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Magnetic resonance imaging (MRI), a non-ionizing radiation imaging method, is widely utilized in diagnosing Alzheimer's disease (AD) due to its excellent imaging ability for brain soft tissues and high-resolution slice imaging. However, the enormous size of 3D MRI makes it difficult to process and analyze it. Therefore, a challenge is to accurately mine encephalatrophy patches from 3D MRI and build a patch-based diagnostic model. The existing patch-based methods mainly extract and fuse features of each patch in isolation, ignoring mutual information between patches, which makes the diagnosis performance unsatisfactory. We propose a novel dual-branch AD diagnostic model based on distinguishing atrophic patch localization. (1) We propose a Distinguishing Atrophic Patch Localization (DAPL) algorithm based on Distinguishing Index (DI) and Spatial Contact Ratio (SCR) to extract lesion areas that have significant impacts on diagnosis from 3D MRI. Meanwhile, we proposed a Discontinuity-voxel-based Dynamic Voxel Wrapping algorithm (DV 2 W) to calculate DI for each patch. (2) A dual-branch diagnostic network (DBDN) is constructed to obtain intra-patch and inter-patch features synchronously. The intra-feature extraction branch extracts feature from each patch through a parallel multi-channel network and fuse them. The Inter-feature extraction branch defines the Spatial Context Mixing matrix (SCM) and performs feature extraction on SCM to obtain mutual information. The performance evaluation demonstrates that our DBDN model has adequate diagnostic performance compared to state-of-the-art methods. In addition, the distinguishing pathological locations identified by our DAPL algorithm can effectively guide inexperienced clinical doctors to identify lesion areas and guide doctors in diagnosis quickly. [ABSTRACT FROM AUTHOR]

Multiplexing technology creates several orthogonal data channels and dimensions for high‐density information encoding and is irreplaceable in large‐capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin‐multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self‐developed laser‐induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high‐dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra‐high‐capacity storage and real‐time encryption. [ABSTRACT FROM AUTHOR]

A wide range of practical control systems such as robot manipulators and spacecrafts can be modeled by Euler–Lagrange systems. On one hand, the requirements for system control accuracy, response speed, and robustness are ever increasing. On the other hand, the control performances are inevitably degraded by multiple disturbances. In this paper, a novel finite‐time composite control law is proposed for a class of Euler–Lagrange systems subject to multiple heterogeneous disturbances including a neutrally stable disturbance and a norm‐bounded disturbance. More specifically, a refined sliding mode disturbance observer (RSMDO) is proposed to estimate and reject the neutrally stable disturbance in the feedforward channel, while an adaptive nonsingular backstepping control (ANBC) is designed in the feedback channel to stabilize the system and attenuate the observer error as well as the norm‐bounded disturbance. The proposed RSMDO can not only fully utilize the disturbance information, but also quantify the convergence time and steady‐state accuracy of the estimation error. The finite‐time stability analysis is carried out for the closed‐loop system. Finally, an example of spacecraft attitude control system is given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m‐phase GaTe0.5Se0.5 with wide‐spectral photoresponses (250–1064 nm) and stack it with WSe2 are utilizes to construct a two‐dimensional van der Waals heterojunction (2D‐vdWH), enabling the design of a gate‐tunable wide‐spectral photodetector. By utilizing the multi‐photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof‐of‐concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250–1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non‐tunable photoresponse (71.17%). Artificially designed gate‐tunable wide‐spectral 2D‐vdWHs GaTe0.5Se0.5/WSe2‐based photodetector present a promising pathway for the development of miniaturized and cost‐effective remote sensing classification technology. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic. [ABSTRACT FROM AUTHOR]

