nils-herrmann/clean_scirepeval huggingface dataset:
An Autonomous Learning Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks
Venue: IEEE Transactions on Communications

We study the problem of autonomous operation of the device to device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs) The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: 1) the D2D pairs transmit over the dedicated frequency bands and 2) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal to interference plus noise ratio requirements. We formulate this problem as a stochastic non cooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to learn its best strategy (based on the locally observed information) and develop a fully autonomous multi agent Q learning algorithm converging to a mixed strategy Nash equilibrium. The proposed learning method is implemented in a long term evolution advanced network and evaluated via the OPNET based simulations. The algorithm shows relatively fast convergence and near optimal performance after a small number of iterations.
Distributed learning approach for joint channel and power allocation in underlay D2D networks
Venue: 2016 International Conference on Signal Processing and Communication (ICSC)

This paper investigates the problem of distributed joint channel and power allocation using game theoretic learning solutions in an underlay Device to Device (D2D) network where device pairs communicate directly with each other by reusing the spectrum that is being used by cellular users. This reuse, in addition to increasing the capacity, results in new cross tier and co tier interference cases. In order to garner the advantages of the resource reuse in the form of higher bit rates and increased energy efficiency, efficient and scalable resource allocation schemes are required. In the next generation ultra dense networks, the focus is on distributed solutions with low computational complexity and signaling requirements. We formulate the joint channel and power allocation problem as a multi agent learning problem with discrete strategy sets and suggest a fully distributed learning algorithm to determine the channel index and power level to be used by each device pair. The distributed joint channel and power allocation problem is formulated as an interference mitigation game, where the utility of each player is a function of its experienced expected weighted interference. We then propose a completely distributed and uncoupled stochastic learning algorithm which converges to pure strategy NE in a time varying radio environment.
Intra and Inter Cell Resource Management in Full Duplex Heterogeneous Cellular Networks
Venue: IEEE Transactions on Mobile Computing

Full duplex communication is drawing high attention as a means of enhancing wireless capacity considerably by enabling simultaneous transmission and reception on the same frequency spectrum. While it had been considered infeasible for a long time due to strong self interference, recent researches have made substantial progress on addressing this challenge. This paper focuses on designing full duplex cellular networks with co channel femtocells. Due to relatively high transmit power, cancellation of self interference by existing techniques could still be imperfect for cellular systems and thus residual interference may impact performance significantly. To overcome this in OFDMA systems, a new radio resource management scheme assigning downlink and uplink transmissions jointly while considering the gain of self interference cancellation is developed. Three available transmission modes of a frequency resource block and crossover points between their achievable capacities are identified for the mode selection of each resource block. Users are then assigned resource blocks and transmit power levels are determined such that the total utility sum is maximized. To handle new femtocell interference scenarios, the transmit powers of femtocells and their connected users are adjusted by a coordination algorithm such that both data transmission and mode selection of an underlying macrocell are protected.
Efficient feature extraction framework for EEG signals classification
Venue: 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)

Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.
2016 Competency Modeling for Doctor of Korean Medicine Application Plans

The purpose of this study was to develop a competency model for the Korean medicine doctors and find application plans for the future education in Korean medicine.  Based on literature review, we drafted a competency model framework for modeling and defined competencies using generic model overlay method. Also we conducted a FGI with 20 extension specialists in Korean medicine to validate competency model.  Findings are 5 domains and 15 competencies. 5 domains have optimal patient care, reasonable communication skill, professionalism enhancement, performing social accountability, and efficient clinical management. 3 competencies are included in 5 domains each. With this model, 4 ways of application plans are shown to apply for the future competency based education in Korean medicine.  Developed 2016 competency model for the Korean medicine doctors can be a first huge step to innovate education in Korean medicine toward competency based educational system.
Utility of uterine artery Doppler and pulsatility index at 11 14 weeks of normal pregnancy in prediction of preeclampsia in third trimester

