nils-herrmann/clean_scirepeval huggingface dataset:
From type 2 diabetes to antioxidant activity : a systematic review of the safety and efficacy of common and cassia cinnamon bark 1

Common (Cinnamomum verum, C. zeylanicum) and cassia (C. aromaticum) cinnamon have a long history of use as spices and flavouring agents. A number of pharmacological and clinical effects have been observed with their use. The objective of this study was to systematically review the scientific literature for preclinical and clinical evidence of safety, efficacy, and pharmacological activity of common and cassia cinnamon. Using the principles of evidence-based practice, we searched 9 electronic databases and compiled data according to the grade of evidence found. One pharmacological study on antioxidant activity and 7 clinical studies on various medical conditions were reported in the scientific literature including type 2 diabetes (3), Helicobacter pylori infection (1), activation of olfactory cortex of the brain (1), oral candidiasis in HIV (1), and chronic salmonellosis (1). Two of 3 randomized clinical trials on type 2 diabetes provided strong scientific evidence that cassia cinnamon demonstrates a therapeutic effect in reducing fasting blood glucose by 10.3%–29%; the third clinical trial did not observe this effect. Cassia cinnamon, however, did not have an effect at lowering glycosylated hemoglobin (HbA1c). One randomized clinical trial reported that cassia cinnamon lowered total cholesterol, low-density lipoprotein cholesterol, and triglycerides; the other 2 trials, however, did not observe this effect. There was good scientific evidence that a species of cinnamon was not effective at eradicating H. pylori infection. Common cinnamon showed weak to very weak evidence of efficacy in treating oral candidiasis in HIV patients and chronic
Direct and indirect pathways of basal ganglia: a critical reappraisal

The basal ganglia are subcortical nuclei controlling voluntary actions and have been implicated in Parkinson's disease (PD). The prevailing model of basal ganglia function states that two circuits, the direct and indirect pathways, originate from distinct populations of striatal medium spiny neurons (MSNs) and project to different output structures. These circuits are believed to have opposite effects on movement. Specifically, the activity of direct pathway MSNs is postulated to promote movement, whereas the activation of indirect pathway MSNs is hypothesized to inhibit it. Recent findings have revealed that this model might not fully account for the concurrent activation of both pathways during movement. Accordingly, we propose a model in which intrastriatal connections are critical and the two pathways are structurally and functionally intertwined. Thus, all MSNs might either facilitate or inhibit movement depending on the form of synaptic plasticity expressed at a certain moment. In PD, alterations of dopamine-dependent synaptic plasticity could alter this coordinated activity.
ECG Based Identification by Deep Learning

Strategies were proposed for Electrocardiogram (ECG) based identification. Firstly, a selecting mechanism based on information entropy was used to obtain whole heart beat signal; Secondly, a Depth Neural Network (DNN) based on Denoising AutoEncoder (DAE) was adopted in feature selection unsupervised, by which, the robustness of the recognition system could be improved in recognizing. Finally, 98.10% and 95.67% recognition rate were obtained on self-collected calm and high pressure data sets respectively, and 94.39% rate on combined data sets of MIT arrhythmia database (mitdb) and self-collected data averagely.
The emerging role of fumarate as an oncometabolite

The drive to understand how altered cellular metabolism and cancer are linked has caused a paradigm shift in the focus of cancer research. The discovery of a mutated metabolic enzyme, isocitrate dehydrogenase 1, that leads to accumulation of the oncometabolite 2-hydroxyglutarate, provided significant direct evidence that dysfunctional metabolism plays an important role in oncogenesis. Striking parallels exist with the Krebs cycle enzyme fumarate hydratase (FH), a tumor suppressor, whose mutation is associated with the development of leiomyomata, renal cysts, and tumors. Loss of FH enzymatic activity results in accumulation of intracellular fumarate which has been proposed to act as a competitive inhibitor of 2-oxoglutarate-dependent oxygenases including the hypoxia-inducible factor (HIF) hydroxylases, thus activating oncogenic HIF pathways. Interestingly, our studies have questioned the role of HIF and have highlighted other candidate mechanisms, in particular the non-enzymatic modification of cysteine residues (succination) that could lead to disruption or loss of protein functions, dysfunctional cell metabolism and cell signaling. Here, we discuss the evidence for proposing fumarate as an onco-metabolite.
Fairness versus Optimality in Ridesharing