In recent years, graph neural networks (GNNs) have developed rapidly. However, GNNs are difficult to deepen because of over-smoothing. This limits their applications. Starting from the relationship between graph sparsification and over-smoothing, for the problems existing in current graph sparsification methods, we propose a novel graph sparsification method SimSparse based on vertex feature similarity and theoretically prove that it can help to relieve the over-smoothing. Furthermore, we also propose its derivatives SimSparse-G (SimSparse with G umbel-Softmax) and SimSparse-GC (SimSparse-G with memory occupancy reduC tion), which can achieve end-to-end learning. Moreover, SimSparse-GC can apply to larger graphs. Based on the three graph sparsification methods, we further propose a general sparse-convolution block SparseConvBlock with a sparsification layer and a graph convolutional layer to construct deep GNNs in which over-smoothing can be better alleviated. Extensive experiments show that the GNNs constructed by SparseConvBlock have better performance when the numbers of the network layers increase (i.e., in deep GNNs). Furthermore, we verify that our graph sparsification methods help to relieve over-smoothing in deep GNNs. [ABSTRACT FROM AUTHOR]

The performance of the gimbal servo system in control moment gyro (CMG), which includes precision, lifespan etc., is one of the crucial factors of spacecraft attitude control. The various practical disturbances, however, will not only deteriorate the velocity‐tracking accuracy but will also cause the abnormal gimbal velocity problem (especially peak phenomenon). To this end, this paper proposes a refined anti‐disturbance control method to deal with velocity output constraints and multiple disturbances. Starting with fully understanding the prior information of multiple disturbances, a refined disturbance observer with a low conservativeness is designed to accurately estimate disturbances. The disturbance‐estimation error is analyzed in detail to ensure convergence to a bounded region. Subsequently, a novel barrier Lyapunov function‐based backstepping controller is proposed that considers the residuals of disturbance estimation to simultaneously achieve multiple disturbances attenuation and compensation, and handle velocity output constraints. Notably, the gimbal's maximum velocity is precisely limited to a pre‐specified low range, which benefits the CMG's lifespan and performance. Finally, both simulation and experimental results show that the proposed method performs better in disturbance estimation, velocity tracking, and robustness. [ABSTRACT FROM AUTHOR]

Graph convolutional networks have developed rapidly these years. Over-smoothing is an important factor that makes it difficult to deepen the networks, affecting the further development of graph convolutional networks. There have been some studies to solve the over-smoothing issue. However, the shapes of the filters in the existing graph convolutional networks are fixed for various data, and the convolution operations in some networks require eigendecomposition. Starting from the relationship between filters and over-smoothing, we propose a novel way to overcome over-smoothing in this paper. In this way, a novel quadratic polynomial filter (QPF) is proposed, and then a quadratic polynomial graph convolutional network (QPGCN) is derived. Without increasing the complexity of the network, QPGCN can adaptively learn the shape of QPF and does not require eigendecomposition, which can better alleviate over-smoothing. The extensive experiments on Cora, Citeseer, Pubmed, DBLP and Ogbn-arxiv datasets show that QPGCN achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

This article studies the leader‐following attitude coordination problem of a group of rigid spacecraft subject to communication constraint, disturbances and uncertain control coefficient matrices. A fully distributed adaptive anti‐disturbance attitude coordinated control scheme with event‐triggering mechanism is developed. First, the event‐triggered adaptive distributed observer is designed for each follower to estimate the leader's information without continuous communication requirement. Based on the estimated information, the adaptive anti‐disturbance attitude tracking controller is designed such that asymptotic coordinated tracking can be achieved under additive disturbances and actuators' partial loss of efficiency. The proposed control scheme ensures that all the closed‐loop signals are bounded and the positive lower bound of inter‐event times exists in each subsystem. Simulation results illustrate the effectiveness and flexibility of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Multiple disturbances are the main constraints that hinder the high-performance velocity tracking of the gimbal servo system in control moment gyro. This article presents a non-cascade structured velocity-tracking controller based on a refined disturbance observer for handling multiple disturbances and improve the tracking performance. Specifically, a disturbance observer is designed to estimate the imbalance disturbance, which can be represented by an exogenous system. The disturbances that cannot be modeled a priori are treated as a lumped term and estimated via extended state observer. By resorting to disturbance estimation, a novel sliding mode controller based on the disturbance estimation values is developed to handle the mismatched problem and chattering issue. Finally, experimental tests are conducted to verify the effectiveness of the proposed anti-disturbance control scheme. [ABSTRACT FROM AUTHOR]