Preeclampsia affects 5 8% of women in pregnancy and leading cause of maternal mortality. The present study was done to predict the development of preeclampsia in patients with uterine artery pulsatility index (PI) >1.71 and the presence of diastolic notch at 11 14 weeks of gestation belonging to low risk population.  Women attending routine antenatal care were offered an early transvaginal ultrasound scan between 11 14 weeks including uterine artery doppler assessment. Mean PI and presence or absence of bilateral early diastolic notch was also noted. All patients were followed up to term for the development of preeclampsia.  Out of 100 patients, 22% developed preeclampsia of which 15 (68.18% cases showed the presence of diastolic notch and 12 (54.54% cases had PI >1.71 (p<0.05) A Total of 12% of patients showed presence of both diastolic notch and PI of >1.71 (p<0.05) Out of 37 nulliparous patients 13 (35.13% developed preeclampsia, 8 (13.79% out of 58 primiparous and 1 (20% out of 5 multipara developed preeclampsia (p<0.05) All 11 patients with systolic blood pressure >140mm of Hg at 11 14 weeks of gestation developed preeclampsia (p<0.05)  Presence of diastolic notch and PI of >1.71 in uterine artery colour doppler at 11 14 weeks of gestation serves as a good predictor of preeclampsia at term, in pregnancies with no other associated risk factors.
Predictive value of angiogenic factors, clinical risk factors and uterine artery Doppler for pre eclampsia and fetal growth restriction in second and third trimester pregnancies in an Ecuadorian population
Venue: The journal of maternal fetal neonatal medicine the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians

To evaluate the performance of angiogenic factors, maternal risks and uterine artery Doppler (UAD) in the prediction of pre eclampsia (PE) and fetal growth restriction (FGR) in a high risk Ecuadorian population.  Patients with singleton pregnancies (n 346) were investigated at two clinical visits (18 25 weeks and 28 32 weeks) Mean uterine artery (UA) pulsatility index (PI) and maternal biomarkers (soluble fms like tyrosine kinase 1, placental growth factor, sFlt 1/PLGF ratio) were obtained. The main endpoints were PE and FGR. UA PI and angiogenic factor levels were compared for the groups with PE (n 34) FGR (n 26) PE FGR (n 14) and controls (n 272) Multivariable stepwise logistic regression was used to construct prediction models.  Pregnancies with either FGR or PE FGR exhibited in the second trimester a significantly higher mean UA PI and sFlt 1/PLGF ratio and lower PLGF values compared to controls. In the third trimester, all groups with adverse outcome demonstrated significantly lower PLGF levels and a higher sFlt 1/PLGF ratio compared to normal pregnancies. Differences were most pronounced for pregnancies that developed PE and FGR for both time intervals. The combination of UAD and sFlt 1/PLGF ratio improved the predictive capacity for PE and FGR compared to each parameter alone. The best performance was obtained by integrating anamnestic risk factors, resulting in an area under the receiver operating curve for PE of 0.85 and 0.89 and for FGR of 0.79 and 0.77 in the second and third trimester, respectively.  In a high altitude Ecuadorian population, angiogenic factors and UA PI were useful tools in the prediction of PE and/or FGR. The highest performance was achieved by the combination of these factors, including obstetric and medical history.
Soft Radial Basis Cellular Neural Network (SRB CNN) based robust low cost truck detection using a single presence detection sensor

This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state of the art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees) For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.
Economic evaluation of wireless charging tram system
Venue: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

In many nations of Europe, tram is still used as main public transportation system. Despite of its many advantages, overhead lines installed along the track line cause severe problems, such as safety issue, maintenance and difficulty in renewing road system. To solve these problems, wireless charging segment is developed to replace the overhead lines, catenary, and pantograph. With wireless charging segment installed underground, tram can operate freely without any support from overhead lines. In this paper, a model of wireless charging tram system is suggested considering all main parts, such as tractive effort, battery modeling, and driving cycle. In order to find out the minimum cost, optimization of battery capacity, power track allocation and pickup capacity of wireless charging tram system is conducted using heuristic algorithm. Moreover, the optimized result and location of power track installation based on Prague tram line are derived. Lastly, initial installation cost between conventional and proposed tram system is evaluated.
Evaluation of Denoising Strategies to Address Motion Correlated Artifacts in Resting State Functional Magnetic Resonance Imaging Data from the Human Connectome Project
Venue: Brain Connect.

