Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Health Status of Female Veterans: Contributions of Sex Partnership and In-Military Rape

Objective The aim of this study was to determine whether current physical health status in female veterans is associated with rape during military service and same-sex partnership. Methods Retrospective computer-assisted telephone interviews of 1004 Midwestern US female veterans identified from Veterans Affairs electronic records were conducted. Data included rape history including rape in military, sex partnership history, demographics, and medical history including chronic pain, mental health (depression and posttraumatic stress disorder [PTSD]), and the physical health component of the Short-Form 12-item interview (PCS-12). Results Physical health in this sample was lower than norm values [PCS-12: mean (standard deviation) = 43 [12]; norm: mean (standard deviation) = 50 [10]). Fifty-one percent of the participants reported rape in their lifetime, 25% reported rape in military, 11% reported history of women as sex partners, and 71% reported history of chronic pain. Multiple regression analysis indicated that physical health (PCS-12) was associated with chronic pain history (&bgr; = −.40, p < .001), rape in military (&bgr; = −.09, p = .002), and current PTSD (&bgr; = .07, p = .03), adjusting for demographic data. Mediational analysis indicated that chronic pain history significantly mediated relationships of women who have sex with women, childhood rape, PTSD, depression, and current substance use disorder with PCS-12. Conclusions Both rape and sex partnership are adversely associated with lower physical functioning in female veterans. Clinicians evaluating the physical health of this population should therefore consider obtaining detailed sexual histories, and a multidisciplinary team is needed to address mental health issues in female veterans.
Stereo matching on low intensity quantization images

This paper addresses the problem of how the image intensity quantization affects the stereo matching algorithms. We compute the disparity using the stereo images represented by various intensity quantization levels. It is shown that, depending on the stereo matching algorithms, even the image pairs with low intensity quantization are able to produce fairly good disparity results. Experiments on Middlebury datasets demonstrate that the global algorithms such as GC and BP are suitable for stereo matching using low intensity quantization images.
Contemporary Academic Publishing: Democratization and Differentiation in Careers

ABSTRACT This study examined how publishing figures in the careers of academics as a means to study the social organization of higher education. Publishing is a means by which academics legitimate themselves. Yet previous work has demonstrated that most academics publish comparatively little. A classic literature in the sociology of science has used employing organization and career phase as chief devices by which to understand how academic fields are stratified. The study treated the field of sociology as a case by which to examine publication processes across cohorts of academics and stratified tiers of academic work. This article argues that a major transformation marks contemporary academic careers: Publication in science and scholarship is increasingly democratized. This article concludes with a discussion on three sets of macro conditions—massification, saturated labor markets, and neoliberalism—that may be understood for their democratizing effects on the career patterns of contemporary academics.
Use of Long Term Molecular Dynamics Simulation in Predicting Cancer Associated SNPs

Computational prediction of cancer associated SNPs from the large pool of SNP dataset is now being used as a tool for detecting the probable oncogenes, which are further examined in the wet lab experiments. The lack in prediction accuracy has been a major hurdle in relying on the computational results obtained by implementing multiple tools, platforms and algorithms for cancer associated SNP prediction. Our result obtained from the initial computational compilations suggests the strong chance of Aurora-A G325W mutation (rs11539196) to cause hepatocellular carcinoma. The implementation of molecular dynamics simulation (MDS) approaches has significantly aided in raising the prediction accuracy of these results, but measuring the difference in the convergence time of mutant protein structures has been a challenging task while setting the simulation timescale. The convergence time of most of the protein structures may vary from 10 ns to 100 ns or more, depending upon its size. Thus, in this work we have implemented 200 ns of MDS to aid the final results obtained from computational SNP prediction technique. The MDS results have significantly explained the atomic alteration related with the mutant protein and are useful in elaborating the change in structural conformations coupled with the computationally predicted cancer associated mutation. With further advancements in the computational techniques, it will become much easier to predict such mutations with higher accuracy level.
ADVANCES IN THE SCIENCE AND TECHNOLOGY OF CARBON NANOTUBE COMPOSITES

In recent years there has been tremendous research advances made in the scientific base for carbon nanotubes, and this has enabled significant improvements in the technology of carbon nanotube-based composites. The interplay between basic science and engineering has resulted in some of the most exciting accomplishments in the development of multifunctional materials. This paper highlights these accomplishments based on the hierarchical structural levels of carbon nanotubes (CNTs) used in composites. Both single walled (SWCNTs) and multi-walled (MWCNTs) carbon nanotubes have been utilized. These structural levels, ranging from 1-D to 2-D to 3-D, are highlighted below.
Optimized Representation for Classifying Qualitative Data

