LLukas22/scidocs huggingface dataset:
High prevalence of childhood emotional, physical and sexual trauma among a Canadian cohort of HIV-seropositive illicit drug users.

BACKGROUND
The psychosocial impacts of various types of childhood maltreatment on vulnerable illicit drug-using populations remain unclear. We examined the prevalence and correlates of antecedent emotional, physical and sexual abuse among a community-recruited cohort of adult HIV-seropositive illicit drug users.


METHODS
We estimated the prevalence of childhood abuse at baseline using data from the Childhood Trauma Questionnaire, a 28-item validated instrument used to retrospectively assess childhood maltreatment. Logistic regression was used to estimate relationships between sub-types of childhood maltreatment with various social-demographic, drug-using and clinical characteristics.


RESULTS
Overall, 233 HIV-positive injection drug users (IDU) were included in the analysis, including 83 (35.6%) women. Of these, moderate or severe emotional childhood abuse was reported by 51.9% of participants, emotional neglect by 36.9%, physical abuse by 51.1%, physical neglect by 46.8% and sexual abuse by 41.6%. In multivariate analyses, emotional, physical and sexual abuses were independently associated with greater odds of recent incarceration. Emotional abuse and neglect were independently associated with a score of ≥16 on the Centre for Epidemiologic Studies Depression Scale. There was no association between any form of childhood maltreatment and clinical HIV variables, including viral load, CD4+ count and history of antiretroviral therapy use.


CONCLUSION
These findings underscore the negative impact of childhood maltreatment on social functioning and mental health in later life. Given the substantial prevalence of childhood maltreatment among this population, there is a need for evidence-based resources to address the deleterious effect it has on the health and social functioning of HIV-positive IDU.
Breast Cancer Image Enhancement using Median Filter and CLAHE

Aziz Makandar, Bhagirathi Halalli Abstract— Breat cancer is one of the major causes of death for woman in world wide. Mammography is the basic screening technique for early detection of tomour in the breast. The major objective of mammography is to detecting the small lumps at an earliest because they may lead for cancer. But it is difficult to identify the very small tomours in the conventional mammography because they are very noisy, low contrast, blur and fuzzy kind of images, it is necessary to enhance the mammography. Enhancement is done to bring out specific features of the mammography such as mass and microcalcification and to heighlight certain characteristics for early detection and easy diagnosis of breast cancer. This paper comprises the different denoising and cintrast enhancement techniques to recognize the suitable enhancement technique for mammography. Mammograms are denoised by linear and nonlinear filtering techniques and efficiency is measured by Root Meam Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) and then contrast of the image is enhanced by histogram based techniques.
Identifying cyber risk hotspots: A framework for measuring temporal variance in computer network risk

Modern computer networks generate significant volume of behavioural system logs on a daily basis. Such networks comprise many computers with Internet connectivity, and many users who access the Web and utilise Cloud services make use of numerous devices connected to the network on an ad-hoc basis. Measuring the risk of cyber attacks and identifying the most recent modus-operandi of cyber criminals on large computer networks can be difficult due to the wide range of services and applications running within the network, the multiple vulnerabilities associated with each application, the severity associated with each vulnerability, and the ever-changing attack vector of cyber criminals. In this paper we propose a framework to represent these features, enabling real-time network enumeration and traffic analysis to be carried out, in order to produce quantified measures of risk at specific points in time. We validate the approach using data from a University network, with a data collection consisting of 462,787 instances representing threats measured over a 144 hour period. Our analysis can be generalised to a variety of other contexts. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
The similitude of blossoms : a critical biography of Izumi Kyōka (1873-1939), Japanese novelist and playwright

This work asserts that Kyoka's writings were a refinement of a vision that came into focus around 1900; that this narrative archetype formed the aesthetic and ethical bases of his work; and that Kyoka, despite modernist imports from the West, did not jettison Japanese literary tradition.
Abnormal event detection in crowded scenes using sparse representation

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k2) to O(k) where k is the number of samples. We use the column wise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our 1This work was supported in part by the Nanyang Assistant Professorship (M4080134), JSPSNTU joint project (M4080882), Natural Science Foundation of China (61105013), and National Science and Technology Pillar Program (2012BAI14B03). Part of this work was done when Yang Cong was a research fellow at NTU. Preprint submitted to Pattern Recognition January 30, 2013 method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.
Predicting stock market index using fusion of machine learning techniques

The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10 years of historical data of these two indices. The predictions are made for 1–10, 15 and 30 days in advance. The paper proposes two stage fusion approach involving Support Vector Regression (SVR) in the first stage. The second stage of the fusion approach uses Artificial Neural Network (ANN), Random Forest (RF) and SVR resulting into SVR–ANN, SVR–RF and SVR–SVR fusion prediction models. The prediction performance of these hybrid models is compared with the single stage scenarios where ANN, RF and SVR are used single-handedly. Ten technical indicators are selected as the inputs to each of the prediction models. 2014 Elsevier Ltd. All rights reserved.
Validation of the action research arm test using item response theory in patients after stroke.

