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Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task.

Highlights • Development of a biological neural network model that allows long term simulation of brain activity. • Optimization of the model using multiple techniques that led to a speed-up of X200. • Presentation of alternative simulation frameworks for long term simulations., Simulations of neural networks can be used to study the direct effect of internal or external changes on brain dynamics. However, some changes are not immediate but occur on the timescale of weeks, months, or years. Examples include effects of strokes, surgical tissue removal, or traumatic brain injury but also gradual changes during brain development. Simulating network activity over a long time, even for a small number of nodes, is a computational challenge. Here, we model a coupled network of human brain regions with a modified Wilson-Cowan model representing dynamics for each region and with synaptic plasticity adjusting connection weights within and between regions. Using strategies ranging from different models for plasticity, vectorization and a different differential equation solver setup, we achieved one second runtime for one second biological time.

The chemi-thermo-mechanical pulping (CTMP) is an important process that produces fibers for paper-making, whose output fiber length distribution (FLD) directly determines the energy consumption and product quality of the subsequent papermaking production. Therefore, study on modeling and control of the output FLD is essential for improving pulp quality and saving energy in refining process. However, the output FLD of refining process has non-Gaussian stochastic distribution property, making it difficult to use conventional methods to establish the output FLD model effectively. Under the framework of stochastic distribution control theory, this paper presents a novel modeling approach for output probability density function (PDF) of fiber length in CTMP by combining with the improved wavelet neural network (WNN). In this context, the square root B-spline approximation principle is firstly adopted to extract the B-spline weights of fiber length PDF shape as the target outputs for the WNN based B-spline weights model. Secondly, a novel two-stage hybrid learning algorithm is proposed to establish the parameters system for the WNN based weighs model by defining a multi-objective evaluation index for modeling accuracy in advance. This learning algorithm integrates multi-objective NSGA II algorithm in the first stage for better initial solutions at a global scope, and gradient descent method is employed in the second stage for accurate solutions of WNN model parameters inside a local range. As a result, the final output PDF of fiber length is reconstructed by the estimated B-spline weights using the B-spline approximation principle again. Experiments using actual industrial data have demonstrated the superiority and practicability of the proposed method.

The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectation. A distributional RL algorithm may be characterised by two main components, namely the representation of the distribution together with its parameterisation and the probability metric defining the loss. The present research work considers the unconstrained monotonic neural network (UMNN) architecture, a universal approximator of continuous monotonic functions which is particularly well suited for modelling different representations of a distribution. This property enables the efficient decoupling of the effect of the function approximator class from that of the probability metric. The research paper firstly introduces a methodology for learning different representations of the random return distribution (PDF, CDF and QF). Secondly, a novel distributional RL algorithm named unconstrained monotonic deep Q-network (UMDQN) is presented. To the authors' knowledge, it is the first distributional RL method supporting the learning of three, valid and continuous representations of the random return distribution. Lastly, in light of this new algorithm, an empirical comparison is performed between three probability quasi-metrics, namely the Kullback-Leibler divergence, Cramer distance, and Wasserstein distance. The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance., Comment: Research paper accepted for publication in the peer-reviewed Neurocomputing journal edited by Elsevier

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning.

Real-time bidding (RTB) has become a critical way of online advertising. In RTB, an advertiser can participate in bidding ad impressions to display its advertisements. The advertiser determines every impression's bidding price according to its bidding strategy. Therefore, a good bidding strategy can help advertisers improve cost efficiency. This paper focuses on optimizing a single advertiser's bidding strategy using reinforcement learning (RL) in RTB. Unfortunately, it is challenging to optimize the bidding strategy through RL at the granularity of impression due to the highly dynamic nature of the RTB environment. In this paper, we first utilize a widely accepted linear bidding function to compute every impression's base price and optimize it by a mutable adjustment factor derived from the RTB auction environment, to avoid optimizing every impression's bidding price directly. Specifically, we use the maximum entropy RL algorithm (Soft Actor-Critic) to optimize the adjustment factor generation policy at the impression-grained level. Finally, the empirical study on a public dataset demonstrates that the proposed bidding strategy has superior performance compared with the baselines.

