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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]

Goal ordering is very important for the incremental planning, and special attention has been paid to determine the ordering constraints between the atomic goals to achieve the orderings for all possible plans. However, not all goal orderings work well on most benchmarks. This paper introduces a new-class goal ordering, called the admissible goal ordering, which is less restrictive and matches with the characteristics of most benchmarks better than the existing goal orderings. Since the proposed ordering relation is hard to decide, an approximate approach for computing the admissible ordering relation that can be determined in polynomial time is presented. Furthermore, an algorithm for extraction of the total-ordered subsets of goals based on the admissible relation is developed. The total-ordered subsets lead to the partition of an original task into smaller subtasks that can be incrementally planned. The experimental results obtained by using almost all propositional STRIPS benchmarks from the international planning competitions show that the proposed goal ordering can significantly improve the planning performance compared with the state-of-the-art satisficing planning system. In comparison with the reasonable ordering and no-goal ordering, the performance improvements in the proposed ordering are substantial and almost dramatic on larger tasks. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the asymptotic behaviours on the lasso-type estimators for diffusion-type processes with a small noise. By constructing the objective function on the estimation, in view of convexity argument, it is proved that the estimator for different values of γ satisfies the iterated logarithm law. The result also presents the exponential convergence principle for the estimator converging to the true value. [ABSTRACT FROM AUTHOR]

• The different-level nonlinear system is first investigated. • The zeroing equivalency between different levels is proposed. • The new algorithm is proposed based on the equivalency and a discretization formula. • The problem is solved in a discrete-time manner with the future-instant solution predicted. • The problem of robotic system control with the end-effector orientation and fault tolerance considered simultaneously is solved. In this paper, we consider a special kind of time-dependent nonlinear system that includes different-level subsystems (i.e., a subsystem with respect to time-dependent solution and a subsystem with respect to its time derivative). To solve this kind of time-dependent nonlinear system, an equivalency between different-level subsystems is proposed and termed as zeroing equivalency based on the zeroing neural network method. This kind of time-dependent nonlinear system can be solved in a discrete-time manner with future-instant solution predicted at current instant based on the zeroing equivalency and a discretization formula. Prediction ability can perfectly meet the requirements of real-time computation, which is crucial in engineering fields. In addition, different-level algorithms are further employed to solve the problem of robotic system control considering additional restrictions. The position tracking control and end-effector orientation control are simultaneously achieved with even fault tolerance by utilizing the proposed algorithms. [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]

Finite difference schemes have been widely studied because of their fundamental role in numerical analysis. However, most finite difference formulas in the literature are not suitable for discrete time-varying problems because of intrinsic limitations and their relatively low precision. In this paper, a high-precision 1-step-ahead finite difference formula is developed. This 5-instant finite difference (5-IFD) formula is used to approximate and discretize first-order derivatives, and it helps us to compute discrete time-varying generalized matrix inverses. Furthermore, as special cases of generalized matrix inverses, time-varying matrix inversion, and scalar reciprocals are generally deemed as independent problems and studied separately, which are solved unitedly in this paper. The precision of the 5-IFD formula and the convergence behavior of the corresponding discrete-time models are derived theoretically and shown in numerical experiments. Conventional useful formulas, such as the Euler forward finite difference (EFFD) formula and the 4-instant finite difference (4-IFD) formula are also used for comparisons and to show the superiority of the 5-IFD formula. [ABSTRACT FROM AUTHOR]

Nonlinear optimization is widely important for science and engineering. Most research in optimization has dealt with static nonlinear optimization while little has been done on time-varying nonlinear optimization problems. These are generally more complicated and demanding. We study time-varying nonlinear optimizations with time-varying linear equality constraints and adapt Z-type neural-dynamics (ZTND) for solving such problems. Using a Lagrange multipliers approach we construct a continuous ZTND model for such time-varying optimizations. A new four-instant finite difference (FIFD) formula is proposed that helps us discretize the continuous ZTND model with high accuracy. We propose the FDZTND-K and FDZTND-U discrete models and compare their quality and the advantage of the FIFD formula with two standard Euler-discretization ZTND models, called EDZTND-K and EDZTND-U that achieve lower accuracy. Theoretical convergence of our continuous and discrete models is proved and our methods are tested in numerical experiments. For a real world, we apply the FDZTND-U model to robot motion planning and show its feasibility in practice. [ABSTRACT FROM AUTHOR]

