nils-herrmann/clean_scirepeval huggingface dataset:
Staff Analysis of Market Data Related to Credit Default Swap Transactions
Year: 2012

The Division of Risk, Strategy, and Financial Innovation has completed an analysis of single name credit default swap transaction and position activity. The first analysis is based on a sample of all new, risk transfer, dollar adjusted, gold record transactions in both corporate and sovereign single name credit default swaps (CDS) submitted to the Depository Trust and Clearing Corporation's Trade Information Warehouse (DTCCTIW) between January 1, 2011 and December 31, 2011 (the sample period) The second analysis is based on monthly position data in single name CDS over the same sample period and provided by DTCC TIW. The analysis provides information that characterizes the level of trading activity and positions in the credit default swap market. It is intended to assist the Commission in the development of final rules that further define the terms "security based swap dealer" and "major security based swap participant" in connection with the implementation of the Dodd Frank Act. In part, this information should help evaluate the impact of alternative approaches (1) to implementing the de minimis exception to the "security based swap dealer" definition by quantifying, under certain assumptions, the number of persons who may be required to register as "security based swap dealers" or "major security based swap participants," and (2) to evaluating the security based swap activities and positions of such persons. The memo is organized as follows. Section I first discusses potential criteria which, when analyzed in combination with the single name CDS dataset available from DTCCTIW, may be indicative of dealer activity. Next, the section outlines the methodological approach. An analysis of each criterion based on 12 month aggregate gross notional dollar value of single name CDS transactions and illustrated by charts and tables follows, as well as an analysis of the effect of combining multiple criteria. Section I concludes with an analysis of transaction activity between counterparties and special entities. Section II provides an analysis of aggregate gross notional positions that may be relevant to predicting the number of entities that need to determine whether they qualify as major security based swap participants. Finally, an appendix repeats the criteria analysis from Sections I and 2 for index CDS.
Nature sports, health and ageing: the value of euphoria
Year: 2020

Using autoethnographic approaches, I analyse ageing trajectories in 10 adventurous outdoor nature sports using boards, paddles, sails and wings in water, snow and air. Skill and risk are intermediate. Training, experience, and safety precautions are required, but risk of death is low. Chronic pain levels increase with age, and capabilities and challenges faced decline. These are offset, however, by greater attention and appreciation of natural surroundings. Of particular significance, enjoyment and opportunities for euphoria persist despite ageing, and euphoria can temporarily override chronic pain, stress and fatigue. In wealthy urbanised nations, chronic pain, and poor mental and emotional health, impose increasing social and economic costs as lifespans increase. By providing opportunities for euphoria as well as exercise, adventurous outdoor nature sports can make substantial contributions to the physical, mental and social health of older individuals, reducing the costs of aged care. Therefore, health policies should encourage lifelong nature sports.
Nonlinear dimension reduction for EEG based epileptic seizure detection
Venue: 2016 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Year: 2016

Approximately 0.1 percent of epileptic patients die from unexpected deaths. In general, for intractable seizures, it is crucial to have an algorithm to accurately and automatically detect the seizures and notify care givers to assist patients. EEG signals are known as definitive diagnosis of seizure events. In this work, we utilize the frequency domain features (normalized in band power spectral density) for the EEG channels. We applied a nonlinear data embedding technique based on stochastic neighbor distance metric to capture the relationships among data elements in high dimension and improve the accuracy of seizure detection. This proposed data embedding technique not only makes it possible to visualize data in two or three dimensions, but also tackles the inherent difficulties regarding high dimensional data classification such as time complexity and memory requirement. We also applied a patient specific KNN classification to detect seizure and non seizure events. The results indicate that our nonlinear technique provides significantly better visualization and classification efficiency (F measure greater than 87% compared to conventional dimension reduction approaches.
Energy efficient chain based routing protocol for underwater wireless sensor networks
Venue: J. Netw. Comput. Appl.
Year: 2017

