nils-herrmann/clean_scirepeval huggingface dataset:
On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks

RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs. Currently these methods limit their inputs to coordinates of joints and improve the accuracy mainly by extending RNN models to spatial domains in various ways. While such models explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, we select a set of simple geometric features, motivated by the evolution of previous work. With experiments on a 3-layer LSTM framework, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and achieve state-of-art results on four datasets. Further, we show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.
Parallel coupled numerical simulation of electric and turbulent gas flow field for SF/sub 6/ circuit breaker

The dielectric recovery characteristic of high voltage circuit breaker (HVCB) is determined by the interaction of the electric field and the flow field in interrupting course, the calculation field of electric and gas flow are different, and the initial computation and boundary conditions are also different. For obtaining the corresponding data to numerically solve the dielectric recovery characteristic of the CB, the parallel coupled strategy is proposed and employed for computing the coupled electric and flow field. Moreover, in the pre-processing, the sub-region meshing method is used and the elements in some region with rapid variation are densified. The feasibility and practicability of the proposed method with high efficiency has been verified by the demonstration example. And the distribution of the flow field, the electric field and the curve of the dielectric recovery characteristic in the whole interrupting course have been obtained. The location of the minimum of the dielectric recovery characteristic during the course of interrupting is traced. Furthermore, the coupled strategy in parallel can effectively calculate the dielectric recovery strength. And discussions about the acquired results are presented
A new method for reference network considering network topology optimization

In this paper, a new method for reference network considering network topology optimization is proposed. The actual operation mode optimization and the correction control with transmission switching under the contingency situation are taken into account in the proposed method. Based on the forecasted load values of each period in predictive multi-time scale and candidate transmission line schemes of future planning time horizon, a new optimal model for reference network is built. In the proposed model, the objective function is minimizing annual power generation cost and transmission cost, and the constraints are the safety operation technical requirements under normal operation state and N-1 accident operation state. The proposed model is a complex mixed-integer nonlinear problem mathematically, and can be converted to a mixed-integer linear programming problem. Finally, case studies are conducted to illustrate the effectiveness of the proposed method.
Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting

Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e.g, face recognition and 3D face reconstruction. Recently, there have been a few attempts to solve this problem, but still more research is needed to achieve highly accurate results. In this paper, we propose a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM. We formulate the face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are estimated by a cascade of CNN-based regressors. The dense 3D shape allows us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging databases (AFLW and AFW), with comparison to the state of the art.
Broadband wire grid polarizer application in millimeter wave frequency

This paper presents a wire grid polarizer (WGP) working at the frequency ranges between 1 GHz to 500 GHz. The WGP is extensively used in polarization sensitive elements for separating different polarized signals at the same channel. The spacing pitch-to-width ratio should be chosen seriously for lower cross polarization levels in double polarizers. The influences between single and double layers WGP are also discussed simultaneously. The single layer simulation results of reflectance and transmittance coefficients are more than 0.96 in double polarization directions, which has been fabricated successfully. The double layers WGP can improve obviously the reflection coefficient and make the transmission coefficient oscillated.
A new automatic method for mass detection in mammography with false positives reduction by supported vector machine

Mass localization is important in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, we present an automated classification system for the detection of masses in mammographic images. Suspicious regions are located with an adaptive region growing firstly. Then, the initial regions are further refined with narrow band based active contour, which can improve the segmentation accuracy of masses. CLBP (Complete Local Binary Pattern) texture features are extracted from the ROIs (regions of interest) containing the segmented suspicious regions. Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist's diagnosis. The method was evaluated on a dataset with 231 images, containing 245 masses. Among them, 125 images containing 133 masses are used to optimize the parameters and are used to train SVM. The remaining 106 images are used to test the performance. It obtained 1.36 FPsI at the sensitivity 76.8%. It shows that the proposed method is a promising approach to achieve low FPsI while maintain a high sensitivity.
Non-contact high-speed rotation of micro targets by vibration of single piezoelectric actuator

