nils-herrmann/clean_scirepeval huggingface dataset:
Comorbid childhood gender identity disorder in a boy with Asperger syndrome
Venue: Psychiatry and clinical neurosciences
Year: 2008

IN AUTISTIC PSYCHOPATHY, Hans Asperger described that 'the autistic personality is an extreme variant of male intelligence' Consistent with this notion, Baron Cohen proposed the 'extreme male brain theory of autism' By quoting this theory, gender dysphoria in female subjects with Asperger syndrome (AS) could be explained logically. But a literature search yielded no boys with AS and gender identity disorder (GID) Hereby we present such a case. Informed written consent was obtained from the patient's parents. A 5 year old boy was referred to Child Psychiatry, Sapporo Ayuminosono because of his poor social skills. He had no peri /neonatal complications. In his infancy he passed early motor and language landmarks without significant delay. Single words were developed at the age of 1 year and communicative phrases were observed at 2.2 years of age. Preoccupations with specific colors and figures were observed. He had limited interaction with others, difficulty in developing peer relationships and was underresponsive in social situations. He liked making his own rules and frequently lost his temper when these were broken. On the Wechsler Intelligence Scale for Children III he had a full scale IQ of 92 (verbal IQ 90, performance IQ 96) at the age of 7.6 years. Although his appearance was obviously that of a boy, he spoke in a girlish manner and covered his mouth with a hand when he laughed, as female individuals commonly do. He had very limited interaction with boys and his preferred playmates were girls. He was not interested in stereotypical boy's toys and had a marked preoccupation with feminine activities. He liked cute characters in TV animations and always painted cute girls surrounded with many lovely hearts and flowers. At home he tended to dress in girlish clothes and wear a ribbon in his hair. He regretted being a boy and often showed his desire to be a girl and asserted that he would grow up into a woman. These symptoms have been observed throughout the follow up period of more than 2 years. Based on these symptoms, we diagnosed him as having AS and GID in accordance with the ICD 10 criteria. Most of the gender related symptoms in autistic spectrum disorders (ASD) could be related to behavioral and psychological characteristics of autism. For example, a boy with ASD might have a sense of belonging to the female sex after being bullied by male peers. Transvestism in ASD may arise from a preoccupation with specific clothes such as a flared skirt which satisfies their tactile sensation. In their youth, ASD subjects can sometimes develop a unique confusion of identity that occasionally expands to gender related problems. But these views do not explain the present case. For the diagnosis of GID in ASD, sufficient language abilities and sufficient follow up time are essential. The present case fulfills these requirements. If the present patient continues to have genderrelated symptoms into adulthood, we would need to treat him following an international standard.
Cross Cultural Communication in Oncology: Challenges and Training Interests.
Venue: Oncology nursing forum
Year: 2016

To survey oncology nurses and oncologists about difficulties in taking care of culturally and linguistically diverse patients and about interests in cross cultural training.  Descriptive, cross sectional.  Web based survey. SAMPLE 108 oncology nurses and 44 oncologists.  31 item questionnaire derived from preexisting surveys in the United States and Switzerland.  Self rated difficulties in taking care of culturally and linguistically diverse patients and self rated interests in cross cultural training.  All respondents reported communication difficulties in encounters with culturally and linguistically diverse patients. Respondents considered the absence of written materials in other languages, absence of a shared common language with patients, and sensitive subjects (e.g. end of life, sexuality) to be particularly problematic. Respondents also expressed a high level of interest in all aspects of cross cultural training (task oriented skills, background knowledge, reflexivity, and attitudes) Nurses perceived several difficulties related to care of migrants as more problematic than physicians did and were more interested in all aspects of cross cultural training.  The need for cross cultural training is high among oncology clinicians, particularly among nurses.  The results reported in the current study may help nurses in decision making positions and educators in introducing elements of cross cultural education into oncology curricula for nurses. Cross cultural training should be offered to oncology nurses.
Forecasting histogram time series with k nearest neighbours methods
Year: 2009

Histogram time series (HTS) describe situations where a distribution of values is available for each instant of time. These situations usually arise when contemporaneous or temporal aggregation is required. In these cases, histograms provide a summary of the data that is more informative than those provided by other aggregates such as the mean. Some fields where HTS are useful include economy, official statistics and environmental science. This article adapts the k Nearest Neighbours (k NN) algorithm to forecast HTS and, more generally, to deal with histogram data. The proposed k NN relies on the choice of a distance that is used to measure dissimilarities between sequences of histograms and to compute the forecasts. The Mallows distance and the Wasserstein distance are considered. The forecasting ability of the k NN adaptation is illustrated with meteorological and financial data, and promising results are obtained. Finally, further research issues are discussed.
Fraud Detection in Credit Card Transactions; Using Parallel Processing of Anomalies in Big Data
Year: 2016