This paper concerns about the safe approaching in rendezvous with spherical envelops of spacecraft. Based on the nonnegative characteristic of positive systems, a novel strategy of controlling the relative distance positive and convergent is proposed to guarantee no‐collision approaching. The dynamics of the relative distance is decoupled from that of the relative angles. A nonlinear disturbance observer is designed, and two anti‐disturbance controllers are constructed to reject the effect of disturbances for the subsystems of relative distance and angles. Under the controllers, the closed‐loop subsystem of relative distance is a positive and stable system, while that of relative angles is a general stable system satisfying the given time‐varying constraints. Particularly, a theorem is proposed to present the conditions of controllers to ensure the system positivity under a class of external inputs without nonnegativity default. Finally, simulation results are presented to illustrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

Introduction: The Amplatzer and Watchman left atrial appendage closure (LAAC) devices are the two most frequently used devices for LAAC devices worldwide. This meta-analysis aimed to compare the safety and efficacy of the two devices. Methods: We searched the PubMed, EMBASE, and the Cochrane Library for studies up to February 6, 2022 that compared the safety and efficacy of the Amplatzer and Watchman devices. Results: Fifteen studies including 2,150 patients in randomized controlled trials and 2,526 patients in observational studies were included in the meta-analysis. Amplatzer device was associated with higher rates of major procedure-related complications (odds ratio [OR]: 1.99, 95% confidence interval [CI]: 1.45–2.74, p < 0.0001) and device embolization (OR: 1.99, 95% CI: 1.09–3.64, p = 0.03). However, Amplatzer device had lower rates of total peridevice leak (PDL) (OR: 0.48, 95% CI: 0.27–0.83, p = 0.009), significant PDL (OR: 0.27, 95% CI: 0.12–0.57, p = 0.0007) and device-related thrombus (DRT) (OR: 0.67, 95% CI: 0.48–0.95, p = 0.02). No statistical differences were observed between the two devices in other safety and efficacy endpoints, such as pericardial effusion, cardiac tamponade, air embolism, vascular complications, ischemic stroke/transient ischemic attack (TIA), hemorrhagic stroke, all-cause death, cardiovascular death, and bleeding. Conclusions: Amplatzer LAAC device was associated with higher rates of major procedure-related complications, especially in device embolization. Watchman LAAC device was associated with higher rates of PDL and DRT. There were no significant differences between two devices in ischemic stroke/TIA, hemorrhagic stroke, all-cause death, cardiovascular death, and bleeding. [ABSTRACT FROM AUTHOR]

This paper investigates the velocity-tracking problem of the gimbal system in a single gimbal control moment gyro (SGCMG) suffering from high-frequency disturbance. In order to accurately and quickly reject the high-frequency disturbance resulted from nonuniform mass distribution and manufacturing imperfection, a finite-time harmonic disturbance observer (FTHDO) is constructed according to the available information about the high-frequency disturbance. Then, a composite controller is developed to achieve simultaneous disturbance rejection and attenuation. The nominal control performance can be recovered under the high-frequency disturbance. It has been demonstrated that the velocity-tracking error can be stabilized by the proposed composite controller within finite time. Finally, numerical simulation results are presented to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a composite anti-disturbance control of disturbance rejection and attenuation for a class of nonlinear positive systems is proposed for the first time to achieve both positivity and stability. The disturbance observer-based control (DOBC) method is utilized to compensate for the effects of the modeled disturbances, while the $L_{1}$ control strategy is adopted to suppress the effects of the disturbances which are not compensable to satisfy the $L_{1}$ index performance. Besides, two analysis schemes of the closed-loop system are presented. The first scheme is commonly used and is easy for analysis, but it has some conservativeness. The second scheme is proposed novelly with less conservativeness, which releases the positivity constraint of disturbance estimation errors. The corresponding theorems and algorithms are proposed for necessary conditions and solutions. Finally, two examples are presented to illustrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This article is concerned with a quasiperiodic disturbance estimation problem for dynamic control systems without prior knowledge on frequency. As a major challenge of our work, the quasiperiodic disturbance to be treated is always submerged by untargeted waves, leading to complicated coupling between disturbance separation and frequency identification. Existing approaches on quasiperiodic disturbance rejection have circumvented, rather than overcome, this challenge by assuming either a known frequency or a measurable disturbance signal. In this work, an expectation–maximization (EM) framework is proposed where disturbance signal separation and frequency identification are carried out in an iterative manner. In the E-step, the expected log-likelihood function is evaluated via reconstruction of the quasiperiodic signal based on the latest frequency estimate; and in the M-step, the frequency estimate is updated by maximizing the log-likelihood function obtained in the E-step. To facilitate recursive frequency estimation, an online EM algorithm is also developed based on the forward-only smoothing techniques. Furthermore, we show that the proposed method can be easily extended to deal with nonlinear system models and time-varying frequencies. [ABSTRACT FROM AUTHOR]

