nils-herrmann/clean_scirepeval huggingface dataset:
Epilepsy: new advances

Epilepsy affects 65 million people worldwide and entails a major burden in seizure-related disability, mortality, comorbidities, stigma, and costs. In the past decade, important advances have been made in the understanding of the pathophysiological mechanisms of the disease and factors affecting its prognosis. These advances have translated into new conceptual and operational definitions of epilepsy in addition to revised criteria and terminology for its diagnosis and classification. Although the number of available antiepileptic drugs has increased substantially during the past 20 years, about a third of patients remain resistant to medical treatment. Despite improved effectiveness of surgical procedures, with more than half of operated patients achieving long-term freedom from seizures, epilepsy surgery is still done in a small subset of drug-resistant patients. The lives of most people with epilepsy continue to be adversely affected by gaps in knowledge, diagnosis, treatment, advocacy, education, legislation, and research. Concerted actions to address these challenges are urgently needed.
Motion planning for autonomous driving with a conformal spatiotemporal lattice

We present a motion planner for autonomous highway driving that adapts the state lattice framework pioneered for planetary rover navigation to the structured environment of public roadways. The main contribution of this paper is a search space representation that allows the search algorithm to systematically and efficiently explore both spatial and temporal dimensions in real time. This allows the low-level trajectory planner to assume greater responsibility in planning to follow a leading vehicle, perform lane changes, and merge between other vehicles. We show that our algorithm can readily be accelerated on a GPU, and demonstrate it on an autonomous passenger vehicle.
Optimal Exact Strring Matching Based on Suffix Arrays

Using the suffix tree of a string S, decision queries of the type “Is P a substring of S?” can be answered in O(|P |) time and enumeration queries of the type “Where are all z occurrences of P in S?” can be answered in O(|P |+z) time, totally independent of the size of S. However, in large scale applications as genome analysis, the space requirements of the suffix tree are a severe drawback. The suffix array is a more space economical index structure. Using it and an additional table, Manber and Myers (1993) showed that decision queries and enumeration queries can be answered in O(|P |+log |S|) and O(|P |+log |S|+z) time, respectively, but no optimal time algorithms are known. In this paper, we show how to achieve the optimal O(|P |) and O(|P |+ z) time bounds for the suffix array. Our approach is not confined to exact pattern matching. In fact, it can be used to efficiently solve all problems that are usually solved by a top-down traversal of the suffix tree. Experiments show that our method is not only of theoretical interest but also of practical relevance.
Fungi—an unusual source for cosmetics

Fruiting bodies of some wild and cultivatable mushrooms contain medicinal compounds which are being used in traditional medicines and cosmetics. There are numerous potential medicinal products from mushrooms that could be used in cosmeceuticals (products applied topically, such as creams, lotions, and ointments) or nutricosmetics (products that are ingested orally). This paper provides a review of the fungi presently used in cosmeceuticals and nutricosmetics with some examples of cosmetic types and products. Species presently used, or patented to be used, in cosmeceuticals and nutricosmetics include Agaricus subrufescens (= A. blazei, A. brasiliensis) Choiromyces maeandriformis Cordyceps sinensis, Ganoderma lucidum, Grifola frondosa, Hypsizygus ulmarium, Inonotus obliquus, Lentinula edodes, Polyporus spp., Trametes versicolor, Tremella fuciformis, Tuber spp., Schizophyllum commune and many other lesser used taxa. Cosmetics incorporating fungi include those for skin care such as anti-aging, anti-oxidants, skin revitalizing, skin whitening and hair products. The mushrooms presently used are traditionally known to produce medicinal compounds and thus were the first to be incorporated in cosmetic applications. There are, however, numerous other mushroom species that are untested, undescribed or not yet cultivatable and that have huge potential for use in the cosmetic industry. Some fungi are also used in biotransformation and the products such as lactic acid and ceramides could potentially be used in cosmetics.
A case of autophagia: a man who was mutilating his fingers by biting them.