Extracting knowledge out of qualitative data is an ever-growing issue in our networking world. Opposite to the widespread trend consisting of extending general classification methods to zero/one-valued qualitative variables, we explore here another path: we first build a specific representation for these data, respectful of the non-occurrence as well as presence of an item, and making the interactions between variables explicit. Combinatorics considerations in our Midova expansion method limit the proliferation of itemsets when building level k+1 on level k, and limit the maximal level K. We validate our approach on three of the public access datasets of University of California, Irvine, repository: our generalization accuracy is equal or better than the best reported one, to our knowledge, on Breast Cancer and TicTacToe datasets, honorable on Monks-2 near-parity problem.
Effect and snags of provision of in-service education and training for teachers in basic schools for the deaf

Regular and effective provision of in-service training for teachers is a necessary practice for the educational development of any country. The provision of effective in-service training and education for special teachers is however bedeviled with many challenges. To appraise the effect and problems of in-service training in Ghana, a survey involving 90 teachers from three schools for the deaf namely: Savelugu, Wa and Gbeogo Schools for the Deaf were sampled in this study. A set of 94 questionnaire and 4 exclusive separate interviews sessions were used to collect data for the study. The data was tallied into frequency tables and percentage distribution tables were generated from MS Excel for further interpretation and discussion. The study revealed that in-service training programmes are few and highly ineffective. There is a general delink between what the special teacher needs and what is provided leading to the little use of knowledge and skills acquired from the training. Lack of material resources, teaching aids, modern equipment and funding opportunities have served as a huge blockage to the provision of effective in-service training and education. In-service training can only be made effective if it is preceded by needs assessment with funding made available for the training of teachers of special schools. Key words: In-service, education, effect, problems, training, education, deaf, special teacher.
VAST VOLATILITY MATRIX ESTIMATION FOR HIGH-FREQUENCY FINANCIAL DATA

High-frequency data observed on the prices of financial assets are commonly modeled by diffusion processes with micro-structure noise, and realized volatility-based methods are often used to estimate integrated volatility. For problems involving a large number of assets, the estimation objects we face are volatility matrices of large size. The existing volatility estimators work well for a small number of assets but perform poorly when the number of assets is very large. In fact, they are inconsistent when both the number, p, of the assets and the average sample size, n, of the price data on the p assets go to infinity. This paper proposes a new type of estimators for the integrated volatility matrix and establishes asymptotic theory for the proposed estimators in the framework that allows both n and p to approach to infinity. The theory shows that the proposed estimators achieve high convergence rates under a sparsity assumption on the integrated volatility matrix. The numerical studies demonstrate that the proposed estimators perform well for large p and complex price and volatility models. The proposed method is applied to real high-frequency financial data.
Multiple lights on the dark side of personality

This chapter reviews key lines in the history of the conceptualization and assessment of dark side personality or dysfunctional personality tendencies, with specific attention of its implications for the employability of persons and how such tendencies are manifested in work behavior. Direct and indirect ways to assess dark traits are reviewed and discussed, including the prevalence of dysfunctional tendencies and their subclinical manifestations. The chapter closes with the identification of key issues that need the immediate attention of both researchers and practitioners to advance this emergent field both scientifically and professionally.
A smart-device news recommendation technology based on the user click behavior

Studies have been conducted in regard to personalized news recommendation using collaborative filtering mechanisms based on users' click behaviors. However, few existing studies have focused on news recommendations depending on the rates of news-category interests. In this paper, we present a personalized news-recommendation system that builds profiles of users' news-category interests, determines the number of news articles to be recommended for each news category in proportion to news-category interests and ranks news articles according to the user's news interests. In order to find the news categories that are interesting to read, the smart device collects the web pages viewed by the user and classifies the contents. We use machine learning techniques in order to classify web pages into different categories, and the results of our experimentation show that Naive Bayes achieved the highest F-measure. The news preference is automatically calculated by the news reading time and the length of the news text. News articles with high preference generated by the collaborative filtering technique are recommended based on the rate of each category. The recommendation system we have implemented is based on collaborative filtering using Mahout Recommender API on Hadoop. Through user testing, we also assess whether the proposed system is useful.
A semi-automated technique for vertebrae detection and segmentation from CT images of spine