Micromanipulation provides researchers a new way to fabricate complex structures through assembling small components, and it is also a useful tool for single cell manipulation. Many micromanipulation methods have been proposed, but only the few are capable of rotation of the micro targets. In this paper, we present a simple non-contact high-speed rotation method based on the local flow generated by vibrating a single piezoelectric actuator in the fluids. A micro glass pipette is attached to the end of the piezoelectric actuator, and the other end is connected to a copper bar. When we input specific electric wave into the piezoelectric actuator, the tip of the micropipette will move circularly and generate a local swirl flow in the fluids. It can be applied to rotating micro targets with a high-speed. The mechanism of this method are analyzed, and simulation is utilized to analyze the vibration-induced local flow. Control strategies including the on-off control, coarse rotation speed control and fine rotation speed control are also designed. Experiments of rotating microbeads are carried out to prove the effectiveness, operability and efficiency of this method.
Dense Face Alignment

Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection. In this paper, for the first time, we aim at providing a very dense 3D alignment for large-pose face images. To achieve this, we train a CNN to estimate the 3D face shape, which not only aligns limited facial landmarks but also fits face contours and SIFT feature points. Moreover, we also address the bottleneck of training CNN with multiple datasets, due to different landmark markups on different datasets, such as 5, 34, 68. Experimental results show our method not only provides high-quality, dense 3D face fitting but also outperforms the state-of-the-art facial landmark detection methods on challenging datasets. Our model can run at real time during testing and it's available at http:///cvlab.cse.msu.edu/project-pifa.html.
Design of a novel multiband antenna for mobile terminals

A multiband compact planar terminal antenna is designed and proposed in this paper, which is suitable for multi-functional wireless communication systems. The proposed antenna consists of a patch with two slots on it and a meandered strip loaded on the top. Two parasitic stubs are etched at the back of the antenna to widen the operating frequency band. The operating bandwidths of the antenna cover GSM900, DCS, PCS, UMTS, LTE2500, and LTE's low-frequency band (450-470MHz). Both of simulation and measurement results are shown to illustrate the good performance of the proposed antenna. It is very suitable for terminal applications in wireless communication systems.
VIIRS mission-long ocean color data reprocessing: Evaluations of data product and sensor performance

In this paper, we provide an overview of the progress on producing accurate and consistent ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). To meet requirements from broad users (e.g., operational, research, modeling, etc.), we have proposed and now been routinely producing two VIIRS ocean color data streams, i.e., the near-real-time (NRT) and delayed science quality ocean color product data. The implementation details for the two data streams will be discussed. In addition, with significantly improved satellite data processing algorithms, as well as considerably improved sensor on-orbit radiometer calibration using both solar and lunar methods, VIIRS mission-long ocean color data have been successfully reprocessed using the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. VIIRS ocean color data have been significantly improved over the global open ocean, as well as turbid coastal and inland waters. In particular, VIIRS-derived water property data over global high altitude lakes (e.g., Lake Victoria in South Africa) are now very much improved, providing a significant progress for satellite remote sensing of inland water properties. Some detailed evaluation of VIIRS ocean color products, as well as sensor performance, will be discussed. Results show that VIIRS can provide high-quality global ocean color products in support of the science researches and operational applications.
Optimization design and application of converter parameters of DC vacuum circuit breaker

With the R&D of high voltage direct current (HVDC) transmission, HVDC circuit breaker as the protection equipment has been widely applied in the power system. In this paper, the HVDC vacuum circuit breaker (VCB) based on artificial zero mode has been analyzed, and the genetic algorithm (GA) has been used to optimize the parameters of DC VCB. Moreover, an optimization design method of converting parameters for HVDC VCB has been presented and applied to the optimization of converter parameters for DC VCB. The simulation result has shown that the proposed method can improve the interrupting performance of the DC VCB.
A signal-to-noise ratio based multiple thresholds cooperative spectrum sensing scheme