To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition. [ABSTRACT FROM AUTHOR]

Singularly perturbed systems (SPSs) wildly exist in practical engineering practice; however, the disturbance rejection in SPSs is still a problem. As well-known disturbance rejection methods, composite hierarchical anti-disturbance control (CHADC) can effectively compensate and suppress disturbance, which improves the control performances of complex systems. To enhance the capability of anti-disturbance and control precision, this article proposes a novel composite hierarchical anti-disturbance (CHAD) dynamic surface control (DSC) for the fast subsystem considering highly dynamic disturbance, and CHAD proportion-integration (PI) control for the slow subsystem with slow-varying harmonic disturbance. The disturbances are supposed to be generated by an exogenous system based on partially known information with model uncertainty, and the stabilities of both fast and slow closed-loop subsystems are analyzed. Meanwhile, the compensability of disturbances is discussed. Finally, simulations for an F-8 aircraft are given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This article presents a single‐hidden‐layer feedforward network (SLFN) aided fault‐tolerant control (FTC) scheme for a class of nonlinear systems subject to actuator faults. First, the upper bounds of the norms of the unknown functions, which cover information of actuator faults and amplitude saturation degrees, are deployed. The upper bounds are approximated by SLFNs with adaptive techniques. Subsequently, a sliding mode control approach is presented to allow for prompt corrective reactions, with explicit consideration of multivariable conditions and control input constraints. Finally, simulation results based on a nonlinear Boeing 747 aircraft model are presented to demonstrate the effectiveness of the proposed FTC scheme. [ABSTRACT FROM AUTHOR]

Adversarial attack on neural networks has become an important problem restricting its security applications, and among adversarial attacks oriented towards the sample set, the universal perturbation design causing most sample output errors is critical to the study. This paper takes the neural network for image classification as the research object, summarizes the existing universal perturbation generation algorithm, proposes a universal perturbation generation algorithm combining batch stochastic gradient rise and spherical projection search, achieves loss function reduction through the iterative training of stochastic gradient rise in batch samples, and limits the universal perturbation search to a high-dimensional sphere with radius ε to reduce the search space of universal perturbation. Moreover, the regularized technology is introduced to improve the generation quality of universal perturbations. The experimental results show that compared with the baseline algorithm, the attack success rate increases by more than 10%, the solution efficiency of universal perturbation is improved by one order of magnitude, and the quality controllability of universal perturbation is better. [ABSTRACT FROM AUTHOR]

