Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Novel Simulation Tools for Lubricants / Overcoats / ABS Design in HAMR Technology

Design in HAMR Technology [01] Front matter a. Date: 04/28/2011 b. Abstract As heat assisted magnetic recording (HAMR) systems are set to be commercialized in the very near future, head disk interface (HDI) research focusing on the reliability and mechanical integration become the major technical barriers to be resolved [1]. To select the optimal HDI materials and configuration for HAMR among the enormous number of candidates for lubricant / overcoat systems, we need to fully understand multiscale phenomena containing numerous combinatorial complexities of highly interactive layers as well as consider various environmental parameters such as repeating thermal stress cycles, while maintaining the integrity of the corrosion protection. Current experimental and theoretical methods are limited in synthesizing materials and systems due to the extremely large set of decision parameters. Much effort and time must be applied to fully explore the combinatorial possibilities of composite HDI materials. In this proposal, we plan to examine HDI in HAMR through multiscale simulations which will holistically integrate lubricants, overcoats, and air bearing system (ABS) in non-isothermal conditions, and further provide the optimal design criteria for HDI modeling to obtain the best performance. We will first elucidate the mechanisms underlying the critical issues in HDI with conventional lubricants and overcoats (i.e., lubricant adsorption / desorption, evaporation, and degradation). We will then propose a novel overcoat known as graphene which has superior thermal and mechanical properties with a single atomic layer thickness that could potentially double the areal density without changing any specs. Innovative candidates for lubricant systems (hybrids of perfluoropolyethers (PFPEs) and carbon nanotube (CNT) or fullerene) and a “buffer layer” concept, ideal for tuning the adhesion and self healing properties will be explored. The interaction between graphene or other custom designed overcoats and lubricants at high temperature conditions will be examined at atomistic and molecular level simulations. Mesoscale level theory will be incorporated with pairings of novel lubricant / overcoat systems providing an innovative ABS design criteria under harsh environments operating in high temperature gradients. Our simulation result can be coupled with sponsor’s experimental data, or student internships can be used to assist in measurements, to facilitate synergy. c. Proponent(s) and affiliation(s): Myung S. Jhon, Professor, Department of Chemical Engineering and Data Storage Systems Center Carnegie Mellon University, Pittsburgh, PA 15213 mj3a@andrew.cmu. edu d. Designated contact person: Yiao-Tee Hsia yiao-tee.hsia@wdc.com
Rule Sharing for Fraud Detection via Adaptation ( full version )

Writing rules to capture precisely fraudulent transactions is a challenging task where domain experts spend significant effort and time. A key observation is that much of this difficulty originates from the fact that such experts typically work as “lone rangers” or in isolated groups, or work on detecting frauds in one context in isolation from frauds that occur in another context. However, in practice there is a lot of commonality in what different experts are trying to achieve. In this paper, we present the GOLDRUSH system, which facilitates knowledge sharing via effective adaptation of fraud detection rules from one context to another. GOLDRUSH abstracts the possible semantic interpretations of each of the conditions in the rules at the source context and adapts them to the target context. Efficient algorithms are used to identify the most effective rule adaptations w.r. t a given cost-benefit metric. Our extensive set of experiments, based on real-world financial datasets, demonstrate the efficiency and effectiveness of our solution, both in terms of the accuracy of the fraud detection and the actual money saved.
Critical Discourse Analysis An Introduction to Major Approaches Moslem Ahmadvand

Meaning is not a monolithic construct; it is a multidimensional and slippery concept with amazing complexity. Understanding the silent meaning of a text, be it spoken or written is a highly-needed skill in the modern era, for the exposure to information and media is so vast that one can never be sure of the validity of the surrounding information. Critical Discourse Analysis along with other related disciplines attempts to reveal hidden meanings, that is, the ideological loads of the discourse. This paper tries to review the origins of CDA and introduces some of the influential theoretical schools on it. Although these approaches differ in terms of the theoretical foundations and analytic tools, they share three concepts of critique, power, and ideology. This article emphasizes two major points. One is the dialectic relation between society, culture, politics, and language that this complex relation requires multidisciplinary research. The other is the fact that CDA cannot be taken as theory due to its tendency to adopt an eclectic approach in the analysis of the data.
This is a repository copy of Unit Test Generation During Software Development : EvoSuite Plugins for Maven