In parallel to the increasing use of electronic cards, especially in the banking industry, the volume of transactions using these cards has grown rapidly. Moreover, the financial nature of these cards has led to the desirability of fraud in this area. The present study with Map Reduce approach and parallel processing, applied the Kohonen neural network model to detect abnormalities in bank card transactions. For this purpose, firstly it was proposed to classify all transactions into the fraudulent and legal which showed better performance compared with other methods. In the next step, we transformed the Kohonen model into the form of parallel task which demonstrated appropriate performance in terms of time; as expected to be well implemented in transactions with Big Data assumptions.
Burden of disease in Brazil, 1990 2016: a systematic subnational analysis for the Global Burden of Disease Study 2016
Venue: The Lancet
Year: 2018

Political, economic, and epidemiological changes in Brazil have affected health and the health system. We used the Global Burden of Disease Study 2016 (GBD 2016) results to understand changing health patterns and inform policy responses.  We analysed GBD 2016 estimates for life expectancy at birth (LE) healthy life expectancy (HALE) all cause and cause specific mortality, years of life lost (YLLs) years lived with disability (YLDs) disability adjusted life years (DALYs) and risk factors for Brazil, its 26 states, and the Federal District from 1990 to 2016, and compared these with national estimates for ten comparator countries.  Nationally, LE increased from 68*4 years (95% uncertainty interval [UI] 68*0 68*9) in 1990 to 75*2 years (74*7 75*7) in 2016, and HALE increased from 59*8 years (57*1 62*1) to 65*5 years (62*5 68*0) All cause age standardised mortality rates decreased by 34*0% (33*4 34*5) while all cause age standardised DALY rates decreased by 30*2% (27*7 32*8) the magnitude of declines varied among states. In 2016, ischaemic heart disease was the leading cause of age standardised YLLs, followed by interpersonal violence. Low back and neck pain, sense organ diseases, and skin diseases were the main causes of YLDs in 1990 and 2016. Leading risk factors contributing to DALYs in 2016 were alcohol and drug use, high blood pressure, and high body mass index.  Health improved from 1990 to 2016, but improvements and disease burden varied between states. An epidemiological transition towards non communicable diseases and related risks occurred nationally, but later in some states, while interpersonal violence grew as a health concern. Policy makers can use these results to address health disparities. Funding Bill Melinda Gates Foundation and the Brazilian Ministry of Health.
An Extended Knowledge Representation Learning Approach for Context Based Traceability Link Recovery: Extended Abstract
Venue: 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)
Year: 2020

Software requirements traceability links have been widely recognized as an essential means for effective system evolution when requirements change. However, creating and maintaining traceability links is not an easy task in practice, especially when faced time pressure. Therefore, an automatic and accurate traceability link recovery approach is needed. Existing methods usually recover the traceability links through information retrieval models  which calculate text similarity among software artifacts. Although the text similarity plays an essential role in correlating software artifacts, we argue that the context of software artifacts also renders important clues for establishing the traceability links among software artifacts. For example, for each software use case, its includes/extends use cases can be seen as its context information, contributing to comprehensively profiling the use case. The collection of software artifacts can be modeled as a graph structure through a variety of explicit relationships.  Embodied Knowledge Representation Learning (DKRL)  is a widely accepted method, which can effectively capture the structural information of explicit relationship and description information of entities. By effectively and precisely embedding such a graph, the context information can be meaningfully represented, contributing to the identification of requirements traceability links. In this paper, we propose a Traceability Link Recovery Knowledge Representation Learning (TLR KRL) to recover requirements traceability links between use cases and code based on DKRL. This work has been accepted in The 32nd International Conference on Software Engineering& Knowledge Engineering. TLR KRL can comprehensively characterize software artifacts by embedding both text information and structural relationships, an overview of which is shown in Fig. 1. Specifically, we follow a systematic process to extend the DKRL model, improving its negative sampling method and thus minimizing false negative samples generated by the original DKRL model. In such a way, we are able to obtain more precise embedding of software artifacts. Such meaningful embeddings are then used to train traceability link classifiers by using supervised machine learning algorithms. All traceability link candidates obtained from the classifier will be further screened by using triple classification in order to retrieve more correct traceability links. To verify the effectiveness of our method, we have carried out experiments on four datasets, including eTour, EAnci, Clinic, and ITrust. The evaluation results on each of the datasets have all shown that our approach can outperform existing work.
Towards User Scheduling for 6G: A Fairness Oriented Scheduler Using Multi Agent Reinforcement Learning
Venue: ArXiv
Year: 2020

