nils-herrmann/clean_scirepeval huggingface dataset:
Learning on Big Graph: Label Inference and Regularization with Anchor Hierarchy

Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. However, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To overcome this issue, we propose a novel Hierarchical Anchor Graph Regularization (HAGR) approach by exploring multiple-layer anchors with a pyramid-style structure. In HAGR, the labels of datapoints are inferred from the coarsest anchors layer by layer in a coarse-to-fine manner. The label smoothness regularization is performed on all datapoints, and we demonstrate that the optimization process only involves a small-size reduced Laplacian matrix. We also introduce a fast approach to construct our hierarchical anchor graph based on an approximate nearest neighbor search technique. Experiments on million-scale datasets demonstrate the effectiveness and efficiency of the proposed HAGR approach over existing methods. Results show that the HAGR approach is even able to achieve a good performance within 3 minutes in an 8-million-example classification task.
Semisupervised Multiview Distance Metric Learning for Cartoon Synthesis

In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters. In this paper, we introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. However, it is improper to concatenate these three features into a long vector, because they have different physical properties, and simply concatenating them into a high-dimensional feature vector will suffer from the so-called curse of dimensionality. Hence, we propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously, under the umbrella of graph-based semisupervised learning. SSM-DML discovers complementary characteristics of different feature sets through an alternating optimization-based iterative algorithm. Therefore, SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement. On the basis of SSM-DML, we develop a novel system that composes the modules of multiview cartoon character classification, multiview graph-based cartoon synthesis, and multiview retrieval-based cartoon synthesis. Experimental evaluations based on the three modules suggest the effectiveness of SSM-DML in cartoon applications.
Spatial-division multiplexed hybrid Raman and Brillouin distributed sensor employing multicore fiber

Employing the multiple spatially independent channels offered by multicore fiber (MCF), spatial-division multiplexed (SDM) hybrid Raman and Brillouin distributed sensor has been demonstrated. The interrogation of Raman and Brillouin scattering are performed in distinct cores of the MCF, allowing for flexible management of the input powers between each of them, which is actually incompatible in hybrid Raman/Brillouin distributed system using single mode fibers (SMFs) due to the limitation of nonlinear effects. The proposed SDM system employs shared devices to generate pump pulse, but separate interrogation fiber channels. The combination of Raman and Brillouin sensing provides the capability of discriminative measurement between temperature and strain.
GaaS workload characterization under NUMA architecture for virtualized GPU

Graphics-as-a-service (GaaS) is gaining popularity in cloud computing community. There is an emerging trend of running GaaS workload using virtualized GPU in current data center deployment. This paper provides a detailed characterization of GaaS workload under virtualized GPU NUMA environment, and found that: (1) GaaS workloads exhibit different behavior with GPGPU workloads by having more frequent real-time data exchange between CPU and GPU; (2) GaaS workloads have no NUMA overhead, whether considering the influence of remote memory access or the resource contention of CPU uncore. We also test the performance and power tradeoff among the frequency scaling of CPU clock, GPU core clock, and GPU memory clock. Characterization results show that (1) ondemand CPU frequency scaling achieves the best balance between performance and power consumption; (2) GaaS workloads are GPU-computation intensive. GPU memory frequency can be set lower to save energy with little performance sacrifice.
Is Second-Order Information Helpful for Large-Scale Visual Recognition?

By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from lowlevel to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-theart works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. We take a step towards addressing this problem. Our method consists in covariance pooling, instead of the most commonly used first-order pooling, of highlevel convolutional features. The main challenges involved are robust covariance estimation given a small sample of large-dimensional features and usage of the manifold structure of covariance matrices. To address these challenges, we present a Matrix Power Normalized Covariance (MPNCOV) method. We develop forward and backward propagation formulas regarding the nonlinear matrix functions such that MPN-COV can be trained end-to-end. In addition, we analyze both qualitatively and quantitatively its advantage over the well-known Log-Euclidean metric. On the ImageNet 2012 validation set, by combining MPN-COV we achieve over 4%, 3% and 2.5% gains for AlexNet, VGG-M and VGG-16, respectively; integration of MPN-COV into 50-layer ResNet outperforms ResNet-101 and is comparable to ResNet-152. The source code will be available on the project page: http://www.peihuali.org/MPN-COV.
Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement

Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
Distributed User Selection in Multi-Cell Massive MIMO Systems with Pilot Contamination

In this paper, we consider a time-division duplex (TDD) multi-cell massive MIMO system. Channel estimation is hampered by pilot contamination from neighbouring cells. We propose a low-complexity distributed user selection scheme to improve the throughput performance and reduce pilot contamination. The scheduling decision is made among the users according to their virtual SINR, and only the self-selected users can be co- scheduled for data transmission. The advantages of our proposed scheme are twofold. Firstly, users experiencing a weak channel are less likely to be self-selected; this will increase the throughput performance of the system. Secondly, users that contribute to significant pilot contamination are also less likely to be selected; this will improve the accuracy of channel estimation. Analytical expressions for setting the user selection threshold and the average mean squared error (MSE) performance are derived. Finally, numerical results are also presented to provide further insights.
Electromechanical coupling based influence of structural error on radiation and scattering performance of array antennas

A new structure and scattering array factor coupling model, for estimating the effect of element position error of active phased-array antenna on its radar cross section (RCS) is established, based on the relationship between structural position error and phase difference. The model greatly reduces the computational complexity of the RCS for the evaluation of array antenna performance. Based on the model, the influence of the element position error on the gain and RCS is analysed with the results showing that a reasonable reduction in element position accuracy may effectively decrease the RCS. Thus, the developed model can be applied in engineering to balance the radiation and scattering properties. With the necessary radiation and scattering performance guaranteed, the antenna processing and installation tolerances can be properly relaxed to effectively reduce the calibration difficulty and cost.
Math Detective: Digital Game-Based Mathematical Error Dectection, Correction and Explanation

Students may inevitably make errors when learning mathematics. We should positively regard these errors, which could be used as a valuable learning resource. For this reason, this study designed a computer-based learning game, called Math Detective. The game comprised a learning process of error detection, correction and explanation. In this game, students can play the role of detectives to find out errors in multiplication and figure out the reasons. A questionnaire was carried out to preliminarily evaluate students' opinions about their own performance in the activity and the functions of the system. Although the results did not show any tendency for their self-rated performance, students thought the functions of the system helped them a lot. Even so, more design on assisting them to explain errors still needed to be provided and investigated in the future.
2D-to-3D image conversion by learning depth from examples

Among 2D-to-3D image conversion methods, those involving human operators have been most successful but also time-consuming and costly. Automatic methods, that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality as they often rely on assumptions that are easily violated in practice. In this paper, we adopt the radically different approach of “learning” the 3D scene structure. We develop a simplified and computationally-efficient version of our recent 2D-to-3D image conversion algorithm. Given a repository of 3D images, either as stereopairs or image+depth pairs, we find k pairs whose photometric content most closely matches that of a 2D query to be converted. Then, we fuse the k corresponding depth fields and align the fused depth with the 2D query. Unlike in our original work, we validate the simplified algorithm quantitatively on a Kinect-captured image+depth dataset against the Make3D algorithm. While far from perfect, the presented results demonstrate that online repositories of 3D content can be used for effective 2D-to-3D image conversion.
Optimization of electric vehicle charging station location based on game theory

The industrialization process of electric vehicles depends largely on a reasonable solution of battery charging. There are huge search spaces and multiple objectives for the location optimization of charging station, and many factors are difficult to be quantified. In this paper, on the basis of analyzing the traditional site selection evaluation method, game theory is proposed to optimize the electric vehicle charging station location. The optimization game model is built and its algorithm steps are given. The results indicate that this optimization method based on game theory can make charging station location more rational and scientific.
Status of the Primary Test Stand (PTS) for Z-pinch Studies

