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Abstract 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 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 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.

Simultaneous localization and mapping algorithm is a technique to simultaneously construct a map of the environment by the sensors it carries. In this paper, we focus on the SLAM (Simultaneous Localization and Mapping) algorithm based on machine vision-depth information, overlap region determination among local sub-maps, and fusion algorithm of 3D dense maps, and analyze four modules of front-end visual odometry, back-end optimization, loopback detection, and map construction. The front-end visual odometry adopts the ORB (Oriented FAST and Rotated BRIEF) algorithm based on the feature point method to extract and match the features between image frames and uses the iterative nearest point method to solve the camera motion between adjacent images. Meanwhile, a 3D feature descriptor based on the histogram of spatial distribution is designed to encode the feature information around the key points, and the Kd-Tree algorithm is used for radial nearest neighbor search to complete the feature matching. Finally, the 3D map is fused by the 3D-ICP algorithm. The experiments prove that the low-dimensional local feature descriptor designed has good descriptiveness and is superior, and the 3D map fusion based on this descriptor has a good fusion effect and can meet the accuracy requirements of practical applications.

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.

When tracking a tumbling non-cooperative target, the position and attitude maneuver control with high-precision as well as fast-response performances is required for flexible spacecrafts. However, the unknown rigid-flexible coupling and model uncertainties will degrade the control performances significantly. Therefore, this paper proposes a refined disturbance rejection-based composite control method for spacecraft position and attitude tracking. First of all, the disturbances including the rigid-flexible coupling and model uncertainties are described by an exogenous model by fully using the partially known disturbance information (e.g., modal frequency and damping). Then, a novel exogenous model-based sliding mode disturbance observer (SMDO) is proposed to achieve the refined disturbance estimation. Next, by combining the SMDO in the feedforward loop and an adaptive terminal sliding mode control (ATSMC) in the feedback loop, a finite-time composite control law is proposed to ensure the fast disturbance rejection and position/attitude tracking simultaneously. In the composite controller, a novel nonsingular terminal sliding mode surface with arctangent function is proposed. Moreover, a time-varying boundary Lyapunov function (BLF) is designed to preassign the transient and steady-state performances of sliding mode surface. Finally, the effectiveness of proposed method is verified via numerical simulation.

As a common malignant disease, brain tumor has high mortality. The automatic segmentation of brain tumor has significance for clinical diagnosis and surgery treatment. With the development of deep learning, CNN (Convolutional Neural Network) achieves remarkable performance in image processing and computer vision. Researchers have proposed a large number of CNN-based segmentation models such as FCN (Fully Convolutional Network) and Unet from the perspective of network architecture, loss function and attention mechanism. However, most of them are based on the traditional pooling operations such as average pooling and maximum pooling, which will lead to the loss of significant features or average features. Especially in brain tumor segmentation, tissues are usually quite small, so feature losing is more serious. More importantly, the fixed pooling patterns such as maximum pooling and average pooling, which cannot accommodate to varying data, may not be able to accurately express their features in down-sampling. In this study, we first unify maximum pooling and average pooling, and then propose a novel generalized pooling (GP) method with adaptive weights. This is the first work to improve models from the perspective of pooling operations for brain tumor segmentation. The experimental results show that our generalized pooling method is effective to segment brain tumors, outperforming the traditional pooling methods.

Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.

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.

As a type of distributed computing, volunteer computing (VC) has provided unlimited computing capacity at a low cost in recent decades. The architecture of most volunteer computing platforms (VCPs) is a master–worker model, which defines a master–slave relationship. Therefore, VCPs can be considered asymmetric multiprocessing systems (AMSs). As AMSs, VCPs are very promising for providing computing services for users. Users can submit tasks with deadline constraints to the VCPs. If the tasks are completed within their deadlines, VCPs will obtain the benefits. For this application scenario, this paper proposes a new task assignment problem with the maximum benefits in VCPs for the first time. To address the problem, we first proposed a list-based task assignment (LTA) strategy, and we proved that the LTA strategy could complete the task with a deadline constraint as soon as possible. Then, based on the LTA strategy, we proposed a maximum benefit scheduling (MBS) algorithm, which aimed at maximizing the benefits of VCPs. The MBS algorithm determined the acceptable tasks using a pruning strategy. Finally, the experiment results show that our proposed algorithm is more effective than current algorithms in the aspects of benefits, task acceptance rate and task completion rate.

