nils-herrmann/clean_scirepeval huggingface dataset:
Real-time interactive heart illustration platform via hardware accelerated rendering

Real-time interactive visualization plays an important role in the analysis of 3D cardiac imaging data and the assessment of electrophysiological simulation result. Recently, the graphics programmable processor unit (GPU), an increasingly convincing acceleration approach, has been widely adopted in various graphics, visualization, and scientific computing applications. In this paper, we present a GPU-based volume visualization approach for real-time interactive human heart illustration. In our scheme, we first design the transfer function by constructing a lookup table, and adopt a slice-based direct volume rendering method for real-time visualization. Then, a real time heart tissue isolation strategy is used to illustrate each tissue individually. Finally, we design several interactive tools and implement a platform for the effective visualization of cardiac tissues.
Current filamentation in bulk GaAs photoconductive semiconductor switches

Current filamentation during the high gain or non-linear switching mode in GaAs photoconductive semiconductor switches (PCSS) was reported in many papers. Optical properties which influence triggering include wavelength, temporal width, spatial size, and uniformity. The most important electrical property is the initial field produced across the switch. In this paper a high speed camera is used to monitor the infrared photoluminescence of the filaments during fast initiation of high gain switching for bulk GaAs PCSS (e. g. 15×6×3 mm3). The dependencies of the location of the current filaments on the optical properties including spatial size, uniformity and the electrical properties of the initial field produced will be discussed.
Data quality management of synchrophasor data in power systems by exploiting low-dimensional models

This extended summary introduces our recent work on phasor measurement unit (PMU) data analysis by exploiting the low-dimensional structures in spatial-temporal blocks of PMU data. An efficient online missing data recovery algorithm is proposed to leverage the low-rank property of the Hankel PMU data matrix to recover data losses even when all the measurements are lost simultaneously. A unified approach to data reduction, privatization, and recovery is proposed. Adding noise and applying quantization enhance the data privacy and reduce the communication burden. A data recovery method from quantized measurements is proposed to approximate the actual data asymptotically. The methods are accompanied with theoretical analysis and numerical evaluations on actual PMU datasets.
Design development of virtual video general-purpose tester

VM700T manufactured by Tektronix is the main instrument for video measurement. It is the basic premise to know about and become familiar with it so as to be skilled at it. However, due to many factors such as expensive equipment and field limitation, there is difficulty in related experimental operation trainings. Therefore, by designing the system to realize non-field limitation, the convenient and intuitive distance learning can be realized. The specific process of realizing virtual simulation video measuring instrument with Flash and programming language is introduced in great detail in this paper. By giving out the functional module for realizing specific testing, the problem about operating measuring instrument through virtual simulation method is better solved.
Path Pattern Query Processing on Large Graphs

There are plentiful and diverse applications of graph data management and mining in the real-world scientific research and business activities. As one of the most basic operations, uniform path pattern query processing on graph data faces three big challenges. In this paper, we deal with these challenges by the following points. Firstly, a new query language on graph, called G-Path, is presented, which focuses on complex path pattern query processing on a very large graph. Also, the design of a system called HDGL is proposed, which is based on a BSP-like model as well as MapReduce model, and can effectively handle distributed graph data operations and queries. Secondly, the implementation of HDGL on the de facto cloud platform - Hadoop - is brought forward. Based on the concept of distributed state machine, the query processing of a G-Path statement in HDGL is detailed. In addition, as the query optimization of G-Path queries, several tricks are utilized to improve dramatically the performance of query execution. Finally, extensive experiments on several graph data sets are conducted to show the usability of G-Path query language and the effectiveness of HDGL.
The Propagation of Stress Wave in the PZT-5H Composite Target and the Influence of Load Resistance on the Electrical Output Under the Strong Shock Loading

To introduce the application background the self-powered technology for Fuze bridge fire, high overload microthermal battery activation, rocket ignition, and the aircraft escape actual condition lifesaving system ignition power supply, which intends to use the high-speed impact of piezoelectric ceramic in the process of missile launching high overload pressure or missile and target intersection in the process of high overload stress wave of piezoelectric ceramics and the transformation of power, for closer to confession technology for Fuze bridge ignition, high overload microthermal battery activation, rocket ignition, and aircraft escape system engineering problems. The stress wave propagation in the PZT-5H piezoelectric ceramic composite target and load resistance effect to the electrical output characteristics is investigated under the strong shock loading; the experiments have been performed about cylindrical steel projectiles normal impact on PZT-5H piezoelectric ceramic composite target by using polyvinylidene fluoride stress test system and electrical output test system combining with one-stage light gas gun loading system. The experimental results show that the stress duration and peak stress are about  $9~\mu \text{s}$  and 232 MPa, respectively, when the cylindrical projectile impacts on piezoelectric ceramic composite target at the impact velocity of 345 m/s. The outputs of current pulse peak, voltage pulse peak, and energy peak are 20 A, 734 V, and 11 mJ under the stress interactions of 200–300 MPa. At the near-impact stress conditions, the electrical output characteristics of piezoelectric ceramics are greatly affected by the load resistance. The output voltage pulse increases with the increasing of load resistance value, and the values of the load resistance have a significant effect on output energy; output energy conversion rate is up to 82% when the load resistance is  $50~\Omega $ .
Transport of High Bit Rate Media Signals over IP Networks