Spine or backbone forms a supportive structure for all vertebrates, which is composed of complex bones known as vertebrae. Spine related pathologies are common and they are analyzed with help of various medical imaging techniques. Thus the detection and segmentation of vertebrae is gaining prime importance, as it can be used for the clinical analysis of spine. There are many semi-automated and automated approaches which are used to detect and segment individual vertebra from computed tomography (CT) images. But most of these approaches do not provide satisfactory results due to the anatomical similarities between the adjacent vertebrae and the effect of underlying artifacts. So to overcome these problems, a framework for individual vertebrae segmentation from CT images of spine is put forward. First of all, the vertebral body is identified with the help of iterative Normalized-cut algorithm which uses eigenvalue decomposition for the detection procedure and the segmentation of individual vertebra is done using region based active contour method.
Fixed-camera visual servo control for planar robots

We present a new controller to solve the visual servo control problem for planar robots in the fixed-camera configuration. For a static target we characterize the global closed loop attractor using the full robot dynamics, and prove local asymptotic stability of the end-effector tracking error. Neither the inverse kinematics nor the inverse Jacobian are used in the controller. Experimental results on a 2-DOF direct drive manipulator are presented.
An Automata Based Authorship Identification System Recommended Citation an Automata Based Authorship Identification System

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Updating the User Model on the Basis of the Recognition of the User's Plans

In information-seeking dialogues the user often asks questions about speciic aspects of the tasks s/he wants to perform and, using that partial knowledge, it is diicult to unambiguously understand her/his overall intentions. The information in the user model is crucial for reducing the ambiguity on the user's goals. So, it is important to extend the user model using every possible source of information. The relationship between the knowledge collected in the ongoing dialogue and that speciic to the user has been long debated. Clearly, the plans hypothesized during a dialogue are relevant, because they represent the system's beliefs on the user's intentions. In general, however, the system has alternative hypotheses on the user's plans and adding them to the user model is problematic , since they are concerned with diierent interpretations of her/his utterances. In this paper we propose to extract their common part for selecting intentions that can be ascribed to the user with a high degree of certainty. Moreover, we show how the expansion of the user model allows drawing new inferences that may be used to reduce the ambiguity on the user's intentions.
Group cognitive analytic music therapy: a quasi-experimental feasibility study conducted in a high secure hospital

ABSTRACT This study conducted a feasibility patient preference quasi-experimental study of group cognitive analytic music therapy (G-CAMT) for mentally disordered offenders. Participants either chose or were randomised to 16 sessions of manualised G-CAMT (N = 10) plus treatment as usual (TAU) or TAU alone (N = 10). Self-rated and staff-rated outcomes were assessed at baseline, post-intervention and 8-weeks post-intervention. Residency was assessed at 2-year follow-up. Results indicate that G-CAMT was easily implemented; 9/10 participants completed G-CAMT and attendees had high satisfaction with the approach. Session attendance was high; 4/10 participants attended all sessions. At the 8-week follow-up, 3/9 G-CAMT participants had reliable reductions (i.e. statistically reliable pre to 8-week follow-up change results) in intrusive/possessive behaviours and fear of separation/abandonment. On the staff-rated outcome measure G-CAMT participants as a group were statistically significantly friendlier compared to TAU at 8-week follow-up (U = 0.50, p = 0.009, d = 1.92, CI 0.44 to 3.11). There were no differences between the arms in terms of residency outcomes at 2-year follow-up. The study is discussed in terms of G-CAMT’s theoretical grounding and high acceptability. The study is limited by its small sample size, but indicates the possibility of progressing onto a full trial.
Applying rule ensembles to the search for super-symmetry at the large hadron collider

In this note we give an example application of a recently presented predictive learning method called Rule Ensembles. The application we present is the search for super-symmetric particles at the Large Hadron Collider. In particular, we consider the problem of separating the background coming from top quark production from the signal of super-symmetric particles. The method is based on an expansion of base learners, each learner being a rule, i.e. a combination of cuts in the variable space describing signal and background. These rules are generated from an ensemble of decision trees. One of the results of the method is a set of rules (cuts) ordered according to their importance, which gives useful tools for diagnosis of the model. We also compare the method to a number of other multivariate methods, in particular Artificial Neural Networks, the likelihood method and the recently presented boosted decision tree method. We find better performance of Rule Ensembles in all cases. For example for a given significance the amount of data needed to claim SUSY discovery could be reduced by 15% using Rule Ensembles as compared to using a likelihood method.
External Knowledge and Information Technology: Implications for Process Innovation Performance

