nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge-guided recurrent neural network learning for task-oriented action prediction

This paper aims at task-oriented action prediction, i.e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The main challenges lie in how to model task-specific knowledge and integrate it in the learning procedure. In this work, we propose to train a recurrent longshort term memory (LSTM) network for handling this problem, i.e., taking a scene image (including pre-located objects) and the specified task as input and recurrently predicting action sequences. However, training such a network usually requires large amounts of annotated samples for covering the semantic space (e.g., diverse action decomposition and ordering). To alleviate this issue, we introduce a temporal And-Or graph (AOG) for task description, which hierarchically represents a task into atomic actions. With this AOG representation, we can produce many valid samples (i.e., action sequences according with common sense) by training another auxiliary LSTM network with a small set of annotated samples. And these generated samples (i.e., task-oriented action sequences) effectively facilitate training the model for task-oriented action prediction. In the experiments, we create a new dataset containing diverse daily tasks and extensively evaluate the effectiveness of our approach.
Generalized spring tensor models for protein fluctuation dynamics and conformation changes

In the last decade, various coarse-grained elastic network models have been developed to study the large-scale motions of proteins and protein complexes where computer simulations using detailed all-atom models are not feasible. Among these models, the Gaussian Network Model (GNM) and Anisotropic Network Model (ANM) have been widely used. Both models have strengths and limitations. GNM can predict the relative magnitudes of the fluctuations well, but due to its isotropic assumption, it can not be applied to predict the directions of the fluctuations. In contrast, ANM adds the ability to do the latter, but it loses a significant amount of precision in the prediction of the magnitudes. In this article, we develop a generalized spring tensor model (STeM) that is able to predict well both the magnitudes and the directions of the fluctuations. STeM also outperforms ANM in explaining protein conformation changes. All of these are accomplished without sacrificing the essential features that have made ANM and GNM attractive.
Study of Superconducting Fault Current Limiters for System Integration of Wind Farms

As electrical energy will be provided from renewable sources, the connection of a large number of wind farms to existing distribution networks may lead to the increasing fault levels beyond the capacity of existing switchgear, especially in urban areas. Fault current limiters (FCLs) are essentially expected to control the prospective short circuit currents. In this paper, investigations were carried out to assess the effectiveness of the resistive superconducting fault current limiters (SFCLs) for fault level management in wind power system. System studies confirmed that the superconducting fault current limiter (SFCL) could not only control the fault currents but also suppress the inrush currents, when wind farm has adopted in the case of the system interconnection. As a result, the highly efficient operation of the wind power system becomes more possible by introducing the superconducting fault current limiters.
Deep Structured Scene Parsing by Learning with Image Descriptions

This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations) that finely accords with human perception. We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixelwise object labeling and ii) a recursive neural network (RNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative user annotations (e.g., manually labeling semantic maps and relations), we train our deep model in a weakly-supervised manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and facilitate these trees discovering the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments suggest that our model is capable of producing meaningful and structured scene configurations and achieving more favorable scene labeling performance on PASCAL VOC 2012 over other state-of-the-art weakly-supervised methods.
Improving performance of TCP Vegas for high bandwidth-delay product networks

A critical design issue of TCP is its congestion control that allows the protocol to adjust the end-to-end communication rate to the bandwidth on the bottleneck link. However, TCP congestion control may function poorly in high bandwidth-delay product networks because of its slow response with large congestion windows. In this paper, we propose an improved version of TCP Vegas called quick Vegas, in which we present an efficient congestion window control algorithm for a TCP source. Our algorithm is based on the increment history and estimated amount of extra data to update the congestion window intelligently. Simulation results show that quick Vegas significantly improve the performance of connections as well as remain fair and stable when the bandwidth-delay product increases.
Mechanism analysis of water powered bellows in ancient western illustrations

The purpose of this paper is to identify the mechanism of water powered bellows in the questionable illustrations of ancient western books. The water powered bellows are classified by the types of water wheels and the ways of power transmissions. The characteristics of topological structures and the feasibility of designs are presented based on historical records and mechanism analysis. Focusing on the unreasonable or unclear designs, the components of mechanisms are analyzed so as to present their corresponding mechanical drawings.
Augmenting flower recognition by automatically expanding training data from web