Self-mutilating behaviors could be minor and benign, but more severe cases are usually associated with psychiatric disorders or with acquired nervous system lesions and could be life-threatening. The patient was a 66-year-old man who had been mutilating his fingers for 6 years. This behavior started as serious nail biting and continued as severe finger mutilation (by biting), resulting in loss of the terminal phalanges of all fingers in both hands. On admission, he complained only about insomnia. The electromyography showed severe peripheral nerve damage in both hands and feet caused by severe diabetic neuropathy. Cognitive decline was not established (Mini Mental State Examination score, 28), although the computed tomographic scan revealed serious brain atrophy. He was given a diagnosis of impulse control disorder not otherwise specified. His impulsive biting improved markedly when low doses of haloperidol (1.5 mg/day) were added to fluoxetine (80 mg/day). In our patient's case, self-mutilating behavior was associated with severe diabetic neuropathy, impulsivity, and social isolation. The administration of a combination of an antipsychotic and an antidepressant proved to be beneficial.
B9. Equivalent circuit with frequency-independent lumped elements for plasmonic graphene patch antenna using particle swarm optimization technique

Graphene patch microstrip antenna has been investigated for 600 GHz applications. The graphene material introduces a reconfigurable surface conductivity in terahertz frequency band. The input impedance is calculated using the finite integral technique. A five-lumped elements equivalent circuit for graphene patch microstrip antenna has been investigated. The values of the lumped elements equivalent circuit are optimized using the particle swarm optimization techniques. The optimization is performed to minimize the mean square error between the input impedance of the finite integral technique and that calculated by the equivalent circuit model. The effect of varying the graphene material chemical potential and relaxation time on the radiation characteristics of the graphene patch microstrip antenna has been investigated. An improved new equivalent circuit model has been introduced to best fitting the input impedance using a rational function and PSO. The Cauer's realization method is used to synthesize a new lumped-elements equivalent circuits.
Does Gender Play a Role in Online Consumer Behavior ?

Electronic commerce (online shopping) is increasing in popularity nowadays due to the popularity of computer usage and the prevalence of internet. Electronic commerce can produce numerous benefits to both sellers and consumers. Hence, the study of online consumer behavior can generate a better online atmosphere to facilitate greater profit to sellers and better online purchasing experience to consumer. Gender has universal characteristics, regardless of culture and time period. In this study, the gender differences in different aspects of online consumer behavior is concerned and investigated.
Improving ChangeDistiller Improving Abstract Syntax Tree based Source Code Change Detection

This document sets the direction for my diploma thesis on the subject how applying similarity measures might improve abstract syntax tree based source code change detection. It defines the main tasks, as well as the envisioned outcome of my work and serves as tentative schedule by specifying work packages, i. e. Milestones, and associated deadlines. The remainder of this document is organized as follows. In Section 1.2, I outline the scope of my work, whereas subsection 1.3 describes the results I intend to achieve during my diploma thesis. Section 2 mentions some of the background material that will provide a basis for my work. In Section 3 in general, I address issues related to the project management: Major objectives and priorities during my thesis are listed in Section 3.1, followed by the criteria for success in Section 3.2. Section 3.3 shall describe my method of work. Section 3.4 concerns quality management issues including documentation (Section 3.4.1) and the steps taken for validation (Section 3.4.1). Finally, I provide a milestone plan in Section 5 consisting of the project steps (Section 5.1) and a tentative schedule (Section 5.2).
Road marking detection and classification using machine learning algorithms

This paper presents a novel approach for road marking detection and classification based on machine learning algorithms. Road marking recognition is an important feature of an intelligent transportation system (ITS). Previous works are mostly developed using image processing and decisions are often made using empirical functions, which makes it difficult to be generalized. Hereby, we propose a general framework for object detection and classification, aimed at video-based intelligent transportation applications. It is a two-step approach. The detection is carried out using binarized normed gradient (BING) method. PCA network (PCANet) is employed for object classification. Both BING and PCANet are among the latest algorithms in the field of machine learning. Practically the proposed method is applied to a road marking dataset with 1,443 road images. We randomly choose 60% images for training and use the remaining 40% images for testing. Upon training, the system can detect 9 classes of road markings with an accuracy better than 96.8%. The proposed approach is readily applicable to other ITS applications.
Electrical simulations of series and parallel PV arc-faults

Arcing in PV systems has caused multiple residential and commercial rooftop fires. The National Electrical Code® (NEC) added section 690.11 to mitigate this danger by requiring arc-fault circuit interrupters (AFCI). Currently, the requirement is only for series arc-faults, but to fully protect PV installations from arc-fault-generated fires, parallel arc-faults must also be mitigated effectively. In order to de-energize a parallel arc-fault without module-level disconnects, the type of arc-fault must be identified so that proper action can be taken (e.g., opening the array for a series arc-fault and shorting for a parallel arc-fault). In this work, we investigate the electrical behavior of the PV system during series and parallel arc-faults to (a) understand the arcing power available from different faults, (b) identify electrical characteristics that differentiate the two fault types, and (c) determine the location of the fault based on current or voltage of the faulted array. This information can be used to improve arc-fault detector speed and functionality.
Effects of dependency injection on maintainability

