nils-herrmann/clean_scirepeval huggingface dataset:
Immunomodulation of experimental allergic encephalomyelitis by cinnamon metabolite sodium benzoate.

Experimental allergic encephalomyelitis (EAE) is an animal model of multiple sclerosis (MS), the most common human demyelinating disease of the central nervous system. Sodium benzoate (NaB), a metabolite of cinnamon and a FDA-approved drug against urea cycle disorders in children, is a widely used food additive, which is long known for its microbicidal effect. However, recent studies reveal that apart from its microbicidal effects, NaB can also regulate many immune signaling pathways responsible for inflammation, glial cell activation, switching of T-helper cells, modulation of regulatory T cells, cell-to-cell contact, and migration. As a result, NaB alters the neuroimmunology of EAE and ameliorates the disease process of EAE. In this review, we have made an honest attempt to analyze these newly-discovered immunomodulatory activities of NaB and associated mechanisms that may help in considering this drug for various inflammatory human disorders including MS as primary or adjunct therapy.
Ground-Shielded Dual-Type High-Voltage Electrode for Corona Charging Applications

Corona discharge generated by various electrode arrangements is commonly employed for several electrostatic applications, such as charging nonwoven fabrics for air filters and insulating granules in electrostatic separators. The aim of this paper is to analyze the effects of the presence of a grounded metallic shield in the proximity of a high-voltage corona electrode facing a grounded plate electrode. The metallic shield was found to increase the current intensity and decrease the inception voltage of the corona discharge generated by this electrode arrangement, both in the absence and in the presence of a layer of insulating particles at the surface of the plate electrode. With the shield, the current density measured at the surface of the collecting electrode is higher and distributed on a larger area. As a consequence, the charge acquired by millimeter-sized HDPE particles forming a monolayer at the surface of the grounded plate electrode is twice as high as in the absence of the shield. These experiments are discussed in relation with the results of the numerical analysis of the electric field generated by the wire-plate configuration with and without shield.
Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters

We present an approach at identifying a specific class of events, movement action events (MAEs), in ca. 2,800 personal letters exchanged by the German architect Erich Mendelsohn and his wife, Luise. A backend system uses these and other semantic analysis results as input for an authoring environment that curators can use to produce new pieces of content. The human expert will receive recommendations from the system with the goal of putting together a travelogue, i. e., a description of the trips and journeys undertaken by the couple. We describe the components and also apply the system to news data.
An On-Chip High-Speed Current Sensor Applied in the Current-Mode DC–DC Converter

A current-mode dc-dc converter with an on-chip current sensor is presented in this letter. The current sensor has significant improvement on the current-sensing speed. The sensing ratio of the current sensor has low sensitivity to the variation of the process, voltage, temperature and loading. The current sensor combines the sensed inductor current signal with the compensation ramp signal and the output of the error amplifier smoothly. The settling time of the current sensor is less than 10 ns. In the current-mode dc-dc converter application, the differential output of the current sensor can be directly sent to the pulse-width modulation comparator. With the proposed current sensor, the dc-dc converter could realize a low duty cycle with a high switching frequency. The dc-dc converter has been fabricated by CSMC 0.5-μm 5-V CMOS process with a die size of 2.25 mm 2. Experimental results show that the current-mode converter can achieve a duty cycle down to 0.11 with a switching frequency up to 4 MHz. The measured transient response time is less than 6 μs as the load current changes between 50 and 600 mA, rapidly.
GaN high power amplifier MMIC for 30W Compact Doherty amplifier for LTE active antenna system

In this paper, an high efficiency and high output power amplifier MMIC based on 0.25 um GaN HEMT proress is presented. The MMIC has been designed with an output impedance moving technique to help a Doherty amplifier to be implemented in a small area. A compact Doherty amplifier for an active antenna system of a LTE base station has been implemented with the developed GaN high power amplifier (HPA) MMIC. The Doherty amplifier has been measured with power added efficiency (PAE) of 42 % at 7 dB back-off output power of 38 dBm, the maximum PAE of 55 % at the peak saturated output power of 45dBm in the frequency range 2.62 GHz ~ 2.69 GHz. The Doherty amplifier has the linear gain more than 27 dB in the frequency range and ACLR performance better than -31 dBc with the 20 MHz bandwidth WCDMA signal without helping any linearization technique. The Doherty amplifier has been assembled in PCB with the size of 25 mm × 25 mm including input LTCC divider and output combining networks.
The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults.

Health literacy is associated with a range of poor health-related outcomes. Evidence that health literacy contributes to disparities in health is minimal and based on brief screening instruments that have limited ability to assess health literacy. The purpose of this study was to assess whether health literacy contributes, through mediation, to racial/ethnic and education-related disparities in self-rated health status and preventive health behaviors among older adults.



We undertook a cross-sectional study of a nationally representative sample of 2,668 US adults aged 65 years and older from the 2003 National Assessment of Adult Literacy. Multiple regression analysis was used to assess for evidence of mediation.