The Primary Test Stand (PTS) is an ultra-high power facility designed and built for Z-pinch studies at Institute of Fluid Physics, China Academy of Engineering Physics. The total stored energy of PTS is 7.2 MJ. It can produce 10 MA electrical current with rise time of 90 ns. The PTS consists of 24 modules connected in parallel. Each module is based on the Marx generator-PFL technique scheme with laser-triggered switch. The design of the PTS is described in the paper. The prototype of the PTS module has been tested successfully. The results of the prototype will be given. One quarter of the PTS is being constructed and is to be finished this year.
A Partition-Enabled Multi-Mode Band Approach to Arterial Traffic Signal Optimization

Arterial traffic signal coordination makes traffic flow more efficient and safer. This paper presents a partition-enabled multi-mode band (PM-BAND) model that is designed to solve the signal coordination problem for arterials with multiple modes, i.e., passenger cars and transit vehicles. The proposed method permits the progression bands to be broken if necessary and optimizes system partition and signal coordination in one unified framework. The impacts of traffic demand of passenger cars and transit vehicles as well as the geometry characteristics of the arterials are taken into account. Signal timings and waiting time of transit vehicles at stations are optimized simultaneously. The PM-BAND model is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical example results have demonstrated that the PM-BAND model can significantly reduce the average number of stops and delay compared with the other models, i.e., MAXBAND and MULTIBAND. Moreover, the progression bands generated by the PM-BAND model have a higher reliability and effectiveness.
Narrow Band Disturbance Attenuation Method for AC Drives: A Complex Pole-Zero Placement Approach

The unwanted periodic external excitations on AC drives, such as fluctuation in the DC bus voltage or dead time in the gate drive signals, often act as narrow band disturbances in rela-tively low frequency region. The consequent influences cannot be filtered out by the commonly used low pass filter in hard-ware configuration, thus special attention has to be paid to this kind of disturbances. A robust discrete-time controller using complex pole-zero placement with the assistance of sensitivity function shaping is proposed, which, specifically, utilizes a sec-ond-order notch filter. Comparatively speaking, the widely used proportional-integral controller only employs first- order polynomials in its expression, while the complex pole-zero placement method has no restriction on the degrees of the pol-ynomials, thus provides more freedom in design to meet extra requirements. Moreover, the presence of the notch filter can attenuate disturbances in specific frequency region as well as maintain the robust characteristic. The effectiveness and feasi- bility of the architecture is verified by a design example.
Demagnetization Weakening and Magnetic Field Concentration With Ferrite Core Characterization for Efficient Wireless Power Transfer

To improve the power transfer efficiency in wireless power transfer systems, a novel configuration of a magnetic resonant structure is proposed in this paper. According to the analytical expression of mutual inductance, the magnetic field can be improved by weakening the demagnetizing factor for coils with a ferrite core. In this paper, the demagnetizing factors of a cylindrical ferrite core and a novel ferrite core are investigated. Compared with the cylindrical ferrite core, the proposed ferrite core has a smaller demagnetizing factor and an increased mutual inductance. In addition, the magnetic field through the receiving coil is enhanced due to the thin ferrite axis where the magnetic field is concentrated. The proposed ferrite core is designed and analyzed. Moreover, the effects of pitch and lateral misalignment, as well as the number of turns of the receiving coil, the core loss, and the load, are studied. Experimental prototypes are established to validate the performance of the wireless power transfer system with different types of receiving structures. The results indicate that the proposed structure is superior to others and that it shows better performance in power transfer efficiency and transfer distance, which is in good agreement with the theoretical and simulation analysis.
Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models

In traditional vision systems, high level information is usually inferred from images or videos captured by cameras, or depth images captured by depth sensors. These images, whether gray-level, RGB, or depth, have a human-readable 2D structure which describes the spatial distribution of the scene. In this paper, we explore the possibility to use distributed color sensors to infer high level information, such as room occupancy. Unlike a camera, the output of a color sensor has only a few variables. However, if the light in the room is color controllable, we can use the outputs of multiple color sensors under different lighting conditions to recover the light transport model (LTM) in the room. While the room occupancy changes, the LTM also changes accordingly, and we can use machine learning to establish the mapping from LTM to room occupancy.
Study of a Log-Periodic Slow Wave Structure for Ka-band Radial Sheet Beam Traveling Wave Tube