As a spatio-temporal data-management problem, taxi ridesharing has received a lot of attention recently in the database literature. The broader scientific community, and the commercial world have also addressed the issue through services such as UberPool and Lyftline. The issues addressed have been efficient matching of passengers and taxis, fares, and savings from ridesharing. However, ridesharing fairness has not been addressed so far. Ridesharing fairness is a new problem that we formally define in this paper. We also propose a method of combining the benefits of fair and optimal ridesharing, and of efficiently executing fair and optimal ridesharing queries.
Rehabilitation of Concussion and Post-concussion Syndrome

Prolonged symptoms after concussion are called post-concussion syndrome (PCS), which is a controversial disorder with a wide differential diagnosis.



MEDLINE and PubMed searches were conducted for the years 1966 to 2011 using the search terms brain concussion/complications OR brain concussion/diagnosis OR brain concussion/therapy AND sports OR athletic injuries. Secondary search terms included post-concussion syndrome, trauma, symptoms, metabolic, sports medicine, cognitive behavioral therapy, treatment and rehabilitation. Additional articles were identified from the bibliographies of recent reviews.



Of 564 studies that fulfilled preliminary search criteria, 119 focused on the diagnosis, pathophysiology, and treatment/rehabilitation of concussion and PCS and formed the basis of this review. Rest is the primary treatment for the acute symptoms of concussion. Ongoing symptoms are either a prolonged version of the concussion pathophysiology or a manifestation of other processes, such as cervical injury, migraine headaches, depression, chronic pain, vestibular dysfunction, visual dysfunction, or some combination of conditions. The pathophysiology of ongoing symptoms from the original concussion injury may reflect multiple causes: anatomic, neurometabolic, and physiologic.



Treatment approaches depend on the clinician's ability to differentiate among the various conditions associated with PCS. Early education, cognitive behavioral therapy, and aerobic exercise therapy have shown efficacy in certain patients but have limitations of study design. An algorithm is presented to aid clinicians in the evaluation and treatment of concussion and PCS and in the return-to-activity decision.
Synthesizing Test Data for Fraud Detection Systems

This paper reports an experiment aimed at generating synthetic test data for fraud detection in an IP based videoon-demand service. The data generation verifies a methodology previously developed by the present authors [7] that ensures that important statistical properties of the authentic data are preserved by using authentic normal data and fraud as a seed for generating synthetic data. This enables us to create realistic behavior profiles for users and attackers. The data can also be used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment. Here we aim to verify the usability and applicability of the synthetic data, by using them to train a fraud detection system. The system is then exposed to a set of authentic data to measure parameters such as detection capability and false alarm rate as well as to a corresponding set of synthetic data, and the results are compared.
Investigating the Effects of Process Variations and System Workloads on Reliability of STT-RAM Caches

In recent years, STT-RAMs have been proposed as a promising replacement for SRAMs in on-chip caches. Although STT-RAMs benefit from high-density, non-volatility, and low-power characteristics, high rates of read disturbances and write failures are the major reliability problems in STTRAM caches. These disturbance/failure rates are directly affected not only by workload behaviors, but also by process variations. Several studies characterized the reliability of STTRAM caches just for one cell, but vulnerability of STT-RAM caches cannot be directly derived from these models. This paper extrapolates the reliability characteristics of one STTRAM cell presented in previous studies to the vulnerability analysis of STT-RAM caches. To this end, we propose a highlevel framework to investigate the vulnerability of STT-RAM caches affected by the per-cell disturbance/failure rates as well as the workloads behaviors and process variations. This framework is an augmentation of gem5 simulator. The investigation reveals that: 1) the read disturbance rate in a cache varies by 6 orders of magnitude for different workloads, 2) the write failure rate varies by 4 orders of magnitude for different workloads, and 3) the process variations increase the read disturbance and write failure rates by up to 5.8x and 8.9x, respectively.
Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application