Aiming to improve recognition rate, we propose a novel flower recognition system that automatically expands the training data from large-scale unlabeled image pools without human intervention. Existing flower recognition approaches often learn classifiers based on a small labeled dataset. However, it is difficult to build a generalizable model (e.g., for real-world environment) with only a handful of labeled training examples, and it is labor-intensive for manually annotating large-scale images. To resolve these difficulties, we propose a novel framework that automatically expands the training data to include visually diverse examples from large-scale web images with minimal supervision. Inspired by co-training methods, we investigate two conceptually independent modalities (i.e., shape and color) that provide complementary information to learn our discriminative classifiers. Experimental results show that the augmented training set can significantly improve the recognition accuracy (from 65.8% to 75.4%) with a very small initially labeled training set. We also conduct a set of sensitivity tests to analyze different learning strategies (i.e., co-training and self-training) and show that co-training is more efficient in our multi-view flower dataset.
A “Try before you buy” approach for networked digital home appliances and services

Though many services are developed and provided for digital homes, a user would both pay for a physical device (appliance) and supporting software (applications and/or services). However, a service or an appliance may only be needed in a period, such as a rental service, or a trial usage. Hence we propose a “Try before you buy” solution for networked digital home appliances and services. The idea is to have an authentication scheme to identify the appliance and/or the service based on the device in reasonable legal use at the right place and in the right time period. The appliance has the ability of self checking about the rental agreement and target environment (the user information), and the appliance or the service based on it connects to the home network gateway to check the lease term itself or check with the service provider.
A Learning Approach with Under-and Over-Sampling for Imbalanced Data Sets

It is difficult for learning models to achieve high classification performance with imbalanced data sets. To conquer the problem, this study presents a strategy involving the reduction of size of majority data set and the generation of synthetic samples of minority data set. Parkinson's disease data set is used to examine and to compare the performance of classification methods. The paired t-tests are also used to show the effectiveness of the proposed method compari.ng with that of the other methods.
Study of Dynamic Evolutionary Model of Regional Talent Agglomeration System

The purpose of this study was to explore the evolutionary rules of regional talent agglomeration system. From the perspective of complex adaptive system theory, this paper revealed spatial and hierarchical structure and process of evolution, then constructed dynamic evolutionary model of regional talent agglomeration system based on the analysis of characteristics of complex adaptation in regional talent agglomeration system. The results are conducive to the selection and implementation of regional talent agglomeration strategy.
Implementation of maximum-power-point-tracker for photovoltaic arrays

The major purpose of this paper is to present an efficient way to search for the photovoltaic array's (PA's) maximum power point (MPP). The incremental conductance is used as the basis for the search process. The algorithm consists of two stages. First, it tries to reach the nearby MPP determined by the operating voltage based on the empirical formula. A modified incremental conductance algorithm is then utilized to reach the real MPP. Finally, a digitized high-performance solar photovoltaic power conversion system is developed and verified. The design method based on the adaptive MPPT algorithm is proposed to generate the optimal power output. A DSP-based control system for synchronization of plural AC power sources has also been implemented.
An implementation of parallel dynamic load balancing for adaptive computing in VLSI device simulation

A new parallel semiconductor device simulation using the dynamic load balancing approach is presented. This semiconductor device simulation based on adaptive finite volume error estimation, and monotone iterative methods has been developed and implemented on a Linux-cluster with MPI library. Two different parallel versions of the algorithm to perform a complete device simulation are proposed. The first one is a dynamic parallel domain decomposition approach. The second version is a parallel current-voltage characteristic points simulation. The implementation shows that a well-designed load balancing simulation can reduce the execution time up to an order of magnitude. Compared with the measured data, numerical simulations on P-N diode, N-MOSFET, and DTMOS devices are presented to show the accuracy and efficiency of the method.
Optimal path planning on 3D space using a DNA computing algorithm

This paper proposes a new DNA computation-based optimal path planning algorithm on 3D space. In the proposed approach, the working space is converted into several slices by the DNA coding scheme during the evolutionary process, where less blocks of every slices correspond to the areas containing loosely dense obstacles and vice versa. The molecular programming algorithm imitates the biological evolution mechanism through artificial programming to enhance the opportunities for searching the shortest moving path while avoiding obstacles. Simulation results are presented to show the effectiveness of the proposed approach.
A 94% efficiency near-constant frequency self-oscillating class-D audio amplifier with voltage control resistor