Different techniques to automatically generate unit tests for object oriented classes have been proposed, but how to integrate these tools into the daily activities of software development is a little investigated question. In this paper, we report on our experience in supporting industrial partners in introducing the EVOSUITE automated JUnit test generation tool in their software development processes. The first step consisted of providing a plugin to the Apache Maven build infrastructure. The move from a research-oriented point-and-click tool to an automated step of the build process has implications on how developers interact with the tool and generated tests, and therefore, we produced a plugin for the popular IntelliJ Integrated Development Environment (IDE). As build automation is a core component of Continuous Integration (CI), we provide a further plugin to the Jenkins CI system, which allows developers to monitor the results of EVOSUITE and integrate generated tests in their source tree. In this paper, we discuss the resulting architecture of the plugins, and the challenges arising when building such plugins. Although the plugins described are targeted for the EVOSUITE tool, they can be adapted and their architecture can be reused for other test generation tools as well.
Prefetched Address Translation

With explosive growth in dataset sizes and increasing machine memory capacities, per-application memory footprints are commonly reaching into hundreds of GBs. Such huge datasets pressure the TLB, resulting in frequent misses that must be resolved through a page walk -- a long-latency pointer chase through multiple levels of the in-memory radix tree-based page table. Anticipating further growth in dataset sizes and their adverse affect on TLB hit rates, this work seeks to accelerate page walks while fully preserving existing virtual memory abstractions and mechanisms -- a must for software compatibility and generality. Our idea is to enable direct indexing into a given level of the page table, thus eliding the need to first fetch pointers from the preceding levels. A key contribution of our work is in showing that this can be done by simply ordering the pages containing the page table in physical memory to match the order of the virtual memory pages they map to. Doing so enables direct indexing into the page table using a base-plus-offset arithmetic. We introduce Address Translation with Prefetching (ASAP), a new approach for reducing the latency of address translation to a single access to the memory hierarchy. Upon a TLB miss, ASAP launches prefetches to the deeper levels of the page table, bypassing the preceding levels. These prefetches happen concurrently with a conventional page walk, which observes a latency reduction due to prefetching while guaranteeing that only correctly-predicted entries are consumed. ASAP requires minimal extensions to the OS and trivial microarchitectural support. Moreover, ASAP is fully legacy-preserving, requiring no modifications to the existing radix tree-based page table, TLBs and other software and hardware mechanisms for address translation. Our evaluation on a range of memory-intensive workloads shows that under SMT colocation, ASAP is able to reduce page walk latency by an average of 25% (42% max) in native execution, and 45% (55% max) under virtualization.
Large-area MACE Si nano-inverted-pyramids for PERC solar cell application

Silicon (Si) nanostructures are regarded as a competitive candidate for the front texture of future high-efficiency Si solar cells owing to their excellent light-trapping properties. In this paper, we present nano-inverted-pyramids (NIPs) textures on the surface of Cz Si wafer with an area of 156×156mm fabricated by employing the metalassisted chemical etching (MACE) technique. The optical properties of the surface are investigated in detail before and after the application of a SiOx/SiNx antireflection coating. A passivated emitter and rear contact (PERC) solar cell with NIPs (NIPs-PERC) is analyzed at the cell as well module level using simulation. The simulation results indicate that the NIPs-PERC solar cell possesses a higher efficiency (η) of 1.4% relatively than that of the traditional PERC solar cell, benefiting from a significant better reflection performance in both the short as well as long wavelength range. Furthermore, simulations show that a 60-cell module with NIPs-PERC solar cells can yield a peak power of 310W which is 8W higher than a traditional PERC module. The novel NIPsPERC solar cell shows high potential for mass production and opens a broad way for the application of NIPs textures to other high-performance solar cells.
METHODS OF LARGE-SCALE SIGNALS TRANSFORMATION FOR DIAGNOSIS IN NEURAL NETWORK MODELS

Context. The problem of dimensionality reduction of diagnosis signals for their use in neural network models is considered. The object of the study was the process of transformation of diagnosis input signals for their subsequent use in the synthesis of predictive models. Objective. The goal of the work is the creation of the methods for the conversion of diagnosis signals as a result of the application of which new signals will be obtained, which in turn will be used in the construction of neural network predictive models and will significantly reduce the synthesis time of the model by reducing their dimension and the allocation of the necessary components that characterize the state of the individual elements of the object of diagnosis. Method. The methods of reducing the dimension of the input signals of diagnosis and isolation of their components, which characterize the state of the individual elements of the object of diagnosis on the basis of expert knowledge about the process of diagnosis are proposed. The developed methods are based on the methods of digital signal processing. Based on the expert knowledge of the object and the process of diagnosis, the necessary signal conversion procedures and their parameters are selected. In accordance with the requirements for the desired accuracy and detail of the forecast, the optimal degree of averaging of the signal is selected, which directly affects the speed of constructing the predictive model. The proposed methods can be used in the transformation of diagnosis signals of various diagnostic processes where there is a need to build neural network predictive models based on high-dimensional signals. The developed methods were investigated for the conversion of diagnostic signals obtained on a complex object of technical diagnostics, namely, on the transmission of the helicopter. On the basis of the received signals, a neural network model was synthesized, the training of which requires much less computational resources, while the prediction accuracy remains optimal. Results. The developed methods are implemented programmatically and investigated in solving the problem of predicting the future state of the helicopter transmission during the diagnosis process. Conclusions. The experiments have confirmed the effectiveness of the developed methods and allow us to recommend them for use in practice in solving diagnostic problems. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in practice for solving the problems of diagnosis and automatic classification on the features. The prospects for further research may include the search for the best parameters of the developed methods, optimization of their software implementations, as well as experimental study of the proposed methods on a large set of practical problems of diagnosing complex objects of different nature by their diagnostic signals.
Efficient Virtualization Schema for Load Management in Cloud Data Centers

