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On this cardstock, we propose a manuscript structures referred to as Multi-Scale Residual Mix Community (MSRF-Net), that's engineered regarding medical image segmentation. The actual recommended MSRF-Net is able to exchange multi-scale features of varying sensitive career fields using a Dual-Scale Thick Mix (DSDF) stop. Each of our DSDF prevent can exchange information rigorously throughout two various solution scales, as well as our MSRF sub-network uses multiple DSDF hindrances in string to do multi-scale blend. This gives your availability regarding solution, enhanced information circulation and also selleck kinase inhibitor propagation of equally high- along with low-level features to get accurate division maps. The recommended MSRF-Net permits for you to seize subject variabilities and offers improved benefits on different biomedical datasets. Extensive studies in MSRF-Net demonstrate that the actual recommended strategy outperforms your technologically advanced health-related picture division strategies about several freely available datasets. We achieve the Cube Coefficient (DSC) associated with 2.9217, Zero.9420, along with 3.9224, 3.8824 upon Kvasir-SEG, CVC-ClinicDB, 2018 Data Scientific disciplines Dish dataset, as well as ISIC-2018 skin color patch division problem dataset respectively. Many of us even more conducted generalizability assessments that also reached the very best DSC credit score together with Zero.7921 and also 2.7575 upon CVC-ClinicDB and Kvasir-SEG, respectively.Adenine-5'-triphosphate (ATP) is often a direct energy source for assorted activities of cells as well as tissue within the body. The production regarding ATP systems requires the assistance of ATP-binding protein. For that reason, your identification regarding ATP-binding meats can be of effective significance for the research in organisms. To date, there are numerous methods for guessing ATP-binding meats. Nevertheless, the particular accuracies of such techniques are very minimal that the expected meats tend to be wrong. Below, all of us created a fresh approach, known as DeepRCI (depending on Heavy convolutional neurological system and Residue-residue Contact Information), pertaining to projecting ATP-binding proteins. DeepRCI achieved an accuracy of Ninety three.61\% around the analyze arranged that was an important enhancement over the state-of-the-art techniques.Identifying place problems regarding Graves' ophthalmopathy (Proceed) individuals making use of electronic website image resolution unit (EPID) transmission fluence maps is useful inside monitoring treatment method. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods.The performance of previous machine learning models for gait phase is only satisfactory under limited conditions. First, they produce accurate estimations only when the ground truth of the gait phase (of the target subject) is known. In contrast, when the ground truth of a target subject is not used to train an algorithm, the estimation error noticeably increases. Expensive equipment is required to precisely measure the ground truth of the gait phase. Thus, previous methods have practical shortcoming when they are optimized for individual users. To address this problem, this study introduces an unsupervised domain adaptation technique for estimation without the true gait phase of the target subject. Specifically, a domain-adversarial neural network was modified to perform regression on continuous gait phases. Second, the accuracy of previous models can be degraded by variations in stride time. To address this problem, this study developed an adaptive window method that actively considers changes in stride time. This model considerably reduces estimation errors for walking and running motions. Finally, this study proposed a new method to select the optimal source subject (among several subjects) by defining the similarity between sequential embedding features.The abnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision owns the high robustness and accuracy for detection. However, executing CNN model on the cloud possible incurs a privacy disclosure problem during data transmission, and the high computation overhead makes difficult to execute the model on edge-end IoT devices with a well real-time performance. In this paper, we realize a skeleton-based abnormal behavior detection, and propose a secure partitioned CNN model (SP-CNN) to extract human skeleton keypoints and achieve safely collaborative computing by deploying different CNN model layers on the cloud and the IoT device. Because, the data outputted from the IoT device are processed by the several CNN layers instead of transmitting the sensitive video data, objectively it reduces the risk of privacy disclosure. Moreover, we also design an encryption method based on channel state information (CSI) to guarantee the sensitive data security. At last, we apply SP-CNN in abnormal behavior detection to evaluate its effectiveness. The experiment results illustrate that the efficiency of the abnormal behavior detection based on SP-CNN is at least 33.2% higher than the state-of-the-art methods, and its detection accuracy arrives to 97.54%.In recent years, clustering