Computational Ethology studies focused on human beings is usually referred as Human Activity Recognition (HAR). Specifically, this paper belongs to a line of work on the identification of broad cognitive activities that users carry out with computers. The keystone of this kind of systems is the noninvasive detection of the subject's gaze fixations in selected display areas. Noninvasiveness is ensured by using the conventional laptop cameras without additional illumination or tracking devices. The gaze ethograms, composed as sequences of gaze fixations, are the basis to identify the user activities. To determine the gaze fixation display areas with the highest accuracy, this paper explores the use of a transfer learning approach applied to several well-known deep learning network (DLN) architectures whose input is the eye area extracted from the face image,and output is the identification of the gaze fixation area in the computer screen. Two different datasets are created and used in the validation experiments. We report encouraging results that may allow the general use of the system. This work has been supported by FEDER funds through MINECO project TIN2017-85827-P. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777720. XinZhe Jin contributed some early computational experiences.

Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations in each layer are computed through propagating and aggregating the neighboring node features with respect to the graph. By stacking to multiple layers, GNNs are able to capture the long-range dependencies among the data on the graph and thus bring performance improvements. To train a GNN with multiple layers effectively, some normalization techniques (e.g., node-wise normalization, batch-wise normalization) are necessary. However, the normalization techniques for GNNs are highly task-relevant and different application tasks prefer to different normalization techniques, which is hard to know in advance. To tackle this deficiency, in this paper, we propose to learn graph normalization by optimizing a weighted combination of normalization techniques at four different levels, including node-wise normalization, adjacency-wise normalization, graph-wise normalization, and batch-wise normalization, in which the adjacency-wise normalization and the graph-wise normalization are newly proposed in this paper to take into account the local structure and the global structure on the graph, respectively. By learning the optimal weights, we are able to automatically select a single best or a best combination of multiple normalizations for a specific task. We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task. Source code is released here https://github.com/cyh1112/GraphNormalization., Comment: 15 pages, 3 figures, 6 tables

Image recognition based on deep learning methods has gained remarkable achievements by feeding with abundant training data. Unfortunately, collecting a tremendous amount of annotated images is time-consuming and expensive, especially in grocery product recognition tasks. It is challenging to recognise grocery products accurately when the deep learning model is trained with insufficient data. This paper proposes multi-angle Generative Adversarial Networks (MAGAN), which can generate realistic training images with different angles for data augmentation. Mutual information is employed in the novel GAN to achieve the learning of angles in an unsupervised manner. This paper aims to create training images containing grocery products from different angles, thus improving grocery product recognition accuracy. We first enlarge the fruit dataset by using MAGAN and the state-of-the-art GAN variants. Then, we compare the top-1 accuracy results from CNN classifiers trained with different data augmentation methods. Finally, our experiments demonstrate that the MAGAN exceeds the existing GANs for grocery product recognition tasks, obtaining a significant increase in the accuracy.

This paper investigates noise/fault tolerant incremental algorithms for the extreme learning machine (ELM) concept. Existing incremental ELM algorithms can be classified into two approaches: non-recomputation and recomputation. This paper first formulates a noise/fault aware objective function for nonlinear regression problems. Instead of developing noise/fault aware algorithms for the two computational approaches in a one-by-one manner, this paper uses two representative incremental algorithms, namely incremental ELM (I-ELM) and error minimized ELM (EM-ELM), to develop two noise/fault aware incremental algorithms. The proposed algorithms are called generalized I-ELM (GI-ELM) and generalized EM-ELM (GEM-ELM). The GI-ELM adds k hidden nodes into the existing network at each incremental step without recomputing the existing weights. To have a fair comparison, we consider a modified version of I-ELM as a comparison algorithm. The simulation demonstrates that the noise/fault tolerance of the proposed GI-ELM is better than that of the modified I-ELM. In the GEM-ELM, k hidden nodes are added into the existing network at each incremental step. Meanwhile, all output weights are recomputed based on a recursive formula. We also consider a modified version of EM-ELM as a comparison algorithm. The simulation demonstrates that the noise/fault tolerance of the proposed GEM-ELM is better than that of the modified EM-ELM. Moreover, we demonstrate that the multiple set concept can further enhance the performance of the two proposed algorithms. Following our research results, one can make some non-noise/fault tolerant incremental algorithms to be noise/fault tolerant.