A self-oscillating constant frequency class-D audio amplifier with voltage control resistor (VCR) technique is proposed for system-on-a-chip (SoC) applications. The VCR control circuit is used to ensure stable switching frequency over a wide range of the modulation index. The proposed method also improves the THD performance at high output power condition. The proposed self-oscillating class-D amplifier can have extremely good performance since the carrier distortion is avoided and the intrinsic harmonic can be attenuated by the second-order loop filter. The VCR control can dynamically adjust the switching frequency according to distinct modulation index. In addition, stable frequency operation can reduce the switching frequency at low output amplitude to improve power efficiency. Experimental results demonstrate that the proposed class-D amplifier can delivers 1.4W to the 8Ω load with a 5V supply. The power efficiency is achieved over 95% and the output THD can be smaller than 0.001%.
Video Stylization: Painterly Rendering and Optimization With Content Extraction

We present an interactive video stylization system for transforming an input video into a painterly animation. The system consists of two phases: a content extraction phase to obtain semantic objects, i.e., recognized content, in a video and establish dense feature correspondences, and a painterly rendering phase to select, place, and propagate brush strokes for stylized animations based on the semantic content and object motions derived from the first phase. Compared with the previous work, the proposed method has the following three advantages. First, we propose a two-pass rendering strategy and brush strokes with mixed colors in order to render expressive visual effects. Second, the brush strokes are warped according to global object deformations, so that the strokes appear to be naturally attached to the object surfaces. Third, we propose a deferred rendering and backward completion method to draw brush strokes on emerging regions and simulate a damped system to reduce stroke scintillation effect. Moreover, we discuss the graphics processing unit-based implementation of our system, which is demonstrated to greatly improve the efficiency of producing stylized videos. In experiments, we verify this system by applying it to a number of video clips to produce expressive oil-painting animations and compare it with the state-of-the-art approaches.
Visualization method for spectrum analysis of vibration acceleration signals

Vibration acceleration signals of mechanical equipment have complicated spectrum structures. It is very difficult to extract the state information from acceleration spectrums by a simple and effective algorithm. In this paper, a visualization method for spectrum analysis of vibration acceleration signals is proposed. First, the spectrums of vibration acceleration signals are automatic segmented and the summation of amplitudes (SOA) of each frequency band is counted. Then, the visualization method of ThemeRiver is used to depict the SOA of each frequency band, which reflects the running state of the machinery equipment in its whole life cycle. Finally, the analysis method is verified by the bearing vibration acceleration signals.
Design of dynamic genetic memory

In electronic systems, dynamic random access memory (DRAM) is one of the core modules in the modern silicon computer. As for a bio‐computer, one would need a mechanism for storage of bio‐information named ‘data’, which, in binary logic, has two levels, logical high and logical low, or in the normalised form, ‘1’ and ‘0’. This study proposes a possible genetic DRAM based on the modified electronic configuration, which uses the biological reaction to fulfil an equivalent RC circuit constituting a memory cell. The authors implement fundamental functions of the genetic DRAM by incorporating a genetic toggle switch for data hold. The results of simulation verify that the basic function can be used on a bio‐storage module for the future bio‐computer.
Ruggedness Characterization of Bonding Wire Arrays in LDMOSFET-Based Power Amplifiers

Electro-thermo-stress (ETS) failure of bonding wire array in a laterally diffused metal oxide semiconductor (LDMOS) field-effect transistor (FET)-based power amplifier during its high power’s ruggedness experiment is investigated in this paper. A special measurement system is at first set up to get reliability of the bonding wire array, where a microscope is used to capture its fractures. The temperature distribution of the LDMOSFET die surface and bonding wire array is also captured by a thermal infrared scanner to investigate the principle underlying the failure of ETS fractures. Simulation studies are also performed. The temperature distribution is closely related to the profiles of the bonding wire array. Numerical methods are employed to calculate the bonding wire array, and the temperature distributions are demonstrated by using our finite-element method code. It also works well in solving the transient temperature of the bond wire array with the stationary current and the heat source from the silicon region. The simulated thermal results agree well with the testing results. Finally, an improved LDMOS design in optimizing the bonding wire profile is proposed. This research can serve as a guide for the design of an LDMOS PA with enhanced reliability.
Development of HTS transmission power cable

