Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units.  https://www.selleckchem.com/products/amg510.html  We compare three numerical methods for the integration of these equations the Euler method, and two methods we have developed and present here a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known nonlinear connectionist model of retrieval in single-digit multiplication, and show that, in many regimes, the semi-analytical and modified Runge Kutta methods outperform the Euler method, in some regimes by more than three orders of magnitude. Given the outstanding difference in execution time of the methods, and that the EM is widely used, we conclude that the researchers in the field can greatly benefit from our analysis and developed methods.Upper-limb prostheses are subject to high rates of abandonment. Prosthesis abandonment is related to a reduced sense of embodiment, the sense of self-location, agency, and ownership that humans feel in relation to their bodies and body parts. If a prosthesis does not evoke a sense of embodiment, users are less likely to view them as useful and integrated with their bodies. Currently, visual feedback is the only option for most prosthesis users to account for their augmented activities. However, for activities of daily living, such as grasping actions, haptic feedback is critically important and may improve sense of embodiment. Therefore, we are investigating how converting natural haptic feedback from the prosthetic fingertips into vibrotactile feedback administered to another location on the body may allow participants to experience haptic feedback and if and how this experience affects embodiment. While we found no differences between our experimental manipulations of feedback type, we found evidence that embodiment was not negatively impacted when switching from natural feedback to proximal vibrotactile feedback. Proximal vibrotactile feedback should be further studied and considered when designing prostheses.Lower-limb exoskeletons often use torque control to manipulate energy flow and ensure human safety. The accuracy of the applied torque greatly affects how well the motion is assisted and therefore improving it is always of interest. Feed-forward iterative learning, which is similar to predictive stride-wise integral control, has proven an effective compensation to feedback control for torque tracking in exoskeletons with complicated dynamics during human walking. Although the intention of iterative learning was initially to benefit average tracking performance over multiple strides, we found that, after proper gain tuning, it can also help improve real-time torque tracking. We used theoretical analysis to predict an optimal iterative-learning gain as the inverse of the passive actuator stiffness. Walking experiments resulted in an optimum gain equal to 0.99 ± 0.38 times the predicted value, confirming our hypothesis. The results of this study provide guidance for the design of torque controllers in robotic legged locomotion systems and will help improve the performance of robots that assist gait.Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based ose. CoMB-Deep maintains two classification categories binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.[This corrects the article DOI 10.3389/fncom.2020.00029.].Background Individuals' information processing includes automatic and effortful processes and the latter require sustained concentration or attention and larger amounts of cognitive "capacity." Event-related potentials (ERPs) reflect all neural activities that are related to a certain stimulus. Investigating ERP characteristics of effortful cognitive processing in people with schizophrenia would be helpful in further understanding the neural mechanism of schizophrenia. Methods Both schizophrenia patients (SCZ, n = 33) and health controls (HC, n = 33) completed ERP measurements during the performance of the basic facial emotion identification test (BFEIT) and the face-vignette task (FVT). Data of ERP components (N100, P200, and N250), BFEIT and FVT performances were analyzed. Results Schizophrenia patients' accuracies of face emotion detection in the BFEIT and vignette emotion detection in the FVT were both significantly worse than the performance of the HC group. Repeated-measures ANOVAs performed on mean amplitudes and latencies revealed that the interaction effect for group × experiment × site (prefrontal, frontal, central, parietal, and occipital site) was significant for N250 amplitude.