Spectrum sensing is a key technology in Cognitive Radio (CR). It is an important function to be performed by every cognitive radio. Either single or multiple users can take part in spectrum sensing process. This paper presents a multiple thresholds cooperative spectrum sensing algorithm based on signal-to-noise ratio (SNR). In the first phase, the cognitive nodes that satisfy the request of double thresholds apply the cooperative sensing. In the second phase, the nodes that in the doubt-window participate in cooperation according to the SNR threshold, if there is no final decision by the fusion center in first phase. The fusion center combines the detection results by weighting factor which is calculated by the SNR. Simulation results show that the proposed scheme could improve the system performance. And the proposed scheme is more suitable in the environment of medium to low SNRs.
Analysis on breakdown characteristics of triple-electrode switch in vacuum

Aiming at the system of triple-electrode switch, the fluid-chemistry model of plasma is established based on the particles' diffusion, collision, ionization and excitation and the finite element method. The micro process of self-breakdown of triple-electrode switch system is simulated in vacuum and SF6. The simulation results clearly revealed that the physical process from initial discharge to plasma channel formed. The plasma channel has more electron number in vacuum compared with it in SF6, and the electron temperature is lower. It lays a theoretical foundation for accurately and fully revealing the physical mechanism of the triple-electrode switch system and helping to find the method to improve the performance of the system.
Face Alignment Across Large Poses: A 3D Solution

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45 degree), lacking the ability to align faces in large poses up to 90 degree. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.
Compact triple band antenna array for GNSS application

A compact triple band antenna array for GNSS application, covers GPS L1, L2 and L5 frequency bands, is proposed in this paper. The array is comprised of 4 radiating elements spaced 0.47 wavelength apart (center to center) at the GPS L1 band and filled with dielectric material and three stacked patches. It is observed that due to the compactness of the antenna array the mutual coupling degrades the radiation properties. We present a modification in the ground-plane to mitigate the inter-element coupling and improve the axial ratio property. It reduces the axial ratio below 3dB in the main-lobe directions, which is required for the navigation systems.
Image Super-Resolution via Deep Recursive Residual Network

Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks. Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks, recursive learning is used to control the model parameters while increasing the depth. Extensive benchmark evaluation shows that DRRN significantly outperforms state of the art in SISR, while utilizing far fewer parameters. Code is available at https://github.com/tyshiwo/DRRN_CVPR17.
Unconstrained 3D face reconstruction

This paper presents an algorithm for unconstrained 3D face reconstruction. The input to our algorithm is an “unconstrained” collection of face images captured under a diverse variation of poses, expressions, and illuminations, without meta data about cameras or timing. The output of our algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data or texture information. 3D face reconstruction from a collection of unconstrained 2D images is a long-standing computer vision problem. Motivated by the success of the state-of-the-art method, we developed a novel photometric stereo-based method with two distinct novelties. First, working with a true 3D model allows us to enjoy the benefits of using images from all possible poses, including profiles. Second, by leveraging emerging face alignment techniques and our novel normal field-based Laplace editing, a combination of landmark constraints and photometric stereo-based normals drives our surface reconstruction. Given large photo collections and a ground truth 3D surface, we demonstrate the effectiveness and strength of our algorithm both qualitatively and quantitatively.
Mass detection based on integrated region growing and level set method

In this paper, an automatically method for mass detection was introduced, which combines multiple layers concentric (MLC) and narrow band region-based active contour (NBAC) technique. We used an improved level set method to segment the mass for contour refinement, after the boundary of a mass is found, texture features from Gray Level Cooccurrence Matrix (GLCM) are extracted from the surrounding area of the boundary of the mass. The extracted texture features are used to reduce the false positive. Mammography images from DDSM were used in the experiments and the method was evaluated, it obtained 1.38 FPsI at the sensitivity 79.3%. The result shows the effectiveness of the proposed method.
Non-contact transportation and rotation of micro objects by vibrating glass needle circularly under water

