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Useful neural disorders: components and therapy.
We present an approach to the numerical simulation of open quantum many-body systems based on the semiclassical framework of the discrete truncated Wigner approximation. We establish a quantum jump formalism to integrate the quantum master equation describing the dynamics of the system, which we find to be exact in both the noninteracting limit and the limit where the system is described by classical rate equations. We apply our method to simulation of the paradigmatic dissipative Ising model, where we are able to capture the critical fluctuations of the system beyond the level of mean-field theory.We report tunable excitation-induced dipole-dipole interactions between silicon-vacancy color centers in diamond at cryogenic temperatures. These interactions couple centers into collective states, and excitation-induced shifts tag the excitation level of these collective states against the background of excited single centers. this website By characterizing the phase and amplitude of the spectrally resolved interaction-induced signal, we observe oscillations in the interaction strength and population state of the collective states as a function of excitation pulse area. Our results demonstrate that excitation-induced dipole-dipole interactions between color centers provide a route to manipulating collective intercenter states in the context of a congested, inhomogeneous ensemble.Transcranial temporal interference stimulation (tTIS) has been proposed as a new neuromodulation technology for non-invasive deep-brain stimulation (DBS). However, few studies have detailed the design method of a tTIS device and provided system validation. Thus, a detailed design and validation scheme of a novel tTIS device for animal brain stimulation are presented in this study. In the proposed tTIS device, a direct digital synthesizer (DDS) was used to generate a sine wave potential of different frequencies, which was converted to an adjustable sine wave current. A current transformer was used to produce electrical isolation of different channels, which eliminated the current crosstalk between channels and greatly increased the load capacity by amplifying the output voltage. Several in vitro experiments were first conducted to validate the tTIS device. Our results indicated that the error percentages of the stimulation currents were within ±2%. Current crosstalk between channels was almost completely eliminated. Then, in vivo electric field measurement shows that the 2-pole arrangement may provide better cortical targeting than the 4-pole mode. A pilot animal experiment was conducted in which evoked motion and electromyographic activation of the contralateral forelimb were observed, which indicated that the 2-pole tTIS had successfully activated the primary motor cortex in a rat. Motor activation induced by the 2-pole tTIS demonstrated the feasibility and safety potential when applying our tTIS device for neuromodulation.Ship target detection in synthetic aperture radar (SAR) images is an important application field. Due to the existence of sea clutter, especially the SAR imaging in huge wave area, SAR images contain a lot of complex noise, which brings great challenges to the effective detection of ship targets in SAR images. Although the deep semantic segmentation network has been widely used in the detection of ship targets in recent years, the global information of the image cannot be fully utilized. To solve this problem, a new convolutional neural network (CNN) method based on wavelet and attention mechanism was proposed in this paper, called the WA-CNN algorithm. The new method uses the U-Net structure to construct the network, which not only effectively reduces the depth of the network structure, but also significantly improves the complexity of the network. The basic network of WA-CNN algorithm consists of encoder and decoder. Dual tree complex wavelet transform (DTCWT) is introduced into the pooling layer of the encoder to smooth the speckle noise in SAR images, which is beneficial to preserve the contour structure and detail information of the target in the feature image. The attention mechanism theory is added into the decoder to obtain the global information of the ship target. Two public SAR image datasets were used to verify the proposed method, and good experimental results were obtained. This shows that the method proposed in this article is effective and feasible.
With the emergence of novel vaccines and new applications for older vaccines, co-administration is increasingly likely. The immunomodulatory effects of BCG could theoretically alter the reactogenicity of co-administered vaccines. Using active surveillance in a randomised controlled trial, we aimed to determine whether co-administration of BCG vaccination changes the safety profile of influenza vaccination.

Participants who received influenza vaccine alone (Influenza group) were compared with those who also received BCG-Denmark vaccine in the contralateral arm (Influenza+BCG group). Data on the influenza vaccination site were collected using serial questionnaires and active follow-up for 3 months post vaccination.

Of 1351 participants in the Influenza+BCG group and 1418 participants in the Influenza group, 2615 (94%) provided influenza vaccine safety data. There was no significant difference in the proportion of participants with any local adverse reaction between the Influenza+BCG group and the Influenza group (918/1293 [71.0%] versus (906/1322 [68.5%], p = 0.17). The proportion of participants reporting any pain, erythema and tenderness at the influenza vaccination site were similar in both groups. Swelling was less frequent (81/1293 [6.3%] versus 119/1322 (9.0%), p = 0.01) and the maximal diameter of erythema was smaller (mean 1.8 cm [SD 2.0] versus 3.0 cm [SD 2.5], p<0.001) in the Influenza+BCG group. Sixteen participants reported serious adverse events 9 participants in the Influenza+BCG group and 7 in the Influenza group.