OBJECTIVE
To validate the unidimensionality of the Action Research Arm Test (ARAT) using Mokken analysis and to examine whether scores of the ARAT can be transformed into interval scores using Rasch analysis.


SUBJECTS AND METHODS
A total of 351 patients with stroke were recruited from 5 rehabilitation departments located in 4 regions of Taiwan. The 19-item Action ARAT was administered to all the subjects by a physical therapist. The data were analysed using item response theory by non-parametric Mokken analysis followed by Rasch analysis.


RESULTS
The results supported a unidimensional scale of the 19-item ARAT by Mokken analysis, with the scalability coefficient H = 0.95. Except for the item "pinch ball bearing 3rd finger and thumb", the remaining 18 items have a consistently hierarchical order along the upper extremity function's continuum. In contrast, the Rasch analysis, with a stepwise deletion of misfit items, showed that only 4 items ("grasp ball", "grasp block 5 cm(3)", "grasp block 2.5 cm(3)", and "grip tube 1 cm(3)") fit the Rasch rating scale model's expectations.


CONCLUSION
Our findings indicated that the 19-item ARAT constituted a unidimensional construct measuring upper-extremity function in stroke patients. However, the results did not support the premise that the raw sum scores of the ARAT can be transformed into interval Rasch scores. Thus, the raw sum scores of the ARAT can provide information only about order of patients on their upper extremity functional abilities, but not represent each patient's exact functioning.
A literature review and classification of electronic commerce research

In this paper, we present a literature review and classification scheme for electronic commerce (EC) research. The former consists of 275 journal articles published between 1993 and 1999 in nine journals that are appropriate outlets for EC research. The results show that an increasing volume of EC research has been conducted for a diverse range of areas. The articles are classified and results of these are presented, based on a scheme that consists of four main categories: application areas, technological issues, support and implementation, and others. A comprehensive list of references is presented. Hopefully, this review will provide a source for anyone interested in EC research and help simulate further interest. Purchase Export
Understandable Big Data: A survey

This survey presents the concept of Big Data. Firstly, a definition and the features of Big Data are given. Secondly, the different steps for Big Data data processing and the main problems encountered in big data management are described. Next, a general overview of an architecture for handling it is depicted. Then, the problem of merging Big Data architecture in an already existing information system is discussed. Finally this survey tackles semantics (reasoning, coreference resolution, entity linking, information extraction, consolidation, paraphrase resolution, ontology alignment) in the Big Data context. c ⃝ 2015 Elsevier Inc. All rights reserved.
Creep and Shrinkage Prediction Model for Analysis and Design of Concrete Structures : Model B 3 By

Synopsis: The present paper presents in chapter 1 a model for the characterization of concrete creep and shrinkage in design of concrete structures (Model B3), which is simpler, agrees better with the experimental data and is better theoretically justified than the previous models. The model complies with the general guidelines recently formulated by RILEM TC-107ß1. Justifications of various aspects of the model and diverse refinements are given in Chapter 2, and many simple explanations are appended in the commentary at the end of Chapter 1 (these parts do not to be read by those who merely want to apply the model). The prediction model B3 is calibrated by a computerized data bank comprising practically all the relevant test data obtained in various laboratories throughout the world. The coefficients of variation of the deviations of the model from the data are distinctly smaller than those for the latest CEB model (1990), and much smaller than those for the previous model in ACI 209 (which was developed in the mid-1960’s). The model is simpler than the previous models (BP and BPKX) developed at Northwestern University, yet it has comparable accuracy and is more rational. The effect of concrete composition and design strength on the model parameters is the main source of error of the model. A method to reduce this error by updating one or two model parameters on the basis of short-time creep tests is given. The updating of model parameters is particularly important for high-strength concretes and other special concretes containing various admixtures, superplasticizers, water-reducing agents and pozzolanic materials. For the updating of shrinkage prediction, a new method in which the shrinkage half-time is calibrated by simultaneous measurements of water loss is presented. This approach circumvents the large sensitivity of the shrinkage extrapolation problem to small changes in the material parameters. The new model allows a more realistic assessment of the creep and shrinkage effects in concrete structures, which significantly affect the durability and long-time serviceability of civil engineering infrastructure.
Organic farming in the Nordic countries--animal health and production.

