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This article is concerned with the asymptotic theory of estimates of unknown parameters in autoregressive quantile processes. We assume random errors form a strictly stationary ϕ -mixing sequences. In view of the approach of argmins and blocking argument, we prove the parameter estimators satisfy the functional moderate deviation principle (MDP). Further, we give the law of the iterated logarithm under some standard conditions. Based on the contraction principle, the moderate deviation principles of L-estimators on the autoregression quantile (ARQ) and autoregression rank scores (ARRS's) are also discussed. This method can be extended to a fair range of different statistical estimation problems. [ABSTRACT FROM AUTHOR]

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources. [ABSTRACT FROM AUTHOR]

There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudgment of the fault location and type may occur. In order to solve these problems, this paper proposes a bearing fault diagnosis method based on wavelet denoising and machine learning. We use sensors to detect the operating conditions of rolling bearings under different working conditions to obtain datasets of different types of bearing failures. On the basis of using the wavelet denoising algorithm to reduce noise, we comprehensively evaluated five machine learning models, including K-means clustering, decision tree, random forest, and support vector machine to classify bearing faults and compare their results. By designing the fault classification evaluation prediction criteria, the following conclusions are drawn. The model proposed in this paper is significantly better than other traditional diagnostic models for bearing faults. In order to solve the problem of weak signal strength and background noise interference, this paper selects a better noise reduction algorithm under different quantitative evaluation indicators for wavelet denoising, which can better restore the true characteristics of the fault signal. Using unsupervised learning and supervised machine learning classification algorithms, the evaluation indicators before and after denoising are compared to make the classification results more accurate and reliable. This article will help researchers to intelligently diagnose the faults of rolling bearing equipment in rotating machinery. [ABSTRACT FROM AUTHOR]

Medical visual question answering (VQA) aims to correctly answer a clinical question related to a given medical image. Nevertheless, owing to the expensive manual annotations of medical data, the lack of labeled data limits the development of medical VQA. In this paper, we propose a simple yet effective data augmentation method, VQAMix, to mitigate the data limitation problem. Specifically, VQAMix generates more labeled training samples by linearly combining a pair of VQA samples, which can be easily embedded into any visual-language model to boost performance. However, mixing two VQA samples would construct new connections between images and questions from different samples, which will cause the answers for those new fabricated image-question pairs to be missing or meaningless. To solve the missing answer problem, we first develop the Learning with Missing Labels (LML) strategy, which roughly excludes the missing answers. To alleviate the meaningless answer issue, we design the Learning with Conditional-mixed Labels (LCL) strategy, which further utilizes language-type prior to forcing the mixed pairs to have reasonable answers that belong to the same category. Experimental results on the VQA-RAD and PathVQA benchmarks show that our proposed method significantly improves the performance of the baseline by about 7% and 5% on the averaging result of two backbones, respectively. More importantly, VQAMix could improve confidence calibration and model interpretability, which is significant for medical VQA models in practical applications. All code and models are available at https://github.com/haifangong/VQAMix. [ABSTRACT FROM AUTHOR]

Differing from the common linear matrix equation, the future different-level linear matrix system is considered, which is much more interesting and challenging. Because of its complicated structure and future-computation characteristic, traditional methods for static and same-level systems may not be effective on this occasion. For solving this difficult future different-level linear matrix system, the continuous different-level linear matrix system is first considered. On the basis of the zeroing neural network (ZNN), the physical mathematical equivalency is thus proposed, which is called ZNN equivalency (ZE), and it is compared with the traditional concept of mathematical equivalence. Then, on the basis of ZE, the continuous-time synthesis (CTS) model is further developed. To satisfy the future-computation requirement of the future different-level linear matrix system, the 7-instant discrete-time synthesis (DTS) model is further attained by utilizing the high-precision 7-instant Zhang et al. discretization (ZeaD) formula. For a comparison, three different DTS models using three conventional ZeaD formulas are also presented. Meanwhile, the efficacy of the 7-instant DTS model is testified by the theoretical analyses. Finally, experimental results verify the brilliant performance of the 7-instant DTS model in solving the future different-level linear matrix system. [ABSTRACT FROM AUTHOR]