The process of aligning one image, in the coordinate system of another is called registration. Image registration is vital in the medical domain and is used for diagnosis, therapy planning, and treatment of diseases. Due to its huge applicability and importance, medical image registration has emerged to be a separate domain of research. Beginning from the invasive landmark based registration, innumerable algorithms have been proposed to register two images of the human physiology. However, a breakthrough in the literature occurred when an information theory based measure, mutual information was used to register two images. Since then a large number of new algorithms have developed, which use mutual information for fully automatic registration of medical images. This paper is a survey of these algorithms. Beginning from its development, it discusses about some of the major works done on mutual information based image registration. Some comparative studies with other algorithms have also been discussed. The paper ends with a discussion of the developments in medical image registration algorithms post mutual information, which primarily include deep neural network (DNN) based algorithms.

In this paper, we present an extensive review on deep learning methods for image restoration tasks. Deep learning techniques, led by convolutional neural networks, have received a great deal of attention in almost all areas of image processing, especially in image classification. However, image restoration is a fundamental and challenging topic and plays significant roles in image processing, understanding and representation. It typically addresses image deblurring, denoising, dehazing and super-resolution. There are substantial differences in the approaches and mechanisms in deep learning methods for image restoration. Discriminative learning based methods are able to deal with issues of learning a restoration mapping function effectively, while optimisation models based methods can further enhance the performance with certain learning constraints. In this paper, we offer a comparative study of deep learning techniques in image denoising, deblurring, dehazing, and super-resolution, and summarise the principles involved in these tasks from various supervised deep network architectures, residual or skip connection and receptive field to unsupervised autoencoder mechanisms. Image quality criteria are also reviewed and their roles in image restoration are assessed. Based on our analysis, we further present an efficient network for deblurring and a couple of multi-objective training functions for super-resolution restoration tasks. The proposed methods are compared extensively with the state-of-the-art methods with both quantitative and qualitative analyses. Finally, we point out potential challenges and directions for future research.

This paper proposes a sensor network information fusion based distributed state estimation algorithm for tracking non-cooperative maneuvering target. In order to lower the communication burden and energy consumption, an event-triggered mechanism is introduced. The distributed state estimation algorithm for maneuvering target tracking is designed by two stages, namely, local filtering stage and consensus fusion stage. The algorithm proposed in this paper can be used to obtain the high accurate state estimation of the target by introducing a multiple suboptimal fading factor even when the target makes big maneuvering. Moreover, a contribution factor is designed in the information fusion stage to improve the accuracy of maneuvering target state estimation and reduce the consensus iteration times. Besides, the stochastic boundedness of the event-triggered distributed estimation algorithm is proved by introducing a stochastic process. Finally, Monte Carlo numerical simulation example is designed to illustrate the effectiveness of the algorithm.

The target of this paper is studying the problem of quasi-synchronization of heterogeneous Lur’e networks with uncertain parameters and impulsive effect. Considering the heterogeneity of Lur’e networks, quasi-synchronization instead of synchronization is discussed. Noting that the uncertain factors exist extensively in reality, uncertain parameters are introduced to the system in this paper. By using the Schur complement lemma, stability theory and the principle of comparison, the sufficient criteria for the heterogeneous networks to achieve quasi-synchronization are derived, and the upper bound of exponential quasi-synchronization errors is calculated. In addition, two corollaries are presented in consideration of homogeneous systems and weighted undirected graphs. Finally, three numerical simulations are given to illustrate the effectiveness of the control protocol and theoretical results.

Video-based person re-identification plays a critical role in intelligent video surveillance by learning temporal correlations from consecutive video frames. Most existing methods aim to solve the challenging variations of pose, occlusion, backgrounds and so on by using attention mechanism. They almost all draw attention to the occlusion and learn occlusion-invariant video representations by abandoning the occluded area or frames, while the other areas in these frames contain sufficient spatial information and temporal cues. To overcome these drawbacks, this paper proposes a comprehensive attention mechanism covering what, where, and when to pay attention in the discriminative spatial-temporal feature learning, namely What-Where-When Attention Network (W3AN). Concretely, W3AN designs a spatial attention module to focus on pedestrian identity and obvious attributes by the importance estimating layer (What and Where), and a temporal attention module to calculate the frame-level importance (when), which is embedded into a graph attention network to exploit temporal attention features rather than computing weighted average feature for video frames like existing methods. Moreover, the experiments on three widely-recognized datasets demonstrate the effectiveness of our proposed W3AN model and the discussion of major modules elaborates the contributions of this paper.