To explore the vast ocean, internet of underwater things has become the most attractive area of research. Underwater Smart things are deployed to facilitate the discovery of unexplored regions of ocean. Unique characteristics of underwater wireless sensor networks pose many constraints in transmission of data packets and energy efficient routing is one of them. Motivated by the significance of less costly energy efficient routing protocol, we propose energy efficient chain based routing protocol for underwater wireless sensor networks (E CBCCP) While keeping in view the complex features of underwater dynamics, dynamic network topology and node mobility, energy of the cluster heads (CHs) relay nodes (RNs) and cluster coordinators (CCOs) has been considered during the transmission of data and role of the CHs, CCOs and RNs is changed after some time duration to maintain the load on the nodes. Distance based communication is based on the location aware nodes and can be used in monitoring domains during steady state but in dynamic state, location free communication is required therefore RN communication is based on hop to hop. Confidence level of the sensor nodes is computed to select the optimal RN and to improve the reliability. New prototype has shown the improvement over CARP a routing protocol in terms of data packets transmission and energy. Simulations in MATLAB validate our methodology which has decreased the communication cost of sensor nodes and hence prolonged the network lifetime.
Choosing native tree species for establishing man made forest: A new perspective for sustainable forest management in changing world
Year: 2016

Subiakto A, Rachmat HH, Sakai C. 2016. Choosing native tree species for establishing man made forest: A new perspective for sustainable forest management in changing world. Biodiversitas 17: 620 625. Establishment of tree plantation on degraded lands and forest clearly favored some exotic species such as Gmelina arborea, Acacia, and Eucalyptus. High productivity, less harvesting time, and deeper silvicultural knowledge are the beneficial factor for choosing those exotics species. However, the use of a wide variety of native tree species becomes more significantly important in reforestation projects due to the greater biodiversity benefits and wider environmental services. This research was carried out as a multiyear observation and continuous experiment to value how native tree species can be prospective alternatives in providing and supporting human need. The performances of two native Indonesian Shorea species, Shorea leprosula and Shorea selanica, were evaluated at a dipterocarp planting trial in two different sites in Indonesia. Growth data was obtained from 15 and 17 years old plots, twelve 100 m X 100 m square plots on mineral soils (Gunung Dahu Experimental Forest/GDEF, Bogor) and eight resembled plots on frequently flooded peat land (PT. Arara Abadi/PT. AA, Riau) Survival rates were varied, ranged from 36 77% diameter at breast height from 13.7 24.9 cm, tree height from 10.8 16.9 m, mean volume from 0.119 m/tree 0.567 m/tree, and total volume from 79.420 m/ha 215.412 m/ha. Growth rates of planted saplings were affected by species, site and spacing distance. The development of man made dipterocarps forest in the tropic, especially in South East Asia can be as prospective as developing an exotic fast growing plantation. Eventhough in situ conservation would give the most benefit in conserving genetic resources of native tree species, establishing man made dipterocarp forest still have higher environmental value than using exotic tree species such as acacia. Moreover, establishing man made dipterocarp forest is considered more environmental friendly and possess lower to no risk of species invasion compare to those of developing exotic trees plantation.
Development and validation of an electronic medical record (EMR) based computed phenotype of HIV 1 infection
Venue: J. Am. Medical Informatics Assoc.
Year: 2018

Electronic medical record (EMR) computed algorithms allow investigators to screen thousands of patient records to identify specific disease cases. No computed algorithms have been developed to detect all cases of human immunodeficiency virus (HIV) infection using administrative, laboratory, and clinical documentation data outside of the Veterans Health Administration. We developed novel EMR based algorithms for HIV detection and validated them in a cohort of subjects in the Duke University Health System (DUHS) Methods We created 2 novel algorithms to identify HIV infected subjects. Algorithm 1 used laboratory studies and medications to identify HIV infected subjects, whereas Algorithm 2 used International Classification of Diseases, Ninth Revision (ICD 9) codes, medications, and laboratory testing. We applied the algorithms to a well characterized cohort of patients and validated both against the gold standard of physician chart review. We determined sensitivity, specificity, and prevalence of HIV between 2007 and 2011 in patients seen at DUHS.  A total of 172 271 patients were detected with complete data; 1063 patients met algorithm criteria for HIV infection. In all, 970 individuals were identified by both algorithms, 78 by Algorithm 1 alone, and 15 by Algorithm 2 alone. The sensitivity and specificity of each algorithm were 78% and 99% respectively, for Algorithm 1 and 77% and 100% for Algorithm 2. The estimated prevalence of HIV infection at DUHS between 2007 and 2011 was 0.6% Conclusions EMR based phenotypes of HIV infection are capable of detecting cases of HIV infected adults with good sensitivity and specificity. These algorithms have the potential to be adapted to other EMR systems, allowing for the creation of cohorts of patients across EMR systems.
Counterfactual Processing of Economic Action Outcome Alternatives in Obsessive Compulsive Disorder: Further Evidence of Impaired Goal Directed Behavior
Venue: Biological Psychiatry
Year: 2014

