Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.
Artificial rearing alters intestinal microbiota and induces inflammatory response in piglets

With the ongoing genetic selection for high prolificacy in sow lines and the improvements in environment and farm management, litter size has increased in recent years. Artificial rearing is becoming widely used to raise the surplus piglets in pig industry. This study aimed to investigate the changes that happened in the morphology, microbiota, mucosal barrier function, and transcriptome caused by artificial rearing in piglet colon. Two hundred and forty newborn piglets were randomly assigned into three treatments, sow rearing until weaning (CON group), artificial rearing from day 21 (AR21 group), and artificial rearing from day 7 (AR7 group). On day 35, the piglets were euthanized to collect colon samples. The results showed that the artificially reared-piglets displayed increased pre-weaning diarrhea incidence and reduced growth performance. Artificial rearing changed the diversity and structure of colonic microbiota and increased relative abundance of harmful bacteria, such as Escherichia-Shigella. In addition, the morphological disruption was observed in AR7 group, which was coincided with decreased tight junction proteins and goblet cell numbers. Moreover, the expression of TNFSF11, TNF-α, IL-1β, TLR2, TLR4, MyD88, NF-κB, COX-2, PTGEs, iNOS, IL-2, IL-6, IL-17A, and IFN-γ was upregulated in the colon of the artificially reared-piglets, while the expression of IL-1Ra and IκBα was downregulated, indicating that artificial rearing induced inflammatory response through the activation of NF-κB pathway. Furthermore, artificial rearing regulated SLC family members, which affected solute transport and destroyed intestinal homeostasis. In conclusion, artificial rearing caused microbiota alteration, morphology disruption, the destruction of mucosal barrier function, and inflammatory response, and thus, led to subsequent increased diarrhea incidence and reduced growth performance.
Liberating Virtual Machines from Physical Boundaries through Execution Knowledge

Abstract : Hardware virtualization enables remote instantiation of computation through the preserved executability of encapsulated software. The large size of virtual machines (VMs), however, poses challenges in exploiting this strong feature under the existence of resource constraints. In this thesis, we claim that the use of execution knowledge achieves the efficiency and timeliness of VM state transfer in such environments. We demonstrate its effectiveness in two concrete contexts in which the challenges materialize: 1) VM delivery over WANs, with network resource limitations, and 2) urgent migration of VMs under contention, with strict time requirements. In the context of VM delivery over WANs, we take advantage of the knowledge about past VM execution instances. We conduct the evaluation of vTube, a system for efficiently streaming virtual appliances, from both systems and human-centric perspectives. In the context of urgent migration of VMs under contention, we leverage current execution knowledge at the guest OS level. Our approach, called enlightened post-copy, uses this knowledge to expedite the resolution of contention between VMs. Our proposed solutions address the corresponding problems by providing VM performance as defined by critical metrics in their specific contexts.
Motivating Students: Identifying and Motivating Reluctant Learners to Reach Their Academic Potential

MOTIVATING STUDENTS: IDENTIFYING AND MOTIVATING RELUCTANT LEARNERS TO REACH THEIR ACADEMIC POTENTIAL By Kim Yeager February 27, 2008 Motivation plays an important role in determining how well a child will perform in school (Philips & Lindsay, 2006). Regardless of how intelligent or capable a student may be ifthe student is not motivated to do well then their academic performance will be less than it could have been with some motivation. Teachers need to understand why some students are motivated to try hard in school and why some students are not. This understanding will lead teachers to understand why their students are not reaching their academic potential. The purpose of this project was to design a guide that helps teachers motivate otherwise unmotivated students. The project includes ways teachers can identify students who may be below standard because of a lack of motivation, instead of a lack of ability. The project then proposes a program to help teachers motivate reluctant learners to try their best in school. v EDUCATIONAL TECHNOLOGY CENTER CENTRAL WASHINGTON UNIVERSITY TABLE OF CONTENTS Chapter Page I BACKGROUND OF THE PROJECT . Introduction . I Significance of the Project. 5 Purpose of the Project . 5 Limitations of the Study . 5 Definition of Terms . 6 II REVIEW OF RELATED LITERATURE . Introduction . 7 Reasons for Amotivation in Students . 7 Recognizing Less Motivated Students . 11 Factors Influencing Motivation . 15 Strategies for Increasing Student Motivation . 19 Summary . 25 III PROCEDURES . 27 IV THE PROJECT . 31 V SUMMARY, CONCLUSIONS, RECOMMENDATIONS . 74 Summary . 74 Conclusion . 7 5 Recommendations . 7 5 REFERENCES . 77
Study of Railway Passenger Flow Forecasting Method Based on Spatio-Temporal Data Mining