Like all resting state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high motion time points and for high motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance dependent spatially specific artifacts. We evaluated several denoising methods: censoring high motion time points, motion regression, the FMRIB independent component analysis based X noiseifier (FIX) and mean grayordinate time series regression (MGTR; as a proxy for global signal regression) The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high motion time points resulted in a small reduction of distance dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high and low motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion correlated artifacts was a combination of FIX and MGTR.
Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh

Contamination of heavy metals in sediment is regarded as a global crisis with a large share in developing countries like Bangladesh. Four heavy metals such as arsenic (As) chromium (Cr) cadmium (Cd) and lead (Pb) in sediments and water were investigated from Karnaphuli River in Bangladesh. The decreasing trend of metals were observed in water as Cr As Pb Cd and in sediment Cr Pb As Cd. The ranges of heavy metals in water were 13.31 53.87, 46.09 112.43, 2.54 18.34 and 5.29 27.45 mg/L and in sediments were 11.56 35.48, 37.23 160.32, 0.63 3.56 and 21.98 73.42 mg/kg for As, Cr, Cd and Pb. The level of studied metals in water samples exceeded the safe limits of drinking water, indicated that water from this river is not safe for drinking and/or cooking. Contamination factor (CF) confirmed that the sediment samples were moderate to high contamination by As, Cd and Pb. The pollution load index (PLI) values were above one >1) indicates advanced decline of the sediment quality. This study recommended that continuous monitoring of As, Cd and Pb in water; sediment and other aquatic biota of Karnaphuli River should be directed to assess the risk of these vital metals to safe the ecology in the vicinity of this river.
Emergence of wheat blast in Bangladesh was caused by a South American lineage of Magnaporthe oryzae
Venue: BMC Biology

BackgroundIn February 2016, a new fungal disease was spotted in wheat fields across eight districts in Bangladesh. The epidemic spread to an estimated 15,000 hectares, about 16 of the cultivated wheat area in Bangladesh, with yield losses reaching up to 100 Within weeks of the onset of the epidemic, we performed transcriptome sequencing of symptomatic leaf samples collected directly from Bangladeshi fields.ResultsReinoculation of seedlings with strains isolated from infected wheat grains showed wheat blast symptoms on leaves of wheat but not rice. Our phylogenomic and population genomic analyses revealed that the wheat blast outbreak in Bangladesh was most likely caused by a wheat infecting South American lineage of the blast fungus Magnaporthe oryzae.ConclusionOur findings suggest that genomic surveillance can be rapidly applied to monitor plant disease outbreaks and provide valuable information regarding the identity and origin of the infectious agent.
Reasons for Preference of Home Delivery with Traditional Birth Attendants (TBAs) in Rural Bangladesh: A Qualitative Exploration
Venue: PloS one

Although Bangladesh has made significant progress in reducing maternal and child mortality in the last decade, childbirth assisted by skilled attendants has not increased as much as expected. An objective of the Bangladesh National Strategy for Maternal Health 2014 2024 is to reduce maternal mortality to 50/100,000 live births. It also aims to increase deliveries with skilled birth attendants to more than 80% which remains a great challenge, especially in rural areas. This study explores the underlying factors for the major reliance on home delivery with Traditional Birth Attendants (TBA) in rural areas of Bangladesh.  This was a qualitative cross sectional study. Data were collected between December 2012 and February 2013 in Sunamganj district of Sylhet division and data collection methods included key informant interviews (KII) with stakeholders; formal and informal health service providers and health managers; and in depth interviews (IDI) with community women to capture a range of information. Key questions were asked of all the study participants to explore the question of why women and their families prefer home delivery by TBA and to identify the factors associated with this practice in the local community.  The study shows that home delivery by TBAs remain the first preference for pregnant women. Poverty is the most frequently cited reason for preferring home delivery with a TBA. Other major reasons include; traditional views, religious fallacy, poor road conditions, limited access of women to decision making in the family, lack of transportation to reach the nearest health facility. Apart from these, community people also prefer home delivery due to lack of knowledge and awareness about service delivery points, fear of increased chance of having a caesarean delivery at hospital, and lack of female doctors in the health care facilities.  The study findings provide us a better understanding of the reasons for preference for home delivery with TBA among this population. These identified factors can inform policy makers and program implementers to adopt socially and culturally appropriate interventions that can improve deliveries with skilled attendants and thus contribute to the reduction of maternal and neonatal mortality and morbidity in rural Bangladesh.
Aluminum in Childhood Vaccines Is Unsafe