A novel slow wave structure (SWS) named angular log-periodic meander-line is proposed for a radial traveling wave tube (TWT). In this paper, a 30 log-periods microstrip angular log-periodic meander-line SWS is studied. The dispersion of this kind of SWS is weak, which shows that it can work in a wide operating bandwidth. The more important advantage is that the operating voltage is much lower than that of the conventional TWT at the same operating frequency and the geometrical dimension is also much smaller than that of the conventional TWT. The beam–wave interaction of the angular log-periodic meander-line TWT is calculated using the particle-in-cell method. When the operating voltage is 1624 V, this kind of TWT can give 156.5-W output power at 35 GHz, the gain is 21.9 dB, and the electron efficiency is  ${\sim}17.7\%$. With this kind of TWT as mentioned, the concept of radial integrated angular log-periodic meander-line TWT is proposed, which can provide a new way to obtain higher output power.
Energy Efficient Power and Subchannel Allocation in Dense OFDMA Small Cell Networks

This paper studies energy efficient power and subchannel allocation in dense small cell networks. After giving the subchannel allocation algorithm, we transform the fractional energy efficient problem into an equivalent iterative optimization problem which has a subtractive form. With the developed utility function of the equivalent problem, we introduce a non-cooperative game to optimize power allocation. The existence and uniqueness of the Nash equilibrium of the game is proved. Besides, the proposed distributed power update needs minor signalling to get the optimal solution. System-level simulation results show that the proposed scheme can quickly reach the convergence and efficiently improve the energy efficiency in small cell networks.
Scale-Aware Spatially Guided Mapping

The scale information in images is important for guiding image-filtering configuration. The authors propose a scale-aware spatially guided mapping (SaSGM) model, which formulates and combines multiple spatial influences of simple edge responses under different levels of detail. The SaSGM model is thus more sensitive to image patterns at a large scale. The authors further incorporate the SaSGM into several image processing models, such as detail enhancement and image stylization models. Experiments show that by inheriting the characteristics of the SaSGM, the extended models are able to differentiate image contents in terms of their scales and thus generate more natural or diversified visual effects. This article is part of a special issue on quality modeling.
User sensing based on MIMO cognitive radio sensor networks

In this paper, we consider cooperative spectrum sensing in OFDM based on energy detection in MIMO cognitive radio sensor networks. Soft combination of the observed energy values from different cognitive radio users is investigated. Square-law-combining (SLC) is theoretically proved to be nearly optimal in low signal-to-noise ratio (SNR) region, an usual scenario in the context of MIMO cognitive radio sensor networks. In particularly, the OFDM based MIMO cognitive radio sensor networks detect the primary user OFDM signal, where CR receiver is equipped with multiple antennas based energy detector. We observed significant impromvement in primary user detection with SLC based energy detection at the MIMO cognitive radio sensor networks.
Research on electromagnetic scattering characteristics of strait composite random rough surface based on improved hybrid SBR/PO method

Due to simplicity, hybridization of shooting and bouncing ray (SBR) and physical optics (PO) has been widely used for fast scattering analyses. A composite random rough surface model is presented for describing the concave structure of strait environment. Monte Carlo method is used to build strait composite random rough surface, gauss spectral function is used to simulate ground part of strait, and Pierson Moskowitz (PM) spectral function is used to simulate ocean part of strait. Weighted inverse tangent function is presented to smooth adjacent region. The ray-density normalization (RDN) theory is introduced to improve the SBR method. In consideration of the inartificial cavity construction of strait in natural environment, the improved SBR/PO is used to calculate the electromagnetic scattering of the strait composite rough surface. Electromagnetic scattering characteristics of ground, ocean, and strait composite random rough surface are compared. The impacts of open area of strait concave structure on the curves of bistatic scattering coefficient (BSC) of horizontal-to-horizontal (HH) polarization of strait environment are analyzed. The influence of root mean square (RMS) of ground region on BSC of HH polarization is discussed. The numerical results can use for reference in the fields of remote sensing, detection and inversion of electromagnetic environment.