Semantic reconstruction of continuous language from MEG signals

Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their low quality. In this study, we proposed a data-driven approach for decoding semantic of language from Magnetoencephalography (MEG) signals recorded while subjects were listening to continuous speech. First, a multi-subject decoding model was trained using contrastive learning to reconstruct continuous word embeddings from MEG data. Subsequently, a beam search algorithm was adopted to generate text sequences based on the reconstructed word embeddings. Given a candidate sentence in the beam, a language model was used to predict the subsequent words. The word embeddings of the subsequent words were correlated with the reconstructed word embedding. These correlations were then used as a measure of the probability for the next word. The results showed that the proposed continuous word embedding model can effectively leverage both subject-specific and subject-shared information. Additionally, the decoded text exhibited significant similarity to the target text, with an average BERTScore of 0.816, a score comparable to that in the previous fMRI study

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure

Highly reversible transition metal migration in superstructure-free Li-rich oxide boosting voltage stability and redox symmetry

The further practical applications of Li-rich layered oxides are impeded by voltage decay and redox asymmetry, which are closely related to the structural degradation involving irreversible transition metal migration. It has been demonstrated that the superstructure ordering in O2-type materials can effectively suppress voltage decay and redox asymmetry. Herein, we elucidate that the absence of this superstructure ordering arrangement in a Ru-based O2-type oxide can still facilitate the highly reversible transition metal migration. We certify that Ru in superstructure-free O2-type structure can unlock a quite different migration path from Mn in mostly studied cases. The highly reversible migration of Ru helps the cathode maintain the structural robustness, thus realizing terrific capacity retention with neglectable voltage decay and inhibited oxygen redox asymmetry. We untie the knot that the absence of superstructure ordering fails to enable a high-performance Li-rich layered oxide cathode material with suppressed voltage decay and redox asymmetry

A Backstepping Controller Based on a Model-Assisted Extended State Observer for a Slice Rotor Supported by Active Magnetic Bearings

To improve the robustness of a slice rotor system supported by active magnetic bearings (AMBs), here, we propose a backstepping controller based on a model-assisted extended state observer (MESO-BC). Based on a generalized extended state observer (GESO), a model-assisted extended state observer is studied with consideration of the linear model of AMBs. The model-assisted extended state observer can estimate the unknown disturbances of the active magnetic bearing system, such as model inaccuracy and external disturbance, and is superior to the generalized extended state observer with respect to observation errors and the speed of convergence errors. In addition, it is compared with the backstepping controller based on a generalized extended state observer (GESO-BC) and conventional adaptive backstepping controller (ABC), and the simulation and experimental results verify the effectiveness of the proposed method. The experimental results demonstrate that the overshoot of the MESO-BC decrease by 5.94% and 13.2% as compared with the GESO-BC and ABC under the effect of pulse disturbance, respectively, and the rotor displacement of the MESO-BC reduce by 40.3% and 54.6% as compared with the GESO-BC and ABC under the effect of the sinusoidal disturbance, respectively

Assessing the Effect of Game System for Rehabilitation on Rehabilitation of Autism and Cerebral Palsy

This work presented a system to encourage children and adolescents with autism and cerebral palsy (CP) to improve their abilities of self-care, mobility and sociality. A study was conducted with 51 children with autism and 36 children with CP using the Game System for Rehabilitation with Kinect at intervals of one month. The scope of this study was aimed to evaluate the effects of Game System for Rehabilitation in terms of self-care, mobility and social function of children and adolescents with autism and CP. Pediatric Evaluation of Disability Inventory (PEDI) was used to determine the functional abilities of self-care, mobility and sociability. Data obtained allowed us to conclude that the positive effects of the Game System for Rehabilitation on the functional abilities of self-care, mobility and social function with disabilities were significant. The Game System for Rehabilitation applied for children and adolescents with autism and CP was effective for the targeted population. And it gives a new light for children with autism and CP to continue their rehabilitation successfully

DWNN: Deep Wavelet Neural Network for Solving Partial Differential Equations

In this paper, we propose a deep wavelet neural network (DWNN) model to approximate the natural phenomena that are described by some classical PDEs. Concretely, we introduce wavelets to deep architecture to obtain a fine feature description and extraction. That is, we constructs a wavelet expansion layer based on a family of vanishing momentum wavelets. Second, the Gaussian error function is considered as the activation function owing to its fast convergence rate and zero-centered output. Third, we design the cost function by considering the residual of governing equation, the initial/boundary conditions and an adjustable residual term of observations. The last term is added to deal with the shock wave problems and interface problems, which is conducive to rectify the model. Finally, a variety of numerical experiments are carried out to demonstrate the effectiveness of the proposed approach. The numerical results validate that our proposed method is more accurate than the state-of-the-art approach

Performance Analysis of Acceleration and Inertial Force of Electromagnetic Suspension Inertial Stabilizer

In this paper, the structural characteristics of electromagnetic suspension (EMS) inertial stabilizers are analyzed firstly, and then a mechanical analysis of a single mass block and double mass block is carried out. The relationship model between the inertial anti-rolling mass block and inertial force transmitted to the ship is established. The inertial force is determined by the number of coil turns, coil current, mass block, mass of the ship, electromagnet current, rate of change of the electromagnet current, air gap between the electromagnet and inertial mass block, and rotational angular speed. Through theoretical analysis, it is found that the response speed of inertia force is directly related to the electromagnetic coil current, the voltage at both ends of the electromagnetic coil, the coil resistance and the air gap. It is concluded that the response speed of the inertia force can be controlled by controlling the coil current, adjusting the voltage at both ends of the coil and adjusting the air gap. The inductance of the electromagnetic coil will also increase the nonlinearity of the inertial anti-roll system. On the basis of theoretical analysis, a digital simulation of EMS inertial stabilizer is carried out by MATLAB and ANSYS MAXWELL2D. Finally, a single mass block system of EMS inertial stabilizer is designed and tested. During the test, a 1.5 V sinusoidal excitation voltage is added to the electromagnetic coil after the mass block is suspended stably, and the maximum acceleration values of the inertial anti-rolling mass block and hull are 10.29 m/s2 and 1.27 m/s2. Finally, the theoretical analysis results, digital simulation results and experimental results are analyzed, which verifies the correctness of the acceleration and inertia force performance analysis of the EMS inertial stabilizer