nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing Compact Marx Generator Networks

Compact high-voltage Marx generators have found wide ranging applications for driving resistive and capacitive loads. Parasitic or leakage capacitance in compact low-energy Marx systems has proved useful in driving resistive loads, but it can be detrimental when driving capacitive loads where it limits the efficiency of energy transfer to the load capacitance. In this paper, we show how manipulating network designs consisting of these parasitic elements along with internal and external components can optimize the performance of such systems.
Machine vision techniques for motorcycle safety helmet detection

Although motorcycle safety helmets are known for preventing head injuries, in many countries, the use of motorcycle helmets is low due to the lack of police power to enforcing helmet laws. This paper presents a system which automatically detect motorcycle riders and determine that they are wearing safety helmets or not. The system extracts moving objects and classifies them as a motorcycle or other moving objects based on features extracted from their region properties using K-Nearest Neighbor (KNN) classifier. The heads of the riders on the recognized motorcycle are then counted and segmented based on projection profiling. The system classifies the head as wearing a helmet or not using KNN based on features derived from 4 sections of segmented head region. Experiment results show an average correct detection rate for near lane, far lane, and both lanes as 84%, 68%, and 74%, respectively.
Extracellular Vesicles in Metabolic Syndrome.

Metabolic syndrome defines a cluster of interrelated risk factors for cardiovascular disease and diabetes mellitus. These factors include metabolic abnormalities, such as hyperglycemia, elevated triglyceride levels, low high-density lipoprotein cholesterol levels, high blood pressure, and obesity, mainly central adiposity. In this context, extracellular vesicles (EVs) may represent novel effectors that might help to elucidate disease-specific pathways in metabolic disease. Indeed, EVs (a terminology that encompasses microparticles, exosomes, and apoptotic bodies) are emerging as a novel mean of cell-to-cell communication in physiology and pathology because they represent a new way to convey fundamental information between cells. These microstructures contain proteins, lipids, and genetic information able to modify the phenotype and function of the target cells. EVs carry specific markers of the cell of origin that make possible monitoring their fluctuations in the circulation as potential biomarkers inasmuch their circulating levels are increased in metabolic syndrome patients. Because of the mixed components of EVs, the content or the number of EVs derived from distinct cells of origin, the mode of cell stimulation, and the ensuing mechanisms for their production, it is difficult to attribute specific functions as drivers or biomarkers of diseases. This review reports recent data of EVs from different origins, including endothelial, smooth muscle cells, macrophages, hepatocytes, adipocytes, skeletal muscle, and finally, those from microbiota as bioeffectors of message, leading to metabolic syndrome. Depicting the complexity of the mechanisms involved in their functions reinforce the hypothesis that EVs are valid biomarkers, and they represent targets that can be harnessed for innovative therapeutic approaches.
Modelling Compression with Discourse Constraints

Sentence compression holds promise for many applications ranging from summarisation to subtitle generation. The task is typically performed on isolated sentences without taking the surrounding context into account, even though most applications would operate over entire documents. In this paper we present a discourse informed model which is capable of producing document compressions that are coherent and informative. Our model is inspired by theories of local coherence and formulated within the framework of Integer Linear Programming. Experimental results show significant improvements over a state-of-the-art discourse agnostic approach.
A Half-Turn Transformer With Symmetry Magnetic Flux for High-Frequency-Isolated DC/DC Converters

A half-turn transformer with symmetrical magnetic flux is proposed for high-frequency-isolated dc/dc converters. The transformer structure can reduce the minimum number of turns from 1 to 0.5. By adopting a novel winding and circuit configuration, a symmetrical magnetic flux is realized to make full utilization of the ferrite core. A simple EI core, which is easily manufacture and commercially available, can be used to make the half-turn transformer. The operation principles of the transformer are analyzed and simulated. The performance of the transformer is experimentally evaluated on a 750-kHz, 380/12-V, 600-W LLC converter using gallium nitride devices with a peak efficiency of 98%.
TrafficIntel: Smart traffic management for smart cities