methods based on deep generative models have received great attention in various unsupervised applications, due to their capabilities for learning promising latent embeddings from original data. This article proposes a novel clustering method based on variational autoencoder (VAE) with spherical latent embeddings. The merits of our clustering method can be summarized as follows. First, instead of considering the Gaussian mixture model (GMM) as the prior over latent space as in a variety of existing VAE-based deep clustering methods, the von Mises-Fisher mixture model prior is deployed in our method, leading to spherical latent embeddings that can explicitly control the balance between the capacity of decoder and the utilization of latent embedding in a principled way. Second, a dual VAE structure is leveraged to impose the reconstruction constraint for the latent embedding and its corresponding noise counterpart, which embeds the input data into a hyperspherical latent space for clustering. Third, an augmented loss function is proposed to enhance the robustness of our model, which results in a self-supervised manner through the mutual guidance between the original data and the augmented ones. The effectiveness of the proposed deep generative clustering method is validated through comparisons with state-of-the-art deep clustering methods on benchmark datasets. The source code of the proposed model is available at https//github.com/fwt-team/DSVAE.In this article, an event-based near-optimal tracking control algorithm is developed for a class of nonaffine systems. First, in order to gain the tracking control strategy, the costate function is established through the iterative dual heuristic dynamic programming (DHP) algorithm. Then, the event-based control method is employed to improve the utilization efficiency of resources and ensure that the closed-loop system has an excellent control performance. Meanwhile, the input-to-state stability (ISS) is proven for the event-based tracking plant. In addition, three kinds of neural networks are used in the event-based DHP algorithm, which aims to identify the nonaffine nonlinear system, estimate the costate function, and approximate the tracking control law. Finally, a numerical experimental simulation is conducted to verify the effectiveness of the proposed scheme. Moreover, in order to further validate the feasibility, the algorithm is applied to the wastewater treatment plant to effectively control the concentrations of dissolved oxygen and nitrate nitrogen.In this article, minimal pinning control for oscillatority (i.e., instability) of Boolean networks (BNs) under algebraic state space representations method is studied. First, two criteria for oscillatority of BNs are obtained from the aspects of state transition matrix (STM) and network structure (NS) of BNs, respectively. A distributed pinning control (DPC) from these two aspects is proposed one is called STM-based DPC and the other one is called NS-based DPC, both of which are only dependent on local in-neighbors. As for STM-based DPC, one arbitrary node can be chosen to be controlled, based on certain solvability of several equations, meanwhile a hybrid pinning control (HPC) combining DPC and conventional pinning control (CPC) is also proposed. In addition, as for NS-based DPC, pinning control nodes (PCNs) can be found using the information of NS, which efficiently reduces the high computational complexity. The proposed STM-based DPC and NS-based DPC in this article are shown to be simple and concise, which provide a new direction to dramatically reduce control costs and computational complexity. Finally, gene networks are simulated to discuss the effectiveness of theoretical results.Exponential function is a basic form of temporal signals, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast data acquisition in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by imitating the iterative process in the model-based state-of-the-art exponentials' reconstruction method with the low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better than compared methods.Deep learning based on deep convolutional neural networks (CNNs) is extremely efficient in solving classification problems in speech recognition, computer vision, and many other fields. But there is no enough theoretical understanding about this topic, especially the generalization ability of the induced CNN algorithms. In this article, we develop some generalization analysis of a deep CNN algorithm for binary classification with data on spheres. An essential property of the classification problem is the lack of continuity or high smoothness of the target function associated with a convex loss function such as the hinge loss. This motivates us to consider the approximation of functions in the Lp space with 1≤ p ≤ ∞. We provide rates of Lp -approximation when the approximated function lies in a Sobolev space and then present generalization bounds and learning rates for the excess misclassification error of the deep CNN classification algorithm. Our novel analysis is based on efficient cubature formulae on spheres and other tools from spherical analysis and approximation theory.Prevalent domain adaptation approaches are suitable