Prior information systems research highlights the vital role of information technology (IT) for innovation in firms. At the same time, innovation literature has shown that accessing and integrating knowledge from sources that reside outside the firm, such as customers, competitors, universities, or consultants, is critical to firms' innovative success. In this paper, we draw on the knowledge-based view of the firm to investigate how search in external knowledge sources and information technology for knowledge absorption jointly influence process innovation performance. Our model is tested on a nine-year panel (2003-2011) of Swiss firms from a wide range of manufacturing industries. Using instrumental variables, and disaggregating by type of IT, we find that data access systems and network connectivity hold very different potential for the effective absorption of external knowledge, and the subsequent realized economic gains from process innovation. Against the backdrop of today's digital transformation, our findings demonstrate how firms should coordinate strategies for sourcing external knowledge with specific IT investments in order to improve their innovation performance.
Sudden unexpected death in infants in the Oxford Record Linkage Area: The mother

Of the 206 cases of sudden unexpected death in infancy (SUD) 170 were linked with the maternity information and birth certificates on the Oxford Record Linkage files. Statistically highly significant correlations were demonstrated with low maternal age, high parity, and low social class, the last two associations being more marked among the mothers of infants who died after the 12th week. Each case of SUD was then matched with three control livebirths for maternal age, parity, civil state of mother, social class, year and hospital of delivery, and as closely as possible for area of residence. A comparison of mothers of cases with those of the controls revealed a highly significant preponderance of women who were born outside the area, and no significant effect with maternal religion, previous pregnancy loss, or ABO blood group. Other pregnancies occurring to the case and control mothers between 1965 and 1971 were also traced. It was shown that the infant dying an SUD was more likely to have been conceived within six months of the birth of his preceding sib.
The collision velocity of the bullet cluster in conventional and modified dynamics

We consider the orbit of the bullet cluster 1E 0657−56 in both cold dark matter (CDM) and Modified Newtonian Dynamics (MOND) using accurate mass models appropriate to each case in order to ascertain the maximum plausible collision velocity. Impact velocities consistent with the shock velocity (∼ 4700 km s −1 ) occur naturally in MOND. CDM can generate collision velocities of at most ∼3800 km s −1 , and is only consistent with the data, provided that the shock velocity has been substantially enhanced by hydrodynamical effects.
Novel Assistive Robot for Self-Feeding

Assistive robots, with which users can interact directly, have attracted worldwide attention. They can assist people with disabilities and older persons in the activities of daily living. Assistive robots could be employed for improving quality of life as they can be adjusted according to demographic changes. There are several crucial issues to be considered with regard to these robots, such as customizing them according to the specific culture of the users as well as ensuring cost-effectiveness (Mann, 2005). In Korea, the official number of registered people with disabilities due to illnesses, injuries, and the natural aging process has already exceeded two million (Employment Development Institute, 2009). More than one-third of these disabled people are the elderly. Moreover, due to longer life spans and a decline in birthrate, the elderly make up over 10% of the population in Korea. As a result, effective caregiving with restricted resources is an urgent problem. In order to achieve efficient caregiving for people with disabilities and elderly persons, caregivers should physically interact with the people. For example, caregivers have to assist people in performing the routine activities of their daily lives, such as eating, changing clothes, changing their posture, moving from one location to another, and bathing. Among these activities, eating meals is one of the most essential daily activities. In this regard, caregivers must interact with people frequently to assist with food selection, feeding interval, etc. Existing robotic technologies can be utilized to take over the functions of the caregivers. Thus, assistive robots represent one of the solutions by which disabled or elderly people can receive support for performing the activities of daily life. The design of assistive robots to help with self-feeding depends strongly on the specific culture of the user. Korean food consists chiefly of boiled rice, soup, and side dishes such as Kimchi. The procedure of having a meal is as follows: the user eats the boiled rice first and then the side dishes. These steps are performed repetitively. In comparison with foreign boiled rice, Korean boiled rice sticks together very well after cooking. Handling this sticky boiled rice can be problematic. In addition, Korean soup includes meat, noodles, and various vegetables, thus the existing feeding robots find it difficult to handle Korean foods. Various assistive robots have been developed since the late 1980s, as shown in Fig. 1. Handy1 (Topping & Smith, 1999) is an assistive robot for daily activities such as eating, drinking, washing, shaving, teeth cleaning, and applying make-up. Handy1 consists of a five-DOF (degree-of-freedom) robot, a gripper, and a tray unit. The major function of Handy1 is to help with eating. Handy1 allows a user to select food from any part of the tray. A cup is attached to enable users to drink water with their meal. The walled columns of a