Miracle-dz/UniFAR-FTUs huggingface dataset:
Contraceptive conversations : power, discourse and the social construction of contraceptive use during nurse consultations with women in family planning clinics.

This thesis aims to make a significant contribution towards ' a sociology of contraception'. It takes as its starting point the lack of a contraceptive 'strand within the sociological literature on the body generally, and on women's reproductive sexual health specifically. It takes as its focus the under-researched area of nurse/woman contraceptive consultations in family planning clinics, and explores how contraceptive use is discussed. This is undertaken by utilising Foucault's work on discourse and power, and the body theory work of Turner and Frank. Using a 'constructivist' grounded theory approach, incorporating a Foucauldian perspective, 49 consultations between nurses and women were audio taped in two large family planning clinics in the North of England. Interviews with 15 family planning nurses were also conducted. Three categories were identified from the consultation data: 'Body education', 'Body surveillance' and 'Regimen'. Further analysis resulted in the development of two overlapping core categories: Surveillance and Compliance, within which the three initial categories were placed. In the context of these categories, nurses employ discursive techniques to develop the women into effective users of contraception. It is suggested that these discursive techniques construct the 'Reproductively vulnerable body' and utilise this concept to encourage women to become involved in selfsurveillance and self-care practices and to adhere to a contraceptive regimen • further constructing the: 'Active yet compliant patient'. The manner in which the Foucauldian notion of 'Productive power' is utilised within this process is discussed extensively. The thesis concludes by proposing that the contraceptive consultations result in the construction of a 'contraceptive-using body', one that has developed a 'contraceptive habitus' congruent with Foucault's 'techniques of the self'. Furthermore, this process also enables the evaluation of Frank's somewhat neglected 'body types in action' and 'body typology' work.
Longitudinal association between alcohol use and physical activity in US college students: Evidence for directionality

Abstract Objective: To investigate directionality of the association between alcohol use and physical activity in a college student sample, longitudinally across three time points. Participants: A total of 396 undergraduate students from a large university in the United States (62% females) participated in this study. Methods: Self-report data of alcohol use and physical activity were collected at three timepoints with 3-month lags between waves. Random-intercept cross-lagged panel modeling was used to disentangle within- and between-person effects. Results: Despite no significant between-person effects, there were significant cross-lagged paths from alcohol use to vigorous physical activity at the within-person level. Specifically, when individuals consumed more alcohol than normal at earlier timepoints, they reported more subsequent vigorous physical activity at 3-month follow-ups. Conclusion: We provide evidence that alcohol use may positively predict later physical activity in college students over the course of a school year. These findings advance theoretical understanding of how these two health behaviors are linked using sophisticated methods.
Area-time-power tradeoffs in parallel adders

In this paper, several classes of parallel, synchronous adders are surveyed based on their power, delay and area characteristics. The adders studied include the linear time ripple carry and Manchester carry chain adders, the square-root time carry skip and carry select adders, the logarithmic time carry lookahead adder and its variations, and the constant time signed-digit and carry-save adders. Most of the research in the last few decades has concentrated on reducing the delay of addition. With the rising popularity of portable computers, however, the emphasis is on both high speed and low power operation. In this paper we adopt a uniform static CMOS layout methodology whereby short circuit power mininization is used as the optimization criterion. The relative merits of the different adders are evaluated by performing a detailed transistor-level simulation of the adders using HSPICE. Among the two's complement adders, a variation of the carry lookahead adder, called ELM, was found to have the best power-delay product. Based on the results of our experiments, a large adder design space is formulated from which an architect can choose an adder with the desired characteristics.
Introducing Microsoft Silverlight

Get a running start with Silverlight 3the cross-platform, cross-browser plug-in for rich interactive applications and cutting-edge media experiences. Featuring advance insights from inside the Microsoft Silverlight teamthis book provides practical, grounded advice, rich examples, and ready code to help inspire your next solutions. Discover how to: Create your first simple sites Build applications that run out of the browser Use XAML to render, scale, and animate graphics on-screen Work with Silverlight Tools for Visual Studio Explore core and advanced controlsand build your own Exploit new graphics support3-D effects, H.264 video, GPU acceleration Experiment with media, ink, DeepZoom, and Photosynth * Access data services to build connected applications Dynamically add content using Ruby and Python Manage Silverlight applications from ASP.NETbased servers Learn how to deliver DRM-protected video content
Conceptual Design of an IP-based Satellite Bus using Internet Technologies