User scheduling is a classical problem and key technology in wireless communication, which will still plays an important role in the prospective 6G. There are many sophisticated schedulers that are widely deployed in the base stations, such as Proportional Fairness (PF) and Round Robin Fashion (RRF) It is known that the Opportunistic (OP) scheduling is the optimal scheduler for maximizing the average user data rate (AUDR) considering the full buffer traffic. But the optimal strategy achieving the highest fairness still remains largely unknown both in the full buffer traffic and the bursty traffic. In this work, we investigate the problem of fairnessoriented user scheduling, especially for the RBG allocation. We build a user scheduler using Multi Agent Reinforcement Learning (MARL) which conducts distributional optimization to maximize the fairness of the communication system. The agents take the cross layer information (e.g. RSRP, Buffer size) as state and the RBG allocation result as action, then explore the optimal solution following a well defined reward function designed for maximizing fairness. Furthermore, we take the 5% tile user data rate (5TUDR) as the key performance indicator (KPI) of fairness, and compare the performance of MARL scheduling with PF scheduling and RRF scheduling by conducting extensive simulations. And the simulation results show that the proposed MARL scheduling outperforms the traditional schedulers.
Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform
Venue: IEEE Transactions on Instrumentation and Measurement
Year: 2020

Online partial discharge (PD) monitoring is an important means to detect insulation deterioration. However, it is difficult to extract the PD signal due to various interferences in the field. Noisy PD signal is used to judge the status of insulation, which would affect the conclusion; therefore, denoising PD signal is a major task in online PD monitoring. Common methods for PD denoising include the empirical mode decomposition (EMD) and wavelet transform; however, the denoising results are highly dependent on the modal aliasing, the selection of mother wavelets, and decomposition levels. This article proposes a method to solve these problems. This method uses traditionally singular value transform [singular value decomposition (SVD) to reconstruct narrowband interference and remove it. Next, the empirical wavelet transform (EWT) is carried out for the PD signal that has residual white noise. Then, the noisy signal is decomposed into several modes corresponding to each spectrum segment. The $3~\sigma principle is used to denoise the modes with large kurtosis, and the modes are combined into a reference signal. The start end positions of PD signal are then obtained from the reference signal. Finally, the PD signal is obtained by time domain denoising. The results from both simulated and actual field detection signals show the excellent performance of this method.
Impossibility of Perfect Recovering Cosinusoidal Signal of Any Phase Sampled with Nyquist Rate
Year: 2020

In this paper, a problem of a perfect recovering cosinusoidal signal of any phase being sampled critically is considered. It is shown that there is no general solution to this problem. Its detailed analysis presented here shows that recovering both the original cosinusoidal signal amplitude and its phase is not possible at all. Only one of this quantities can be recovered under the assumption that the second one is known. And even then, performing some additional calculations is needed. As a byproduct, it is shown here that a transfer function of the recovering filter that must be used in the case of the critical sampling differs from the one which is used when a cosinusoidal signal is sampled with the use of a sampling frequency greater than the Nyquist rate. All the results achieved in this paper are soundly justified by thorough derivations. http:/www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 14 Number 3 September 2020
Simple Solvothermal Routes to Synthesize 3D BiOBrxI1 x Microspheres and Their Visible Light Induced Photocatalytic Properties
Year: 2011

The band structure controlled solid solution of BiOBrxI1 x was successfully synthesized by a simple solvothermal route. The prepared samples were characterized by X ray diffraction, scanning electron microscopy, UV vis diffuse reflectance spectroscopy, and nitrogen sorption/desorption. The resulting BiOBrxI1 x samples were phase pure and of three dimensional (3D) microspheres composed of nanoplates. The samples with different x values exhibited composition dependent absorption properties in the visible light region and the bandgaps were estimated to be between 1.89 and 2.53 eV. Rhodamine B (RhB) photocatalytic degradation experiments showed that these samples possessed excellent and composition dependent performance. The highest catalytic performance of the 3D BiOBr0.2I0.8 microspheres may derive from a synergetic effect, including higher surface area, porous structure, and enhancement of light absorbance. Moreover, on the basis of the analysis of the valence band and conduction band, a possible mechanism.
On whether zero is in the global attractor of the 2D Navier Stokes equations
Year: 2014