for a close-set scenario where the source domain and the target domain are assumed to share the same data categories. However, this assumption is often violated in real-world conditions where the target domain usually contains samples of categories that are not presented in the source domain. This setting is termed as open set domain adaptation (OSDA). Most existing domain adaptation approaches do not work well in this situation. In this article, we propose an effective method, named joint alignment and category separation (JACS), for OSDA. Specifically, JACS learns a latent shared space, where the marginal and conditional divergence of feature distributions for commonly known classes across domains is alleviated (Joint Alignment), the distribution discrepancy between the known classes and the unknown class is enlarged, and the distance between different known classes is also maximized (Category Separation). These two aspects are unified into an objective to reinforce the optimization of each part simultaneously. The classifier is achieved based on the learned new feature representations by minimizing the structural risk in the reproducing kernel Hilbert space. Extensive experiment results verify that our method outperforms other state-of-the-art approaches on several benchmark datasets.The tracking performance of discriminative correlation filters (DCFs) is often subject to unwanted boundary effects. Many attempts have already been made to address the above issue by enlarging searching regions over the last years. However, introducing excessive background information makes the discriminative filter prone to learn from the surrounding context rather than the target. In this article, we propose a novel context restrained correlation tracking filter (CRCTF) that can effectively suppress background interference via incorporating high-quality adversarial generative negative instances. Concretely, we first construct an adversarial context generation network to simulate the central target area with surrounding background information at the initial frame. Then, we suggest a coarse background estimation network to accelerate the background generation in subsequent frames. By introducing a suppression convolution term, we utilize generative background patches to reformulate the original ridge regression objective through circulant property of correlation and a cropping operator. Finally, our tracking filter is efficiently solved by the alternating direction method of multipliers (ADMM). CRCTF demonstrates the accuracy performance on par with several well-established and highly optimized baselines on multiple challenging tracking datasets, verifying the effectiveness of our proposed approach.Based on radial basis function neural networks (RBF NNs) and backstepping techniques, this brief considers the consensus tracking problem for nonlinear semi-strict-feedback multiagent systems with unknown states and disturbances. The adaptive event-triggered control scheme is introduced to decrease the update times of the controller so as to save the limited communication resources. To detect the unknown state, external disturbance, and reduce calculation workload, the state observer and disturbance observer as well as the first-order filter are first jointly constructed. It is shown that all the output signals of followers can uniformly track the reference signal of the leader and all the error signals are uniformly bounded. A simulation example is carried out to further prove the effectiveness of the proposed control scheme.Traditionally, neural networks are viewed from the perspective of connected neuron layers represented as matrix multiplications. We propose to compose these weight matrices from a set of orthogonal basis matrices by approaching them as elements of the real matrices vector space under addition and multiplication. Making use of the Kronecker product for vectors, this composition is unified with the singular value decomposition (SVD) of the weight matrix. The orthogonal components of this SVD are trained with a descent curve on the Stiefel manifold using the Cayley transform. Next, update equations for the singular values and initialization routines are derived. Finally, acceleration for stochastic gradient descent optimization using this formulation is discussed. Our proposed method allows more parameter-efficient representations of weight matrices in neural networks. These decomposed weight matrices achieve maximal performance in both standard and more complicated neural architectures. Furthermore, the more parameter-efficient decomposed layers are shown to be less dependent on optimization and better conditioned. As a tradeoff, training time is increased up to a factor of 2. These observations are consequently attributed to the properties of the method and choice of optimization over the manifold of orthogonal matrices.Dexterous manipulation of objects heavily relies on the feedback provided by the tactile afferents innervating the fingertips. Previous studies have suggested that humans might take advantage of partial slip, localized loss of grip between the skin and the object, to gauge the stability of a contact and react appropriately when it is compromised, that is, when slippage