In India the unbalanced growth in the freight volume as compared to the road length has led to severe issues of traffic congestion, road safety and health hazards, which, as estimated by recent studies will surely escalate very rapidly within coming years. The use of a real time dynamic traffic management system to intelligently navigate the vehicles can optimize the traffic flow on the roads, thus utilizing the infrastructure efficiently and providing an environment conducive to solving gridlock in cities. The authors propose a real time traffic management system (RTMS) consisting of real time traffic monitoring system formed by small network of road side units, junction units and mobile units to dynamically decide the time of traffic lights to discourage formation of gridlock, coupled with a web based application for vehicle drivers that will derive the data from real time traffic analysis to indicate the local traffic flow and suggest the incoming vehicles to make use of alternative routes in order to further alleviate growth of the gridlock.
A low loss X-band filter using 3-D Polyjet technology

In this paper, the Polyjet technology was applied to build high-Q X-band resonators and low loss filters for the first time. As one of state-of-the-art 3-D printing technologies, the Polyjet technique produces RF models with finest resolution and outstanding surface finish in a clean, fast and affordable way. The measured resonator with 0.3% frequency shift yielded a quality factor of 214 at 10.26 GHz. A Vertically stacked two-cavity bandpass filter with an insertion loss of 2.1 dB and 5.1% bandwidth (BW) was realized successfully. The dimensional tolerance of this process was found to be less than 0.5%. The well matched performance of the resonator and the filter, as well as the fine feature size indicate that the Polyjet process is suitable for the implementation of low loss and low cost RF devices.
New irradiation facility for biomedical applications at the RA-3 reactor thermal column.

A new irradiation facility has been developed in the RA-3 reactor in order to perform trials for the treatment of liver metastases using boron neutron capture therapy (BNCT). RA-3 is a production research reactor that works continuously five days a week. It had a thermal column with a small cross section access tunnel that was not accessible during operation. The objective of the work was to perform the necessary modifications to obtain a facility for irradiating a portion of the human liver. This irradiation facility must be operated without disrupting the normal reactor schedule and requires a highly thermalized neutron spectrum, a thermal flux of around 10(10) n cm(-2)s(-1) that is as isotropic and uniform as possible, as well as on-line instrumentation. The main modifications consist of enlarging the access tunnel inside the thermal column to the suitable dimensions, reducing the gamma dose rate at the irradiation position, and constructing properly shielded entrance gates enabled by logical control to safely irradiate and withdraw samples with the reactor at full power. Activation foils and a neutron shielded graphite ionization chamber were used for a preliminary in-air characterization of the irradiation site. The constructed facility is very practical and easy to use. Operational authorization was obtained from radioprotection personnel after confirming radiation levels did not significantly increase after the modification. A highly thermalized and homogenous irradiation field was obtained. Measurements in the empty cavity showed a thermal flux near 10(10) n cm(-2)s(-1), a cadmium ratio of 4100 for gold foils and a gamma dose rate of approximately 5 Gy h(-1).
Broadband high gain SIW cavity square slot antenna for X-band applications

In this paper, square slot (SS) upheld by the substrate integrated waveguide (SIW) cavity is presented. A simple 50 Ω microstrip line is employed to feed this cavity. Then slot matched cavity modes are coupled to the slot and radiated efficiently. The proposed antenna features the following structural advantages, compact size, light weight and easy low cost fabrication. Concerning the electrical performance, it exhibits 15% impedance bandwidth for the reflection coefficient less than -10 dB and the realized gain touches 8.5 dB frontier.
Nonlinear adaptive speed control for permanent magnet synchronous motors under the unbalanced resistances of phase windings

In this paper, we propose nonlinear adaptive speed control under three-phase voltage unbalance of permanent magnet synchronous motors. An adaptive control applied in a-b-c frame dynamics is proposed to compensate three-phase voltage unbalance by different values of resistance. At this method can estimate actual resistances. For velosity tracking, it is proven that the tracking error is globally asymptotically stable via Lyapunov theorem. The proposed method is performed to validate via the simulation.
Comparative Study of Artificial Neural Networks and Hidden Markov Model for Financial Time Series Prediction

Financial Time Series analysis and prediction is one of the interesting areas in which past data could be used to anticipate and predict data and information about future. There are many artificial intelligence approaches used in the prediction of time series, such as Artificial Neural Networks (ANN) and Hidden Markov Models (HMM). In this paper HMM and HMM approaches for predicting financial time series are presented. ANN and HMM are used to predict time series that consists of highest and lowest Forex index series as input variable. Both of ANN and HMM are trained on the past dataset of the chosen currencies (such as EURO/ USD which is used in this paper). The trained ANN and HMM are used to search for the variable of interest behavioral data pattern from the past dataset. The obtained results was compared with real values from Forex (Foreign Exchange) market database . The power and predictive ability of the two models are evaluated on the basis of Mean Square Error (MSE). The Experimental results obtained are encouraging, and it demonstrate that ANN and HMM can closely predict the currency market, with a small different in predicting performance.
An effective hop-by-hop Interest shaping mechanism for CCN communications