The goal of this paper is to develop a generic, reconfigurable spacecraft bus architecture that implements IP-based protocols and networking hardware that is common to terrestrial networks. First, a description of the communications architecture for an operational Earth Science mission is presented. The Tropical Rainfall Measuring Mission (TRMM) was selected as an example that shows a typical Earth science mission with a nice complement of varying data rate instruments. We will be able to show through the satellite architecture where IP-based protocols will benefit a new design. Secondly, we develop an IP-based satellite bus design with an Ethernet backbone using standard terrestrial networking components and protocols. The design will be highly configurable to meet many different mission requirements. Adapting the design to the TRMM communications architecture will test the feasibility. We will indicate the subsystems that are part of the design and show examples of how TCP/IP will operate on board the satellite bus. Finally, we present the type of research needed to make IP-based missions a reality. This roadmap will provide NASA the guidance to design complex architectures that will become part of their mission portfolio in the next decade.
Location Service Framework for Private Cloud Storage System Based on Logical Disk

We have designed and implemented a location service framework in the private cloud storage system based on logical disk. Location service is very important in the private cloud storage system and many people are eager to improve the performance of the location service. But the algorithm for location service is fixed so that we can’t change the algorithm even we find a better one. In order to adopt the new algorithm, we need to redesign the location service system. Therefore, we design and implement a location service framework to solve the problem. The location service framework makes development more fast and flexible. The developers and designers can concentrate on algorithm and need not to worry about redesigning the location service system. What’s more, the location service framework can provide the fundamental features of the location service. At last, we prove the effectiveness through simulation testing.
"We are the memory representation of our parents": Intergenerational legacies of genocide among descendants of rape survivors in Rwanda.

INTRODUCTION The 1994 genocide against the Tutsi in Rwanda subjected thousands of women to rape as part of a range of other genocidal atrocities. This article explores what it means in everyday life to be a descendant of such mothers. METHODS A qualitative study was conducted in eastern Rwanda. The twelve respondents, all descendants of genocide-rape survivor mothers, participated in focus group discussions and semi-structured interviews. Topics focused on different aspects of the intergenerational transmission of trauma and the mitigation of this transmission by the psychosocial support from which their mothers benefited. The phenomenological method as developed by Giorgi (2012) was used to analyze the transcripts. FINDINGS All respondents, regardless of their birth circumstances, are marked by growing up with a severely traumatized mother. Children conceived during rape are specifically marked by the absence of a perpetrator father unknown to them, the others by the lack of many (extended) family members. They all benefited from the psychosocial support provided to their mothers. DISCUSSION Genocidal rape causes specific kinds of suffering and specific identity problems for the children born as a consequence of genocide-rape. However, even if the children were not conceived during the rape, their level of suffering is similar. CONCLUSION The effects of the intergenerational transmission of trauma related to the 1994 genocide against the Tutsi in Rwanda should be recognized among all youth deeply affected by it. Appropriate policies and programs should be designed and implemented to moderate the effects and strengthen resilience to ensure a peaceful future on an individual, interpersonal, and inter-relational community levels.
Inversion of dynamically repositioned multi-axis electromagnetic data for ordnance characterization

The challenges associated with removing UXO and explosive remnants of war have led to a variety of methods for detection and discrimination of buried metallic objects using time-domain electromagnetic induction (EMI). Recent work has shown that parameters recovered from physics-based inversions can discriminate and classify buried ordnance from non-ordnance. We present results of applying advanced processing to data from a dynamically repositioned multiaxis EMI instrument. Data are collected using an adaptive sampling process to find the center of the anomaly and collect minimal data while maintaining model fidelity. An ortho-normalized volume magnetic source (ONVMS) model is used to resolve various targets at different depths. The ONVMS model is a generalized volume dipole model, with the single dipole model being a special limiting case. Using the ONVMS model, an object's response to a sensor's primary magnetic field is modeled mathematically by a set of equivalent magnetic dipoles distributed inside a volume containing the object. We assess the utility and veracity of the dynamic sampling strategy coupled with the ONVMS model on data acquired over a set of calibration and simulant targets. Rapid target characterization codes are aggregated into a software package with particular focus on ease of use for non-expert users.
Emotional labour at work and at home among Greek health‐care professionals