In the current era of the new media, because of the influence of new media, the traditional radio and television is transforming to the IP network structure. This paper has referred the standard SMPTE ST2022-6-2012 to detail of the high bit rate media signals transmission in the IP network data mapping, protocol encapsulation and requirements. It has given a brief introduction to the trend of television center's infrastructure trans to IP network development status. This paper analyzes the real-time transport protocol to study a number of issues which associated with reliable real-time transmission, including audio and video data compression and decompression, transmission protocol encapsulation header format. SMPTE ST2022 defines the encapsulation formats and transmission standards of professional baseband IP video signal. To achieve SDI over IP technology, it has to convert SDI signal into data and network environment, so that it can establish comprehensive network transmission and data error correction protocol.
Study of Pressure Drop Model for Self-Priming Venturi Scrubber

Pressure drop is one of the most important parameter to evaluate the performance of venturi scrubber. A prototype self-priming venturi scrubber having throat length of 0.11m and diameter 0.034m is used. The throat gas varies from 30 to 220 m/s and liquid flow rate from 0.2 to 1.7 l/m3. Yung's model for pressure drop is modified to predict pressure drop in self-priming venturi scrubber. The experimental results show good agreement with the proposed model.
A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data

With the installation of many new multi-channel phasor measurement units (PMUs), utilities and power grid operators are collecting an unprecedented amount of high-sampling rate bus frequency, bus voltage phasor, and line current phasor data with accurate time stamps. The data owners are interested in efficient algorithms to process and extract as much information as possible from such data for real-time and off-line analysis. Traditional data analysis typically analyze one channel of PMU data at a time, and then combine the results from the individual analysis to arrive at some conclusions. In this paper, a spatial-temporal framework for efficient processing of blocks of PMU data is proposed. A key property of these PMU data matrices is that they are low rank. Using this property, various data management issues such as data compression, missing data recovery, data substitution detection, and disturbance triggering and location can be processing using singular-value based algorithms and convex programming. These functions are illustrated using some historical data from the Central New York power system.
Transistor open-circuit fault disgnosis of three phase voltage-source inverter fed induction motor based on information fusion

In this paper, an improved Dempster-Shafer evidence theory fusing Back-propagation neural network and Classification and regression tree was presented to improve the accuracy of three-phase voltage-source inverter transistor open-circuit fault diagnosis. Firstly, wavelet transform is used to preprocess load current signals. Secondly, open-circuit faults are diagnosed by the processed currents based on BPNN and CART, respectively. Finally, diagnosis results of BPNN and CART are fused in the improved D-S evidence theory on decision level. Experimental results show better accuracy than any single fault diagnosis method, high effectiveness is evaluated.
GNSS signal acquisition method on the high earth orbits spacecraft

To resolve the contradiction between the sensitivity and the resource limitation of Global Navigation Satellite System (GNSS) signal acquisition on the high Earth orbit spacecraft, a novel method based on comb filtering with high-speed processing is proposed. A comb filter is used to increase correlation gain by integration of multiple code cycles. And the integral output is processed in fast clock to reduce computational cost of correlation. The FFT/IFFT transform is carried out to search code phases in parallel. The Doppler compensation is analyzed to reduce high-dynamic signal loss and its mathematical expression is also derived. A FPGA implementation routine is presented and hardware-in-loop tests are also taken. The tests show that the acquisition sensitivity is better than −173 dBW. Compared to the NASA Navigator receiver, with similar acquisition sensitivity, the correlational computation reduces by 42%, the acquisition time reduces 61% and the acquisition Doppler range per search enlarges 2.5 times approximately. The method can acquire Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) signals and it has been verified on the high Earth orbit spacecraft, i.e. the Shijian 17 satellite.
The troposcatter array signal receiving model and processing algorithm