Organic farming (or ecological agriculture) is of growing importance in the agricultural sector worldwide. In the Nordic countries, 1-10% of the arable land was in organic production in 1999. Organic farming can be seen as an approach to agriculture where the aim is to create integrated, humane, environmentally and economically sustainable agricultural production systems. Principles like nutrient recycling, prevention rather than treatment and the precautionary principle are included in aims and standards. Animal welfare is another hallmark of organic livestock production but despite this, several studies have indicated severe health problems e.g. in organic poultry production in Denmark. Also the quality of animal food products in relation to human health, particularly the risk of zoonotic infections, has been debated. For these reasons there is a need for improvement of production methods and animal health status. Vets play an important role in this development through work in clinical practice and in research. On-farm consultancy should be tailored to the individual farmers needs, and the practitioner should be willing to take up new ideas and when needed, to enter a critical dialogue in relation to animal welfare. Better base line data on animal health and food safety in organic food systems are needed.
Improved size-tunable synthesis of monodisperse gold nanorods through the use of aromatic additives.

We report an improved synthesis of colloidal gold nanorods (NRs) by using aromatic additives that reduce the concentration of hexadecyltrimethylammonium bromide surfactant to ~0.05 M as opposed to 0.1 M in well-established protocols. The method optimizes the synthesis for each of the 11 additives studied, allowing a rich array of monodisperse gold NRs with longitudinal surface plasmon resonance tunable from 627 to 1246 nm to be generated. The gold NRs form large-area ordered assemblies upon slow evaporation of NR solution, exhibiting liquid crystalline ordering and several distinct local packing motifs that are dependent upon the NR's aspect ratio. Tailored synthesis of gold NRs with simultaneous improvements in monodispersity and dimensional tunability through rational introduction of additives will not only help to better understand the mechanism of seed-mediated growth of gold NRs but also advance the research on plasmonic metamaterials incorporating anisotropic metal nanostructures.
Title Mixture models for statistical flood frequency analysis

Norsk Regnesentral (Norwegian Computing Center, NR) is a private, independent, non-profit foundation established in 1952. NR carries out contract research and development projects in information and communication technology and applied statistical-mathematical modelling. The clients include a broad range of industrial, commercial and public service organisations in the national as well as the international market. Our scientific and technical capabilities are further developed in cooperation with The Research Council of Norway and key customers. The results of our projects may take the form of reports, software, prototypes, and short courses. A proof of the confidence and appreciation our clients have in us is given by the fact that most of our new contracts are signed with previous customers. Abstract Statistical flood frequency analysis is commonly used to estimate design floods based on a series of annual maximum discharge data. The current guidelines for flood estimation in Norway recommend the use of a regional model when only a short series of at-site data is available, for 30-50 years of at-site data the two-parameter Gumbel distribution is the recommended statistical model while the three-parameter generalized extreme value (GEV) distribution should be used for more than 50 years of at-site data. This note investigates a mixture model approach that combines all three models with the mixture weights depending on the amount of available at-site data. The mixture weights are derived by assessing the predictive performance of the three models on out-of-sample data using proper scoring rules. In a case study of 60 discharge series from Norway it is found that the resulting weighting scheme depends heavily on the scoring rule that is used to obtain the weights. A trend similar to that of the current guidelines is most apparent when using scoring rules that focus on the upper tail, i.e. the quantile score and the quantile-weighted continuous ranked probability score. When the models are estimated based on 10-50 years of data, the weight corresponding to the regional model increases as the number of observations in the training set decreases and opposite for the local GEV and Gumbel models. The Gumbel model receives larger weights than the GEV model, indicating and overall better performance.
Geography, political economy and the ‘personality of Britain’

SUMMARY: This paper engages with the historical archaeology of the British Isles (With one or two exceptions, I follow the usage of Kearney 2006 in preferring the term ‘British Isles’ to ‘Atlantic archipelago’, preferring the more ideologically loaded, but familiar, term over the arguably more neutral but obscure term.) as a whole. It advocates an approach that foregrounds geography and political economy, via quite simple and traditional ways of mapping variation, for example the work of Cyril Fox. It seeks to play to archaeology’s strengths: rather than seeking abstract origins, it examines how practices later labelled as ‘colonial’ emerged from an intersection of concrete material practices.
Selective difficulty in recognising facial expressions of emotion in boys with ADHD