Neural network technology has achieved good results in many tasks, such as image classification. However, for some input examples of neural networks, after the addition of designed and imperceptible perturbations to the examples, these adversarial examples can change the output results of the original examples. For image classification problems, we derive low-dimensional attack perturbation solutions on multidimensional linear classifiers and extend them to multidimensional nonlinear neural networks. Based on this, a new adversarial example generation algorithm is designed to modify a specified number of pixels. The algorithm adopts a greedy iterative strategy, and gradually iteratively determines the importance and attack range of pixel points. Finally, experiments demonstrate that the algorithm-generated adversarial example is of good quality, and the effects of key parameters in the algorithm are also analyzed. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) is now being widely built all over the world. Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic information for travelers to save their time, but also assists traffic management agencies to manage the traffic system. We find that the existing papers, including neural networks, do not perform well due to the complex association features between the road sections. In addition, the lack of a better evaluation standard for traffic congestion also makes the effect of traffic congestion prediction worse. In this paper, we propose a method to generate traffic congestion index by mining free-stream speed and free-stream flow. Considering the association features of road segments in the road network, we propose a road segment grouping method based on association subgraph to pre-train deep learning model and realize information sharing between road segments. Combining the features of traffic data and CNN model, we propose a traffic congestion prediction model named SG-CNN which training process is optimized by road segment grouping algorithm. The experiments demonstrate that our proposed model has higher accuracy rate than other models. [ABSTRACT FROM AUTHOR]

For a practical engineering system, disturbances usually have a negative impact on system performance. Especially, when the system physical constraints (e.g., actuator amplitude saturation and input channel constraint) are present, the study for antidisturbance controllability (ADC) is of paramount importance. This article presents the concepts of disturbance estimability, disturbance compensability, and an enhanced antidisturbance control (EADC). From the viewpoint of control system design, an example of spacecraft attitude control is given. The control constraints and multiple disturbances are explicitly analyzed for the sake of attitude control system design of flexible spacecraft. Subsequently, an EADC approach with integration of the disturbance observer-based control (DOBC) and active disturbance rejection control (ADRC) is proposed, based on the characteristics of disturbances exposed. Moreover, prescribed performance approach is incorporated into the spacecraft attitude tracking control. In consequence, the presented control can not only achieve superior antidisturbance capability than DOBC or ADRC, but also quantify the control performances. Numerical simulations and hardware-in-the-loop tests exemplify the applicability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Precise attitude control of space manipulators plays an important role in advanced on-orbit assembly tasks. The vibration of flexible appendage and inertial uncertainties encountered in the operating process, however, may cause attitude error or even safety threats to the space manipulator system. In this article, a high-precision attitude control scheme of a space manipulator system is designed via a combination of disturbance observer (DO), prescribed performance-based ${H} _{\infty }$ control, and iterative learning control (ILC) techniques. The proposed control scheme consists of three portions: 1) a DO that estimates the vibration disturbance caused by flexible appendage of base satellite; 2) a robust ${H} _{\infty }$ controller with prescribed performance to attenuate the inertial uncertainties resulting from capture of an unknown object; and 3) an ILC for improving the transient and steady-state process in the presence of a repetitive on-orbit assembly task. This novel control scheme can not only handle the flexible vibration and inertial uncertainty of the space manipulator but also achieve satisfactory tracking performance. Both simulation and experimental results confirm the superiority of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid anti-disturbance composite control scheme is presented to address the problem subject to systems remarkably affected by external disturbances and discrete dynamics of actuators. By explicitly considering the hybrid feature resulting from discrete actuation signals acted on a continuous system, the hybrid disturbance observer is designed to estimate the effect of external disturbance. Meanwhile, H∞ technique is used to mitigate the effect of continuous command executing error caused by discontinuous operation, which cannot be modeled in priori. Within the proposed composite control scheme, the combination of the disturbance estimation information and the H∞ technique enables the entire hybrid system precisely performing. The stability of the whole continuous–discrete control system is analyzed. Finally, based on the Mars lander dynamics, comparative simulation studies illustrate that the developed control scheme can preserve a satisfactory level of performance, even in the presence of external disturbances and actuator quantization errors. [ABSTRACT FROM AUTHOR]