Software maintenance consumes around 70% of the software life cycle. Improving software maintainability could save software developers significant time and money. This paper examines whether the pattern of dependency injection significantly reduces dependencies of modules in a piece of software, therefore making the software more maintainable. This hypothesis is tested with 20 sets of open source projects from sourceforge.net, where each set contains one project that uses the pattern of dependency injection and one similar project that does not use the pattern. The extent of the dependency injection use in each project is measured by a new Number of DIs metric created specifically for this analysis. Maintainability is measured using coupling and cohesion metrics on each project, then performing statistical analysis on the acquired results. After completing the analysis, no correlation was evident between the use of dependency injection and coupling and cohesion numbers. However, a trend towards lower coupling numbers in projects with a dependency injection count of 10% or more was observed.
Education Improvement through ISO 9000 Implementation : Experiences in Taiwan *

This paper describes the experiences of introducing ISO 9000 into Taiwan's higher education systems. Based on an empirical investigation and a case study, the authors argue that the implementation of ISO 9000 quality systems has a positive impact on the education quality. The benefits of ISO 9000 certification are further depicted for those interested in complying with the Standard. We also justify the current progress of the ISO 9000 implementation in Taiwan with recommendations for improvement.
Heart Track: Automated ECG Analysis for Detecting Myocardial Infarction

The paper presents a system, Heart Track, which aims for automated ECG (Electrocardiogram) analysis. Different modules and algorithms which are proposed and used for implementing the system are discussed. The ECG is the recording of the electrical activity of the heart and represents the depolarization and repolarization of the heart muscle cells and the heart chambers. The electrical signals from the heart are measured non-invasively using skin electrodes and appropriate electronic measuring equipment. ECG is measured using 12 leads which are placed at specific positions on the body [2]. The required data is converted into ECG curve which possesses a characteristic pattern. Deflections from this normal ECG pattern can be used as a diagnostic tool in medicine in the detection of cardiac diseases. Diagnosis of large number of cardiac disorders can be predicted from the ECG waves wherein each component of the ECG wave is associated with one or the other disorder. This paper concentrates entirely on detection of Myocardial Infarction, hence only the related components (ST segment) of the ECG wave are analyzed.
Parallel implementation of the NIST Statistical Test Suite

Randomness test suites constitute an essential component within the process of assessing random number generators in view of determining their suitability for a specific application. Evaluating the randomness quality of random numbers sequences produced by a given generator is not an easy task considering that no finite set of statistical tests can assure perfect randomness, instead each test attempts to rule out sequences that show deviation from perfect randomness by means of certain statistical properties. This is the reason why several batteries of statistical tests are applied to increase the confidence in the selected generator. Therefore, in the present context of constantly increasing volumes of random data that need to be tested, special importance has to be given to the performance of the statistical test suites. Our work enrolls in this direction and this paper presents the results on improving the well known NIST Statistical Test Suite (STS) by introducing parallelism and a paradigm shift towards byte processing delivering a design that is more suitable for today's multicore architectures. Experimental results show a very significant speedup of up to 103 times compared to the original version.
Emotion in Motion: A Study of Music and Affective Response

‘Emotion in Motion’ is an experiment designed to understand the emotional reaction of people to a variety of musical excerpts, via self-report questionnaires and the recording of electrodermal response (EDR) and pulse oximetry (HR) signals. The experiment ran for 3 months as part of a public exhibition, having nearly 4000 participants and over 12000 listening samples. This paper presents the methodology used by the authors to approach this research, as well as preliminary results derived from the self-report data and the physiology.
Resistant hypertension: a review of diagnosis and management.

Resistant hypertension-uncontrolled hypertension with 3 or more antihypertensive agents-is increasingly common in clinical practice. Clinicians should exclude pseudoresistant hypertension, which results from nonadherence to medications or from elevated blood pressure related to the white coat syndrome. In patients with truly resistant hypertension, thiazide diuretics, particularly chlorthalidone, should be considered as one of the initial agents. The other 2 agents should include calcium channel blockers and angiotensin-converting enzyme inhibitors for cardiovascular protection. An increasing body of evidence has suggested benefits of mineralocorticoid receptor antagonists, such as eplerenone and spironolactone, in improving blood pressure control in patients with resistant hypertension, regardless of circulating aldosterone levels. Thus, this class of drugs should be considered for patients whose blood pressure remains elevated after treatment with a 3-drug regimen to maximal or near maximal doses. Resistant hypertension may be associated with secondary causes of hypertension including obstructive sleep apnea or primary aldosteronism. Treating these disorders can significantly improve blood pressure beyond medical therapy alone. The role of device therapy for treating the typical patient with resistant hypertension remains unclear.
Adversarially Robust Malware Detection Using Monotonic Classification