Obsessive compulsive disorder (OCD) is a disorder of automatic, uncontrollable behaviors and obsessive rumination. There is evidence that OCD patients have difficulties performing goal directed actions, instead exhibiting repetitive stimulus response habit behaviors. This might result from the excessive formation of stimulus response habit associations or from an impairment in the ability to use outcome value to guide behavior. We investigated the latter by examining counterfactual decision making, which is the ability to use comparisons of prospective action outcome scenarios to guide economic choice.  We tested decision making (forward counterfactual) and affective responses (backward counterfactual) in 20 OCD patients and 20 matched healthy control subjects using an economic choice paradigm that previously revealed attenuation of both the experience and avoidance of counterfactual emotion in schizophrenia patients and patients with orbitofrontal cortex lesions.  The use of counterfactual comparison to guide decision making was diminished in OCD patients, who relied primarily on expected value. Unlike the apathetic affective responses previously shown to accompany this decision style, OCD patients reported increased emotional responsivity to the outcomes of their choices and to the counterfactual comparisons that typify regret and relief.  Obsessive compulsive disorder patients exhibit a pattern of decision making consistent with a disruption in goal directed forward modeling, basing decisions instead on the temporally present (and more rational) calculation of expected value. In contrast to this style of decision making, emotional responses in OCD were more extreme and reactive than control subjects. These results are in line with an account of disrupted goal directed cognitive control in OCD.
Perceived Risk in a Strategic Household Purchase: Consumer Home Buying Behavior
Year: 2007

The purpose of this study is to develop and empirically test a conceptual model of a perceived risk for a prefabricated house purchase. The results of our quantitative study suggest that perceived risk as a multidimensional construct plays a less important role in the process of purchasing a prefabricated house; higher values have been assigned mostly to financial and delivery aspects of risk. According to our findings, important antecedents are subjective knowledge, situational involvement and perceived financial sacrifice, while among consequences perceived benefits and information search are of greater meaning.
HOW I DO IT Parathyroid 4
Year: 2013

Number 1 January 2014 n radiology.rsna.org 15 1 From the Department of Radiology, Division of Neuroradiology, Duke University Medical Center, Box 3808, Erwin Rd, Durham NC, 27710 (J.K.H. W.K.S. M.B. and Department of Radiology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY (C.D.P. Received December 20, 2012; revision requested January 30, 2013; revision received February 13; accepted March 19; final version accepted April 30. Address correspondence to J.K.H.
3D Deep Residual Encoder Decoder CNNS with Squeeze and Excitation for Brain Tumor Segmentation
Venue: BrainLes@MICCAI
Year: 2019

Segmenting brain tumors from multimodal MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Due to the highly heterogeneous appearance and shape, segmentation of brain tumors in multimodal MRI scans is a challenging task in medical image analysis. In recent years, many segmentation algorithms based on neural network architecture are proposed to address this task. Observing the previous state of the art algorithms, not only did we explore multimodal brain tumor segmentation in 2D space, 2.5D space and 3D space respectively, we also made a lot of attempts in attention block to improve the segmentation result. In this paper, we describe a 3D deep residual encoder decoder CNNS with Squeeze and Excitation block for brain tumor segmentation. In order to learn more effective image features, we have utilized an attention module after each Res block to weight each channel, which emphasizes useful features while suppresses invalid ones. To deal with class imbalance, we have formulated a weighted Dice loss function. We find that 3D segmentation network with attention block which can enhance context features can significantly improve the performance. In addition, the results of data preprocessing have a great impact on segmentation performance. Our method obtained Dice scores of 0.70, 0.85 and 0.80 for segmenting enhancing tumor, whole tumor and tumor core, respectively on the testing data set.
Marketing based on user behavior in Facebook social network through recommender system design
Venue: 2015 International Congress on Technology, Communication and Knowledge (ICTCK)
Year: 2015

