Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process

An experimental investigation on the mechanical properties of the interface between large-sized graphene and a flexible substrate

In this paper, the interfacial mechanical properties of large-sized monolayer graphene attached to a flexible polyethylene terephthalate (PET) substrate are investigated. Using a micro-tensile test and Raman spectroscopy, in situ measurements are taken to obtain the full-field deformation of graphene subjected to a uniaxial tensile loading and unloading cycle. The results of the full-field deformation are subsequently used to identify the status of the interface between the graphene and the substrate as one of perfect adhesion, one showing slide or partial debonding, and one that is fully debonded. The interfacial stress/strain transfer and the evolution of the interface from one status to another during the loading and unloading processes are discussed and the mechanical parameters, such as interfacial strength and interfacial shear strength, are obtained quantitatively demonstrating a relatively weak interface between large-sized graphene and PET

Prediction and compensation of contour error of CNC systems based on LSTM neural-network

This paper proposes a contour error estimation and compensation method for computer numerical control (CNC) systems based on the long short-term memory neural network (LSTM-NN). This is achieved by performing modeling of each axis to predict the tracking error, calculating the actual trajectory, estimating the contour error, and modifying the reference trajectory. First, linear feature selection based on a simplified single-axis model and nonlinear feature selection based on a circular test are performed to achieve tracking error prediction. Then, a spline-approximation-based contour error estimation method is proposed to estimate the contour error between the reference trajectory and the predicted trajectory. Finally, contour error compensation is performed on the reference trajectory before it is run on CNC systems. The proposed method is validated through experiments on a three-axis CNC system

Design of a core-shell catalyst : an effective strategy for suppressing side reactions in syngas for direct selective conversion to light olefins

An elegant catalyst is designedviathe encapsulation of metallic oxide Zn-Cr inside of zeolite SAPO34 as a core-shell structure (Zn-Cr@SAPO) to realize the coupling of methanol-synthesis and methanol-to-olefin reactions. It can not only break through the limitation of the Anderson-Schulz-Flory distribution but can also overcome the disadvantages of physical mixture catalysts, such as excessive CO2formation. The confinement effect, hierarchical structure and extremely short distance between the two active components result in the Zn-Cr@SAPO capsule catalyst having better mass transfer and diffusion with a boosted synergistic effect. Due to the difference between the adsorption energies of the Zn-Cr metallic oxide/SAPO zeolite physical mixture and capsule catalysts, the produced water and light olefins are easily removed from the Zn-Cr@SAPO capsule catalyst after formation, suppressing the side reactions. The light olefin space time yield (STY) of the capsule catalyst is more than twice that of the typical physical mixture catalyst. The designed capsule catalyst has superior potential for scale-up in industrial applications while simultaneously extending the capabilities of hybrid catalysts for other tandem catalysis reactions through this strategy. © The Royal Society of Chemistry 2020

Validating quantum-supremacy experiments with exact and fast tensor network contraction

The quantum circuits that declare quantum supremacy, such as Google Sycamore [Nature \textbf{574}, 505 (2019)], raises a paradox in building reliable result references. While simulation on traditional computers seems the sole way to provide reliable verification, the required run time is doomed with an exponentially-increasing compute complexity. To find a way to validate current ``quantum-supremacy" circuits with more than $50$ qubits, we propose a simulation method that exploits the ``classical advantage" (the inherent ``store-and-compute" operation mode of von Neumann machines) of current supercomputers, and computes uncorrelated amplitudes of a random quantum circuit with an optimal reuse of the intermediate results and a minimal memory overhead throughout the process. Such a reuse strategy reduces the original linear scaling of the total compute cost against the number of amplitudes to a sublinear pattern, with greater reduction for more amplitudes. Based on a well-optimized implementation of this method on a new-generation Sunway supercomputer, we directly verify Sycamore by computing three million exact amplitudes for the experimentally generated bitstrings, obtaining an XEB fidelity of $0.191\%$ which closely matches the estimated value of $0.224\%$. Our computation scales up to $41,932,800$ cores with a sustained single-precision performance of $84.8$ Pflops, which is accomplished within $8.5$ days. Our method has a far-reaching impact in solving quantum many-body problems, statistical problems as well as combinatorial optimization problems where one often needs to contract many tensor networks which share a significant portion of tensors in common.Comment: 7 pages, 4 figures, comments are welcome

Radix Rehmanniae Extract Ameliorates Experimental Autoimmune Encephalomyelitis by Suppressing Macrophage-Derived Nitrative Damage

Multiple sclerosis (MS) is a neuroinflammatory disease in central nervous system (CNS) without effective treatment or medication yet. With high prevalence of MS patients worldwide and poor therapeutic outcome, seeking novel therapeutic strategy for MS is timely important. Radix Rehmanniae (RR), a typical Chinese Medicinal herb, has been used for neuroinflammatory diseases in Traditional Chinese Medicine for centuries. However, scientific evidence and underlying mechanisms of RR for MS are unclear. In this study, we tested the hypothesis that RR could attenuate the progress and severity of MS via suppressing macrophage-derived nitrative damage and inflammation by using experimental autoimmune encephalomyelitis (EAE) model for mimicking MS pathology. The results showed the RR treatment effectively ameliorated clinical disease severity, inhibited inflammation/demyelination in spinal cord, and alleviated CNS infiltration of encephalitogenic T cells and activated macrophages. Meanwhile, RR possessed bioactivities of scavenging ONOO− and reducing the expression of iNOS and NADPH oxidases in the spinal cords of the EAE mice. Furthermore, RR treatment suppressed nuclear factor-κB (NF-κB) signaling pathway in the splenocytes of EAE mice. The in vitro experiments on macrophages and neuronal cells exerted consistent results with the in vivo animal experiments. Taken together, we conclude that Radix Rehmanniae extract has therapeutic values for ameliorating EAE/MS pathological process and disease severity and its underlying mechanisms are associated with anti-inflammation and inhibiting macrophage-derived nitrative damages. Further study could yield novel promising therapeutic agent for multiple sclerosis