Troposcatter array signal model and processing algorithm are of great importance when over-the-horizon passive reconnaissance is carried out with troposcatter. This paper studies on troposcatter array signal model, and the diffusion phase between different array elements is found after analyzing the inter-relationship of different array element transfer functions,. Moreover, when analyzing the diffusion phase change of different targets, it is known that the bigger the target azimuth angle is, the further the distance between the transmitting and receiving antenna is, the wider the target azimuth beam width is, the bigger the array element number is, then the more obvious the diffusion phase is. Finally, aiming at the characteristics of troposcatter signal model, this paper improves the MUSIC algorithm, and designs the array signal processing algorithm which can estimate the target spatial distance spectrum.
Low-rank matrix recovery from quantized and erroneous measurements: Accuracy-preserved data privatization in power grids

This paper recovers data from quantized and partially corrupted measurements. The data recovery is achieved through solving a constrained maximum likelihood estimation problem that exploits the low-rank property of the actual measurements. The recovery error is proven to be order optimal and decays in the same order as that of the state-of-the-art method when no corruption exists. The data accuracy is thus maintained while the data privacy is enhanced. A new application of this method for data privacy in power systems is discussed. Experiments on synthetic data and real synchrophasor data in power systems demonstrate the effectiveness of our method.
Design and primary results of x-ray heating aluminum foil experiments at PTS facility

The strong X-ray radiations produced by mega-ampere-current-level fast Z-pinch plasmas offer valuable and indispensible opportunities to create hot and dense radiation-heated plasmas1, serving the laboratory investigations of radiation sciences in the fields of opacity measurements, photon-ionized plasmas and so on. Recently, experiments of x-ray heating aluminum foil has been designed and is being carried out at the Primary Test Stand (PTS) facility2, a 8-10 MA, 70 ns pulsed power driver being able to generate x-ray bursts of 80 TW peak power and 3-5 ns FWHM3. In this paper, we present the simulation results of the aluminum foils heated by x-rays using a 1D radiation hydrodynamic code, the design of the sample foils with CH coatings on each side and the diagnostic results of x-ray power and energy in different spectral regions, transmission spectra through the foils and so on. By analyzing the measurements, the states and properties of the radiation-heated aluminum plasmas are estimated and discussed.
Multimodal Deep Autoencoder for Human Pose Recovery

Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limits recovery performance of these methods. In this paper, we propose a novel pose recovery method using non-linear mapping with multi-layered deep neural network. It is based on feature extraction with multimodal fusion and back-propagation deep learning. In multimodal fusion, we construct hypergraph Laplacian with low-rank representation. In this way, we obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix. In back-propagation deep learning, we learn a non-linear mapping from 2D images to 3D poses with parameter fine-tuning. The experimental results on three data sets show that the recovery error has been reduced by 20%-25%, which demonstrates the effectiveness of the proposed method.
Effects of N2O-annealed sacrificial oxide on the short-channel effects of nMOSFETs

The authors report the effects of N2O-annealed sacrificial oxide on nMOSFETs. It is demonstrated that by adding an N2O-annealing step to the sacrificial oxide which was stripped off before growing the final gate oxide, the reverse short-channel effects (RSCE) can still be effectively suppressed.
Editorial: A Successful Year and Looking Forward to 2017 and Beyond

This issue marks the first anniversary issue since I was honored to serve as the Editor-in-Chief (EiC) of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS). I am happy to report that we had a very successful year and here are a few highlights that I would like to share with the community. • The latest impact factor of TNNLS is 4.854 according to the Journal Citation Reports. This marks a record high impact factor for our journal and places TNNLS as the number one scholarly publication in Computer Science (Hardware & Architecture), number three in Computer Science (Theory & Methods), and number ten in Electrical and Electronic Engineering journals.
Arbitrary Frequency Table Transmission Technology for a High-Power Borehole–Ground Electromagnetic Transmitter

A high-power borehole–ground electromagnetic transmitter is an important device used to detect deep metal mineral resources. This paper presents the control principle of the transmitter and the arbitrary frequency table transmission technology. The latter includes the synthesis and the predetermined time transmission of an arbitrary frequency table and a seamless frequency conversion based on an embedded system design and hardware programming technology. We used the arbitrary frequency table transmission function to reduce the impact on the transmitting system from processing the frequency conversion and achieve a smooth transition for the transmitting system when converting the high-power frequency table. These features improved the stability and the reliability of the transmitter. We also performed several field tests that support the advantages of the arbitrary frequency table transmission technology. The flexibility of the best customized transmission frequency table in the field improves the efficiency during the electromagnetic exploration and provides high-quality electromagnetic data.
Modeling and Analysis of Large-Scale Urban Mobility for Green Transportation