Robust object tracking is still a challenging task in the field of computer vision and has application value in many fields such as automatic driving, human–computer interaction and robot visual navigation. More and more researchers are devoted to researching more accurate object tracking methods. How to better deal with occlusion and deformation has always been the difficult challenges in the object tracking field, and the existing methods cannot solve these problems well. In this regard, we propose a novel, effective and portable module called part-based tracking and assembly (PTA), which is added to the fully convolutional siamese networks to divide the exemplar feature map into several parts. Each part is separately tracked, and then the tracking results of all parts are assembled to obtain the final tracking results. And the experiments on several popular tracking benchmarks show our variant trackers with the PTA module that operate at almost the same tracking speed with the original trackers and achieve superior tracking performance. Moreover, the tracking accuracy is significantly improved on the data with occlusion, deformation and background clutter. Compared with some real-time tracking methods, our variant trackers with the PTA module can achieve the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive neural network (NN) control scheme is proposed for a class of nonlinear systems with unknown control direction. By introducing some differentiable functions and high-order Lyapunov functions, the obstacle caused by unknown control direction in NN control is successfully circumvented and all closed-loop signals are shown to be uniformly bounded up to infinite time. Meanwhile, by introducing an error transformation technique, it is rigorously proved that the argument of the unknown nonlinearities remains within a compact set which can be explicitly calculated a priori, making the NN approximation always valid. Moreover, with the aid of a bound estimation approach, we effectively compress the impact of approximation errors and external disturbances and steer the tracking error into a predefined small residual set. Simulation results illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition. [ABSTRACT FROM AUTHOR]

This paper is devoted to attitude tracking control of fractionated spacecraft with wireless communication. We consider the practical case that the spacecraft suffers from uncertain inertia parameters, external disturbances, and even unknown and time-varying actuator faults. Within the framework of the backstepping method, a novel event-triggered adaptive fault-tolerant control scheme is proposed. In our design, an event-triggering mechanism is introduced to determine the time instants for communication, which successfully avoids continuous communication and Zeno phenomenon. Then, with the aid of a bound estimation approach and a smooth function, the impacts of the actuator faults, as well as the network-induced error, are effectively compensated for. Moreover, by employing the prescribed performance control technique, it is shown that the attitude tracking errors can converge to predefined arbitrarily small residual sets with prescribed convergence rate and maximum overshoot, no matter if there exist unknown actuator faults. Compared with conventional adaptive attitude control schemes, the proposed scheme significantly reduces the communication burden, while providing high reliability and stable, rapid, and accurate response for attitude maneuvers. Simulation results are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this article, a novel spacecraft composite attitude stabilization scheme based on dual disturbances observers (DDOs) and high-precision nonsingular terminal sliding mode control is presented, with explicit consideration of reaction wheel dynamics and multiple disturbances. First, spacecraft attitude coupling dynamics model is constructed covering reaction wheel dynamics and multiple disturbances. These disturbances include matched disturbances caused by motor counter electromotive force and equivalent mismatched disturbance caused by reaction wheel friction and environment disturbances. DDOs are designed to estimate both equivalent matched and mismatched disturbances, respectively. Subsequently, the high-precision control scheme is designed to compensate the estimated disturbances and attenuate the influence of estimated errors via composite nonsingular terminal sliding mode attitude controller. The closed-loop stability and convergence are proved based on the Lyapunov stability theory. Finally, hardware-in-the-loop experiments are conducted to verify the effectiveness of proposed spacecraft attitude stabilization scheme. [ABSTRACT FROM AUTHOR]

Robustness of positive systems with respect to disturbances is a challenging issue of both theoretical and practical importance. In this study, the stabilisation control problem of positive systems with disturbances is investigated. More generalised disturbances are considered here, including positive modelled disturbances and general disturbances. A disturbance observer is designed to estimate the disturbances and a sufficient and necessary condition for stabilisation is derived. For positive disturbances, a sufficient condition with the use of the convergence law for the existence of controller is proposed, and this problem can be formulated as a linear programming problem. Furthermore, the corresponding theory is expanded to generally modelled disturbances, which enriches the theories about anti‐disturbance control of positive systems. Meanwhile, the analysis is given in detail to promote the theoretical completeness, including conservations of hypothesis and positivity constraints of disturbance estimation values. Simulation results show that the proposed scheme can guarantee the positivity of systems and improve the system performances for generalised disturbances. [ABSTRACT FROM AUTHOR]