A 1500 A/6 m HTS DC transmission power cable has been developed and tested at the Institute of Electrical Engineering, Chinese Academy of Sciences. The conductor of the HTS cable consists of 4 layers of Bi-2223/Ag tapes helically wound on a flexible stainless steel former. Ag tapes have been developed by the Northwest Institute for Nonferrous Metal Research and the Beijing General Research Institute for Nonferrous Metals. The cable core has a 45.3 mm outer diameter and 6 m length and is housed in a cryogenic envelope. The E-I characteristic and the total joint resistance of the cable have been measured at 77 K. The critical current of the cable is 1473 A and the total joint resistance is 0.1 /spl mu//spl Omega/ at 77 K. The main properties of the Bi-2223/Ag tape and the development and test results of the HTS cable are presented in this paper.
On Secrecy Capacity of Fast Fading MIMOME Wiretap Channels with Statistical CSIT

In this paper, we consider secure transmissions in ergodic Rayleigh fast-faded multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) wiretap channels with only statistical channel state information at the transmitter (CSIT). When the antennas at the legitimate receiver are more than (or equal to) those at the eavesdropper, we prove the first MIMOME secrecy capacity with partial CSIT by establishing a new secrecy capacity upper-bound. The key step is to form an MIMOME degraded channel by dividing the legitimate receiver's channel matrix into two submatrices, and setting one of the submatrices to be the same as the eavesdropper's channel matrix. Next, under the total power constraint over all transmit antennas, we analytically solve the channel-input covariance matrix optimization problem to fully characterize the MIMOME secrecy capacity. Typically, the MIMOME optimization problems are non-concave. However, thanks to the proposed degraded channel, we can transform the stochastic MIMOME optimization problem to be a Schur-concave one and then find its solution. Besides total power constraint, we also investigate the secrecy capacity when the transmitter is subject to the practical per-antenna power constraint. The corresponding optimization problem is even more difficult since it is not Schur-concave. Under the two power constraints considered, the corresponding MIMOME secrecy capacities can both scale with the signal-to-noise ratios (SNR) when the difference between numbers of antennas at legitimate receiver and eavesdropper are large enough. However, when the legitimate receiver and eavesdropper have a single antenna each, such SNR scalings do not exist for both cases.
Research on ${\rm MgB}_{2}$ Superconducting Magnet With Iron Core for MRI

Magnesium diboride shows a wide application prospect in the field of low field DC application for its relatively simple structure and rather higher critical temperature than LTS. With the development of composite superconductor manufacturing technology and cryogenic technology, developing conduction cooled superconducting MRI system with composite superconductor is becoming increasingly feasible. An superconducting magnet for MRI was developed. The superconducting magnet is used to investigate the fundamental scientific problem of the MRI main magnet, including field uniformity and stability, influencing factors of composite superconductor on field uniformity and stability. The superconducting magnet consists of 2 solenoids wound with wire with the length of 60 m. The magnet has an outer diameter of 169 mm, an iron core diameter of 135 mm, and air gap of 32 mm. The superconducting magnet is fabricated and tested. The central magnetic field reaches 1.0 T when the current of the magnet is 290 A. The central magnetic field is 0.39 T when the current of the magnet is 100 A. In this paper, main magnet for MRI with composite superconductor is analysed and proposed, including magnet design, test results, and field uniformity.
Investigation and Comparison on Switching Performance of Semiconductor Pulsed Power Devices

Pulsed power semiconductor devices play a more and more important role in the field of pulse power, with their high reliability and low switching losses. It includes common power semiconductor devices such as insulated gate bipolar transistor (IGBT), thyristor, and reversely switched dynistor (RSD) that is specially applied in the pulsed power area. Two methods of high-voltage differential probe test for single device and ordinary probe test for voltage-divided serial devices were applied to measure the turn-on voltage of these three types of switches in this paper separately. The switching characteristics of the RSD, thyristor, and IGBT were investigated and compared by the turn-on experiments. The advantage of the RSD in the pulsed power application is shown by the experimental data.
Accurate semantic image labeling by fast Geodesic Propagation

Motivated by recently raised image semantic labeling problem, this paper studies a fast Geodesic Propagation (GP) algorithm that integrates recognition proposal and image compatibility into a graphical representation. Given the recognition proposal map of the image, the initial seeds are selected as confident pixels standing on local proposal peaks by Mean-shift algorithm. The geodesic distance is then defined on a hybrid manifold, combining the color and boundary features with the recognition proposal map. Based on the geodesic distance, the semantic labeling is simultaneously propagated from the initial seeds of all classes to the rest of image pixels. This inference algorithm is capable of multi-labeling an image of 2-mega pixels in one second (with a common PC). In the experiment, we test on 21 generic semantic categories (sky, road, grass …) on MSRC dataset, and 17 categories on LHI dataset to evaluate the performance.