nils-herrmann/clean_scirepeval huggingface dataset:
The role of tree nuts and peanuts in the prevention of coronary heart disease: multiple potential mechanisms.

Epidemiologic and clinical trial evidence has demonstrated consistent benefits of nut and peanut consumption on coronary heart disease (CHD) risk and associated risk factors. The epidemiologic studies have reported various endpoints, including fatal CHD, total CHD death, total CHD, and nonfatal myocardial infarct. A pooled analysis of 4 U.S. epidemiologic studies showed that subjects in the highest intake group for nut consumption had an approximately 35% reduced risk of CHD incidence. The reduction in total CHD death was due primarily to a decrease in sudden cardiac death. Clinical studies have evaluated the effects of many different nuts and peanuts on lipids, lipoproteins, and various CHD risk factors, including oxidation, inflammation, and vascular reactivity. Evidence from these studies consistently shows a beneficial effect on these CHD risk factors. The LDL cholesterol-lowering response of nut and peanut studies is greater than expected on the basis of blood cholesterol-lowering equations that are derived from changes in the fatty acid profile of the diet. Thus, in addition to a favorable fatty acid profile, nuts and peanuts contain other bioactive compounds that explain their multiple cardiovascular benefits. Other macronutrients include plant protein and fiber; micronutrients including potassium, calcium, magnesium, and tocopherols; and phytochemicals such as phytosterols, phenolic compounds, resveratrol, and arginine. Nuts and peanuts are food sources that are a composite of numerous cardioprotective nutrients and if routinely incorporated in a healthy diet, population risk of CHD would therefore be expected to decrease markedly.
An overview of gradient descent optimization algorithms

Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.
Content-Based Image Retrieval Using Multiple Features

Algorithms of Content-Based Image Retrieval (CBIR) have been well developed along with the explosion of information. These algorithms are mainly distinguished based on feature used to describe the image content. In this paper, the algorithms that are based on color feature and texture feature for image retrieval will be presented. Color Coherence Vector based image retrieval algorithm is also attempted during the implementation process, but the best result is generated from the algorithms that weights color and texture. 80% satisfying rate is achieved.
ZVS range extension of 10A 15kV SiC MOSFET based 20kW Dual Active Half Bridge (DHB) DC-DC converter

SiC MOSFET is favorable for high voltage applications due to its fast switching speed, low loss and high voltage rating compared with silicon power devices. This paper presents the switching performance of 10A 15kV SiC MOSFET and analyzes its switching loss when considering the impact of parasitic capacitance. A 20 kHz 20 kW Dual Active Half Bridge (DHB) dc-dc converter based on this new device is designed with full ZVS range. Simulation and experiment results are given to validate the analysis.
A Survey of Graph Mining Techniques for Biological Datasets

Mining structured information has been the source of much research in the data mining community over the last decade. The field of bioinformatics has emerged as important application area in this context. Examples abound ranging from the analysis of protein interaction networks to the analysis of phylogenetic data. In this article we survey the principal results in the field examining them both from the algorithmic contributions and applicability in the domain in question. We conclude this article with a discussion of the key results and identify some interesting directions for future research.
Data Mining in Telecommunications

Telecommunication companies generate a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers. This chapter describes how data mining can be used to uncover useful information buried within these data sets. Several data mining applications are described and together they demonstrate that data mining can be used to identify telecommunication fraud, improve marketing effectiveness, and identify network faults.
From Photos to Travel Itinerary: A Tourism Recommender System for Smart Tourism Destination

Tourism and Travel sector are improving services through the use of a large amount of data gathered from different sources. The ease access to reviews, rating and experiences from different tourists made the touristic planning rich and complex. Hence, a big challenge faced by tourism sector is to use the gathered data for detecting tourists' preferences and provide personalized itinerary to each particular tourist. In this work, we proposed a solution that is able to detect tourist implicit preferences based on social media photos and recommend a set of tourism attractions. We used emerging techniques such as Convolutional Neural Network and fuzzy logic to classify tourists and perform the recommendation. The proposed approach was integrated to an existing real world smart destination platform and assessed its effectiveness against a ground truth.
Orthogonal least squares methods and their application to non-linear system identification

This article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram-Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed. The classical Gram-Schmidt, modified Gram-Schmidt,and Householder transformation algorithms are then extended to combine structure determination, or which terms to include in the model, and parameter estimation in a very simple and efficient manner for a class of multivariable discrete-time non-linear stochastic systems which are linear in the parameters.
Robust vehicle localization in urban environments using probabilistic maps