The influence of social networks among people and at the same time inevitable spread of commercial use of them. Accordingly, in order to sell products, recommender systems designed based on user behavior on social networks, providing a variety of commercial offers tailored to the user. The accuracy of recommender systems that make recommendations to users, and how many of the proposals are accepted by the users is important. In this paper, a recommender system is designed based on user behavior in social network Facebook in two acts and suggests that users purchase their favorite products. The first step is to examine user behavior based on user interests will be given an offer to buy products. In the second stage recommender system uses data mining techniques and suggestions to the user that is associated with their previous purchases. This is real data and the real results of it and it is valid, as well as the results show a high level of accuracy recommender system is designed to offer suggestions to users.
A Convolutional Neural Network Based Method for Human Movement Patterns Classification in Alzheimer's Disease
Venue: UCAmI
Year: 2019

Alzheimer's disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer's patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer's patients, in which a classification success rate of 91% was obtained.
A hybrid recommender system for recommending relevant movies using an expert system
Venue: Expert Syst. Appl.
Year: 2020

Currently, the Internet contains a large amount of information, which must then be filtered to determine suitability for certain users. Recommender systems are a very suitable tool for this purpose. In this paper, we propose a monolithic hybrid recommender system called Predictory, which combines a recommender module composed of a collaborative filtering system (using the SVD algorithm) a content based system, and a fuzzy expert system. The proposed system serves to recommend suitable movies. The system works with favorite and unpopular genres of the user, while the final list of recommended movies is determined using a fuzzy expert system, which evaluates the importance of the movies. The expert system works with several parameters average movie rating, number of ratings, and the level of similarity between already rated movies. Therefore, our system achieves better results than traditional approaches, such as collaborative filtering systems, content based systems, and weighted hybrid systems. The system verification based on standard metrics (precision, recall, F1 measure) achieves results over 80% The main contribution is the creation of a complex hybrid system in the area of movie recommendation, which has been verified on a group of users using the MovieLens dataset and compared with other traditional recommender systems.
Hadamard matrices and the spectrum of quadratic symmetric polynomials over finite fields
Year: 2018

In this article, we present a beautiful connection between Hadamard matrices and exponential sums of quadratic symmetric polynomials over Galois fields. This connection appears when the recursive nature of these sequences is analyzed. We calculate the spectrum for the Hadamard matrices that dominate these recurrences. The eigenvalues depend on the Legendre symbol and the quadratic Gauss sum over finite field extensions. In particular, these formulas allow us to calculate closed formulas for the exponential sums over Galois field of quadratic symmetric polynomials. Finally, in the particular case of finite extensions of the binary field, we show that the corresponding Hadamard matrix is a permutation away from a classical construction of these matrices.
Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank
Venue: EMNLP 2020
Year: 2020

Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these models, whose labeled \emph{and unlabeled} data is too limited to train a monolingual model effectively. We propose the use of additional language specific pretraining and vocabulary augmentation to adapt multilingual models to low resource settings. Using dependency parsing of four diverse low resource language varieties as a case study, we show that these methods significantly improve performance over baselines, especially in the lowest resource cases, and demonstrate the importance of the relationship between such models' pretraining data and target language varieties.
A Priori Method of Using Photon Activation Analysis to Determine Unknown Trace Element Concentrations in NIST Standards
Year: 2009

Photon activation analysis detected elements in two NIST standards that did not have reported concentration values. A method is currently being developed to infer these concentrations by using scaling parameters and the appropriate known quantities within the NIST standard itself. Scaling parameters include: threshold, peak and endpoint energies; photo nuclear cross sections for specific isotopes; Bremstrahlung spectrum; target thickness; and photon flux. Photo nuclear cross sections and energies from the unknown elements must also be known. With these quantities, the same integral was performed for both the known and unknown elements resulting in an inference of the concentration of the un reported element based on the reported value. Since Rb and Mn were elements that were reported in the standards, and because they had well identified peaks, they were used as the standards of inference to determine concentrations of the unreported elements of As, I, Nb, Y, and Zr. This method was tested by choosing oth.