As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels. This study demonstrates that single-port salient mapping channels can ensure that each input channel signal has a relatively strong convolution kernel mapped to the output channel, and vice versa. The dual-port salient mapping channel is a channel with high feature mapping abilities from both the input and output directions. Additionally, the average mapping ability measure index is defined, which is used to control the kernel pruning process of the single-port salient mapping channel to switch to the kernel pruning process of the dual-port salient mapping channel. The experimental results and analysis show that the method proposed in this paper can be used to effectively prune a heavyweight model and a lightweight model and can obtain a better accuracy under the conditions of higher compression ratio and acceleration ratios. For example, when VGG-16 is pruned on CIFAR-10, the compression ratio and acceleration ration are 91.00 × and 15.24 × , respectively, and the classification accuracy of the model decreased slightly by 0.22%. When ResNet-101 is pruned on ImageNet, the compression ratio and acceleration ratio are 3.90 × and 3.38 × , respectively, and the classification accuracy of the model decreased slightly by 0.48%. The proposed method is significantly better than state-of-the-art methods.

Noise and physical constraints of redundant manipulators are the two major challenges in the repetitive motion planning (RMP) problems. Therefore, this paper proposed a power-exponent-type modified recurrent neural network (PET-MRNN) to simultaneously address both noise and physical constraints. Moreover, PET-MRNN model is activated by a new Sbp-sinh type nonlinear activation function proposed in this paper. The Sbp-sinh type activation function is first applied to such time varying quadratic program (TVQP) solving and possesses excellent convergence performance. Theoretical analysis proves that the PET-MRNN model can completely eliminate noise disturbance through learning and compensation during the convergence process, and then converge the residual error to zero and obtain the theoretical solution. Finally, simulation and experiments further proved the superiority of the PET-MRNN and the Sbp-sinh type activation function.

In this paper, we study a projective multi-synchronization problem for fractional-order complex-valued coupled multi-stable neural networks (FCVCMNNs) with time-delays. Using a complex decomposition approach, FCVCMNNs are divided into their real and imaginary components. Our method uses certain conditions for each subnetwork to achieve the multiple local equilibrium points or stable periodic orbits that are exponentially stable, which, when combined with the Lyapunov functions method, result in FCVCMNNs that are projectively multi-synchronized. The FCVCMNNs, on the other hand, are examined directly through the use of the Lyapunov functional method and linear matrix inequality (LMI). Various new sufficient conditions in the form of complex-valued LMIs are presented for the projective multi-synchronization of the considered FCVCMNNs. As a final step, we provide two numerical simulations to verify the effectiveness of the main results derived in this paper.

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient’s safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints.

Recent works have demonstrated the vulnerability of text classifiers to universal adversarial attacks, which are splicing carefully designed word sequences into the original text. These word sequences are natural, and adversarial examples generated by splicing them with the original text are unnatural. In this paper, we propose a framework for generating natural adversarial examples with an adversarially regularized autoencoder (ARAE) model and an inverter model. The framework maps discrete text into the continuous space, get the conversion of adversarial examples by adding universal adversarial perturbations in the continuous space, then generates natural adversarial examples. In order to achieve universal adversarial attacks, we design a universal adversarial perturbations search (UAPS) algorithm with the gradient of the loss function of the target classifier. Perturbations found by the UAPS algorithm can be directly added to the conversion of the original text in the continuous space. On two textual entailment datasets, we evaluate the fooling rate of generated adversarial examples on two RNN-based architectures and one Transformer-based architecture. The results show that all architectures are vulnerable to the adversarial examples. For example, on the SNLI dataset, the accuracy of the ESIM model for the “entailment” category drops from 88.35% to 2.26%. While achieving a high fooling rate, generated adversarial examples have good performance in naturalness. By further analysis, adversarial examples generated in this paper have transferability in neural networks.