iv TABLE OF CONTENTS. v LIST OF FIGURES.vii LIST OF TABLES. viii Chapter 1: Introduction. 1 1.1 Cloud Computing. 1 1.1.1 Characteristics of Cloud Computing. 2 1.1.2 Service Models of Cloud Computing. 3 1.1.3 Deployment of Cloud Computing. 3 1.2 Datacenter. 4 1.3 Virtualization. 5 1.3.1 Hypervisor based platforms. 5 1.3.2 OS-based platforms. 6 1.4 Thesis Outline. 7 Chapter 2: Literature Review. 8 Chapter 3: Problem Statement. 10 3.1 Problem Analysis. 10 3.2 Objectives. 11 Chapter 4: Cloud Infrastructure as a Service. 12 4.1 CIAS Overview. 12 4.1.1 Basic Overview. 12 4.1.2 CIAS architecture and its components. 14 4.1.3 Basic Work Flow of CIAS. 16 4.2 Enabling Virtual Box to CIAS. 17 4.2. 1 Background study. 17 4.2. 2 Virtual Box Scenario. 18 4.2. 3 Technology Used. 18
Social and clinically-relevant cardiovascular risk factors in Asian Americans: NHANES

Little evidence exists examining cardiovascular risk factors among Asian Americans and how social determinants such as nativity status and education pattern risk in the United States (U.S.) context. We used the National Health and Nutrition Examination Survey, which purposely oversampled Asian Americans from 2011 to 2014, and examined prevalence of Type II diabetes, smoking and obesity for Asian Americans (n = 1363) and non-Latino Whites (n = 4121). We classified Asian Americans as U.S. or foreign-born and by years in the U.S. Obesity status was based on standard body mass index (BMI) cut points of ≥ 30 kg/m2 and Asian-specific cut points (BMI ≥ 25 kg/m2) that may be more clinically relevant for this population. We fit separate logistic regression models for each outcome using complex survey design methods and tested for the joint effect of race, nativity and education on each outcome. Diabetes and obesity prevalence (applying Asian-specific BMI cut points) were higher among Asian Americans when compared to non-Latino Whites but smoking prevalence was lower. These patterns remained in fully adjusted models and showed small increases with longer duration in the U.S. Joint effects models showed higher odds of prevalent Type II diabetes and obesity (Asian-specific) for foreign-born Asians, regardless of years in the U.S. and slightly higher risk for low education, when compared to non-Latino Whites with high education. Smoking models showed significant interaction effects between race and education for non-Latino Whites only. Our study supports the premise that social as well as clinical factors should be considered when developing health initiatives for Asian Americans.
Proposal and Evaluation of the Structural Information Extraction Method for Update-conscious XML Labeling

In recent years, the method of assigning labels to the nodes of XML trees for efficient storage and processing in RDBs is getting more attractive. However, in traditional labeling methods, a number of nodes must be relabeled when the XML documents are updated. To address this problem, we proposed DO-VLEI, an update-conscious XML labeling method, and we proposed Compressed-bit-string DO-VLEI (C-DO-VLEI) code to reduce the storage consumption of the DO-VLEI labels. When we process the query for XML documents labeled by C-DO-VLEI codes, we need to extract the structural information from the C-DO-VLEI codes. In this paper, we propose an effective method for extracting structural information of XML documents from the C-DO-VLEI code. The proposed method achieves efficient queries because the structural information can be acquired without traversal from the beginning of the code. Besides, we perform experiments to compare the performance of the proposed method with the ORDPATH.
The Notion of Homeland , “ Imaginary Homeland ” and Wounded Memory in Khaled Hosseini ’ s The Kite

