Application of Question and Answering on Virtual Human Dialogue:a Review and Prediction

Nowadays,virtual human(VH) is becoming a hot research topic in virtualization.VH dialogue can be categorized as an application of natural language processing(NLP) technology,since it is relational to question and answering(QA) technologies.In order to integrate these technologies,this paper reviews some important work on VH dialogue,and predicts some research points on the view of QA technologies.

Evolution of Cooperation in Public Goods Games

We investigate the evolution of cooperation with evolutionary public goods games based on finite populations, where four pure strategies： cooperators, defectors, punishers and loners who are unwilling to participate are considered. By adopting approximate best response dynamics, we show that the magnitude of rationality not only quantitatively explains the experiment results in [Nature （London） 425 （2003） 390], but also it will heavily influence the evolution of cooperation. Compared with previous results of infinite populations, which result in two equilibriums, we show that there merely exists a special equilibrium cooperation. In addition, we characterize that loner＇s and the relevant high value of bounded rationality will sustain payoff plays an active role in the maintenance of cooperation, which will only be warranted for the low and moderate values of loner＇s payoff. It thus indicates the effects of rationality and loner＇s payoff will influence the cooperation. Finally, we highlight the important result that the introduction of voluntary participation and punishment will facilitate cooperation greatly.

Assessing performance of augmented reality-based neurosurgical training

This paper presents a novel augmented reality(AR)-based neurosurgical training simulator which provides a very natural way for surgeons to learn neurosurgical skills.Surgical simulation with bimanual haptic interaction is integrated in this work to provide a simulated environment for users to achieve holographic guidance for pre-operative training.To achieve the AR guidance,the simulator should precisely overlay the 3D anatomical information of the hidden target organs in the patients in real surgery.In this regard,the patient-specific anatomy structures are reconstructed from segmented brain magnetic resonance imaging.We propose a registration method for precise mapping of the virtual and real information.In addition,the simulator provides bimanual haptic interaction in a holographic environment to mimic real brain tumor resection.In this study,we conduct AR-based guidance validation and a user study on the developed simulator,which demonstrate the high accuracy of our AR-based neurosurgery simulator,as well as the AR guidance mode’s potential to improve neurosurgery by simplifying the operation,reducing the difficulty of the operation,shortening the operation time,and increasing the precision of the operation.

Synthesis of wafer-scale graphdiyne/graphene heterostructure for scalable neuromorphic computing and artificial visual systems

Graphdiyne(GDY)is emerging as a promising material for various applications owing to its unique structure and fascinating properties.However,the application of GDY in electronics and optoelectronics are still in its infancy,primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable applications.Here a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene(Gr)surface,providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse array.Essential synaptic behaviors have been realized,and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault tolerance.Logic functions including“NAND”and“NOR”are integrated into the synapse which can be executed in an optical pathway.Moreover,a visible information sensing-memory-processing system is constructed to execute real-time image acquisition,in situ image memorization and distinction tasks,avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual systems.These results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

Dual Branch PnP Based Network for Monocular 6D Pose Estimation

Monocular 6D pose estimation is a functional task in the field of com-puter vision and robotics.In recent years,2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based scenes.However,for monocular 6D pose estimation,these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the per-spective-n-point(PnP)algorithm.There is still a difference in the distance from the expected estimation effect.To obtain a more effective feature representation result,edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on 6D pose regression and comparing the effectiveness of the intermediate representation.Furthermore,although the transformation matrix is composed of rotation and translation matrices from 3D model points to 2D pixel points,the two variables are essentially different and the same network cannot be used for both variables in the regression process.Therefore,to improve the effectiveness of the PnP algo-rithm,this paper designs a dual-branch PnP network to predict rotation and trans-lation information.Finally,the proposed method is verified on the public LM,LM-O and YCB-Video datasets.The ADD(S)values of the proposed method are 94.2 and 62.84 on the LM and LM-O datasets,respectively.The AUC of ADD(-S)value on YCB-Video is 81.1.These experimental results show that the performance of the proposed method is superior to that of similar methods.

Fiber-coupler-based microfluidic system for trapping of DNA biomolecules

A miniature fiber-coupler-based microfluidic system is proposed for trapping of DNA biomolecules. The loop-shaped fiber-coupler is fabricated by using flame tapering technique and integrated in a microfluidic channel. Probe-DNA immobilized on the fiber-coupler surface enables specific recognition of target DNA sequences and effectively facilitates the trapping of target DNA molecules. The binding characteristics of biomolecules on the fiber-coupler surface have been theoretically analyzed and experimentally demonstrated. Experimental results indicate that the spectral response of the loop-shaped fiber coupler immobilized with probe DNA exhibits a red-shift with the trapping of the DNA biomolecules. The proposed microfluidic system possesses such desirable merits as simple structure, label-free method and high integration, which make it a promising candidate for applications in molecular biology and related bioengineering areas.