Recently, intelligent cephalometric landmark detection systems that employ deep convolutional neural networks (DCNNs) have been widely developed. In this paper, we concentrate on two challenging difficulties of DCNNs based cephalometric landmark detection. First, previous DCNN methods usually decreased the resolution of high-resolution cephalometric images. Hence, the trained DCNNs missed the detailed local features of original images. Moreover, the detected landmarks’ locations were recovered using a fixed ratio, so location error was increased after recovery. Second, the performance of cephalometric landmark detection is dependent on DCNNs’ translational invariance because the target landmarks’ locations are influenced by any shift of the cephalometric images. However, modern DCNNs have limited translational invariance, and previous DCNN methods did not take translational invariance into consideration. In this paper, we developed the widely used two-stage framework to resolve the above two challenges. In the first stage, we train a global detection network, which receives the whole images as input, to generate candidate landmarks. In the second stage, we split the input image into patches and train a local refine network, which receives image patches as input, to refine the landmarks’ locations. The proposed local refine network can produce accurate landmarks’ locations because it entirely focuses on the detailed local features of high-resolution cephalometric images. The global detection network and local refine network are structured with translational invariance as their primary consideration. The proposed method achieved robust performance on a private dataset and the public Automatic Cephalometric X-ray Landmark Detection Challenge 2015 dataset.

The existing Pavlov associative memory circuit only realizes the simple conditioned reflex process, secondary conditional reflex can make the simple conditioning process more complicated and make the circuit more bionic, but there is a lack of relevant circuit implementation. In this paper, a Pavlov associative memory circuit with secondary conditional reflex is proposed by utilizing the memristor. The proposed circuit can respond to a conditional stimulus after initial learning and have two kinds of forgetting process. Besides, this circuit can indirectly establish conditioned reflexes through conditioned stimuli, instead of directly establishing conditioned reflex with unconditioned stimulus. The realization of secondary conditional reflex is confirmed in the PSPICE simulation results. Meanwhile, an extended classification circuit based on secondary conditioned reflex is proposed. Based on the features of objects as input, the output of the circuit is used to achieve the function of classification. The accuracy of application circuit proposed in this paper can be verified by the simulation results in PSPICE.

Dynamic networks are popularly used to describe networks that change with time. Although there have been a large number of research works on understanding dynamic networks using link prediction, node classification and community detection, there is rare work that is specially designed to address the challenge of big network size of dynamic networks. To this end, we study in this paper an emerging and challenging problem of network coarsening in dynamic networks. Network coarsening refers to a class of network “zoom-out” operations where node pairs and edges are grouped together for efficient analysis on big networks. However, existing network coarsening approaches can only handle static networks where network structure weights have been predefined before the coarsening calculation. Under the observation that big networks are highly dynamic and naturally change over time, we consider in this paper to embed information diffusion data which reflect the dynamics of networks for network coarsening. Specifically, we present a new Semi-NetCoarsen approach that jointly maximizes the likelihood of observing the information diffusion data and minimizes the network regularization with respect to the predefined network structural data. The learning function is convex and we use the accelerated proximal gradient algorithm to obtain the global optimal solution. We conduct experiments on two synthetic and five real-world data sets to validate the performance of the proposed method.

Interval-valued data are needed to manage either the uncertainty related to measurements, or the variability inherent to the description of complex objects representing group of individuals. A number of regression methods suitable to interval variables describing variability of complex objects are already available. However, less attention has been given to methods that, simultaneously, take into account the full interval information and are resistant to interval outlier observations, even with the frequent presence of atypical observations on interval-valued data sets. This paper proposes a new robust linear regression method for interval variables, where the presence of outliers either in the center or in the radius penalize both the center and the radius regression models. Moreover, the interval observations with outliers on both center and radius are more penalized than those observations with outliers only in the center (or in the radius). Besides, this paper provides a suitable iterative algorithm to estimate the parameters of the proposed method. The algorithm estimates the parameters of the center (or of the radius) model taking into account both information of the center and the radius. The convergence and time complexity of the iterative algorithm are also presented. Finally, the performance of the new method is compared with some previous robust regression approaches and evaluated on synthetic and real interval-valued data sets.

