Gadestout4325

We evaluate CLEAS on numerous sequential classification tasks, and the results demonstrate that CLEAS outperforms other state-of-the-art alternative methods, achieving higher classification accuracy while using simpler neural architectures.This article investigates the finite-time and fixed-time synchronization for memristive neural networks (MNNs) with mixed time-varying delays under the adaptive aperiodically intermittent adjustment strategy. Different from previous works, this article first employs the aperiodically intermittent adjustment feedback control and adaptive control to drive the MNNs to achieve synchronization in finite time and fixed time. First of all, according to the theories of set-valued mappings and differential inclusions, the error MNNs is derived, and its finite-time and fixed-time stability problems are discussed by applying the Lyapunov function method and some LMI techniques. Moreover, by meticulously designing an effective aperiodically intermittent adjustment with adaptive updating law, sufficient conditions that guarantee the finite-time and fixed-time synchronization of the drive-response MNNs are obtained, and the settling time is explicitly estimated. Finally, three numerical examples are provided to illustrate the validity of the obtained theoretical results.Based on the information loss analysis of the blur accumulation model, a novel single-image deblurring method is proposed. We apply the recurrent neural network architecture to capture the attention perception map and the generative adversarial network (GAN) architecture to yield the deblurring image. Considering that the attention mechanism has to make hard decisions about specific parts of the input image to be focused on since blurry regions are not given, we propose a new adaptive attention disentanglement model based on the variation blind source separation, which provides the global geometric restraint to reduce the large solution space, so that the generator can realistically restore details on blurry regions, and the discriminator can accurately assess the content consistency of the restored regions. Since we combine blind source separation, attention geometric restraint with GANs, we name the proposed method BAGdeblur. Extensive evaluations on quantitative and qualitative experiments show that the proposed method achieves the state-of-the-art performance on both synthetic datasets and real-world blurry images.Heterogeneous information networks (HINs) are potent models of complex systems. In practice, many nodes in an HIN have their attributes unspecified, resulting in significant performance degradation for supervised and unsupervised representation learning. We developed an unsupervised heterogeneous graph contrastive learning approach for analyzing HINs with missing attributes (HGCA). HGCA adopts a contrastive learning strategy to unify attribute completion and representation learning in an unsupervised heterogeneous framework. To deal with a large number of missing attributes and the absence of labels in unsupervised scenarios, we proposed an augmented network to capture the semantic relations between nodes and attributes to achieve a fine-grained attribute completion. Extensive experiments on three large real-world HINs demonstrated the superiority of HGCA over several state-of-the-art methods. The results also showed that the complemented attributes by HGCA can improve the performance of existing HIN models.In this brief, we define a self-limiting control term, which has the function of guaranteeing the boundedness of variables. Then, we apply it to a finite-time stability control problem. For nonstrict feedback nonlinear systems, a finite-time adaptive control scheme, which contains a piecewise differentiable function, is proposed. This scheme can eliminate the singularity of derivative of a fractional exponential function. By adding a self-limiting term to the controller and the virtual control law of each subsystem, the boundedness of the overall system state is guaranteed. Then the unknown continuous functions are estimated by neural networks (NNs). The output of the closed-loop system tracks the desired trajectory, and the tracking error converges to a small neighborhood of the equilibrium point in finite time. The theoretical results are illustrated by a simulation example.The record-breaking performance of deep neural networks (DNNs) comes with heavy parameter budgets, which leads to external dynamic random access memory (DRAM) for storage. The prohibitive energy of DRAM accesses makes it nontrivial for DNN deployment on resource-constrained devices, calling for minimizing the movements of weights and data in order to improve the energy efficiency. Driven by this critical bottleneck, we present SmartDeal, a hardware-friendly algorithm framework to trade higher-cost memory storage/access for lower-cost computation, in order to aggressively boost the storage and energy efficiency, for both DNN inference and training. The core technique of SmartDeal is a novel DNN weight matrix decomposition framework with respective structural constraints on each matrix factor, carefully crafted to unleash the hardware-aware efficiency potential. Specifically, we decompose each weight tensor as the product of a small basis matrix and a large structurally sparse coefficient matrix whose nonzero eions and 2) being applied to training, SmartDeal can lead to 10.56x and 4.48x reduction in the storage and the training energy cost, respectively, with usually negligible accuracy loss, compared to state-of-the-art training baselines. Our source codes are available at https//github.com/VITA-Group/SmartDeal.Traditional molecular techniques for SARS-CoV-2 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of SARS-CoV-2 detection using plasmonic gold nanoparticles. The resonance wavelength of a SARS-CoV-2 virus was recently estimated to be in the near-infrared region. By engineering gold nanospheres to specifically bind with the outer surface of the SARS-CoV-2 virus, the resonance frequency can be shifted to the visible range (380 nm - 700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a specific coverage percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. Our observation also suggests that this technique is unaffected by the number of protein spikes present on the virus outer surface, hence can pave a potential path for a label-free COVID-19 diagnostic tool independent of the number of protein spikes.The output of a motor is work, while the output of a clock is information. TVB-3166 Here it is discussed how a molecular motor can produce both, work and information, depending on the load. If the ratio of the backward and forward stepping rates of a molecular motor increases exponentially with load, the change in free energy per step can be used to produce only work (at stall force) or only timing information (at zero force), or anything in between.Rapid detection of mycobacterium tuberculosis bacteria is very important in reducing tuberculosis disease. We propose a label-free graphene-based refractive index sensor using a machine learning approach that detects mycobacterium tuberculosis bacteria. The biosensor is designed for higher sensitivity by analyzing different parameters like substrate thickness, resonator thickness, and angle of incidence. Machine learning is applied to predict the values of absorption for different wavelengths. The machine learning model is applied to four different parameters (angle of incidence, substrate thickness, resonator thickness, graphene chemical potential) of the biosensor. The plus shape metasurface is placed above the graphene-SiO2 hybrid layer to improve the sensitivity. The comparative analysis with other published designs is also presented. The proposed sensor with its higher sensitivity and ability to detect mycobacterium tuberculosis bacteria can be used in biomedical devices for diagnostic applications. Experiments are performed to check the K-Nearest Neighbors (KNN)-regressor model's prediction efficiency for predicting absorption values of intermediate wavelengths. Different values of K and two test cases; R-50, U-50 are used to test the regressor models using the R2 Score as an evaluation metric. It is observed from the experimental results that, high prediction efficiency can be achieved using lower values of K in the KNN-Regressor model.