In micromanipulation, lots of methods have been developed to manipulate objects in microscale. However, few of them can be applied in both the transportation and the rotation of the micro objects. In this paper, we present a novel method to realize the non-contact transportation and rotation of the micro objects based on the vibration-induced swirl flow. A piezo actuator is set between the glass needle and a metal rod. The sine wave with controlled frequency and amplitude is input into the piezo actuator to drive the glass needle to move circularly, which is caused by resonance of the actuator and the metal rod. We place the glass needle under water and keep a limited distance to the bottom. The circular vibration of the glass needle can generate a swirl flow and low pressure around it. The low pressure can trap and transport the micro objects vertically to the glass needle, and the swirl flow can rotate the objects continuously. Finally, we realize the trap and rotation of micro object with only one piezo actuator. Experiments of transportation and rotation of microbeads are carried out, and the results demonstrate it is a simple, low-cost, effective micromanipulation method.
Analysis and design of heterogeneous bioinformatics database integration system based on middleware

With the accomplishment of Human Genome Project, a mass of original data are collected by researchers. In order to handle and research those data better, hundreds of databases of Bioinformatics are formed. However, how to integrate these heterogeneous databases to provide users with a unified application platform? This is a problem to be solved. This paper analyzes the existing bioinformatics database and data integration method at first. And it proposes a system framework which integrates heterogeneous data sources based on middleware technology and gene ontology using XML as a common data model then. It gives out how to implement this model and described based on the technology, the main module of system integration and processing flow. Finally, it introduces the framework of the biological information integration system specific application. It solves the structure of bioinformatics data format and semantic heterogeneity problems.
Research on the simulation of vacuum arc under low current interruption

For the vacuum arc, the parameters of the interelectrode plasma have influences on the electrode process. It is necessary to obtain the parameters and characteristics of the plasma by simulating the plasma among the electrodes. In this paper, based on the analysis of the existing vacuum arc model, combined with the characteristics of vacuum arc fault under different interrupting conditions, the effects of the mean charge number and the ion kinetic energy on the vacuum arc are considered. Moreover, the vacuum arc magnetohydrodynamic (MHD) model is derived and boundary conditions are determined. And using Fluent software as the computing platform and VC++ self-programming method, the basic characteristics of the vacuum arc MHD model under 200A low current interruption are simulated for control the arc behavior.
Design of frequency selective surfaces for a gyro-multiplier output system

This paper presents our on-going work on the development of an output system for a THz gyro-multiplier. The beam-wave interaction and frequency multiplication in the gyro-multiplier generate two high-order modes at two distinguished frequencies. To convert the high-order waveguide modes into quasi-Gaussian beams and separate the two frequencies, a quasi-optical mode converter (QMC) and a frequency selective surface (FSS) are combined in the designed output system. The QMC is used to convert both the high order modes into quasi-Gaussian beams, while the FSS is used to separate the two frequencies. This presentation focuses on the design, fabrication and testing of the required FSS.
A new method for reference network considering nodal injection uncertainties

With the diversified development of electrical loads, and the large-scale integration of volatile renewable generations, power system operation, control and planning face the challenge of increasing injection uncertainties. In this paper, a new method for reference network considering nodal injection uncertainties is proposed. The actual operation mode optimization and reserve configuration of power grid as well as the correction control under the contingency situation are taken into account in the proposed method. Based on the forecasted interval values of nodal injection power of each period in predictive multi-time scale and candidate transmission line schemes of future planning time horizon, a new optimal model for reference network is built. In the proposed model, the objective function is minimizing annual power generation cost, reserve cost and transmission cost, and the constraints are the safety operation technical requirements under normal operation state and N-1 accident operation state. The proposed model is an interval optimization problem mathematically, and can be converted to a linear programming problem. Finally, case studies are conducted to illustrate the effectiveness of the proposed method.