is about to happen. To test this hypothesis, we asked participants to perform point-to-point movements using a manipulandum. Through optical imaging, the device monitored partial slip at the contact interface, and at the same time, the forces exerted by the fingers. The level of friction of the contact material was changed every five trials. We found that the level of grip force was systematically adjusted to the level of friction, and thus partial slip was limited to an amount similar across friction conditions. We suggest that partial slip is a key signal for dexterous manipulation and that the grip force is regulated to continuously maintain an upper bound on partial slip across friction conditions.Developing manipulators for kinesthetic haptic interfaces is challenging due to a large number of design parameters. We propose a novel optimization-driven design approach taking into account the properties of the entire workspace of the human arm instead of a specific task. To achieve this, models of both the human arm and the haptic manipulator are derived and deployed in a suitable objective function, which simultaneously considers poses, velocities, accelerations, as well as displayed forces and torques. A detailed analysis and experiments with real-world motion tracking data show that the proposed method is capable of finding meaningful design parameters to enable good haptic transparency.Data-driven approaches are commonly used to model and render haptic textures for rigid stylus-based interaction. Current state-of-the-art data-driven methodologies synthesize acceleration signals through the interpolation of samples with different input parameters based on neural networks or parametric spectral estimation methods. In this paper, we see the potential of emerging deep learning methods in this area. To this end, we designed a complete end-to-end data-driven framework to synthesize acceleration profiles based on the proposed deep spatio-temporal network. The network is trained using contact acceleration data collected through our manual scanning stylus and interaction parameters, i.e., scanning velocities, directions, and forces. The proposed network is composed of attention-aware 1D CNNs and attention-aware encoder-decoder networks to adequately capture both the local spatial features and the temporal dynamics of the acceleration signals, which are further augmented with attention mechanisms that assign weights to the features according to their contributions. For rendering, the trained network generates synthesized signals in real-time in accordance with the user's input parameters. The whole framework was numerically compared with existing state-of-the-art approaches, showing the effectiveness of the approach. Additionally, a pilot user study is conducted to demonstrate subjective similarity.Wearable devices with bimanual force feedback enable natural and cooperative manipulations within an unrestricted space. Weight and cost have a great influence on the potential applications of a haptic device. This paper presents a wearable robotic interface with bimanual force feedback that has considerably reduced weight and cost. To make the reaction force less perceivable than the interaction force, a waist-worn scheme is adopted. The interface mainly consists of a belt, a fastening tape, two serial robotic arms, and two electronics units and batteries. The robotic arms located on both sides of the belt are capable of 3-DoF position tracking and force feedback for each hand. The whole interface is lightweight (only 2.4 kg) and accessible. Furthermore, it is also easy to wear and the operator can wear it only by putting the belt on the waist and fastening the tape, reducing his/her dependency on additional assistance. The interface is optimized to obtain desirable force output and a dexterous workspace without singularity. To evaluate its performance in bimanual cooperative manipulations, an experiment in the virtual environment was conducted. The experimental results showed the subjects had more efficient and stable cooperative manipulations with bimanual force feedback than without force feedback.Wearable haptic systems can be easily integrated with the human body and represent an effective solution for a natural and unobtrusive stimulus delivery. These characteristics can open interesting perspectives for different applications, such as haptic guidance for human ergonomics enhancement, e.g. during human-robot collaborative tasks in industrial scenarios, where the usage of the visual communication channel can be problematic. In this work, we propose a wearable multi-cue system that can be worn at the arm level on both the two upper limbs, which conveys both squeezing stimuli (provided by an armband haptic device) and vibration, to provide corrective feedback for posture balancing along the user's frontal and sagittal plane, respectively. We evaluated the effectiveness of our system in delivering directional information to control the user's center of pressure position on a balancing board. We compared the here proposed haptic guidance with visual guidance cues. Results show no statistically significant differences in terms of success rate and time for task completion for the two conditions.