Complete tetraplegia can deprive a person of hand function. Assistive technologies may improve autonomy but needs for ergonomic interfaces for the user to pilot these devices still persist. Despite the paralysis of their arms, people with tetraplegia may retain residual shoulder movements. In this work we explored these movements as a mean to control assistive devices.

We captured shoulder movement with a single inertial sensor and, by training a support vector machine based classifier, we decode such information into user intent.

The setup and training process take only a few minutes and so the classifiers can be user specific. We tested the algorithm with 10 able body and 2 spinal cord injury participants. The average classification accuracy was 80% and 84%, respectively.

The proposed algorithm is easy to set up, its operation is fully automated, and achieved results are on par with state-of-the-art systems.

Assistive devices for persons without hand function present limitations in their user interfaces. Our work presents a novel method to overcome some of these limitations by classifying user movement and decoding it into user intent, all with simple setup and training and no need for manual tuning. We demonstrate its feasibility with experiments with end users, including persons with complete tetraplegia without hand function.

Assistive devices for persons without hand function present limitations in their user interfaces. Our work presents a novel method to overcome some of these limitations by classifying user movement and decoding it into user intent, all with simple setup and training and no need for manual tuning. We demonstrate its feasibility with experiments with end users, including persons with complete tetraplegia without hand function.In this study, a three-dimensional (3D) printed soft robotic hand with embedded soft sensors, intended for prosthetic applications is designed and developed to efficiently operate with new-generation myoelectric control systems, e.g., pattern recognition control and simultaneous proportional control. The mechanical structure of the whole hand ('ACES-V2') is fabricated as a monolithic structure using a low-cost and open-source 3D printer. It minimizes the post-processing required for the addition of the embedded sensors in the hand. These are significant benefits for the robotic hand that features low cost, low weight (313 grams), and anthropomorphic appearance. With the soft position sensors added to the fingers, the fingers' positions can be monitored to avoid self-collision of the hand. Besides, it allows a robotic prosthetic hand to eliminate the conventional way of returning to the neutral full open position when switching from one type of gesture to another. This makes the transition between the hand gestures much faster, more efficient, and more intuitive as well.