This paper exploits the random assignment rule embedded in Chinese education system to investigate the external effect of better-educated parents in a class on students' academic outcomes. Using a representative sample of Chinese middle school students, our estimation results show that a higher share of classmates with college-educated parents helps improve test scores of the students in the class. We further show that such external effect may arise from positive interactions between parents. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.techfore.2004.08.004 Byline: Mingzhi Mao, E.C. Chirwa Keywords: Motor vehicle; Fatality; Risk forecasting; Grey model; Crashworthiness; Accident Abstract: A mathematical model known as grey model GM(1, 1) has been herewith employed successfully in the estimation of vehicle fatality risk. Its application to the UK and US data sets yields exact predictions that are of high repeatability with characteristics depicting high reliability and efficiency. Another advantage is that these results are obtained without using any assumptions. In the analysis, the model normally deals directly with original data and searches the intrinsic regularity of data. The forecasting accuracy is related to the sample number n in grey model, and the accuracies for smoothed fatality risk predictions are higher than those for short-term predictions. The total time-dependent trends for these cases show very small variability between the real and prediction values, hence showing the theory to be working. For rollover cases, US data sets show that the risk of occupants to experience fatal injuries is decreasing and the fluctuating amplitude is declining. The theory predicted this trend and showed the time-dependent curves for data set and prediction to be in-phase. Another interesting observation that has come from the study is that UK and US experience similar phases in the time-dependent trend of motor vehicle fatality risk despite the different perception of safety between the USA and UK, especially when it comes to occupant usages of restraint systems. Author Affiliation: Bolton Automotive and Aerospace Research Group, Department of Engineering & Safety, Bolton Institute, Bolton BL3 5AB, UK Article History: Received 15 June 2004; Revised 13 August 2004; Accepted 14 August 2004

Different-level dynamic linear system (DLDLS) is an interesting and challenging topic due to its complicated structure and time-variant characteristic. To solve this difficult problem, the equivalency of solutions at different levels is analyzed and obtained via zeroing neural network (ZNN) method. Based on the equivalency, a continuous ZNN model is proposed to solve the continuous DLDLS. For easier hardware realization, a new Zhang et al. discretization formula with high precision is proposed for the continuous ZNN model discretization, and the corresponding new discrete ZNN (NDZNN) model is proposed to solve discrete DLDLS. Note that the NDZNN model satisfies the requirement of real-time computation because it has the online ability to predict the solution for the future instant. Furthermore, the problems of robot manipulator control with additional restrictions (e.g., joint damage) are formulated as specific discrete DLDLS, and the proposed NDZNN model is employed to solve such problems. Simulation results substantiate the effectiveness of NDZNN model. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the asymptotic properties of quantile regression processes. In view of the exponential tightness and convexity argument, we prove the quantile regression estimators satisfy the functional moderate deviation principle. This method can be extended to a fair range of different statistical estimation problems such as quantile regression estimators with bridge penalized functions. [ABSTRACT FROM AUTHOR]

Improvement of the real-time performance of tracking control is increasingly desirable. It is a routine for most conventional algorithms that the control input at current time instant is to track the current desired output. However, lagging errors resulting from computational time and the fluctuation of the desired output exist for the tracking control. Different from conventional algorithms, a look-ahead scheme of zeroing dynamics (ZD) is established in this paper to achieve the real-time tracking control of both serial and parallel manipulators. With the exploitation of data at current time and that in history, the control inputs generated by the proposed ZD algorithms never lead to lagging errors with the source from the inevitable computational time. To tackle prediction errors for ZD algorithms, a new high-precision discretization formula, as an essential part of ZD algorithms, is presented to confine the prediction error in an ignorable range in comparison with lagging errors. [ABSTRACT FROM AUTHOR]

This paper considers the output tracking control of general-form single-input single-output (SISO) nonlinear system, which may encounter the problem of division by zero (DBZ). First, via the Zhang dynamics (ZD) method, a ZD controller is proposed. Then, based on the ZD controller with the aid of gradient dynamics (GD) method, a Zhang-gradient (ZG) controller is proposed. For comparison, the conventional input-output linearization (IOL) controller is presented. The ZD, ZG and IOL controllers are compared in different relative-degree cases ( i.e., the standard relative-degree case, the loose relative-degree case and the DBZ relative-degree case). Note that the ZG controller is valid in three relative-degree cases, while the ZD and IOL controllers are valid only in the standard relative-degree case and the loose relative-degree case. In addition, performances of ZD and ZG controllers are guaranteed via theoretical analyses and computer simulations for the output tracking of general-form nonlinear system with the DBZ problem conquered. [ABSTRACT FROM AUTHOR]

