition that the backdoor in our U.S. street sign detector can persist even if the network is later retrained for another task and cause a drop in an accuracy of 25% on average when the backdoor trigger is present. These results demonstrate that backdoors in neural networks are both powerful and—because the behavior of neural networks is difficult to explicate—stealthy. This paper provides motivation for further research into techniques for verifying and inspecting neural networks, just as we have developed tools for verifying and debugging software.
Mindfulness Meditation and Anxiety in Adolescents on an Inpatient Psychiatric Unit

This study presents the impact of chaplain-provided mindfulness meditation (MM) groups on state-anxiety in adolescent inpatients on an acute psychiatric unit, an understudied topic that warrants further research given that anxiety is a demonstrated predictor of suicide attempts in adolescents and the elevated suicide risk of this population. A total of 53 adolescent patients, age 13–19, attended optional 30-minute MM groups while hospitalized for inpatient psychiatric care. State-anxiety was assessed immediately before and after each MM session, and psychiatric symptom severity upon admission was compared between patients choosing to attend MM and those who did not. State-anxiety was found to decrease significantly between pre- and post-MM upon first exposure regardless of patient age, sex, and prior experience with MM. Findings also suggest the possibility that patients experiencing symptoms of psychosis may benefit more from MM as compared to other patients. Admission symptom severity was not found to be an indicator of MM attendance. These findings suggest the possibility that MM could be an effective and relatively immediate transdiagnostic intervention to lower state anxiety in adolescents on an inpatient psychiatric unit and invite further implementation and research by staff chaplains on such units.
Genomic and Epidemiological Evidence of a Dominant Panton-Valentine Leucocidin-Positive Methicillin Resistant Staphylococcus aureus Lineage in Sri Lanka and Presence Among Isolates From the United Kingdom and Australia

Objective: To undertake the first detailed genomic analysis of methicillin-resistant Staphylococcus aureus (MRSA) isolated in Sri Lanka. Methods: A prospective observational study was performed on 94 MRSA isolates collected over a 4 months period from the Anuradhapura Teaching Hospital, Sri Lanka. Screening for mecA, mecC, and the Panton-Valentine leucocidin (PVL)-associated lukS-PV/lukF-PV genes and molecular characterization by spa typing was undertaken. Whole genome sequencing (WGS) and phylogenetic analysis was performed on selected multilocus sequence type (MLST) clonal complex 5 (CC5) isolates from Sri Lanka, England, Australia, and Argentina. Results: All 94 MRSA harbored the mecA gene. Nineteen spa types belonging to nine MLST clonal complexes were identified. Where origin of the sample was recorded, most isolates were from skin and soft tissue infections (70/91; 76.9%), with fewer causing bacteremia (16/91; 17.6%), empyema (3/91; 3.3%) and osteomyelitis (2/91; 2.2%). Sixty two (65.9%) isolates were PVL positive with the majority (56 isolates; 90.3%) belonging to a dominant CC5 lineage. This lineage, PVL-positive ST5-MRSA-IVc, was associated with both community and hospital-onset infections. Based on WGS, representative PVL-positive ST5-MRSA-IVc isolates from Sri Lanka, England and Australia formed a single phylogenetic clade, suggesting wide geographical circulation. Conclusions: We present the most detailed genomic analysis of MRSA isolated in Sri Lanka to date. The analysis identified a PVL-positive ST5-MRSA-IVc that is prevalent among MRSA causing clinical infections in Sri Lanka. Furthermore, this clone was also found among isolates from the United Kingdom and Australia.
Conformational tuning of a DNA-bound transcription factor

Abstract Transcription factors are involved in many cellular processes that take place remote from their cognate DNA sequences. The efficiencies of these activities are thus in principle counteracted by high binding affinities of the factors to their cognate DNAs. Models such as facilitated diffusion or dissociation address this apparent contradiction. We show that the MYC associated transcription factor X (MAX) undergoes nanoscale conformational fluctuations in the DNA-bound state, which is consistent with facilitated dissociation from or diffusion along DNA strands by transiently reducing binding energies. An integrative approach involving EPR, NMR, crystallographic and molecular dynamics analyses demonstrates that the N-terminal domain of MAX constantly opens and closes around a bound DNA ligand thereby dynamically tuning the binding epitope and the mode of interaction.
Pragmatic Unit Testing in Java with JUnit