– The emotionally taxing nature of health‐care work has been increasingly recognized. In parallel, the field of work and family has been searching for more specific antecedents of both work interference with family (WFI) and family interference with work (FWI). The current study aims to examine the relationship between surface acting and hiding negative emotions with WFI and FWI among Greek health‐care professionals. , – The research is a cross‐sectional study of 180 Greek doctors and 84 nurses using self‐report measures. , – Results indicated that, for doctors, surface acting at work was positively related to WFI and, for nurses, surface acting at home was positively related to FWI. , – The respondents were sampled on a convenience basis and the non‐random procedure may have introduced unmeasured selection effects. The present study is cross‐sectional and thus the postulated relationships cannot be interpreted causally. , – Emotional management training and opportunities for emotional decompression for Greek health‐care professionals should be explored. In terms of medical education, the need to train students to understand and cope with emotional demands is an important first step. This research highlights the need for communication‐skills training courses facilitating emotional awareness and emotional management. , – These findings position emotional labour as an important antecedent of both WFI and FWI.
Diseño y construcción de un guante prototipo electrónico capaz de traducir el lenguaje de señas de una persona sordomuda al lenguaje de letras

En nuestro medio las personas privadas de hablar y escuchar aprenden la comunicacion manual (mimica dactilologica) y/o la lectura labio facial para poder comunicarse con la sociedad, sin embargo la gran mayoria de personas con la capacidad de hablar no entienden el lenguaje que tales personas utilizan para comunicarse con los demas. Por lo tanto se plantea un guante prototipo que traduzca el lenguaje de senas de una persona sorda, realizando palabras letra por letra las mismas que se puedan visualizar en un computador con el objetivo de que los estudiantes de los institutos dedicados a la ensenanza puedan aprender su lenguaje con mayor rapidez y facilidad.
L'accident de Fukushima Dai Ichi - Le récit du directeur de la centrale. Volume 1 : L'anéantissement

Le 11 mars 2011, le Japon subit l’un des seismes les plus importants de l’histoire. Il est suivi d’un tsunami de grande amplitude. Ce double evenement fait plus de 18 000 morts et provoque d’importants degâts materiels. Cinq centrales nucleaires sont notamment touchees par le sinistre. L’une d’entre elles, Fukushima Dai Ichi, est confrontee a une situation d’urgence nucleaire. Trois reacteurs entrent en fusion et des explosions se produisent dans trois bâtiments reacteurs. Malgre la gravite de l’accident – rapidement compare dans les medias a celui de Tchernobyl – le pire est pourtant evite. Evite grâce au courage et a l’action d’une poignee d’hommes restes aux commandes de la centrale. Ils sont diriges par Masao Yoshida, le directeur de Fukushima Dai Ichi. Cet ouvrage propose au lecteur un recit inedit. Le temoignage, traduit du japonais, de Yoshida a la commission d’enquete gouvernementale sur l’accident. Plus de 400 pages qui devoilent une autre histoire : celle d’une equipe de travailleurs confrontes a un desastre annonce. Une histoire qui, du reste, peut se lire comme un grand roman technique. Face a l’ampleur du chantier de traduction et d’analyse, nous avons choisi de diviser ce projet editorial en quatre volumes. Ce premier volume, soustitre « L’aneantissement », aborde l’incidence de la catastrophe naturelle sur les installations et le debut de l’accident nucleaire. Il expose en outre deux concepts : celui d’ingenierie de l’urgence et de situation extreme.
Computer Testbeds and Mechanism Design

We develop a behavioral model as a a computer testbed we can use to study the probable performance of a wide range of mechanisms prior to testing them in a laboratory or using them in practice. In this paper, we describe an implementation of our model and the computer testbed methodology to Groves-Ledyard (1977) mechanisms for provision of public goods. Previous experimental evidence, and some theory, strongly suggest that the value of a free mechanism parameter is important for the dynamic performance of the mechanism (when it is simulated as a repeated game). In our model messages converge to the Nash Equilibrium for all of the values of the mechanism parameter that we studied. However, the convergence times depend on the value of the parameter. Our analysis suggests there are values of the free parameter that result in the fastest convergence. The range of values is robust with respect to the changes in the behavioral model’s parameter values and details of the updating procedures. This prediction is validated with data from experiments with human subjects. JEL classification: D83, C63, C92, H41
Research of Medical Image Segmentation Based on Improved Watershed Algorithm

Considered the uniqueness of medical image the watershed algorithm is improved and applied to the segmentation of the medical image. Watershed algorithm is commonly applied to the segmentation, but there is a sever problem of over-segmentation. This paper is to expound some improvements based on the current base of watershed algorithm. These improvements are: before watershed algorithm, to introduce a floating-point active-image as the input of watershed algorithm and after watershed algorithm, to incorporate some small segmented regions into the near bigger region based on the area control, and at the same time according to the incorporated criterions of area and contrast control. By means of the improved method, the phenomena of over-segmentation are well restrained, and small regions with pathological change are well segmented. The effect of this method is very well.
Addressing the Annotation Bottleneck for Clinical Natural Language Processing: Testing the Feasibility of Domain Adaptation for Medical Text