This brief investigates the high-precision trajectory tracking control of space manipulator after capturing an unknown target. The neutral uncertainty induced by parameter variation poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. In order to address the aforementioned difficulties, a novel control scheme is proposed in this brief. First, to reduce the conservativeness in the descriptions of uncertainties, the dynamic of space manipulator is described by an uncertain system with neutral uncertainty, state-dependent uncertainty, and norm-bounded uncertainty for the first time. Then, by incorporating a novel deadzone compensation control effort, a composite controller is proposed to attenuate these uncertainties and compensate the deadzone nonlinearity. Finally, stability and robustness analyses are carried out, and simulation results are given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Due to the long-range data communication and complex Mars environment, the Mars lander needs to promote the ability to autonomously adapt uncertain situations ensuring high precision landing in future Mars missions. Based on the analysis of multiple disturbances, this study demonstrates an enhanced predictor–corrector guidance method to deal with the effect of atmospheric uncertainties during the entry phase of the Mars landing. In the proposed method, the predictor–corrector guidance algorithm is designed to autonomously drive the Mars lander to the parachute deployment. Meanwhile, the disturbance observer is designed to onboard estimate the effect of fiercely varying atmospheric uncertainties resulting from rapidly height decreasing. Then, with the estimation of atmospheric uncertainties compensated in the feed-forward channel, the composite guidance method is put forward such that both anti-disturbance and autonomous performance of the Mars lander guidance system are improved. Convergence of the proposed composite method is analysed. Simulations for a Mars lander entry guidance system demonstrates that the proposed method outperforms the baseline method in consideration of the atmospheric uncertainties. [ABSTRACT FROM AUTHOR]

Mechanical stress and hypoxia during episodes of ocular hypertension (OHT) trigger glial activation and neuroinflammation in the retina. Glial activation and release of pro-inflammatory cytokines TNFα and IL-1β, complement, and other danger factors was shown to facilitate injury and loss of retinal ganglion cells (RGCs) that send visual information to the brain. However, cellular events linking neuroinflammation and neurotoxicity remain poorly characterized. Several pro-inflammatory and danger signaling pathways, including P2X7 receptors and Pannexin1 (Panx1) channels, are known to activate inflammasome caspases that proteolytically activate gasdermin D channel-formation to export IL-1 cytokines and/or induce pyroptosis. In this work, we used molecular and genetic approaches to map and characterize inflammasome complexes and detect pyroptosis in the OHT-injured retina. Acute activation of distinct inflammasome complexes containing NLRP1, NLRP3 and Aim2 sensor proteins was detected in RGCs, retinal astrocytes and Muller glia of the OHT-challenged retina. Inflammasome-mediated activation of caspases-1 and release of mature IL-1β were detected within 6 h and peaked at 12–24 h after OHT injury. These coincided with the induction of pyroptotic pore protein gasdermin D in neurons and glia in the ganglion cell layer (GCL) and inner nuclear layer (INL). The OHT-induced release of cytokines and RGC death were significantly decreased in the retinas of Casp1−/−Casp4(11)del, Panx1−/− and in Wild-type (WT) mice treated with the Panx1 inhibitor probenecid. Our results showed a complex spatio-temporal pattern of innate immune responses in the retina. Furthermore, they indicate an active contribution of neuronal NLRP1/NLRP3 inflammasomes and the pro-pyroptotic gasdermin D pathway to pathophysiology of the OHT injury. These results support the feasibility of inflammasome modulation for neuroprotection in OHT-injured retinas. [ABSTRACT FROM AUTHOR]

The requirements for the control performances of space manipulators, especially for the stability and accuracy of the attitude control systems of the base spacecrafts, are ever increasing during the space target capturing tasks. However, the system uncertainties caused by parameter variations will degrade the system performances severely. This paper investigates the precise and fast trajectory tracking control problem for the free-flying space manipulator, after capturing a space target with uncertain mass. To compensate the system uncertainty with complex and uncertain dynamics, a novel adaptive sliding mode disturbance observer (ASMDO) is proposed. Then, a composite controller with prescribed transient and steady-state performances is developed. It is proved that the estimation error of ASMDO can be stabilized in finite-time, though the bound of the derivative of system uncertainty is unknown. Meanwhile, the trajectory tracking error can also be stabilized in finite-time and has preassigned maximum overshoot and steady-state error. Finally, numerical simulations and experimental studies are presented to demonstrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