This article explores the idea of homeland, “imaginary homeland” and wounded memory in Khaled Hosseini’s debut novel, The Kite Runner (2003). The idea of homeland, which includes the illustration of a person’s nostalgia towards homeland, is a significant topic of discussion among scholars of diaspora studies. The article demonstrates that Afghanistan as depicted in Hosseini’s novel represents the author’s nostagic feeling towards his home country. The novel also illustrates the ideas of a shattered dream and fragmented memories of diaspora subjects which are caused by the loss of homeland as discussed in Salman Rushdie (2006)’s seminal article, “Imaginary Homelands”. Hence, the article investigates the notion of “imaginary homeland” which is portrayed through the main characters’s experiences, both inner and outer. Apart from the notion of nostalgia, homeland, and “imaginary homeland”, the memory of homeland also plays an important role in shaping the life of diaspora people. This memory can be presented in a positive or negative light, and can be a link between diaspora subjects and their home country. While the word diaspora suggests the idea of dispersion, scattering and exile, it also implies the collective memory and trauma involved in such dispersion. Therefore, the second part of this article investigates the idea concerning the memory of diaspora subjects, especially the wounded memory which shapes a crucial aspect of life in their new home. The study reveals that the protagonist’s recollections of Afghanistan is darkened by and tainted with his own guilts and mistakes. As a result, the ghosts of the past urge him to wholeheartedly adopt America as his new home since it is a place of freedom which he can seek refuge from all guilts and wounded memory.
Zinc de fi ciency decreases osteoblasts and osteoclasts associated with the reduced expression of Runx 2 and RANK

Abbreviations: ALP, alkaline phosphatase; BMP, b Col1α1, collagen type I α1; CFU-M, colony forming cell telopeptide of type I collagen; Dkk1, Dickkopf 1; Dlx5, d GSK3β, glycogen synthase kinase 3β; IGF-I, insulin-like g ceptor; InsR, insulin receptor; LRP5, low-density lipopr 5; Osx, Osterix; RANK, receptor for activation of NF-κB; of NF-κB ligand; Runx2, runt-related transcription facto thiobarbituric acid-reactive substances; TRAP, tartrate-r ⁎ Corresponding author. Fax: +81 742 20 3254. E-mail address: itsuka@cc.nara-wu.ac. jp (I. Tsukamo
Using Contextual Information to Improve Blood Glucose Prediction

Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and evaluate signals about such temporal context into prediction models. Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves. Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood glucose prediction task. We find this approach outperforms common methods for multi-variate data, as well as using the blood glucose values in isolation. Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using contextual information and may provide a significant shift in blood glucose prediction research and practice.
An Anonymous Reputation System Resistant Against Tracking and Intersection Attacks CPSC 490 Final Project

AnonRep is an anonymous reputation system developed by Ennan Zhai in 2016 that enables users to anonymously post new messages, tagged with their reputation scores, to a global message board. Users can provide feedback on previously posted messages without revealing their identity or any otherwise sensitive information [1]. Unlike traditional reputation systems, AnonRep prevents attackers from linking identities between messages by providing stronger guarantees on privacy and anonymity. However, AnonRep is prone to long-term intersection attacks, which can remove the guarantee of anonymity for certain individuals. For example, if a system contains one user with a significantly higher reputation score than all other users, an attacker may be able to link posts of high reputation to the same person. In this paper, we present AnonRep++, an improved version of AnonRep that incorporates blockchain technology to give users control over the amount of reputation that they attach to their posts. Instead of having a reputation explicitly attached to each client in the system, each user has access to a set of reputation wallets. The CoinShuffle protocol is used to preserve anonymity of these client wallets. For this project, I develop the base AnonRep program in Python, from which we implement AnonRep++. Both systems consist of a coordinator, message board, servers, and clients. I also implement the cryptographic primitives needed for the system’s guarantees on security, which include ElGamal encryption and decryption, linkable ring signatures, and verifiable shuffle. I perform analyses on the performance, efficiency, and scalability of both the original AnonRep and our blockchain-based AnonRep++.
Neuroscience Attitudes, Exposure, and Knowledge among Counselors

The purpose of this study was to explore the attitudes, exposure, myths, and knowledge regarding neuroscience among counselors at various stages of their careers. Descriptive statistics were used to highlight the current state of neuroscience attitudes, exposure, myths, and knowledge among a sample of counselors. The results showed that participants held positive attitudes towards neuroscience, experienced exposure to neuroscience information through various methods, believed neuroscience should be integrated in over half of the counselor education curriculum, and possessed high levels of neuroscience knowledge and average levels of neuromyths endorsed. The results provide insights that can guide the infusion of neuroscience into the counselor education curriculum.