The use of artificial neural networks (ANNs) in problems related to water resources has received steadily increasing interest over the last decade or so. The related method of the self-organizing map (SOM) is an unsupervised learning method to analyze, cluster, and model various types of large databases. There is, however, still a notable lack of comprehensive literature review for SOM along with training and data handling procedures, and potential applicability. Consequently, the present paper aims firstly to explain the algorithm and secondly, to review published applications with main emphasis on water resources problems in order to assess how well SOM can be used to solve a particular problem. It is concluded that SOM is a promising technique suitable to investigate, model, and control many types of water resources processes and systems. Unsupervised learning methods have not yet been tested fully in a comprehensive way within, for example water resources engineering. However, over the years, SOM has displayed a steady increase in the number of applications in water resources due to the robustness of the method.
NOVEL REAL-TIME FPGA-BASED QRS DETECTOR USING ADAPTIVE THRESHOLD WITH THE PREVIOUS SMALLEST PEAK OF ECG SIGNAL

The QRS complex of the ECG signal is the reference point for the most ECG applications. In this paper, we aim to describe the design and the implementation of an embedded system for detection of the QRS complexes in real-time. The design is based on the notorious algorithm of Pan & Tompkins, with a novel simple idea for the decision stage of this algorithm. The implementation uses a circuit of the current trend, i.e. the FPGA, and it is developed with the Xilinx design tool, System Generator for DSP. In the authors’ view, the specific feature, i.e. authenticity and simplicity of the proposed model, is that the threshold value is updated from the previous smallest peak; in addition, the model is entirely designed simply with MCode blocks. The hardware design is tested with five 30 minutes data records obtained from the MIT-BIH Arrhythmia database. Its accuracy exceeds 96%, knowing that four records among the five represent the worst cases in the database. In terms of the resources utilization, our implementation occupies around 30% of the used FPGA device, namely the Xilinx Spartan 3E XC3S500.
Data rate User Data rate Spectrum Efficiency Connection Density Latency Mobility Power Efficiency Traffic Density Reliability Machine-to-Machine Critical

5G is envisioned to be a multi-service network supporting a wide range of verticals with a diverse set of performance and service requirements. Slicing a single physical network into multiple isolated logical networks has emerged as a key to realizing this vision. This article is meant to act as a survey, the first to the authors’ knowledge, on this topic of prime interest. We begin by reviewing the state of the art on 5G network slicing and we present a framework for bringing together and discussing existing work in a holistic manner. Using this framework, we evaluate the maturity of current proposals and identify a number of open research questions.
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories

Conversational agents, having the goal of natural language generation, must rely on language models which can integrate emotion into their responses. Recent projects outline models which can produce emotional sentences, but unlike human language, they tend to be restricted to one affective category out of a few (e.g. Zhao et al. (2018)). To my knowledge, none allow for the intentional coexistence of multiple emotions on the word or sentence level. Building on prior research which allows for variation in the intensity of a singular emotion (Ghosh et al., 2017), this research proposal outlines an LSTM (Long Short-Term Memory) language model which allows for variation in multiple emotions simultaneously.
A Review of Convolutional Neural Networks for Inverse Problems in Imaging

In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, super-resolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions: Where does the training data come from? What is the architecture of the CNN? and How is the learning problem formulated and solved? We also bring together a few key theoretical papers that offer perspective on why CNNs are appropriate for inverse problems and point to some next steps in the field.
Estimation of Neutron Production from Accelerator Head Assembly of 15 MV Medical LINAC using FLUKA

For the production of a clinical 15 MV photon beam, the design of accelerator head assembly has been optimized using Monte Carlo based FLUKA code. The accelerator head assembly consists of etarget, flattening filter, primary collimator and an adjustable rectangular secondary collimator. Accelerators used for medical radiation therapy generate continuous energy gamma rays called bremsstrahlung radiations by impinging high energy electrons on high Z materials. The electron accelerators operating above 10 MeV can result in the production of neutrons, mainly due to photo nuclear reaction ( ,n) induced by high energy photons in the accelerator head materials. These neutrons contaminate the therapeutic beam and give a non negligible contribution to patient dose. The gamma dose and neutron dose equivalent at the patient plane were obtained at different field sizes and maximum neutron dose equivalent observed near the central axis of of 30 30 cm.
An information complexity approach to extended formulations