One of the hot topics in graphic processing unit (GPU) research is workload scheduling. For parallel workloads with a large scale, the scheduling strategy can seriously affect system performance. To address this, the authors carry out scheduling of data transfer before workload execution scheduling, and propose an optimal scheduling algorithm for GPU workload. By hiding data transfer in workload execution to the maximum extent, the algorithm can reduce wait time, thus achieving a small timespan. They attribute the problem of hiding data transfer in workload execution to the 0-1 knapsack problem, and propose the pseudo-polynomial time algorithm based on the Dyer-Zemel algorithm. The authors then deduce the fully polynomial-time algorithm scheme for PPTA. By testing on cloud platform equipped with Nvidia Geforce GTX 750, they show that their scheduling algorithm estimates the optimal schedule sequence effectively. [ABSTRACT FROM AUTHOR]

A novel composite anti‐disturbance control scheme is proposed for attitude control system (ACS) of flexible spacecraft with the reaction wheel friction. The proposed control scheme can overcome the performance degradation of controller under the condition of reaction wheel friction and flexible appendage vibration. First, the dynamic model of the flexible spacecraft is modelled, including disturbance torque of the reaction wheel. A sliding mode friction observer is designed to estimate the reaction wheel friction torque for feed‐forward compensation. Then, a flexible appendage vibration observer is constructed to estimate and compensate for the flexible appendage vibration. Finally, an H∞ controller is adopted to attenuate the estimation error and other bounded disturbances, such as the environment disturbance. Simulations on the ACS of a flexible spacecraft demonstrate the robustness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The attitude control problem is much more complicated due to the existence of faults and multiple disturbances simultaneously in satellites. In this paper, an adaptive fault tolerant attitude control method is presented for satellites with both time-varying actuator faults and multiple disturbances. Differing from previous results, the first part of the multiple disturbance is the uncertain modelled disturbance and the second part represents an uncertain variable bounded by a given function. A composite observer is designed to estimate the uncertain modelled disturbance and the time-varying fault simultaneously. Then, a new fault tolerant control strategy consisting of disturbance observer based control, fault accommodation and an adaptive controller is constructed to reconfigure the concerned systems and so as to achieve the anti-disturbance performance. In the proposed method, the modelled disturbance can be rejected by its estimation and the uncertain bounded disturbance can be compensated by the adaptive compensation term. The simulation results are given to show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Objective: A number of studies have evaluated the efficacy and safety of fondaparinux versus low-molecular-weight heparin (LMWH) in patients with acute coronary syndrome (ACS), but the findings were not consistent across these studies. Methods: Electronic databases and article references were searched for studies that assessed fondaparinux versus LMWH in ACS patients. Results: Six studies met the inclusion criteria. There was a lower risk of major adverse cardiac events (MACE) with fondaparinux-based regimens both in randomized controlled trials (RCT; risk ratio, RR: 0.91, p = 0.04) and observational studies (RR: 0.85, p < 0.0001). Mortality decreased in fondaparinux-treated patients in RCT (RR: 0.84, p = 0.02), but not in observational studies (RR: 1.44, p = 0.64). For the analysis of myocardial infarction (MI), recurrent ischemia and stroke, none of the studies showed significant results. In addition, fondaparinux lowered the risk of major bleeding in RCT (RR: 0.62, p < 0.0001) and observational studies (RR: 0.65, p < 0.0001). The net clinical outcome also favored fondaparinux over LMWH in RCT (RR: 0.82, p < 0.0001) and observational studies (RR: 0.84, p < 0.0001). Conclusions: Among ACS patients, a fondaparinux-based regimen presented advantages regarding MACE and major bleeding, and a net clinical benefit compared with LMWH, although the benefit is minimal regarding MACE. For death, MI, recurrent ischemia and stroke, fondaparinux has not shown significant benefits. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]