Machine reading comprehension (MRC) is a challenging task in natural language processing (NLP), which requires machine to determine the corresponding answer to a given passage and question. Whereas, there always exist unanswerable questions in the real world, which poses a new challenge to MRC tasks. Abundant research work has been carried out on the verification mechanism for the answerability of passage-question pairs. However, these researches only focus on its design and implementation, which has limitations in real-world scenarios. Thus, the method proposed in this paper not only verifies the answerability, but also validates and adjusts the predicted answer to obtain an elaborate answer span. Using powerful pre-trained model as encoder block, this paper explores a more comprehensive verification mechanism. Similar to how humans read passages and give answers, we propose a three-stage mechanism called ”Verification for an Elaborate Span” (V4ES): 1) sketchy reading that the model briefly browses the overall information of the passage and question, and then generates an initial answer; 2) intensive reading that it reads the passage and question again, judges the answerability of the question and gives an answer at this stage; 3) verification that it verifies these two answers produced at the previous two stages, and then gives the final prediction. Moreover, the proposed model is evaluated on two MRC challenge datasets: SQuAD2.0 and CMRC2018, and the experiment results show that our model has achieved great improvement compared with the ALBERT and BERT baselines. In conclusion, our proposed verification mechanism has demonstrated its effectiveness through a series of experiments and analysis.

Object detection and semantic segmentation are two fundamental techniques for Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Motivated by recent studies demonstrating that object detection and semantic segmentation are two highly-correlated tasks, this paper handles the problem of joint object detection and semantic segmentation in traffic scenes. Existing methods perform the joint object detection and semantic segmentation by sharing the same backbone network, but always ignore the interactive connection between the subdividing detection branch and segmentation branch, leading to the insufficient interaction between the two branches. Considering this situation, this paper proposes a joint object detection and semantic segmentation model with the cross-attention and inner-attention mechanisms. The cross-attention mechanism enables to build up the essential interaction between the subdividing detection branch and segmentation branch to fully make use of their correlation. In addition, the inner-attention contributes to strengthening the representations of feature maps in the model. Given an image, an encoder-decoder network is firstly used to extract initial feature maps. Then, the inner-attention mechanism is applied to strengthen the initial feature maps to obtain segmentation feature maps. Subsequently, the cross-attention mechanism utilizes the segmentation feature maps to guide the generation of object detection feature maps. Finally, the semantic segmentation is performed on the segmentation feature maps and object detection is performed on the detection feature maps. In the experiments, two well-known public traffic datasets are used to evaluate our model. Our model achieves the highest performance in comparison with several recently-proposed methods. In addition, some ablation studies are conducted to evaluate the proposed inner-attention and cross-attention mechanisms, and experiment results validate their effectiveness.

Central pattern generators (CPG) are neural circuits that generate robust sequences of coordinated neural activity to control motor rhythms. We have recently revealed the presence of dynamical invariants in the relationships between specific intervals that build the CPG sequence and the instantaneous period of the rhythm in the pyloric CPG of crustacean. In this paper, we analyze the variability of the intervals comprising the rhythm in a model of a mollusk feeding CPG. We measured the cycle-by-cycle time intervals of the CPG rhythm under distinct single neuron stimulation. We characterized interval variability and report dynamical invariants found only for specific time intervals building the sequence of the feeding CPG. Together with our previous findings, the results reported in this paper indicate that dynamical invariants can be a universal feature of any sequence generating circuit.

This paper focuses on two related sub-tasks of aspect-based sentiment analysis, namely aspect-term extraction and aspect sentiment classification. The former aims to extract aspect-terms from given sentences and the latter aims to identify the sentiment polarity expressed on the extracted terms. Considering the practical application, researchers use more joint methods rather than pipeline methods. However, existing joint methods cannot model the interaction between aspect-terms and the sentence they belong to, or consider the relevance among the sentiments of different aspect-terms. In this paper, a novel end-to-end generative model based on encoder-decoder, namely Joint Term-Sentiment Generator (JTSG), is presented to generate all aspect term-polarity pairs. Specifically, a pre-trained model based encoder is used to encode the sentences, and specially, the decoder generates the start and end position to determine an aspect-term, rather than generate aspect-terms themselves. This new generative method contributes to avoid generating incomplete aspect-terms. Experimental results demonstrate that the proposed approach yields competitive performance on three benchmark datasets.

Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F1-score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field.