Inevitable noises and limited computational time are major issues for time-variant matrix inversion in practice. When designing a time-variant matrix inversion algorithm, it is highly demanded to suppress noises without violating the performance of real-time computation. However, most existing algorithms only consider a nominal system in the absence of noises, and may suffer from a great computational error when noises are taken into account. Some other algorithms assume that denoising has been conducted before computation, which may consume extra time and may not be suitable in practice. By considering the above situation, in this paper, an enhanced discrete-time Zhang neural network (EDTZNN) model is proposed, analyzed and investigated for time-variant matrix inversion. For comparison, an original discrete-time Zhang neural network (ODTZNN) model is presented. Note that the EDTZNN model is superior to ODTZNN model in suppressing various kinds of bias noises. Moreover, theoretical analyses show the convergence of the proposed EDTZNN model in the presence of various kinds of bias noises. In addition, numerical experiments including an application to robot motion planning are provided to substantiate the efficacy and superiority of the proposed EDTZNN model for time-variant matrix inversion. [ABSTRACT FROM AUTHOR]

This study investigates the tracking‐control problems of the Lorenz, Chen and Lu chaotic systems. Note that the input–output linearisation method cannot solve these tracking‐control problems because of the existence of singularities, at which such chaotic systems fail to have a well‐defined relative degree. By combining Zhang dynamics and gradient dynamics, an effective controller‐design method, termed Zhang‐gradient (ZG) method, is proposed for tracking control of the three chaotic systems. This ZG method, with singularities conquered, is capable of solving the tracking‐control problems of the chaotic systems. Both theoretical analyses and simulative verifications substantiate that the tracking controllers based on the ZG method can achieve satisfactory tracking accuracy and successfully conquer singularities encountered during the tracking‐control process. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the asymptotic behaviors on the minimizers of convex processes. In view of the convexity argument, it is proved that the minimizers of convex processes with parameterized objective functions satisfy the functional moderate deviation principle. As some applications, the estimators in two basic models (threshold regression models and stochastic dynamical systems) are studied. In particular, the exponential convergence principles on the estimators converging to true parameters are proved. [ABSTRACT FROM AUTHOR]

Optical access networks, metro networks and mobile data networks are facing rapidly evolving demands, not only is it essential to satisfy the unyielding need for increased user bandwidths, but future networks must also support the growing wide variation in traffic dynamics and characteristics, due to various emerging technologies, such as cloud-based services, the Internet-of-Things (IoT) and 5G mobile systems, and due to growing trends, such as the proliferation of mobile devices and the rapidly increasing popularity of video-on-demand services. To be cost-effective and commercially sustainable, future optical networks must offer features, such as, dynamic reconfigurability, highly efficient use of network resources, elastic bandwidth provisioning with fine granularity, network sliceabilty and software defined networking (SDN). To meet these requirements Cloud Access Networks (CANs) are proposed which require a number of flexible, adaptive and reconfigurable networking elements. By exploiting digital signal processing (DSP) we have proposed a digital orthogonal filter-based multiplexing technique to implement CANs with multiplexed, independent optical channels at the wavelength, sub-wavelength, and orthogonal sub-band levels. This paper reviews the overall CAN concept, the operating principles of the various CAN network elements and presents an overview of the research work we have undertaken in order to validate the feasibility of the proposed technologies which includes real-time DSP-based demonstrations. [ABSTRACT FROM AUTHOR]

Abstract Discrete time-varying linear system (LS) is a fundamental topic in science and engineering. However, conventional methods essentially designed for time-invariant LS generally assume that LS is time-invariant during a small time interval (i.e., sampling gap) for solving time-varying LS. This assumption quite limits their precision because of the existing of lagging errors. Discarding this assumption, Zhang neural dynamics (ZND) method improves the precision for LS solving, which is a great alternative for the solving of discrete time-varying problems. Note that precision solutions to discrete time-varying problems depend on discretization formulas. In this paper, we propose a new ZND model to solve the discrete time-varying LS. The discrete time-varying division is a special case of discrete time-varying LS with the solution being a scalar while it is usually studied alone. Considering the above inner connection, we further propose a special model for solving the discrete time-varying division. Moreover, as an application of discrete time-varying LS, the discrete time-varying quadratic programming (QP) subject to LS is also studied. The convergence and precision of proposed models are guaranteed by theoretical analyses and substantiated by numerous numerical experiments. [ABSTRACT FROM AUTHOR]