Behavior of Collective Cooperation Yielded by Two Update Rules in Social Dilemmas:Combining Fermi and Moran Rules

We combine the Fermi and Moran update rules in the spatial prisoner＇s dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations indicate that, compared to the model with only one update rule, the the role of update dynamics should be paid more attention in cooperation behavior exhibits the richer phenomena, and the evolutionary game theory. Meanwhile, we also observe that the introduction of Moran rule, which needs to consider all neighbor＇s information, can markedly promote the aggregate cooperation level, that is, randomly selecting the neighbor proportional to its payoff to imitate will facilitate the cooperation among agents. Current results will contribute to further understand the cooperation dynamics and evolutionary behaviors within many biological, economic and social systems.

Light field imaging for computer vision:a survey

Light field(LF)imaging has attracted attention because of its ability to solve computer vision problems.In this paper we briefly review the research progress in computer vision in recent years.For most factors that affect computer vision development,the richness and accuracy of visual information acquisition are decisive.LF imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays,acquiring complete three-dimensional(3D)scene information.LF imaging technology improves the accuracy of depth estimation,image segmentation,blending,fusion,and 3D reconstruction.LF has also been innovatively applied to iris and face recognition,identification of materials and fake pedestrians,acquisition of epipolar plane images,shape recovery,and LF microscopy.Here,we further summarize the existing problems and the development trends of LF imaging in computer vision,including the establishment and evaluation of the LF dataset,applications under high dynamic range(HDR)conditions,LF image enhancement,virtual reality,3D display,and 3D movies,military optical camouflage technology,image recognition at micro-scale,image processing method based on HDR,and the optimal relationship between spatial resolution and four-dimensional(4D)LF information acquisition.LF imaging has achieved great success in various studies.Over the past 25 years,more than 180 publications have reported the capability of LF imaging in solving computer vision problems.We summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

First-principles calculations of stability of graphene-like BC_(3) monolayer and its high-performance potassium storage

With increasing demand for renewable energy,graphene-like BC_(3) monolayer as high performance electrode materials for lithium and sodium batteries are drawing more attention recently.However,its structural stability,potassium storage properties and strain effect on adsorption properties of alkali metal ions have not been reported yet.In this work,phonon spectra,AIMD simulations and elastic constants of graphene-like BC_(3) monolayer are investigated.Our results show that graphene-like BC_(3) monolayer possesses excellent structural stability and the maximum theoretical potassium storage capacity can reach up to 1653 mAh/g with the corresponding open circuit voltages 0.66 V.Due to potassium atom can be effectively adsorbed at the most energetically favorable h-CC site with obvious charge transfer,making adsorbed graphene-like BC_(3) monolayer change from semiconductor to metal which is really good for electrode utilization.Moreover,the migrations potassium atom on the graphene-like BC_(3) monolayer is rather fast with the diffusion barriers as low as 0.12 eV,comparing lithium atom with a relatively large diffusion barrier of 0.46 eV.Additionally,the tensile strains applied on the graphene-like BC3 monolayer have marginal effect on the adsorption and diffusion performances of lithium,sodium and potassium atoms.

Ultra-large mode area multi-core orbital angular momentum transmission fiber designed by neural network and optimization algorithms

A large mode area multi-core orbital angular momentum(OAM)transmission fiber is designed and optimized by neural network and optimization algorithms.The neural network model has been established first to predict the optical properties of multi-core OAM transmission fibers with high accuracy and speed,including mode area,nonlinear coefficient,purity,dispersion,and effective index difference.Then the trained neural network model is combined with different particle swarm optimization(PSO)algorithms for automatic iterative optimization of multi-core structures respectively.Due to the structural advantages of multi-core fiber and the automatic optimization process,we designed a number of multi-core structures with high OAM mode purity(>95%)and ultra-large mode area(>3000µm^(2)),which is larger by more than an order of magnitude compared to the conventional ring-core OAM transmission fibers.

Unsupervised image-to-image translation by semantics consistency and self-attention

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on generative adversarial nets(GANs). We use autoencoder structure to extract image features in the generator and add semantic consistency loss on extracted features to maintain the semantic consistency of the generated image. Self-attention mechanism at the end of generator is used to obtain long-distance dependency in image. At the same time, as expanding the convolution receptive field, the quality of the generated image is enhanced. Quantitative experiment shows that our method significantly outperforms previous works. Especially on images with obvious foreground, our model shows an impressive improvement.

A novel denoising method for infrared image based on bilateral filtering and non-local means

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.

