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Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean data structures and have been proved powerful in various application domains such as social networks and e-commerce. While such graph data maintained in real-world systems can be extremely large and sparse, thus employing GNNs to deal with them requires substantial computational and memory overhead, which induces considerable energy and resource cost on CPUs and GPUs. In this work, we present a specialized accelerator architecture, EnGN, to enable high-throughput and energy-efficient processing of large-scale GNNs. The proposed EnGN is designed to accelerate the three key stages of GNN propagation, which is abstracted as common computing patterns shared by typical GNNs. To support the key stages simultaneously, we propose the ring-edge-reduce(RER) dataflow that tames the poor locality of sparsely-and-randomly connected vertices, and the RER PE-array to practice RER dataflow. In addition, we utilize a graph tiling strategy to fit large graphs into EnGN and make good use of the hierarchical on-chip buffers through adaptive computation reordering and tile scheduling. Overall, EnGN achieves performance speedup by 1802.9X, 19.75X, and 2.97X and energy efficiency by 1326.35X, 304.43X, and 6.2X on average compared to CPU, GPU, and a state-of-the-art GCN accelerator HyGCN, respectively.

This paper investigates the influencing factors in passengers' multimodal traffic choice behaviors and provides a decision-making basis and improvement strategies. By collecting large individual-level data through a comprehensive field survey that was carried out at the major transportation hubs in Xian City in China from the 1st to the 10th of March 2018, we compared 21 variables from the data with four travel modes including air, high-speed rail (HSR), traditional passenger train, and express bus. Among the variables, 12 variables were used as the independent variables after the correlation analysis and the collinearity test, including age, car ownership, ticketing method, travel purpose, travel distance, fare rate, inter-city travel time per hundred kilometers, safety, comfort, punctuality, access time, and departure time. The regression relationships between the travel mode choice and the independent variables were studied using Bayesian binomial logistic regression (BBL) and Bayesian multiple logistic regression (BMNL). The receiver operating characteristic (ROC) curve was applied to analyze the predictive performance of the regression models. The results show that the area under curve (AUC) of the ROC curves for the air travel, high-speed train, passenger train, and express bus using BMNL were 0.9785, 0.9263, 0.8614, and 0.9025, respectively. The AUC value of the ROC curve of the express bus using BBL was 0.8797, which indicated that the passenger travel choice models established by Bayesian logistic regressions had good prediction ability. The application of Bayesian logistic regression in traffic mode choice is beneficial to enriching the assessments of mode-choice behaviors, and the findings of this study can provide a basis for formulating traffic management strategies in practice for shareholders and policymakers., Comment: The paper contains serious flaws and has been withdrawn

Metasurfaces possess the outstanding ability to tailor phase, amplitude and even spectral responses of light with an unprecedented ultrahigh resolution, thus have attracted significant interests. Here, we propose and experimentally demonstrate a novel meta-device that integrates color printing and computer-generated holograms within a single-layer dielectric metasurface by modulating spectral and spatial responses at subwavelength scale, simultaneously. In our design, such metasurface appears as a microscopic color image under white light illumination, while encrypting two different holographic images that can be projected at the far-field when illuminated with red and green laser beams. We choose amorphous silicon dimers and nanofins as building components and use a modified parallel Gerchberg-Saxton algorithm to obtain multiple sub-holograms with arbitrary spatial shapes for image-indexed arrangements. Such a method can further extend the design freedom of metasurfaces. By exploiting spectral and spatial control at the level of individual pixels, multiple sets of independent information can be introduced into a single-layer device, the additional complexity and enlarged information capacity are promising for novel applications such as information security and anti-counterfeiting.

Hardware Trojans (HTs) have drawn more and more attention in both academia and industry because of its significant potential threat. In this paper, we proposed a novel HT detection method using information entropy based clustering, named HTDet. The key insight of HTDet is that the Trojan usually be inserted in the regions with low controllability and low observability in order to maintain high concealment, which will result in that Trojan logics appear extremely low transitions during the simulation. This means that the logical regions with the low transitions will provide us with much more abundant and more important information for HT detection. Therefore, HTDet applies information theory technology and a typical density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect all suspicious Trojan logics in circuit under detection (CUD). DBSCAN is an unsupervised learning algorithm, which can improve the applicability of HTDet. Besides, we develop a heuristic test patterns generation method using mutual information to increase the transitions of suspicious Trojan logics. Experimental evaluation with benchmarks demenstrates the effectiveness of HTDet.

Metasurfaces have attracted extensive interests due to their ability to locally manipulate optical parameters of light and easy integration to complex optical systems. Particularly, metasurfaces can provide a novel platform for splitting and diffracting light into several beams with desired profile, which is in contrast to traditional gratings. Here, we propose and experimentally demonstrate a novel method for generating independently selective diffraction orders. Our method is based on complex amplitude modulation with ultrathin dielectric metasurfaces. By tailoring the geometric parameters of silicon nanofin structures, we can spatially control the geometric and dynamic phase as well as the amplitude simultaneously. We compare the results with a metasurface that uses a phase-only modulation, to verify such selective diffraction can be solely efficiently achieved with complex amplitude modulation. Besides, the diffraction angles of each order have been measured, which are consistent with standard grating theory. Our developed method for achieving selective diffraction with metasurfaces has potential applications in beam shaping, parallel laser fabrication, and nanoscale optical detection.

Manipulating the polarization of light is highly desired for versatile applications ranging from super resolution, optical trapping, to particle acceleration. The enormous freedom in metasurface design motivates the implementation of polarization control in ultrathin and compact optical systems. However, the majority of proposed strategies based on metasurfaces have been demonstrated only a spatially homogeneous polarization generation, while less attention has been devoted to spatially variant inhomogeneous vector beams. Here, we demonstrate a novel method for generating arbitrary radial and azimuthal polarization beams with high efficiencies of up to 80% by utilizing transmission-type dielectric metasurfaces. Polarization conversion metasurfaces are suitable candidates for the implementation of polarization encryption, which we demonstrate by encoding a hidden image into the spatial polarization distribution. In addition, we show that the image pattern can be modified by appropriate polarization selection of the transmitted light. Such a method may provide a practical technique for a variety of applications such as imaging, encryption and anti-counterfeiting.

Since its invention, holography has emerged as a powerful tool to fully reconstruct the wavefronts of light including all the fundamental properties (amplitude, phase, polarization, wave vector, and frequency). For exploring the full capability for information storage/display and enhancing the encryption security of metasurface holograms, smart multiplexing techniques together with suitable metasurface designs are highly demanded. Here, we integrate multiple polarization manipulation channels for various spatial phase profiles into a single birefringent vectorial hologram by completely avoiding unwanted cross-talk. Multiple independent target phase profiles with quantified phase relations that can process significantly different information in different polarization states are realized within a single metasurface. For our metasurface holograms, we demonstrate high fidelity, large efficiency, broadband operation, and a total of twelve polarization channels. Such multichannel polarization multiplexing can be used for dynamic vectorial holographic display and can provide triple protection for optical security. The concept is appealing for applications of arbitrary spin to angular momentum conversion and various phase modulation/beam shaping elements.