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.

As a class of autonomous deep-space exploration robots, Mars lander is simultaneously affected by wind disturbance and actuator fault, which hinders precise landing on Mars. In this article, we propose a composite guidance approach by combining disturbance observer and iterative learning observer together. The Mars wind disturbance is dealt by the disturbance observer providing wind estimation which is rejected through feed-forward channel. Meanwhile, the iterative learning observer is used to estimate the deficiency of actuators. The proposed guidance scheme ensures not only the precision but also the reliability of the Mars guidance system. The stability of the closed-loop system is analyzed. Simulations made in different situations demonstrate the performance of the proposed approach.

Background A number of studies have evaluated the efficacy of deferred stenting vs immediate stenting in patients with ST‐segment elevation myocardial infarction, but the findings were not consistent across these studies. This meta‐analysis aims to assess optimal treatment strategies in patient with ST‐segment elevation myocardial infarction. Methods and Results We searched the PubMed, EMBASE, and the Cochrane Library for studies that assessed deferred vs immediate stenting in patients with ST‐segment elevation myocardial infarction. Nine studies including 1456 patients in randomized controlled trials and 719 patients in observational studies were included in the meta‐analysis. No significant differences were observed in the incidence of no‐ or slow‐reflow between deferred stenting and immediate stenting in randomized controlled trials (odds ratio [OR] 0.51, 95%CI 0.17‐1.53, P=0.23, I2=70%) but not in observational studies (OR 0.13, 95%CI 0.06‐0.31, P

In volunteer computing (VC), the expected availability time and the actual availability time provided by volunteer nodes (VNs) are usually inconsistent. Scheduling tasks with precedence constraints in VC under this situation is a new challenge. In this paper, we propose two novel task assignment algorithms to minimize completion time (makespan) by a flexible task assignment. Firstly, this paper proposes a reliability model, which uses a simple fuzzy model to predict the time interval provided by a VN. This reliability model can reduce inconsistencies between the expected availability time and actual availability time. Secondly, based on the reliability model, this paper proposes an algorithm called EFTT (Earliest Finish Task based on Trust, EFTT), which can minimize makespan. However, EFTT may induce resource waste in task assignment. To make full use of computing resources and reduce task segmentation rate, an algorithm IEFTT (improved earliest finish task based on trust, IEFTT) is further proposed. Finally, experimental results verify the efficiency of the proposed algorithms.

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

Supervised monocular depth estimation methods based on learning have shown promising results compared with the traditional methods. However, these methods require a large number of high-quality corresponding ground truth depth data as supervision labels. Due to the limitation of acquisition equipment, it is expensive and impractical to record ground truth depth for different scenes. Compared to supervised methods, the self-supervised monocular depth estimation method without using ground truth depth is a promising research direction, but self-supervised depth estimation from a single image is geometrically ambiguous and suboptimal. In this paper, we propose a novel semi-supervised monocular stereo matching method based on existing approaches to improve the accuracy of depth estimation. This idea is inspired by the experimental results of the paper that the depth estimation accuracy of a stereo pair as input is better than that of a monocular view as input in the same self-supervised network model. Therefore, we decompose the monocular depth estimation problem into two sub-problems, a right view synthesized process followed by a semi-supervised stereo matching process. In order to improve the accuracy of the synthetic right view, we innovate beyond the existing view synthesis method Deep3D by adding a left-right consistency constraint and a smoothness constraint. To reduce the error caused by the reconstructed right view, we propose a semi-supervised stereo matching model that makes use of disparity maps generated by a self-supervised stereo matching model as the supervision cues and joint self-supervised cues to optimize the stereo matching network. In the test, the two networks are able to predict the depth map directly from a single image by pipeline connecting. Both procedures not only obey geometric principles, but also improve estimation accuracy. Test results on the KITTI dataset show that this method is superior to the current mainstream monocular self-supervised depth estimation methods under the same condition.