Privacy regulations created a serious bottleneck for accessing and annotating text in EMR. Statistical natural language processing requires annotated training text. There are publicly available biomedical text sources. We propose to apply domain adaptation to (source) clinical trial announcements and (target) physician discharge summaries (DS) to ease the annotation bottleneck. We tested Augment and InstancePruning. Augment improved the performance of medication name extraction statistically significantly on p=0.0006 level when only few annotated DS were available. Introduction The Health Insurance Portability and Accountability Act (HIPAA) of 1996 presented a set of federal standards for protecting the privacy of personal health information and it seriously limited data access in electronic medical records 1 . Statistical Natural Language Processing (NLP) requires annotated training data, consequently HIPAA created a bottleneck for statistical NLP research. We propose to apply domain adaptation to ease the annotation bottleneck. In this poster we report our preliminary findings and discuss future research directions. Related Work There are supervised (requires labeled target domain text) and unsupervised (no labeled target text) adaptations. Our task falls into the first category. Jiang (2008) discusses two frameworks for adaptation: instance weighting and feature selection 2 . Data and Methods In this experiment, we identify medication names in biomedical text. The source domain (no restrictions) currently includes 100 randomly selected Clinical Trial Announcements. The target domain (strict HIPAA restrictions) includes 110 discharge summaries for training, 35 for development, and 251 for testing. We tested a Maximum Entropy algorithm on the source and target domains using InstancePruning and Augment adaptation algorithms 3 . Results and Discussion We calculated three baselines: (1) source data only, (2) target data only, (3) use both source and target data. (Figure 1) shows the results of the target only baseline and Augmentation algorithms. Augmentation has higher numerical value for all experiments and the difference is statistically significant at p=0.0006 level.
A comparison of features for POS tagging in Kannada

This paper proposes a system of part of speech tagging for the South Indian language Kannada using supervised machine learning. POS tagging is an important step in Natural Language Processing and has varied applications such as word sense disambiguation, natural language understanding etc. Based on extensive research into methods used for POS tagging, Conditional Random fields have been chosen as our algorithm. CRFs are used for sequence modeling in POS tagging, named entity recognition and as an alternative to Hidden Markov Models. Three very large corpora are used and their results are compared. The feature sets for all three corpora are also varied. The best method for the task is determined using these results.
Irregular Partitioning Method Based k-Nearest Neighbor Query Algorithm Using MapReduce

With the dramatic increase of available data, the process of data processing should get higher and higher performance. Most researches on k-Nearest Neighbor (kNN) query algorithm are based on the regular partitioning method which is easy to cause the imbalance of load, even influence the overall performance of the kNN query algorithm. In addition, the traditional kNN query algorithm works on single process or single machine platforms, which cannot obtain high enough efficiency when dealing with big data. Aiming at these two issues, an irregular partitioning method based kNN algorithm is presented and being executed on the distributed parallel computing platform—MapReduce as of in this paper. Experiments show that the irregular partitioning method based kNN algorithm using MapReduce can obtain much higher performance and can guarantee a very efficient query when dealing with big data.
Inclined Image Recognition for Aerial Mapping using Deep Learning and Tree based Models

One of the important capabilities of an unmanned aerial vehicle (UAV) is aerial mapping. Aerial mapping is an image registration problem, i.e., the problem of transforming different sets of images into one coordinate system. In image registration, the quality of the output is strongly influenced by the quality of input (i.e., images captured by the UAV). Therefore, selecting the quality of input images becomes important and one of the challenging task in aerial mapping because the ground truth in the mapping process is not given before the UAV flies. Typically, UAV takes images in sequence irrespective of its flight orientation and roll angle. These may result in the acquisition of bad quality images, possibly compromising the quality of mapping results, and increasing the computational cost of a registration process. To address these issues, we need a recognition system that is able to recognize images that are not suitable for the registration process. In this paper, we define these unsuitable images as “inclined images,” i.e., images captured by UAV that are not perpendicular to the ground. Although we can calculate the inclination angle using a gyroscope attached to the UAV, our interest here is to recognize these inclined images without the use of additional sensors in order to mimic how humans perform this task visually. To realize that, we utilize a deep learning method with the combination of tree-based models to build an inclined image recognition system. We have validated the proposed system with the images captured by the UAV. We collected 192 images and labelled them with two different levels of classes (i.e., coarse- and fine-classification). We compared this with several models and the results showed that our proposed system yielded an improvement of accuracy rate up to 3%.