Autonomous vehicle navigation in dynamic urban environments requires localization accuracy exceeding that available from GPS-based inertial guidance systems. We have shown previously that GPS, IMU, and LIDAR data can be used to generate a high-resolution infrared remittance ground map that can be subsequently used for localization . We now propose an extension to this approach that yields substantial improvements over previous work in vehicle localization, including higher precision, the ability to learn and improve maps over time, and increased robustness to environment changes and dynamic obstacles. Specifically, we model the environment, instead of as a spatial grid of fixed infrared remittance values, as a probabilistic grid whereby every cell is represented as its own gaussian distribution over remittance values. Subsequently, Bayesian inference is able to preferentially weight parts of the map most likely to be stationary and of consistent angular reflectivity, thereby reducing uncertainty and catastrophic errors. Furthermore, by using offline SLAM to align multiple passes of the same environment, possibly separated in time by days or even months, it is possible to build an increasingly robust understanding of the world that can be then exploited for localization. We validate the effectiveness of our approach by using these algorithms to localize our vehicle against probabilistic maps in various dynamic environments, achieving RMS accuracy in the 10cm-range and thus outperforming previous work. Importantly, this approach has enabled us to autonomously drive our vehicle for hundreds of miles in dense traffic on narrow urban roads which were formerly unnavigable with previous localization methods.
Wireless Body Area Network Security and Privacy Issue in E-Healthcare

Wireless Body Area Network (WBAN) is a collection of wireless sensor nodes which can be placed within the body or outside the body of a human or a living person which in result observes or monitors the functionality and adjoining situations of the body. Utilizing a Wireless Body Area Network, the patient encounters a greater physical versatility and is never again constrained to remain in the hospital. As the Wireless Body Area Network sensor devices is being utilized for gathering the sensitive data and possibly will run into antagonistic situations, they require complicated and very secure security medium or structure to avoid the vitriolic communications within the system. These devices represent various security and privacy protection of touchy and private patient medical data. The medical records of patients are a significant and an unsolved situation due to which a changing or exploitation to the system is possible. In this research paper, we first present an overview of WBAN, how they utilized for healthcare monitoring, its architecture then highlight major security and privacy requirements and assaults at different network layer in a WBAN and we finally talk about various cryptographic algorithms and laws for providing solution of security and privacy in WBAN. E-Health; privacy; security; wireless body area networks
Deep multimodal learning for Audio-Visual Speech Recognition

In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR). First, we study an approach where uni-modal deep networks are trained separately and their final hidden layers fused to obtain a joint feature space in which another deep network is built. While the audio network alone achieves a phone error rate (PER) of 41% under clean condition on the IBM large vocabulary audio-visual studio dataset, this fusion model achieves a PER of 35.83% demonstrating the tremendous value of the visual channel in phone classification even in audio with high signal to noise ratio. Second, we present a new deep network architecture that uses a bilinear softmax layer to account for class specific correlations between modalities. We show that combining the posteriors from the bilinear networks with those from the fused model mentioned above results in a further significant phone error rate reduction, yielding a final PER of 34.03%.
Accuracy analysis for TDOA localization in sensor networks

In this paper, passive emitter localization using Time Difference of Arrival (TDOA) measurements within a sensor network is investigated. Contrary to the case of a moving sensor pair the fact of correlated measurements in a single time step arises dependent on the choice of the measurement set. In tracking simulations, the Crame´r Rao Lower Bound (CRLB) and simulation results are often computed assuming constant standard deviations of the incoming measurements. Though, the measurement error is parameter dependent, it depends for example on the emitter-sensors-geometry. In Monte Carlo simulations, a comparison is performed between the localization in a static sensor network with three sensors and the localization using a moving sensor pair. Measurements are generated assuming additive white Gaussian noise with constant and parameter dependent variance. Results confirm the attainable localization accuracy and show the superior performance of an additional sensor compared to additional motion.
Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects.

The hypothesis was tested of whether neurofeedback training (NFT)--applied in order to increase upper alpha but decrease theta power--is capable of increasing cognitive performance. A mental rotation task was performed before and after upper alpha and theta NFT. Only those subjects who were able to increase their upper alpha power (responders) performed better on mental rotations after NFT. Training success (extent of NFT-induced increase in upper alpha power) was positively correlated with the improvement in cognitive performance. Furthermore, the EEG of NFT responders showed a significant increase in reference upper alpha power (i.e. in a time interval preceding mental rotation). This is in line with studies showing that increased upper alpha power in a prestimulus (reference) interval is related to good cognitive performance.
News-based trading strategies

The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decisionmaking based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.
Sports participation and health-related behaviors among US youth.

To examine the relationship between sports participation and health-related behaviors among high school students.



Cross-sectional design using data from the 1997 Centers for Disease Control and Prevention Youth Risk Behavior Survey.



A nationally representative sample of 14,221 US high school students.



Prevalence of sports participation among males and females from 3 ethnic groups and its associations with other health behaviors, including diet, tobacco use, alcohol and illegal drug use, sexual activity, violence, and weight loss practices.