Of older adults in the United States, 29% reported fair or poor health status, and 27% to 39% reported not utilizing 3 recommended preventive health care services in the year preceding the assessment (influenza vaccination 27%, mammography 34%, dental checkup 39%). Health literacy and the 4 health outcomes (self-rated health status and utilization of the 3 preventive health care services) varied by race/ethnicity and educational attainment. Regression analyses indicated that, after controlling for potential confounders, health literacy significantly mediated both racial/ethnic and education-related disparities in self-rated health status and receipt of influenza vaccination, but only education-related disparities in receipt of mammography and dental care.



Health literacy contributes to disparities associated with race/ethnicity and educational attainment in self-rated health and some preventive health behaviors among older adults. Interventions addressing low health literacy may reduce these disparities.
ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification

This paper reports our submissions to the four subtasks of Aspect Based Sentiment Analysis (ABSA) task (i.e., task 4) in SemEval 2014 including aspect term extraction and aspect sentiment polarity classification (Aspect-level tasks), aspect category detection and aspect category sentiment polarity classification (Categorylevel tasks). For aspect term extraction, we present three methods, i.e., noun phrase (NP) extraction, Named Entity Recognition (NER) and a combination of NP and NER method. For aspect sentiment classification, we extracted several features, i.e., topic features, sentiment lexicon features, and adopted a Maximum Entropy classifier. Our submissions rank above average.
Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network

Laparoscopic Roux-en-Y gastric bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body mass index loss (%EBMIL) over 25 kg/m2, which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50 % EBMIL at 1 year postoperatively. Six hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P < .05) were modeled by “traditional” multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365. The mean EBMIL180 and EBMIL365 were 56.4 ± 16.5 % and 73.5 ± 21.5 %, corresponding to total body weight losses of 25.7 ± 5.9 % and 33.6 ± 8.0 %, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B = −6.3 %, P < .001), BMI0 (B = −1.1 %/unit BMI, P < .001), and DM (B = −3.2 %, P < .004). For EBMIL365, independently associated factors were female gender (B = 6.4 %, P < .001), black race (B = −6.7 %, P < .001), BMI0 (B = −1.2 %/unit BMI, P < .001), HTN (B = −3.7 %, P = .03), and DM (B = −6.0 %, P < .001). Pearson r 2 values for the multiple linear regression and ANN models were 0.38 (EBMIL180) and 0.35 (EBMIL365), and 0.42 (EBMIL180) and 0.38 (EBMIL365), respectively. ANN prediction of benchmark 50 % EBMIL at 365 days generated an area under the curve of 0.78 ± 0.03 in the training set (n = 518) and 0.83 ± 0.04 (n = 129) in the validation set. Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR , this or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB.
Taxol from Corylus avellana: paving the way for a new source of this anti-cancer drug

Taxol is amongst the most effective anti-cancer drugs available in market. The increasing demand of this compound due to its use in treating wide range of cancers, as well as its high cost have triggered efforts to find alternative ways to obtain this drug. Hazel (Corylus avellana), which is already cultivated for nutritional aspects, is now attracting attention for its phytochemical content. Notably the discovery of taxol and related taxanes in this plant species prompted extensive interest to explore biotechnological production of these compounds using in vitro cultures of C. avellana. This review emphasizes the potential of C. avellana cells for production of taxol and related taxanes. The botanical description of C. avellana, its pharmacological uses and various biotechnological approaches, such as micropropagation, cell culture and genetic engineering to increase the production of taxol and related taxanes are discussed. To present an overall overview, the experience of researchers working on these aspects is mentioned and major highlights or discoveries are presented. A review of the literature suggests that C. avellana may act as a commercial and alternative source for taxol production in an eco-friendly way, which will meet the ever-increasing demand, and also help reducing the cost of this anti-cancer compound.
PixelSNE: Pixel-Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen-Resolution Precision

Embedding and visualizing large-scale high-dimensional data in a two-dimensional space is an important problem, because such visualization can reveal deep insights of complex data. However, most of the existing embedding approaches run on an excessively high precision, even when users want to obtain a brief insight from a visualization of large-scale datasets, ignoring the fact that in the end, the outputs are embedded onto a fixed-range pixel-based screen space. Motivated by this observation and directly considering the properties of screen space in an embedding algorithm, we propose Pixel-Aligned Stochastic Neighbor Embedding (PixelSNE), a highly efficient screen resolution-driven 2D embedding method which accelerates Barnes-Hut treebased t-distributed stochastic neighbor embedding (BH-SNE), which is known to be a state-of-the-art 2D embedding method. Our experimental results show a significantly faster running time for PixelSNE compared to BH-SNE for various datasets while maintaining comparable embedding quality.
You Can't Make Bricks Without Straw: Designing Systematic Literature Search Systems

Although a rigorous review of literature is essential for any research endeavor, technical solutions that support systematic literature review approaches are still scarce. Systematic literature searches in particular are often described as complex, error-prone and time-consuming, due to the prevailing lack of adequate technical support. In this study, we therefore aim to learn how to design information systems that effectively facilitate systematic literature searches. Using the design science research paradigm, we develop design principles that intend to increase comprehensiveness, precision, and reproducibility of systematic literature searches. The design principles are derived through multiple design cycles that include the instantiation of the principles in form of a prototype web application and qualitative evaluations. Our design knowledge could serve as a foundation for future research on systematic search systems and support the development of innovative information systems that, eventually, improve the quality and efficiency of systematic literature reviews.
In Defense of Locality-Sensitive Hashing

Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search. We claim in this paper that the true merit of LSH lies in transforming the semantic labels to obtain the binary codes, resulting in an effective and efficient two-step hashing framework. Specifically, we developed the locality-sensitive two-step hashing (LS-TSH) that generates the binary codes through LSH rather than any complex optimization technique. Theoretically, with proper assumption, LS-TSH is actually a useful LSH scheme, so that it preserves the label-based semantic similarity and possesses sublinear query complexity for hash lookup. Experimentally, LS-TSH could obtain comparable retrieval accuracy with state of the arts with two to three orders of magnitudes faster training speed.
Points of Significance: Visualizing samples with box plots

of quartiles for box plots is a well-established convention: boxes or whiskers should never be used to show the mean, s.d. or s.e.m. As with the division of the box by the median, the whiskers are not necessarily symmetrical . The 1.5 multiplier corresponds to approximately ±2.7s (where s is s.d.) and 99.3% coverage of the data for a normal distribution. Outliers beyond the whiskers may be individually plotted. Box plot construction requires a sample of at least n = 5 (preferably larger), although some software does not check for this. For n < 5 we recommend showing the individual data points. Sample size differences can be assessed by scaling the box plot width in proportion to √n , the factor by which the precision of the sample’s estimate of population statistics improves as sample size is increased. To assist in judging differences between sample medians, a notch  can be used to show the 95% confidence interval (CI) for the median, given by m ± 1.58 × IQR/√n (ref. 1). This is an approximation based on the normal distribution and is accurate in large samples for other distributions. If you suspect the population distribution is not close to normal and your sample size is small, avoid interpreting the interval analytically in the way we have described for CI error bars2. In general, when notches do not overlap, the medians can be judged to differ significantly, but overlap does not rule out a significant difference. For small samples the notch may span a larger interval than the box . The exact position of box boundaries will be software dependent. First, there is no universally agreedupon method to calculate quartile values, which may be based on simple averaging or linear interpolation. Second, some applications, such as R, use hinges instead of quartiles for box boundaries. The lower and upper hinges are the median of the Points of siGnifiCAnCE
Node similarity in the citation graph

Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval.
FPGA implementation of Real Time acoustic noise suppression by Spectral Subtraction using Dynamic Moving Average Method

The Paper presents the outlines of the Field Programmable Gate Array (FPGA) implementation of Real Time speech enhancement by Spectral Subtraction of acoustic noise using Dynamic Moving Average Method. It describes an stand alone algorithm for Speech Enhancement and presents a architecture for the implementation. The traditional Spectral Subtraction method can only suppress stationary acoustic noise from speech by subtracting the spectral noise bias calculated during non-speech activity, while adding the unique option of dynamic moving averaging to it, it can now periodically upgrade the estimation and cope up with changes in noise level. Signal to Noise Ratio (SNR) has been tested at different noisy environment and the improvement in SNR certifies the effectiveness of the algorithm. The FPGA implementation presented in this paper, works on streaming speech signals and can be used in factories, bus terminals, Cellular Phones, or in outdoor conferences where a large number of people have gathered. The Table in the Experimental Result section consolidates our claim of optimum resouce usage.
Bayesian computing with INLA: New features

The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparameters, without any need for numerical integration.
How video streaming consumes power in 4G LTE networks

With the increasing adoption of mobile 4G LTE networks, video streaming as the major contributor of 4G LTE data traffic, has become extremely hot. However, the battery life has become the bottleneck when mobile users are using online video services. In this paper, we deploy a real mobile system for power measurement and profiling of online video streaming in 4G LTE networks. Based on some designed experiments with different configurations, we measure the power consumption for online video streaming, offline video playing, and mobile background. A RRC state study is taken to understand how RRC states impact power consumption. Then, we profile the power consumption of video streaming and show the results with different impact factors. According to our experimental statistics, the power saving room for online video streaming in 4G LTE networks can be up to 69%.
A Schema-Associative Model of Memory

This paper* describes an innovative model of long term memory (SALT -Schema-Associative Long Term memory). It also presents an implementation of the SALT model, a specification of an agent, and some scenarios of interactions with the agent. The model presented has its roots in two of the most important general theories of human memory, namely the associative network theory and the schema-based theory. The main advantage of the SALT model is its capabilit y of generating context-dependent cognition. The examples selected for ill ustrating the functioning of the implementation were chosen from the field of personnel evaluation.
Reliability Analysis of Converter Valves for VSC-HVDC Power Transmission System

In this paper reliability model of the converter valves in VSC-HVDC system is analyzed. The internal structure and functions of converter valve are presented. Taking the StakPak IGBT from ABB Semiconductors for example, the mathematical reliability model for converter valve and its sub-module is established. By means of calculation and analysis, the reliability indices of converter valve under various voltage classes and redundancy designs are obtained, and then optimal redundant scheme is chosen. KeywordsReliability Analysis; VSC-HVDC; Converter Valve