The synchronization of chaotic systems has been studied recently due to its fundamental role in engineering fields. However, many conventional synchronization controllers are designed without considering the parameter perturbation, model uncertainty or external disturbance and the design processes of most conventional controllers are relatively quite complicated. Motivated by the above considerations and inspired by the zeroing dynamics (ZD) method, a quite simple and effective ZD-achieving controller is designed for the synchronization of chaotic systems with the simultaneous existence of parameter perturbation, model uncertainty and external disturbance. Theoretical analysis and numerical simulations show the strong robustness of the ZD-achieving controller, and show that the precision can be enhanced by adjusting the value of the parameter in the controller. Moreover, a modified ZD-achieving controller is designed to further improve the ZD-achieving controller. Besides, for comparison, the conventional ZD-derived controller and the linear active controller are presented. Finally, both the synchronization of two isomorphic chaos systems and the synchronization of two heteroideous chaos systems are investigated to substantiate the effectiveness and superiority of the ZD-achieving controller. [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]

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]

Abstract: This paper mainly discusses the asymptotic properties of multi-casting autoregressive processes. By using the -dependence of random vectors, we prove that the least squares (LS) estimator of the unknown parameters satisfies the moderate deviation principle. Two examples of regular cases are also presented. [Copyright &y& Elsevier]

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]

We consider the occupation time variance in the asymmetric exclusion process. In the case where the mean m ≠ 0 (m is the mean hopping rate) and ρ = 1/2 (ρ is the filling probability for a state), we find that the variance is bounded above by O(t 3/2 ). [ABSTRACT FROM AUTHOR]

Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [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]

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]

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]

Knowledge Graph (KG) is identified as a major area in artificial intelligence, which is used for many real-world applications. The task of knowledge graph reasoning has been widely used and proven to be effective, which aims to find these reasonable paths for various relations to solve the issue of incompleteness in KGs. However, many previous works on KG reasoning, such as path-based or reinforcement learning-based methods, are too reliant on the pre-training , where the paths from the head entity and the target entity must be given to pre-train the model, which would easily lead the model to overfit on the given paths seen in the pre-training. To address this issue, we propose a novel reasoning model named MemoryPath with a deep reinforcement learning framework, which incorporates Long Short Term Memory (LSTM) and graph attention mechanism to form the memory component. The well-designed memory component can get rid of the pre-training so that the model doesn't depend on the given target entity for training. A tailored mechanism of reinforcement learning is presented in this proposed deep reinforcement framework to optimize the training procedure, where two metrics, Mean Selection Rate (MSR) and Mean Alternative Rate (MAR), are defined to quantitatively measure the complexities of the query relations. Meanwhile, three different training mechanisms, Action Dropout, Reward Shaping and Force Forward, are proposed to optimize the training process of the proposed MemoryPath. The proposed MemoryPath is validated on two datasets from FB15K-237 and NELL-995 on different tasks including fact prediction, link prediction and success rate in finding paths. The experimental results demonstrate that the tailored mechanism of reinforcement learning make the MemoryPath achieves state-of-the-art performance comparing with the other models. Also, the qualitative analysis indicates that the MemoryPath can store the learning process and automatically find the promising paths for a reasoning task during the training, and shows the effectiveness of the memory component. [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]

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]

• A secure halftone image steganographic scheme is investigated. • Vertical swapping is proved to be the optimal pair swapping strategy in halftone images. • A novel distortion measurement for halftone images is proposed. • High statistical security and high embedding capacity with few distortions is achieved. In halftone image data hiding, pixel pairs containing master pixels and slave pixels are common operating units. In most previous researches, master pixels are selected at a set of pseudo-random locations, which degrade the image quality. In this paper, a secure halftone image steganographic scheme based on pair swapping is presented, which aims at minimizing the embedding distortions. Different from most previous researches, there is no master-slave relationship in the proposed scheme and the steganographic performance depends on the selection of pixel pairs instead of slave pixels. Based on a human visual system (HVS) model of halftone images, the superiority of pair swapping is proved and vertical swapping is further demonstrated to be the optimal way to improve visual quality among all pair swapping strategies. Finally, a statistical model is developed to predict the vertical pair pattern by considering its neighboring region, based on which, a distortion measurement is proposed to evaluate the embedding distortions on both vision and statistics. To play the advantage of the distortion measurement, syndrome-trellis code (STC) is employed to minimize the embedding distortions. Experimental results show that the proposed steganographic scheme achieves high statistical security with high embedding capacity without degrading the visual quality. [ABSTRACT FROM AUTHOR]