By analyzing the limitation of current passenger flow forecast approach, this paper presents a new approach to forecast railway passenger flow based on spatio_temporal data mining. The approach first forecasts time sequence of target object using statistical principles, then figures out the spatial influence of neighbor objects using a neural network, and finally combines the two forecasting results using linear regression. The method is used in the forecast of railway passenger flow during the spring festival period in 2004. Comparing with the existing approaches that don't consider the spatial influence, the forecast accuracy of our approach is better.
“Upcycling” known molecules and targets for drug-resistant TB

Despite reinvigorated efforts in Tuberculosis (TB) drug discovery over the past 20 years, relatively few new drugs and candidates have emerged with clear utility against drug resistant TB. Over the same period, significant technological advances and learnings around target value have taken place. This has offered opportunities to re-assess the potential for optimization of previously discovered chemical matter against Mycobacterium tuberculosis (M.tb) and for reconsideration of clinically validated targets encumbered by drug resistance. A re-assessment of discarded compounds and programs from the “golden age of antibiotics” has yielded new scaffolds and targets against TB and uncovered classes, for example beta-lactams, with previously unappreciated utility for TB. Leveraging validated classes and targets has also met with success: booster technologies and efforts to thwart efflux have improved the potential of ethionamide and spectinomycin classes. Multiple programs to rescue high value targets while avoiding cross-resistance are making progress. These attempts to make the most of known classes, drugs and targets complement efforts to discover new chemical matter against novel targets, enhancing the chances of success of discovering effective novel regimens against drug-resistant TB.
Mutation of barley HvPDIL5-1 improves resistance to yellow mosaic virus disease without growth or yield penalties

The soil-borne yellow mosaic virus disease, which is caused by the bymoviruses barley yellow mosaic virus (BaYMV) and/or barley mild mosaic virus (BaMMV), seriously threatens winter barley production in Europe and East Asia. Both viruses are transmitted by the soil-borne plasmodiophorid Polymyxa graminis and are difficult to eliminate through chemical or physical measures in the field, making breeding for resistant cultivars the optimal strategy for disease control. The resistance locus rym1/11 was cloned encoding the host factor gene Protein Disulfide Isomerase Like 5-1 (PDIL5-1), whose loss-of-function variants confer broad-spectrum resistance to multiple strains of BaMMV/BaYMV. Most resistance-conferring variants have been identified in six-rowed barley landraces/historic cultivars, and their introgression into modern two-rowed malting cultivars is difficult because PDIL5-1 is located in a peri-centromeric region with suppressed recombination. In this study, we used CRISPR/Cas9 genome editing to modify PDIL5-1 in the BaYMV/BaMMV-susceptible elite malting barley cv. ‘Golden Promise’ and obtained the mutants pdil5-1-a and pdil5-1-b. PDIL5-1 in the pdil5-1-a mutant encodes a protein lacking a cysteine residue, and pdil5-1-b contains a protein-coding frameshift. Both mutants were completely resistant to BaYMV. The knockout mutant pdil5-1-b showed complete BaMMV resistance, while pdil5-1-a showed decreased viral accumulation but no disease symptoms if compared to ‘Golden Promise’. Both PDIL5-1 edited lines, as well as the previously produced EMS-induced pdil5-1 mutant ‘10253-1-5’ in the elite malting barley cv. ‘Barke’ background, displayed no growth or yield penalties in garden experiments or bymovirus-free field trials. Line ‘10253-1-5’ showed improved resistance and yield performance compared to the wild-type and its sibling line when grown in infectious fields. Therefore, genome editing of the host factor gene PDIL5-1 could facilitate the breeding of barley varieties with resistance to bymoviruses.
Scientific Data Mining with StripMinerTM

This paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMinerTM, a shell program for feature reduction and predictive modeling, integrates the executions of several different machine- learning models (Partial Least Squares Regression, Genetic Algorithms, Support Vector Machines, Neural Networks, and Local Learning). This paper introduces the StripMinerTM code, its functionality, and its options.
A Cloud-Model-Based Remote Sensing Image Segmentation Concerned with Geometrical Features

Cloud model,an information transition model between qualitative and quantitative,is beneficial to the processing of uncertainty in remote sensing image.Aiming to deal with uncertainty better,a new approach for image segmentation which integrates cloud model and fuzzy theory is proposed.This approach adopts watershed algorithm to produce clumps from original image.These original clumps are abstracted to cloud models and described with cloud models parameters.The cloud integration algorithm is constructed via the differential measurement rule which is defined by the cloud notion.And cloud models,similar in color,shape and topology,are merged together by integration of cloud notion.Thus,the clumps are combined in different granularity spaces and the multi-scale remote sensing image segmentation is realized.
Verifiable early-reply with C++

Verifiable Early-Reply with C++. (May 2007) Stephen Wendell Cook, B.S., Colorado School of Mines Chair of Advisory Committee: Dr. Bjarne Stroustrup Concurrent programming can improve performance. However, it comes with two drawbacks. First, concurrent programs can be more difficult to design and reason about than their sequential counterparts. Second, error conditions that do not exist in sequential programs, such as data race conditions and deadlock, can make concurrent programs more unreliable. To make concurrent programming simpler and more reliable, while still providing sufficient performance gains, we present a concurrency framework based on an existing concurrency initiation mechanism called “Early-Reply”. Early-Reply is based on the idea that some functions can produce final return values long before they terminate. Concurrent execution begins when return value of a function is returned to the caller, allowing the rest of the work of the function to be done on an auxiliary thread. The simpler sequential programming model can be used by the caller, because the concurrency is initiated and hidden within the function body. Pike and Sridhar recognized Early-Reply as a way for sequential programs to get the benefits of concurrent execution. They also discussed using object-oriented programming to serialize access to data that needs synchronization. Our work expands on their approach and provides an actual C++ implementation of an Early-Reply based framework. Our framework simplifies concurrent programming for both users and implementers by allowing developers to use sequential reasoning, and by providing a minimal framework interface. Concurrent programming is made more reliable by combining the concurrency synchronization and initiation into one mechanism within the framework, which isolates where race conditions and deadlock can occur. Furthermore, this isolation facilitates the development of a simple set of coding guidelines that can be used by developers (through inspection) or static analysis tools (through verification) to eliminate race conditions and deadlocks.
Inter-eye: Interactive error compensation for eye-tracking devices

This paper presents a new method for systematic errors compensation in modern eye-tracking devices. Systematic errors, together with repeatability errors, reduce the possible use of eye trackers for several applications such as moving into an indoor environment and enabling the user to indicate precisely the target only with the support of his/her eyes. The new method relies on an interactive procedure that enables the system to accurately estimate the systematic effect in few seconds and thus compensate it in a fast and accurate way. Results show that the uncertainty can be dramatically decreased for a low-cost device on a 17 inches screen from 100 pixels to approximately 15 pixels.
Evidence- based medicine among family physicians in Mosul city: knowledge and attitudes

Objectives: The aim of this study is to assess knowledge and attitudes of family physicians in Mosul city toward evidence-based medicine and to describe the major barriers to practicing evidence based medicine. Method: A cross sectional questionnaire based survey has been adopted for this study. For the period between the 1 st of June and 31 st of August 2012; a self-administered questionnaire had been distributed to all 71 family physicians registered in Nineveh Health Directorate and practicing in primary health centers in Mosul city. The main outcome measures were: participants‟ attitudes toward evidence –based medicine (EBM), their knowledge about the important technical terms used in EBM and their perception of barriers to EBM practice. Results: Sixty one family physicians participated in this study, 24 (39.3%) were males and 37 (60.7%) were females. The response rate was 85.9%. The mean age of the participants was 36.15 years. Most of the respondents had positive attitudes toward EBM: 54.1% showed welcoming attitudes towards EBM; 44.3% showed strongly welcoming attitudes towards EBM; 65.6% believed that most of their clinical practice was evidence based; 90% agreed with the concept of practicing EBM improves patients care. The participants showed deficient knowledge regarding the technical terms commonly used in EBM. Patients overload and lack of personal time were the major perceived barriers to practicing EBM (78.7% and 65.6%) respectively. Conclusion: Although this research revealed high support among family physicians in Mosul for the practice of EBM, they showed a deficiency in knowledge about EBM.
3D Measurement with High Accuracy and Robust Estimation for Bin Picking