Adverse events following influenza vaccination are not increased when BCG is co-administered.
Adverse events following influenza vaccination are not increased when BCG is co-administered.Effective tissue clutter filtering and noise removing are essential for ultrafast Doppler imaging. Singular vector decomposition (SVD)-based spatiotemporal method has been applied as a classical method to remove the clutter and strong motion artifacts. However, performance of the SVD-based methods often depends on a proper eigenvector thresholding, i.e., the separation of signal subspaces of small-value blood flow, large-value static tissue, and noise. In the study, a Cauchy-norm-based robust principal component analysis (Cauchy-RPCA) method is developed via Cauchy-norm-based sparsity penalization, which enhances the blood flow extraction of small-vessels. A randomized spatial downsampling strategy and alternating direction method of multipliers (ADMM) are further involved to accelerate the computation. A face-to-face comparison is carried out among the classical SVD, traditional RPCA, blind deconvolution-based RPCA (BD-RPCA), and the proposed Cauchy-RPCA methods. Ultrafast ultrasound imaging dataset recorded from rat brain is used to investigate the performance of the proposed Cauchy-RPCA method in terms of clutter filtering, power Doppler, color Doppler, and functional ultrasound (fUS) imaging. The computational efficiency is finally discussed.Due to the wide range of time scales involved in the ordinary differential equations (ODEs) describing chemical reaction kinetics, multidimensional numerical simulation of chemical reactive flows using detailed combustion mechanisms is computationally expensive. To confront this issue, this article presents an economic data-driven tabulation algorithm for fast combustion chemistry integration. It uses the recurrent neural networks (RNNs) to construct the tabulation from a series of current and past states to the next state, which takes full advantage of RNN in handling long-term dependencies of time series data. The training data are first generated from direct numerical integrations to form an initial state space, which is divided into several subregions by the K-means algorithm. The centroid of each cluster is also determined at the same time. Next, an Elman RNN is constructed in each of these subregions to approximate the expensive direct integration, in which the integration routine obtained from the centroid is regarded as the basis for a storing and retrieving solution to ODEs. Finally, the alpha-shape metrics with principal component analysis (PCA) are used to generate a set of reduced-order geometric constraints that characterize the applicable range of these RNN approximations. For online implementation, geometric constraints are frequently verified to determine which RNN network to be used to approximate the integration routine. The advantage of the proposed algorithm is to use a set of RNNs to replace the expensive direct integration, which allows to reduce both the memory consumption and computational cost. Numerical simulations of a H₂/CO-air combustion process are performed to demonstrate the effectiveness of the proposed algorithm compared to the existing ODE solver.Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g., locations and orientations. Although deep learning (DL) approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion--a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module that can be applied to useful pairs of sensor modalities, such as monocular images and inertial measurements, depth images, and light detection and ranging (LIDAR) point clouds. Our model is a uniform framework that is not restricted to specific modality or task. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and to estimate trajectory at both scale and global pose. In particular, we propose two fusion modules--a deterministic soft fusion and a stochastic hard fusion--and offer a comprehensive study of the new strategies compared with trivial direct fusion. We extensively evaluate all fusion strategies both on public datasets and on progressively degraded datasets that present synthetic occlusions, noisy and missing data, and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself provides insights into the operation of the various models.In this article, a novel model-free dynamic inversion-based Q-learning (DIQL) algorithm is proposed to solve the optimal tracking control (OTC) problem of unknown nonlinear input-affine discrete-time (DT) systems. Compared with the existing DIQL algorithm and the discount factor-based Q-learning (DFQL) algorithm, the proposed algorithm can eliminate the tracking error while ensuring that it is model-free and off-policy. First, a new deterministic Q-learning iterative scheme is presented, and based on this scheme, a model-based off-policy DIQL algorithm is designed. The advantage of this new scheme is that it can avoid the training of unusual data and improve data utilization, thereby saving computing resources. Simultaneously, the convergence and stability of the designed algorithm are analyzed, and the proof that adding probing noise into the behavior policy does not affect the convergence is presented. Then, by introducing neural networks (NNs), the model-free version of the designed algorithm is further proposed so that the OTC problem can be solved without any knowledge about the system dynamics.
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