Brain tumor segmentation using MRI data remains challenging for some reasons. Hence, how to accurately segment the brain tumor is kept as a significant topic in the area of medical image segmentation. To follow the idea of “coarse-to-fine” in practice, this paper proposes an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the braintumor is kept as a significant topic in the area of medical image segmentation. To follow the idea of “coarse-to-fine” in practice, this paper proposes an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the brain tumor segmentation performance by making full use of semantic information in high-level.In the proposed ToStaGAN, the UNET network is adopted as the “coarse” generation network in the first stage, while a U-Shaped contextual autoencoder (ConEnDer), which consists of the proposed fine-grained extraction module (FEM) and dense skip connection, is proposed as the “fine” generation network in the second stage. To be more specific, the FEM is mainly used to effectively extract “fine-grained” features that plays a crucial role in semantic segmentation with only increasing a few parameters. Furthermore, it has the ability to obtain more diverse and abstract features. And by further utilizing the features extracted from the FEM and the coarse prediction map from the first stage, the ConEnDer holds the great potential to generate the optimized segmentation results. We then evaluate the performance of ToStaGAN on the BARATS 2015. Findings from the evaluations suggest that the proposed two-stage generation network achieves a better performance than the one-stage network; thus, also implying the effectiveness of the proposed ConEnder for “fine” segmentation. A comparative summary also shows that the ToStaGAN achieves a better segmentation performance in comparison to other competing approaches.

This paper investigates the distributed output tracking problem via linear sampled-data control for a class of uncertain nonlinear multi-agent systems. Specifically, for the case where the graph topology is directed and the leader is the neighbor of only a small portion of followers, by combining the backstepping method with sampled-data method, a series of linear sampled-data controllers are obtained. Stability analysis is performed by using calculus, comparison principle and so on. The main results of this paper are concluded that all the states of the closed-loop system are globally bounded and the output tracking errors can be tuned arbitrarily small. Finally, a numerical example is given to verify the validity of the conclusion.

Benefiting with the rapid development of communication networks, effective video quality assessment (VQA) models which provide guidance for video transmission and compression technologies are highly demanded. This paper proposes a general-purpose full-reference VQA method combining DenseNet with spatial pyramid pooling and RankNet to not only extract high-level distortion representation and global spatial information of samples but also characterize the temporal correlation among frames. Firstly, the pretrained DenseNet is modified and finetuned to extract high-level features of distorBenefiting with the rapid development of communication networks, effective video quality assessment (VQA) models which provide guidance for video transmission and compression technologies are highly demanded. This paper proposes a general-purpose full-reference VQA method combining DenseNet with spatial pyramid pooling and RankNet to not only extract high-level distortion representation and global spatial information of samples but also characterize the temporal correlation among frames. Firstly, the pretrained DenseNet is modified and finetuned to extract high-level features of distorted videos. Then, spatial pyramid pooling is equipped in the DenseNet module to process flexible inputs with arbitrary spatial resolution. Thus, this kind of input which has the same spatial resolution as the original distorted video is processed by the well-trained DenseNet to generate frame-level quality, which considers the global spatial information of videos directly. Finally, learning to rank is introduced to explore the high-level temporal correlation of distorted videos by taking the RankNet as the temporal pooling function. The experimental results on two public VQA databases show that the proposed algorithm performs consistently with human visual perception.

Quite often a machine learning problem lends itself to be split in several well-defined subproblems, or tasks. The goal of Multi-Task Learning (MTL) is to leverage the joint learning of the problem from two different perspectives: on the one hand, a single, overall model, and on the other hand task-specific models. In this way, the found solution by MTL may be better than those of either the common or the task-specific models. Starting with the work of Evgeniou et al., support vector machines (SVMs) have lent themselves naturally to this approach. This paper proposes a convex formulation of MTL for the L1-, L2- and LS-SVM models that results in dual problems quite similar to the single-task ones, but with multi-task kernels; in turn, this makes possible to train the convex MTL models using standard solvers. As an alternative approach, the direct optimal combination of the already trained common and task-specific models can also be considered. In this paper, a procedure to compute the optimal combining parameter with respect to four different error functions is derived. As shown experimentally, the proposed convex MTL approach performs generally better than the alternative optimal convex combination, and both of them are better than the straight use of either common or task-specific models, With partial support from Spain’s grant TIN2016-76406-P. Work supported also by the UAM–ADIC Chair for Data Science and Machine Learning.