About the Starter Kit Preface 1 Introduction 1.1 Coding With Confidence 1.2 What is Unit Testing? 1.3 Why Should I Bother with Unit Testing? 1.4 What Do I Want to Accomplish? 1.5 How Do I Do Unit Testing? 1.6 Excuses For Not Testing 1.7 Roadmap 2 Your First Unit Tests 2.1 Planning Tests 2.2 Testing a Simple Method. 2.3 More Tests 3 Writing Tests in JUnit 3.1 Structuring Unit Tests 3.2 JUnit Asserts 3.3 JUnit Framework 3.4 JUnit Test Composition 3.5 JUnit Custom Asserts 3.6 JUnit and Exceptions 3.7 More on Naming 3.8 JUnit Test Skeleton 4 What to Test: The Right-BICEP 4.1 Are the Results Right? 4.2 Boundary Conditions 4.3 Check Inverse Relationships 4.4 Cross-check Using Other Means 4.5 Force Error Conditions 4.6 Performance Characteristics 5 CORRECT Boundary Conditions 5.1 Conformance 5.2 Ordering 5.3 Range 5.4 Reference 5.5 Existence 5.6 Cardinality 5.7 Time 5.8 Try It Yourself 6 Using Mock Objects 6.1 Simple Stubs 6.2 Mock Objects 6.3 Testing a Servlet 6.4 Easy Mock Objects 7 Properties of Good Tests 7.1 Automatic 7.2 Thorough 7.3 Repeatable 7.4 Independent 7.5 Professional 7.6 Testing the Tests 8 Testing on a Project 8.1 Where to Put Test Code 8.2 Test Courtesy 8.3 Test Frequency 8.4 Tests and Legacy Code 8.5 Tests and Reviews 9 Design Issues 9.1 Designing for Testability 9.2 Refactoring for Testing 9.3 Testing the Class Invariant 9.4 Test-Driven Design 9.5 Testing Invalid Parameters A Gotchas A.1 As Long As The Code Works A.2 "Smoke" Tests A.3 "Works On My Machine" A.4 Floating-Point Problems A.5 Tests Take Too Long A.6 Tests Keep Breaking A.7 Tests Fail on Some Machines A.8 My main is Not Being Run B Installing JUnit B.1 Command-line installation B.2 Does it work? C JUnit Test Skeleton C.1 Helper Class C.2 Basic Template D Resources D.1 On The Web D.2 Bibliography E Summary: Pragmatic Unit Testing F Answers to Exercises
Hello, Android: Introducing Google's Mobile Development Platform

Android is a new software toolkit for mobile phones, created by Google and the Open Handset Alliance. In a few years, it's expected to be found inside millions of cell phones and other mobile devices, making Android a major platform for application developers. That could be your own program running on all those devices. Getting started developing with Android is easy. You don't even need access to an Android phone, just a computer where you can install the Android SDK and the phone emulator that comes with it. Within minutes, "Hello, Android" will get you creating your first working application: Android's version of "Hello, World. " From there, you'll build up a more substantial example: an Android Sudoku game. By gradually adding features to the game throughout the course of the book, you'll learn about many aspects of Android programming including user interfaces, multimedia, and the Android life cycle. If you're a busy developer who'd rather be coding than reading about coding, this book is for you. To help you find what you need to know fast, each chapter ends with "Fast forward" section. These sections provide guidance for where you should go next when you need to read the book out of order.
Epidemiological study on Japanese encephalitis virus distribution in Ishikawa prefecture, Japan, by serological investigation using wild boar sera

Thirty-seven specimens of wild boar sera were collected from August 2016 to March 2018 in Ishikawa prefecture, Japan. Thirty-two specimens (86.5%) were positive for neutralizing antibodies against Japanese encephalitis virus (JEV). Eight specimens (21.6%) were positive for IgM antibodies against JEV. One sample was obtained from a wild boar captured in February during the winter season. Four other serum specimens obtained during the winter season were positive using a JEV gene-specific PCR assay. Based on IgM and PCR assays, wild boars were infected with JEV during the winter season, suggesting that the prevalence of JEV antibodies in wild boars in Ishikawa is high and JEV activity is possible during winter in this region. In addition, wild boars may play an important role in the infection cycle of JEV.