Abstract China's fallow compensation policy is known to be relatively simple and unsophisticated, and in much need of improvement. In seeking such improvement, this paper designed some alternative compensation schemes that include recultivation insurance, priority right of participation in the fallowing work or free agricultural inputs, but lower income subsidy, and reported on a choice experiment of alternatives with 421 peasant-farming households in Chaling County, Hunan Province – a fallow area of heavy-metal-contaminated farmland. A mixed logit model was used to compare the choices made between pilot and non-pilot villages, and the influencing factors involved. The results showed that most farmers preferred the alternative compensation schemes to the current policy, and income subsidy was not the only factor affecting their choices, which indicated recultivation insurance and a longer period of free agricultural inputs were also important. However, the preferences differed between pilot and non-pilot village farmers, in which the pilot village farmers were more willing to accept less income subsidy in favor of increased recultivation insurance and an extended period of free agricultural inputs. By contrast, non-pilot village farmers preferred to adopt an alternative compensation scheme with a priority right of participation in the fallowing work and high level of income subsidy. The possible reason for the differences was that the publicity and training activities provided by the government had changed the preferences of pilot village farmers. Generally, the effects of various forms of economic compensation and single cash payment were different about fallow compensation policy, and the preferences of farmers from different backgrounds might also vary from person to person. Different regions needed to design the fallow compensation policy according to practical problems in the use of farmland and farmers' preferences. Highlights • Choice experiment is used to compare the preference between pilot and non-pilot village farmers. • The effects of various forms of economic compensation and single cash payment are different. • The preferences of farmers from different backgrounds may be different. • Extra attention should be given to works such as fallow publicity and training. [ABSTRACT FROM AUTHOR]

Being two famous neural networks, the error back-propagation (BP) algorithm based neural networks (i.e., BP-type neural networks, BPNNs) and Hopfield-type neural networks (HNNs) have been proposed, developed, and investigated extensively for scientific research and engineering applications. They are different from each other in a great deal, in terms of network architecture, physical meaning and training pattern. In this paper of literature-review type, we present in a relatively complete and creative manner the common natures of learning between BP-type and Hopfield-type neural networks for solving various (mathematical) problems. Specifically, comparing the BPNN with the HNN for the same problem-solving task, e.g., matrix inversion as well as function approximation, we show that the BPNN weight-updating formula and the HNN state-transition equation turn out to be essentially the same. Such interesting phenomena promise that, given a neural-network model for a specific problem solving, its potential dual neural-network model can thus be developed. [ABSTRACT FROM AUTHOR]

Due to the advancement of deep learning, the performance of salient object detection (SOD) has been significantly improved. However, deep learning-based techniques require a sizable amount of pixel-wise annotations. To relieve the burden of data annotation, a variety of deep weakly-supervised and unsupervised SOD methods have been proposed, yet the performance gap between them and fully supervised methods remains significant. In this paper, we propose a novel, cost-efficient salient object detection framework, which can adapt models from synthetic data to real-world data with the help of a limited number of actively selected annotations. Specifically, we first construct a synthetic SOD dataset by copying and pasting foreground objects into pure background images. With the masks of foreground objects taken as the ground-truth saliency maps, this dataset can be used for training the SOD model initially. However, due to the large domain gap between synthetic images and real-world images, the performance of the initially trained model on the real-world images is deficient. To transfer the model from the synthetic dataset to the real-world datasets, we further design an uncertainty-aware active domain adaptive algorithm to generate labels for the real-world target images. The prediction variances against data augmentations are utilized to calculate the superpixel-level uncertainty values. For those superpixels with relatively low uncertainty, we directly generate pseudo labels according to the network predictions. Meanwhile, we select a few superpixels with high uncertainty scores and assign labels to them manually. This labeling strategy is capable of generating high-quality labels without incurring too much annotation cost. Experimental results on six benchmark SOD datasets demonstrate that our method outperforms the existing state-of-the-art weakly-supervised and unsupervised SOD methods and is even comparable to the fully supervised ones. Code will be released at: https://github.com/czh-3/UADA.