Approximately 70% of male students and 53% of female students reported participating on 1 or more sports teams in school and/or nonschool settings; rates varied substantially by age, sex, and ethnicity. Male sports participants were more likely than male nonparticipants to report fruit and vegetable consumption on the previous day and less likely to report cigarette smoking, cocaine and other illegal drug use, and trying to lose weight. Compared with female nonparticipants, female sports participants were more likely to report consumption of vegetables on the previous day and less likely to report having sexual intercourse in the past 3 months. Among white males and females, several other beneficial health behaviors were associated with sports participation. A few associations with negative health behaviors were observed in African American and Hispanic subgroups.



Sports participation is highly prevalent among US high school students, and is associated with numerous positive health behaviors and few negative health behaviors.
Dynamic Repositioning to Reduce Lost Demand in Bike Sharing Systems

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing data has shown that congestion/starvation is a common phenomenon that leads to a large number of unsatisfied customers resulting in a significant loss in customer demand. In order to tackle this problem, we propose an optimisation formulation to reposition bikes using vehicles while also considering the routes for vehicles and future expected demand. Furthermore, we contribute two approaches that rely on decomposability in the problem (bike repositioning and vehicle routing) and aggregation of base stations to reduce the computation time significantly. Finally, we demonstrate the utility of our approach by comparing against two benchmark approaches on two real-world data sets of bike sharing systems. These approaches are evaluated using a simulation where the movements of customers are generated from real-world data sets.
New step-up DC-DC converters for PV power generation systems

This paper proposes new step-up DC-DC converters with high ratio capability. The proposed DC-DC converters are derived using combination of boost converters and buck-boost converters. A high ratio capability is achieved by parallel input and series output combination so that the efficiency is better than the one that is achieved by using a conventional boost converter. A method to reduce the input and output ripples are also proposed in this paper. Simulated and experimental results are included to show the validity of the proposed converters.
Understanding Pharmaceutical Quality by Design

This review further clarifies the concept of pharmaceutical quality by design (QbD) and describes its objectives. QbD elements include the following: (1) a quality target product profile (QTPP) that identifies the critical quality attributes (CQAs) of the drug product; (2) product design and understanding including identification of critical material attributes (CMAs); (3) process design and understanding including identification of critical process parameters (CPPs), linking CMAs and CPPs to CQAs; (4) a control strategy that includes specifications for the drug substance(s), excipient(s), and drug product as well as controls for each step of the manufacturing process; and (5) process capability and continual improvement. QbD tools and studies include prior knowledge, risk assessment, mechanistic models, design of experiments (DoE) and data analysis, and process analytical technology (PAT). As the pharmaceutical industry moves toward the implementation of pharmaceutical QbD, a common terminology, understanding of concepts and expectations are necessary. This understanding will facilitate better communication between those involved in risk-based drug development and drug application review.
Improving Alzheimer's disease diagnosis with machine learning techniques.

There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.
A Bayesian Nonparametric Approach for Multi-label Classification

Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for prediction. Traditional classification methods have attempted to reveal these correlations in different ways. However, existing methods demand expensive computation complexity for finding such correlation structures. Furthermore, these approaches can not identify the suitable number of label-feature correlation patterns. In this paper, we propose a Bayesian nonparametric (BNP) framework for multi-label classification that can automatically learn and exploit the unknown number of multi-label correlation. We utilize the recent techniques in stochastic inference to derive the cheap (but efficient) posterior inference algorithm for the model. In addition, our model can naturally exploit the useful information from missing label samples. Furthermore, we extend the model to update parameters in an online fashion that highlights the flexibility of our model against the existing approaches. We compare our method with the state-of-the-art multi-label classification algorithms on real-world datasets using both complete and missing label settings. Our model achieves better classification accuracy while our running time is consistently much faster than the baselines in an order of magnitude.
Does age affect the stress and coping process? Implications of age differences in perceived control.

The perceived controllability of situations is thought to influence the types of coping strategies used, and thus is important in adaptive processes. Elderly individuals are widely perceived to have less control over their environment than other adults. This lack of perceived control should have adverse affects on how they cope with stressful situations. However, most studies have shown that older adults differ little from younger adults in their approaches to coping with stress. This contradiction was investigated in a sample of 228 community-residing adults with a mean age of 42.16 (SD = 14.88). Path analysis revealed that appraisals and attributions do affect the use of coping strategies such as instrumental action and escapism in the expected directions, and age is negatively associated with perceived control. However, there was an independent and negative relationship between age and the reported use of escapist coping strategies, which mitigated the adverse effects of perceived lack of control. Neither age nor perceived controllability had direct effects on depression, but they had indirect effects through their influence on the use of coping strategies and perceived efficacy.