The set of nonzero external forces for which the zero function is in the global attractor of the two dimensional Navier Stokes equations is shown to be meagre in a Frechet topology. A criterion in terms of a Taylor expansion in complex time is used to characterize the forces in this set. This leads to several relations between certain Gevrey subclasses of C and a new upper bound for a Gevrey norm of solutions in the attractor, valid in the strip of analyticity in time.
In Situ Confinement of Ultrasmall Pd Clusters within Nanosized Silicalite 1 Zeolite for Highly Efficient Catalysis of Hydrogen Generation.
Venue: Journal of the American Chemical Society
Year: 2016

Well dispersed and ultrasmall Pd clusters in nanosized silicalite 1 (MFI) zeolite have been prepared under direct hydrothermal conditions using [Pd(NH2CH2CH2NH2)2]Cl2 as precursor. High resolution scanning transmission electron microscopy studies indicate that the Pd clusters are encapsulated within the intersectional channels of MFI, and the Pd clusters in adjacent channels visually aggregate, forming nanoparticles (NPs) of ~1.8 nm. The resultant catalysts show an excellent activity and highly efficient H2 generation toward the complete decomposition of formic acid (FA) under mild conditions. Notably, thanks to the further reduced Pd NP size ~1.5 nm) and the additionally introduced basic sites, the Pd/S 1 in K catalyst affords turnover frequency values up to 856 h( 1) at 25 degC and 3027 h( 1) at 50 degC. The easy in situ confinement synthesis of metal clusters in zeolites endows the catalysts with superior catalytic activities, excellent recyclability, and high thermal stability, thus opening new perspectives for the practical application of FA as a viable and effective H2 storage material for use in fuel cells.
Design and Development of AD CGAN: Conditional Generative Adversarial Networks for Anomaly Detection
Venue: IEEE Access
Year: 2020

Whether in the realm of software or hardware, datasets representing the state of systems are mostly imbalanced. This imbalance is because these systems' reliability requirements make the occurrence of an anomaly a rare phenomenon. Hence, most datasets on anomaly detection have a relatively small percentage that captures the anomaly. Recently, generative adversarial networks (GAN) have shown promising results in image generation tasks. Therefore, in this research work, we build on conditional GANs (CGAN) to generate plausible distributions of a given profile to solve the challenge of data imbalance in anomaly detection tasks and present a novel framework for anomaly detection. Firstly, we learn the pattern of the minority class data samples using a single class CGAN. Secondly, we use the knowledge base of the single class CGAN to generate samples that augment the minority class samples so that a binary class CGAN can train on the typical and malicious profiles with a balanced dataset. This approach inherently eliminates the bias imposed on algorithms from the dataset and results in a robust framework with improved generalization. Thirdly, the binary class CGAN generates a knowledge base that we use to construct the cluster based anomaly detector. During testing, we do not use the single class CGAN, thereby providing us with a lean and efficient algorithm for anomaly detection that can do anomaly detection on semi supervised and non parametric multivariate data. We test the framework on logs and image based anomaly detection datasets with class imbalance. We compare the performance of AD CGAN with GAN derived and non GAN derived state of the art algorithms on benchmark datasets. AD CGAN outperforms most of the algorithms in the standard metrics of Precision, Recall, and F 1 Score. Where AD CGAN does not perform better in the parameters used, it has the advantage of being lightweight. Therefore, it can be deployed for both online and offline anomaly detection tasks since it does not use an input sample inversion strategy.
Predictors of malignancy in screen detected breast masses with indeterminate/equivocal (grade 3) imaging features.
Venue: Breast
Year: 2011

The mammographic grading system used by BreastScreen Australia accredited programs requires needle biopsy of lesions with indeterminate features (grade 3) We wished to determine the predictors of malignancy for these common lesions, so as to reduce surgery for benign lesions.  Grade 3 masses assessed between Jan 1996 Dec 2005 are included. New or changing lesions were identified through film review. Imaging, demographic and final outcome data were tabulated. Statistical analysis was performed to determine the predictors of malignancy.  1181 lesions, including 623 new or changing masses are assessed. Of these 98 (8.3% were malignant. Malignancy was found in 3% first round masses versus 13% of new or changing lesions. Withholding needle biopsy results, interval change (OR 2.85, p 0.0001) increasing age (p 0.0001) and diameter were independent predictors of malignancy, lesion diameter having an inverse relationship with malignancy. Once needle biopsy results were included, this parameter became the sole predictor of malignancy as other factors lost significance in a multivariate model.  Surgical biopsy is not indicated in well sampled grade 3 masses with benign needle biopsy results. Surgery is valuable for diagnosis or treatment of the remaining such masses without benign needle biopsy results.