Context-aware applications of vehicular social networks (VSNs) play a significant role to achieve the goal of green transportation by sharing driving experience to reduce gasoline consumption. One of the main challenges is to evaluate the performance of these applications, which relies on the underlying VSN mobility model. In this paper, we investigate big urban traffic data to characterize essential features of urban mobility and construct large-scale green urban mobility models. We exploit the road and traffic information to enhance the trip generation algorithm and traffic assignment technique based on the weighted segments of roads. Besides, we perform extensive observations and corrections on the OpenStreetMap imported to simulation of urban mobility to make it analogous to real-world road topology. The experimental results and validation process show that the generated mobility models reveal realistic behavior required for analysis of context-aware applications of VSNs for green transportation systems.
FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization

Knowledge graphs have received intensive research interests. When the labels of most nodes or datapoints are missing, anchor graph and hierarchical anchor graph models can be employed. With an anchor graph or hierarchical anchor graph, we only need to optimize the labels of the coarsest anchors, and the labels of datapoints can be inferred from these anchors in a coarse-tofine manner. The complexity of optimization is therefore reduced to a cubic cost with respect to the number of the coarsest anchors. However, to obtain a high accuracy when a data distribution is complex, the scale of this anchor set still needs to be large, which thus inevitably incurs an expensive computational burden. As such, a challenge in scaling up these models is how to efficiently estimate the labels of these anchors while keeping classification performance. To address this problem, we propose a novel approach that adds an anchor label predictor in the conventional anchor graph and hierarchical anchor graph models. In the proposed approach, the labels of the coarsest anchors are not directly optimized, and instead, we learn a label predictor which estimates the labels of these anchors with their spectral representations. The predictor is optimized with a regularization on all datapoints based on a hierarchical anchor graph, and we show that its solution only involves the inversion of a small-size matrix. Built upon the anchor hierarchy, we design a sparse intra-layer adjacency matrix over these anchors, which can simultaneously accelerate spectral embedding and enhance effectiveness. Our approach is named Faster Learning on Anchor Graph (FLAG) as it improves conventional anchor-graph-based methods in terms of efficiency. Experiments on a variety of publicly available datasets with sizes varying from thousands to millions of samples demonstrate the effectiveness of our approach.
Spin Contour

Spin image is a powerful shape descriptor, useful in a point set or surface registration. However, the usage of spin images is hampered by issues such as sensitivity to noise and sampling rate and time-consuming matching process. We propose a novel spin-image-based local surface descriptor named spin contour to alleviate these problems. This descriptor is not an image but a 2-D point set. Comparisons show that the spin contour is robust to noise and sampling differences. The matching time is also improved over spin images.
ADMOt: Compressive sensing techniques for channel monitoring in multiple access networks

This paper studies the overhead for channel gain monitoring in wireless networks with time division multiple access. We first investigate the scenario in which a receiver needs to track the channel gains with respect to multiple transmitters. Suppose that there are n transmitters, and no more than k channels suffer significant variations since the last round. We prove that “Θ(k log(n=k)) time slots” is the minimum overhead needed to catch up with the k varied channels. We propose a novel channel-gain monitoring scheme named ADMOT. ADMOT leverages recent advances in compressive sensing in signal processing and interference processing in wireless communication, to enable the receiver to estimate all n channels in a reliable and computationally efficient manner within O(k log(n=k)) time slots. To our best knowledge, all previous channel-tracking schemes require Θ(n) time slots regardless of k.
Simulation of canopy FPAR and its relation with vegetation index: A case study of evergreen coniferous forests

The fraction of absorbed photosynthetically active radiation (FPAR) is an important biophysical variable, and is widely used in satellite-based production efficiency models to estimate GPP or net primary production. To the forest, the vegetation canopy is composed primarily of photosynthetically active vegetation (PAV) and non-photosynthetic vegetation (NPV). But only the PAR absorbed by PAV is used for photosynthesis. Current methods of measuring absorbed PAR in fields measure total PAR absorption. The Scattering by Arbitrary Inclined Leaves (SAIL) model was modified to simulate canopy reflectance and to separate the PAR absorbed by PAV and NPV of the evergreen coniferous forest in two scenarios. The total FPAR, green FPAR and FPAR determined by NPV were calculated and the relations between them and two vegetation index (NDVI and EVI) were analyzed. Our results showed the variation of canopy architecture influenced the canopy absorption of PAR. In high coverage forests, the green FPAR was close to the total FPAR of vegetation canopy and the contribution of NPV to the total FPAR was low. While, in low coverage forests or open forests, the situation was contrary. The significant relationships were found between two vegetation indices and FPAR, but compared with NDVI, EVI was more suitable to describe the variation of green FPAR.