Effect of Growing Size of Interaction Neighbors on the Evolution of Cooperation in Spatial Snowdrift Game

In this paper,we study the influence of the size of interaction neighbors(k) on the evolution of cooperation in the spatial snowdrift game.At first,we consider the effects of noise K and cost-to-benefit ratio r,the simulation results indicate that the evolution of cooperation depends on the combined action of noise and cost-to-benefit ratio.For a lower r,the cooperators are multitudinous and the cooperation frequency ultimately increases to 1 as the increase of noise.However,for a higher r,the defectors account for the majority of the game and dominate the game if the noise is large enough.Then we mainly investigate how k influences the evolution of cooperation by varying the noise in detail.We find that the frequency of cooperators is closely related to the size of neighborhood and cost-to-benefit ratio r.In the case of lower r,the augmentation of k plays no positive role in promoting the cooperation as compared with that of k = 4,while for higher r the cooperation is improved for a growing size of neighborhood.At last,based on the above discussions,we explore the cluster-forming mechanism among the cooperators.The current results are beneficial to further understand the evolution of cooperation in many natural,social and biological systems.

Performance evaluation and segmentation for synthetic aperture radar image using mixture multiscale autoregressive model and bootstrap technique

An unsupervised segmentation and its performance evaluation technique are proposed for synthetic aperture radar （SAR） image based on the mixture multiscale autoregressive （MMAR） model and the bootstrap method. The segmentation-evaluation techniques consist of detecting the number of image regains, esti- mating MMAR parameters by using bootstrap stochastic annealing expectation-maximization （BSAEM） algorithm, and classifying pixels into region by using Bayesian classifier. Experimental results demonstrate that the evaluation operation is robust, and the proposed segmentation method is superior to the tradi- tional single resolution techniques, and considerably reduces the computing time over the EM algorithm.

Exploring the usefulness of light field super-resolution for object detection

In order to solve the impact of image degradation on object detection, an object detection method based on light field super-resolution(LFSR) is proposed. This method takes LFSR as an image enhancement step to provide high-quality images for object detection without using expensive imaging equipment. To evaluate this method, three types of objects: person, bicycle, and car, are chosen and the results are compared from 5 parts: detected object quantity, mean confidence score, detection results in different scenes, error detection, and detection results from different images sizes and detection speed. Experimental results based on the common object in context(COCO) dataset show that the method incorporated LFSR improves performance of object detection models.

Solid-state green-light-emitting upconversion coherent random laser in macroporous LiNbO_3:Er

A solid-state green-light-emitting upconversion coherent random laser was realized by pumping macroporous erbium-doped lithium niobate with a 980 nm laser. The lasing threshold was determined to be about 40 k W∕cm~2.Above the threshold, the emission intensity increased sharply with the increasing pump intensity. Moreover, a narrow multi-peaks structure was observed in the green-light-emission band, and the positions of lasing lines were various at different angles. The results were the direct evidences of coherent random lasing emission from macroporous erbium-doped lithium niobate. These phenomena were attributed to the coexistence of upconversion emission and a multiple scattering feedback mechanism.

Adversarial image detection based on the maximum channel of saliency maps

Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.

An on-line fiber cutting-welding method for the fabrication of Fabry-Perot micro-cavity

A Fabry-Perot micro-cavity is fabricated by on-line fiber cutting-welding method.The asymmetrical fiber Fabry-Perot micro-cavity is designed and produced by cutting a standard single-mode fiber and welding the fiber end with the core-offset structure.The length of the Fabry-Perot micro-cavity could be controlled within a certain range of accuracy based on the on-line fiber cutting-welding method.According to this method,a micro-machined Fabry-Perot micro-cavity with a length of about 147μm is achieved and its spectral characteristic is also investigated in our experiment.This proposed method is suitable to produce a micro-fiber-optic structure with improved and controlled precision,which is attractive for the fiber processing field.Moreover,the fabricated Fabry-Perot micro-cavity also has potential application in the microfluidic system and biochemical detection area.

Data Secure Transmission Model Based on Compressed Sensing and Digital Watermarking Technology

Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly, for node coding end, the digital watermarking technology is used to embed secret information in the conventional data carrier. Secondly, these data are reused to build the target transfer data by the CS algorithm which are called observed signals. Thirdly, these signals are transmitted to the base station through the wireless channel. After obtaining these observed signals, the decoder reconstructs the data carrier containing privacy information. Finally, the privacy information is obtained by digital watermark extraction algorithm to achieve the secret transmission of signals. By adopting the watermarking and compression sensing to hide secret information in the end of node code, the algorithm complexity and energy consumption are reduced. Meanwhile, the security of secret information is increased.The simulation results show that the method is able to accurately reconstruct the original signal and the energy consumption of the sensor node is also reduced significantly in consideration of the packet loss.

Multi-block SSD based on small object detection for UAV railway scene surveillance

A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance.To address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is designed.First,the original input images are segmented into several overlapped patches.Second,each patch is separately fed into an SSD to detect the objects.Third,the patches are merged together through two stages.In the first stage,the truncated object of the sub-layer detection result is spliced.In the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers.The boxes that are not detected in the main-layer are retained.In addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this issue.The deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case study.Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.