We prove an unconditional lower bound that any linear program that achieves an O(n1-ε) approximation for clique has size 2Ω(nε). There has been considerable recent interest in proving unconditional lower bounds against any linear program. Fiorini et al. proved that there is no polynomial sized linear program for traveling salesman. Braun et al. proved that there is no polynomial sized O(n1/2 - ε)-approximate linear program for clique. Here we prove an optimal and unconditional lower bound against linear programs for clique that matches Hastad's celebrated hardness result. Interestingly, the techniques used to prove such lower bounds have closely followed the progression of techniques used in communication complexity. Here we develop an information theoretic framework to approach these questions, and we use it to prove our main result. Also we resolve a related question: How many bits of communication are needed to get ε-advantage over random guessing for disjointness? Kalyanasundaram and Schnitger proved that a protocol that gets constant advantage requires Ω(n) bits of communication. This result in conjunction with amplification implies that any protocol that gets ε-advantage requires Ω(ε2 n) bits of communication. Here we improve this bound to Ω(ε n), which is optimal for any &#949; > 0.
The effectiveness of virtual reality distraction for pain reduction: a systematic review.

Virtual reality technology enables people to become immersed in a computer-simulated, three-dimensional environment. This article provides a comprehensive review of controlled research on the effectiveness of virtual reality (VR) distraction for reducing pain. To be included in the review, studies were required to use a between-subjects or mixed model design in which VR distraction was compared with a control condition or an alternative intervention in relieving pain. An exhaustive search identified 11 studies satisfying these criteria. VR distraction was shown to be effective for reducing experimental pain, as well as the discomfort associated with burn injury care. Studies of needle-related pain provided less consistent findings. Use of more sophisticated virtual reality technology capable of fully immersing the individual in a virtual environment was associated with greater relief. Overall, controlled research suggests that VR distraction may be a useful tool for clinicians who work with a variety of pain problems.
Geographic information extraction, disambiguation and ranking techniques

An important part of textual information around the world contains some kind of geographic features. User queries with geographic references are becoming very common and human expectations from a search engine are even higher. Although several works have been focused on this area, the interpretation of the geographic information in order to better satisfy the user needs continues being a challenge. This work proposes different techniques which are involved in the process of identifying and analyzing the geographic information in textual documents and queries in natural languages. A geographic ontology GeoNW has been built by combining GeoNames, WordNet and Wikipedia resources. Based on the information stored in GeoNW, geographic terms are identified and an algorithm for solving the toponym disambiguation problem is proposed. Once the geographic information is processed, we obtain a geographic ranking list of documents which is combined with a standard textual ranking list of documents for producing the final results. GeoCLEF test collection is used for evaluating the accuracy of the result.
Dimensionality-reduced subspace clustering

Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, whose number, orientations, and dimensions are all unknown. In practice one may have access to dimensionality-reduced observations of the data only, resulting, e.g., from undersampling due to complexity and speed constraints on the acquisition device or mechanism. More pertinently, even if the high-dimensional data set is available it is often desirable to first project the data points into a lower-dimensional space and to perform clustering there; this reduces storage requirements and computational cost. The purpose of this paper is to quantify the impact of dimensionality reduction through random projection on the performance of three subspace clustering algorithms, all of which are based on principles from sparse signal recovery. Specifically, we analyze the thresholding based subspace clustering (TSC) algorithm, the sparse subspace clustering (SSC) algorithm, and an orthogonal matching pursuit variant thereof (SSC-OMP). We find, for all three algorithms, that dimensionality reduction down to the order of the subspace dimensions is possible without incurring significant performance degradation. Moreover, these results are order-wise optimal in the sense that reducing the dimensionality further leads to a fundamentally ill-posed clustering problem. Our findings carry over to the noisy case as illustrated through analytical results for TSC and simulations for SSC and SSC-OMP. Extensive experiments on synthetic and real data complement our theoretical findings.
Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: 1) being capable of discriminating the tracked target from its background 2) being robust to the target’s appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target’s appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via l1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.
Testing Strategies for the Automated Grading of Student Programs

Enrollments in introductory computer science courses are growing rapidly, thereby taxing scarce teaching resources and motivating the increased use of automated tools for program grading. Such tools commonly rely on regression testing methods from industry. However, the goals of automated grading differ from those of testing for software production. In academia, a primary motivation for testing is to provide timely and accurate feedback to students so that they can understand and fix defects in their programs. Testing strategies for program grading are therefore distinct from those of traditional software testing. This paper enumerates and describes a number of testing strategies that improve the quality of feedback for different types of programming assignments.