Aluminum is a neurotoxin, yet infants and young children are repeatedly injected with aluminum adjuvants from multiple vaccines during critical periods of brain development. Numerous studies provide credible evidence that aluminum adversely affects important biological functions and may contribute to neurodegenerative and autoimmune disorders. It is impossible to predetermine which vaccinated babies will succumb to aluminum poisoning. Aluminum free health options are needed.
First Do No Harm: Scientific Evidence Implicating Allopathic Vaccination

Allopathic vaccination has many scientifically documented safety deficits that counteract well publicised benefits. Thimerosal and aluminium, common ingredients in vaccines, can cause neurologic, immunologic and developmental harm. The pertussis vaccine has induced pertussis microorganisms associated with whooping cough to mutate and become more virulent. People vaccinated against pertussis can be asymptomatic carriers and transmitters of the disease, rendering herd immunity, or community immunity, unlikely. People vaccinated against measles can spread the disease to other fully vaccinated people. The MMR (measles mumps rubella) vaccine increases the probability of requiring emergency care. It also increases the risk of seizures and thrombocytopenia, a serious bleeding disorder. Infections experienced during childhood, such as measles, mumps and chickenpox, encourage normal development of the immune system in most children, offering protection against heart disease, strokes and cancer in adulthood. Vaccines which are designed to prevent infections increase cancer rates.
Hiring by Algorithm: Predicting and Preventing Disparate Impact

Major advances in machine learning have encouraged corporations to rely on Big Data and algorithmic decision making with the presumption that such decisions are efficient and impartial. In this Essay, we show that protected information that is encoded in seemingly facially neutral data could be predicted with high accuracy by algorithms and employed in the decision making process, thus resulting in a disparate impact on protected classes. We then demonstrate how it is possible to repair the data so that any algorithm trained on that data are more likely to make nondiscriminatory decisions. Since this data modification is done before decisions are applied to any individuals, this process can be applied without requiring the reversal of decisions. We make the legal argument that such assessments and data modifications should be mandated as an anti discriminatory measure. And akin to Professor Ayres' and Professor Gerarda's Fair Employment Mark, such data repair that is preventative of disparate impact would be certifiable by teams of lawyers working in tandem with software engineers and data scientists. Finally, we anticipate the business necessity defense that such data modifications could degrade the accuracy of algorithmic decision making. While we find evidence for this trade off, we also found that on one data set it was possible to modify the data so that despite previous decisions having had a disparate impact under the four fifths standard, any subsequent decision making algorithm was necessarily non discriminatory while retaining essentially the same accuracy. Such an algorithmic "repair" could be used to refute a business necessity defense by showing that algorithms trained on modified data can still make decisions consistent with their previous outcomes. Assistant Professor of Law, University of the District of Columbia, PhD Candidate, Columbia University, Affiliate, Data and Society Assistant Professor in Computer Science, Haverford College, Fellow, Data and Society Assistant Professor in Computer Science, Arizona State University Assistant Professor in Computer Science, Utah University DRAFT NOT FOR CIRCULATION, DISTRIBUTION, OR CITATION. 4/1/16 2:04 PM 2 _____________ L. REV.