We introduce a rate-based congestion control mechanism for Content-Centric Networking (CCN). It builds on the fact that one Interest retrieves at most one Data packet. Congestion can occur when aggregate conversations arrive in excess and fill up the transmission queue of a CCN router. We compute the available capacity of each CCN router in a distributed way in order to shape their conversations Interest rate and therefore, adjust dynamically their Data rate and transmission buffer occupancy. We demonstrate the convergence properties of this Hop-by-hop Interest Shaping mechanism (HoBHIS) and provide a performance analysis based on various scenarios using our ns2 simulation environment.
A Comparative Study of 6T, 8T and 9T Sram Cell

From last 5 decades, we are scaling down the CMOS devices to achieve the better performance in terms of speed, power dissipation, size and reliability. Our focus is to make the general use device like computer more compact in terms of size, better speed, less power consumption and so we are moving towards the new technology. That can be done by making memories compact and faster and so the scaling CMOS is done to attain high speed and decrease size of memory i.e. SRAM. Due to scaling of device we are facing new challenges day by day like oxide thickness fluctuation, intrinsic parameter fluctuation. Working on low threshold voltage and leakage energy also became main concern. SRAM (Static Random Access Memory) is memory used to store data. The comparison of different SRAM cell on the basis of different parameter is done. 6T, 8T and 9T SRAM cell are compared on basis of followings:1) Read delay, 2)Write delay, 3)Power dissipation.The technology used to implement the 6T (T stands for transistor), 8T and 9T SRAM is 90 nm technology and the software used is ORCAD PSPICE. Schematics of these SRAM have been implemented using ORCAD CAPTURE and analysis is done using PSPICE A/D tool of ORCAD PSPICE.
A Meta-Analysis of the Efficacy of Teaching Mathematics With Concrete Manipulatives

examples in learning math. Science, 320(5875), 454–455. doi: 10.1126/science.1154659 Kaminski, J. A., Sloutsky, V. M., & Heckler, A. (2009). Transfer of mathematical knowledge: The portability of generic instantiations. Child Development Perspectives, 3(3), 151–155.
The workload on parallel supercomputers: modeling the characteristics of rigid jobs

The analysis of workloads is important for understanding how systems are used. In addition, workload models are needed as input for the evaluation of new system designs, and for the comparison of system designs. This is especially important in costly largescale parallel systems. Luckily, workload data are available in the form of accounting logs. Using such logs from three different sites, we analyze and model the job-level workloads with an emphasis on those aspects that are universal to all sites. As many distributions turn out to span a large range, we typically first apply a logarithmic transformation to the data, and then fit it to a novel hyperGamma distribution or one of its special cases. This is a generalization of distributions proposed previously, and leads to good goodness-of-fit scores. The parameters for the distribution are found using the iterative EM algorithm. The results of the analysis have been codified in a modeling program that creates a synthetic workload based on the results of the analysis.
Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models

Recent developments in applying deep learning techniques to train end-to-end communication systems have shown great promise in improving the overall performance of the system. However, most of the current methods for applying deep learning to train physical-layer characteristics assume the availability of the explicit channel model. Training a neural network requires the availability of the functional form all the layers in the network to calculate gradients for optimization. The unavailability of gradients in a physical channel forced previous works to adopt simulation-based strategies to train the network and then fine tune only the receiver part with the actual channel. In this letter, we present a practical method to train an end-to-end communication system without relying on explicit channel models. By utilizing stochastic perturbation techniques, we show that the proposed method can train a deep learning-based communication system in real channel without any assumption on channel models.
Control of microfluidic flow in amphiphilic fabrics.