As a special type of neurodynamic methodology dedicated to finding zeros of equations, zeroing neurodynamics has shown a powerful ability in solving challenging online time-varying problems. Multi-linear systems, on the other hand, are a type of tensor equations with a wide range of applications. In this paper, a zeroing neurodynamics approach for solving multi-linear systems with M -tensors is proposed along with three specific accelerated finite-time convergent zeroing neurodynamics models, which breaks the limitations of a recently presented neural network approach termed continuous time neural network. Activation functions available in constructing zeroing neurodynamics models for solving multi-linear systems with M -tensors are also investigated in the rest of the paper. Theoretical analyses prove that the proposed zeroing neurodynamics approach is stable in the sense of Lyapunov stability theory and the proposed models converge to the theoretical solution in finite time. Finally, computer simulations are provided to substantiate the efficacy and superiority of the proposed zeroing neurodynamics models for solving multi-linear systems with M -tensors.

The complex and varied urban road conditions have always been a difficult and a pivotal component in the study of driverless technology especially at intersections. In China, it is necessary for driverless cars to understand the gestures of traffic police. To identify the traffic police gesture at the intersection, the key step is to detect the traffic police at the intersection. At present, the research on traffic police detection is still in its infancy, there exists common problems such as slow detection speed and other real time problems in this method, and there is not a standardized traffic police data set ether. For the real time problems, this paper introduces the attention mechanism, and proposes a new real-time detection method of traffic police based on attention mechanism. The method proposed in this paper has strong robustness and can quickly complete the target detection task. For the data set problem, this paper analyzes and discloses similar data sets published through research. In the meanwhile, this paper collects 24,530 video data on the actual road, and it extracts and saves 12,000 pictures containing traffic police at frame rate of 30FPS as traffic police detection training and validated data set.

In this paper, adaptive neural tracking control problem is considered for non-strict-feedback high-order nonlinear systems with quantized input signal. Compared with the logarithmic quantizer, the quantizer introduced in this paper can avoid chattering problem. The dynamic surface control (DSC) technique is introduced to solve the problem of ‘explosion of complexity’, which is appeared in the classic adaptive backstepping control of high-order nonlinear systems. The structural properties of radial basis function neural networks (RBF NNs) are used to simplify the design difficulty from the functions of whole state variables. According to the classic adaptive backstepping technique and neural network algorithm, an output tracking controller is designed, which can guarantee that all the signals of the closed-loop system are semiglobally uniformly bounded and the output of the system can track the reference signal. Finally, a numerical example is presented to verify the effectiveness of the proposed method.

In this paper, an adaptive neural network (NN) optimized control strategy is presented to improve the inherent tradeoff between ride comfort of passengers and the suspension travel for full vehicle active suspension system with state constraints. To the best of our knowledge, the automotive suspension system control using hydraulic actuators is a highly complex nonlinear control task, involving external disturbances and uncertainties. To address it, this paper develops the virtual and actual optimal controllers based on backstepping technique and the identifier-actor-critic structure. Meanwhile, the Barrier Lyapunov functions are developed to prevent the systems from the state constraints and the systems states are limited in the preselected compact sets. It is particularly worth mentioning that the proposed optimal control strategy ensures that all the closed-loop signals remain bounded, while the power of the control input as well as the amplitude of the vertical displacement has been minimized. The simulation results show how the full-car system can be controlled optimally and satisfactorily, and confirm the superiority of proposed method.

Scientific literature retrieval provides convenience for researchers to find scientific literature related to the query. It is an important part of scientific research to search related papers given a paper title as query. However, for scientific literature retrieval tasks, most of the existing retrieval methods do not consider sentence-level semantic matching so that the retrieval performance is limited. With the success of neural networks, neural information retrieval methods have been widely studied and achieved good retrieval results. In this paper, we propose a semi-supervised semantic-enhanced scientific literature retrieval framework. The framework is composed of two networks: a self-attention convolutional encoder-decoder network and a sentence-level attention scientific literature retrieval network. By joint training of the two networks, the proposed semi-supervised semantic-enhanced scientific literature retrieval model can fully capture the rich semantic information of scientific text data and leverages human labeled scientific text data to improve the discriminativeness of the learned semantic representation. The retrieval results on two scientific literature datasets demonstrate that the proposed method significantly and consistently outperforms the other baseline methods.