We propose monotonic classification with selection of monotonic features as a defense against evasion attacks on classifiers for malware detection. The monotonicity property of our classifier ensures that an adversary will not be able to evade the classifier by adding more features. We train and test our classifier on over one million executables collected from VirusTotal. Our secure classifier has 62% temporal detection rate at a 1% false positive rate. In comparison with a regular classifier with unrestricted features, the secure malware classifier results in a drop of approximately 13% in detection rate. Since this degradation in performance is a result of using a classifier that cannot be evaded, we interpret this performance hit as the cost of security in classifying malware.
The double burden of undernutrition and excess body weight in Mexico.

In Mexico, stunting and anemia have declined but are still high in some regions and subpopulations, whereas overweight and obesity have increased at alarming rates in all age and socioeconomic groups.



The objective was to describe the coexistence of stunting, anemia, and overweight and obesity at the national, household, and individual levels.



We estimated national prevalences of and trends for stunting, anemia, and overweight and obesity in children aged <5 y and in school-aged children (5-11 y old) and anemia and overweight and obesity in women aged 20-49 y by using the National Health and Nutrition Surveys conducted in 1988, 1999, 2006, and 2012. With the use of the most recent data (2012), the double burden of malnutrition at the household level was estimated and defined as the coexistence of stunting in children aged <5 y and overweight or obesity in the mother. At the individual level, double burden was defined as concurrent stunting and overweight and obesity in children aged 5-11 y and concurrent anemia and overweight or obesity in children aged 5-11 y and in women. We also tested if the coexistence of the conditions corresponded to expected values, under the assumption of independent distributions of each condition.



At the household level, the prevalence of concurrent stunting in children aged <5 y and overweight and obesity in mothers was 8.4%; at the individual level, prevalences were 1% for stunting and overweight or obesity and 2.9% for anemia and overweight or obesity in children aged 5-11 y and 7.6% for anemia and overweight or obesity in women. At the household and individual levels in children aged 5-11 y, prevalences of double burden were significantly lower than expected, whereas anemia and the prevalence of overweight or obesity in women were not different from that expected.



Although some prevalences of double burden were lower than expected, assuming independent distributions of the 2 conditions, the coexistence of stunting, overweight or obesity, and anemia at the national, household, and intraindividual levels in Mexico calls for policies and programs to prevent the 3 conditions.
Transformer shielding technique for common mode noise reduction in isolated converters

In isolated converter, transformer is a main path of common mode current. Methods of how to reduce the noise through transformer have been widely studied. One effective technique is using shield between primary and secondary winding. In this paper, EMI noise transferring path and EMI model for typical isolated converters are analyzed. And the survey about different methods of shielding is discussed. Their pros and cons are analyzed. Then the balance concept is introduced and our proposed double shielding using balance concept for wire winding transformer is raised. It can control the parasitic capacitance accurately and is easy to manufacturing. Next, a newly proposed single layer shielding for PCB winding transformer is discussed. The experiment results are provided to verify the methods.
ata-driven soft sensor development based on deep learning echnique

In industrial process control, some product qualities and key variables are always difficult to measure online due to technical or economic limitations. As an effective solution, data-driven soft sensors provide stable and reliable online estimation of these variables based on historical measurements of easy-tomeasure process variables. Deep learning, as a novel training strategy for deep neural networks, has recently become a popular data-driven approach in the area of machine learning. In the present study, the deep learning technique is employed to build soft sensors and applied to an industrial case to estimate the heavy diesel 95% cut point of a crude distillation unit (CDU). The comparison of modeling results demonstrates that the deep learning technique is especially suitable for soft sensor modeling because of the following advantages over traditional methods. First, with a complex multi-layer structure, the deep neural network is able to contain richer information and yield improved representation ability compared with traditional data-driven models. Second, deep neural networks are established as latent variable models that help to describe highly correlated process variables. Third, the deep learning is semi-supervised so that all available process data can be utilized. Fourth, the deep learning technique is particularly efficient dealing with massive data in practice.
Gl-learning: an optimized framework for grammatical inference

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application scenarios for grammar models. The modular design of our C++ library makes it an efficient and extensible framework for the design of further novel algorithms.