Woven textile fabrics were designed and constructed from hydrophilic and hydrophobic spun yarns to give planar substrates containing amphiphilic microchannels with defined orientations and locations. Polypropylene fibers were spun to give hydrophobic yarns, and the hydrophilic yarns were spun from a poly(ethylene terephthalate) copolyester. Water wicking rates into the fabrics were measured by video microscopy from single drops, relevant for point-of-care microfluidic diagnostic devices, and from reservoirs. intra-yarn microchannels in the hydrophilic polyester yarns were shown to selectively transport aqueous fluids, with the flow path governed by the placement of the hydrophilic yarns in the fabric. By comparing fluid transport in fabric constructions with systematic variations in the numbers of adjacent parallel and orthogonal hydrophilic yarns, it was found that inter-yarn microchannels significantly increased wicking rates. Simultaneous wicking of an aqueous and hydrocarbon fluid into the hydrophilic and hydrophobic microchannels of an amphiphilic fabric was successfully demonstrated. The high degree of interfacial contact and micrometer-scale diffusion lengths of such coflowing immiscible fluid streams inside amphiphilic fabrics suggest potential applications as highly scalable and affordable microcontactors for liquid-liquid extractions.
1 DO NEIGHBORHOODS GENERATE FEAR OF CRIME ? : AN EMPIRICAL TEST USING THE BRITISH CRIME SURVEY

Home Office (Grant number: PTA-033-2005-00028). We gratefully acknowledge the three anonymous reviewers, whose comments and suggestions improved an earlier version of this paper. Criminologists have long contended that neighborhoods are important determinants of how individuals perceive their risk of criminal victimization. Yet, despite the theoretical importance and policy-relevance of these claims, the empirical evidence-base is surprisingly thin and inconsistent. Drawing on data from a national probability sample of individuals, linked to independent measures of neighborhood demographic characteristics, visual signs of physical disorder, and reported crime, we test four hypotheses about the mechanisms through which neighborhoods influence fear of crime. Our large sample size, analytical approach and the independence of our empirical measures enable us to overcome some of the limitations that have hampered much previous research into this question. We find that neighborhood structural characteristics, visual signs of disorder, and recorded crime all have direct and independent effects on individual level fear of crime. Additionally, we demonstrate that individual differences in fear of crime are strongly moderated by neighborhood socioeconomic characteristics; between group differences in expressed fear of crime are both exacerbated and ameliorated by the characteristics of the areas in which people live. interests include criminal statistics, neighborhood effects, missing data problems, and survey methodology. Methods at the University of Southampton. His research interests are in the areas of survey methodology, statistical methods, public opinion, and political behaviour.
Knitty: 3D Modeling of Knitted Animals with a Production Assistant Interface

Knitty is an interactive design system for creating knitted animals. The user designs a 3D surface model using a sketching interface. The system automatically generates a knitting pattern and then visualizes the shape of the resulting 3D animal model by applying a simple physics simulation. The user can see the resulting shape before beginning the actual knitting. The system also provides a production assistant interface for novices. The user can easily understand how to knit each stitch and what to do in each step. In a workshop for novices, we observed that even children can design their own knitted animals using our system.
A Prediction Model of Traffic Congestion Using Weather Data

Weather factors such as temperature and rainfall in residential areas and tourist destinations affect traffic flow on the surrounding roads. In this study, we attempt to find new knowledge between traffic congestion and weather by using big data processing technology. Changes in traffic congestion due to the weather are evaluated by using multiple linear regression analysis to create a prediction model and forecast traffic congestion on a daily basis. For the regression analysis, we use 48 weather forecasting factors and six dummy variables to express the days of the week. The final multiple linear regression model is then proposed based on the three analytical steps of (i) the creation of the full regression model, (ii) the removal of the variables, and (iii) residual analysis. We find that the R-squared value of the proposed model has an explanatory power of 0.6555. To verify its predictability, the proposed model then evaluates traffic congestion in July and August 2014 by comparing predicted traffic congestion with actual traffic congestion. By using the mean absolute percentage error valuation method, we show that the final multiple linear regression model has a prediction accuracy of 84.8%.
Financial Trading as a Game: A Deep Reinforcement Learning Approach

An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used epsilon-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
Building a Low Cost Line Impedance Stabilization Network for EMI Tests

In this paper will be presented a methodology for implementation of the Line Impedance Stabilization Network (LISN), of commutable symmetric kind, as the specified on Standard IEC CISPR 16-1, using low cost easily acquirable components in the electro-electronic business. The Line Impedance Stabilization Network is used for conducted EMI tests in equipment’s witch current is not above 16 A.