Research on emotion understanding in ADHD shows inconsistent results. This study uses control methods to investigate two questions about recognition and understanding of emotional expressions in 36 five- to eleven-year-old boys with ADHD: [1] Do they find this task more difficult than judging non-emotional information from faces, thus suggesting a specific social-cognitive impairment? [2] Are their judgements about faces impaired by general limitations on task performance, such as impulsive responding? In Part 1, 19 boys with ADHD and 19 age-matched typically developing boys matched facial expressions of emotion to situations, and did a control non-emotional face-processing task. Boys with ADHD performed more poorly than age-matches on both tasks, but found the emotion task harder than the non-emotion task. In Part 2, 17 boys with ADHD and 13 five-to six-year-old typically developing boys performed the same tasks, but with an ‘inhibitory scaffolding’ procedure to prevent impulsive responding. Boys with ADHD performed as well as the younger controls on the non-emotional task, but still showed impairments in the emotion task. Boys with ADHD may show poorer task performance because of general cognitive factors, but also showed selective problems in matching facial emotions to situations.
Fluorescence anisotropy (polarization): from drug screening to precision medicine.

INTRODUCTION
Fluorescence anisotropy (FA) is one of the major established methods accepted by industry and regulatory agencies for understanding the mechanisms of drug action and selecting drug candidates utilizing a high-throughput format.


AREAS COVERED
This review covers the basics of FA and complementary methods, such as fluorescence lifetime anisotropy and their roles in the drug discovery process. The authors highlight the factors affecting FA readouts, fluorophore selection and instrumentation. Furthermore, the authors describe the recent development of a successful, commercially valuable FA assay for long QT syndrome drug toxicity to illustrate the role that FA can play in the early stages of drug discovery.


EXPERT OPINION
Despite the success in drug discovery, the FA-based technique experiences competitive pressure from other homogeneous assays. That being said, FA is an established yet rapidly developing technique, recognized by academic institutions, the pharmaceutical industry and regulatory agencies across the globe. The technical problems encountered in working with small molecules in homogeneous assays are largely solved, and new challenges come from more complex biological molecules and nanoparticles. With that, FA will remain one of the major work-horse techniques leading to precision (personalized) medicine.
Passive RFID tomographic imaging for device-free user localization

Localization of users is an important part of location aware systems and smart environments. It forms a major data source for superimposed intention recognition systems. In RF device-free localization (DFL), the person being tracked does not need to wear a RF transmitter or receiver in order to be located. Instead, they are tracked using the changes in signal strength measured on static links in a wireless network. This work presents a new algorithm for RF DFL using passive RFID networks. We formulate and show how a tomographic imaging algorithm provides both low computational complexity and highly accurate position estimates. Using measurements conducted in an indoor environment with various human positions, we find the algorithm can locate the human with as low as 30 cm mean location error.
Strategies for alveolar ridge reconstruction and preservation for implant therapy.

PURPOSE
In dental implant treatment, ridge preservation and immediate or early implant placement are recommended to minimize bone resorption after tooth extraction and achieve esthetic outcomes. However, there is no consensus concerning the efficacy of this surgical method. There is also no consensus on the efficacy of bone and soft tissue grafts and surgical methods for alveolar ridge reconstruction.


STUDY SELECTION
This paper reports ridge alteration in the anterior maxilla after tooth extraction, and summarizes the efficacy of various ridge preservation methods and immediate or early implant placement as alveolar ridge preservation methods to minimize bone resorption after tooth extraction. The advantages and complications of alveolar ridge reconstruction methods, and the efficacy and surgical method of soft tissue graft are reviewed.


RESULTS
The anterior maxilla is in the esthetic zone, and the thickness of the bone on the labial side around the natural tooth is less than 1mm in many cases. Therefore, it is impossible to prevent bone resorption completely, even if ridge preservation and immediate or early implant placement are performed after tooth extraction. It is necessary to obtain stable and long-term esthetics by combining connective tissue and free gingival grafts, in addition to hard tissue augmentation.


CONCLUSIONS
It is important to consider the burden and level of satisfaction of patients, such as in terms of donor site morbidity in hard and soft tissue grafting, and to pay attention to appropriate indications to avoid overtreatment.