多品種変量生産に効率的に対応するために生産工程の自動化, ならびに生産ラインの柔軟性が求められている. 柔軟性のある 生産ラインとは,同一のラインで多品種を生産できる,あるい は最低限のライン組み換えで多くの工業製品を生産可能なライ ンを指す. 生産工程自動化のためには,工業製品を構成する部 品(ワーク)を 1点ずつ組み付けロボットに自動供給できる装置 (自動給材装置)が必要であり,現在は人手やパーツフィーダと 呼ばれる専用装置による供給が行われている [1]. パーツフィー ダは,比較的速い給材(一部品あたり 1.0~1.5秒)が可能な一 方で,一種類一種類の部品に応じて専用設計された特注機械であ るために下記の問題がある. (1)部品の種類数だけパーツフィー ダが必要になる. したがって,部品点数の増加に応じて,設置 スペースとコストが増大する. (2)パーツの種類に変更がない 場合でも大きさが変更されると別のパーツフィーダが必要にな る. (3)形状の複雑な部品を給材することができない. 多品種 少量生産時代に効率的に対応するためには,生産ラインを短時 間かつ低コストで組み替える必要があることから,ばら積み部
Phenomenological Psychopathology and Psychiatric Classification

This chapter provides an overview of phenomenological approaches to psychiatric classification. The aim is to encourage and facilitate philosophical debate over the best ways to classify psychiatric disorders. First, the chapter articulates phenomenological critiques of the dominant approach to classification and diagnosis—that is, the operational approach employed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-10). Second, it describes the type or typification approach to psychiatric classification, which is distinguished into three different versions: ideal types, essential types, and prototypes. It is argued that despite their occasional conflation in the contemporary literature, there are important differences among these approaches. Third, the chapter outlines a new phenomenological-dimensional approach. It shows how this approach, which starts from basic dimensions of human existence, allows us to investigate the full range of psychopathological conditions without accepting the validity of current diagnostic categories.
A COMPUTATIONAL FRAMEWORK FOR DISCRIMINATIVE ANALYSIS OF HIGH DIMENSIONAL BIOMEDICAL IMAGE DATA

In this work we propose, implement and evaluate a discriminative computational pipeline to address the problem of feature generation, feature screening and feature subset selection from biomedical datasets when the ratio of feature dimensions (possibly in millions) to number of samples is very high . The proposed pipeline is modular and can be highly parallel. The framework is applied to a variety of real world discrimination problems including plant specie classification from 2D leaf images, Gender/age/expression/human identification from 3D facial surface mesh and Gender/Age/Disease classification from 3D neural Magnetic Resonance Imaging (MRI) images. By using a unique set of novel features for each application we either achieve better or competitive classification accuracy than existing work or set new benchmarks otherwise. We can also locate discriminative regions for faces and brains which facilitate further scientific discoveries. This work illustrates quantitatively, the effectiveness and the diversity of the proposed feature extraction and machine learning pipelines.
Object tracking algorithm based on CamShift

CamShift is a color-based tracking algorithm.In the process of object tracking,CamShift operates on a back-projection image produced from object histogram model.Computed information is used previously to adjust current search window size and location,then the location and size of the color object are found.Simple motion prediction and twice searching methods to improve tracking per-formance are devised.Finally the experimental results indicate that color object is efficiently tracked even with complex background and irregular motion by using this algorithm.
Adaptive hidden Markov noise modelling for speech enhancement

A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm.
Elastic ORB: Non-Rigid Transformation Based SLAM

We present Elastic ORB, an improved implementation of a novel visual SLAM approach. Our system uses point cloud data to build the map of the environment and for the localization of the robot, odometry data is used. A frame-to-frame loop closure method is used at the back of the SLAM system using the concept of deformation theory. When building the reduced model of the map, the ORB keypoint extraction algorithm and the FPFH feature descriptor are used. Our system is capable of mapping a larger environment overcoming the limitations of existing SLAM systems which use the deformation theory at the backend.