557 research outputs found
Wave breaking in the unidirectional non-local wave model
In this paper we study wave breaking in the unidirectional non-local wave
model describing the motion of a collision-free plasma in a magnetic field. By
analyzing the monotonicity and continuity properties of a system of the
Riccati-type differential inequalities involving the extremal slopes of flows,
we show a new sufficient condition on the initial data to exhibit wave
breaking. Moreover, the estimates of life span and wave breaking rate are
derived
A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data
Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks
Electrocardiogram Recognization Based on Variational AutoEncoder
Subtle distortions on electrocardiogram (ECG) can help doctors to diagnose some serious larvaceous heart sickness on their patients. However, it is difficult to find them manually because of disturbing factors such as baseline wander and high-frequency noise. In this chapter, we propose a method based on variational autoencoder to distinguish these distortions automatically and efficiently. We test our method on three ECG datasets from Physionet by adding some tiny artificial distortions. Comparing with other approaches adopting autoencoders [e.g., contractive autoencoder, denoising autoencoder (DAE)], the results of our experiment show that our method improves the performance of publically available on ECG analysis on the distortions
Ex Perimental Study on Water Absorption of Coal Under Different Pressure Source Conditions
AbstractIn order to study the effect of pressure on the water absorption capability of coal, the water injection experiments of two coal samples were done under different pressure source conditions and room temperature by using self-designed pressurized water device. The experimental results show that pressure has a positive effect on water absorbability; water absorbability gets large as the pressure increases; the earlier water absorbability of coal is rapid, and the water absorbability of coal displays a similar Langmuir-isothermal adsorption curve with the changes of time, and it has saturated water absorbability. The water injection pressure is higher, which not only strengthened the ability of expanding seepage space, but also developed the transporting water and storage water space. The later water absorption curve of coal becomes weaker, and water adsorption process is mainly influenced by capillary force. The pressure water only supplies provisions and time is the main influential factor
A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph
The detection of malware have developed for many years, and the appearance of new machine learning and deep learning techniques have improved the effect of detectors. However, most of current researches have focused on the general features of malware and ignored the development of the malware themselves, so that the features could be useless with the time passed as well as the advance of malware techniques. Besides, the detection methods based on machine learning are mainly static detection and analysis, while the study of real-time detection of malware is relatively rare. In this article, we proposed a new model that could detect malware real-time in principle and learn new features adaptively. Firstly, a new data structure of API-Pair was adopted, and the constructed data was trained with Maximum Entropy model, which could satisfy the goal of weighting and adaptive learning. Then a clustering was practised to filter relatively unrelated and confusing features. Moreover, a detector based on Lont Short Term Memory Network (LSTM) was devised to achieve the goal of real-time detection. Finally, a series of experiments were designed to verify our method. The experimental results showed that our model could obtain the highest accuracy of 99.07% in general tests and keep the accuracies above 97% with the development of malware; the results also proved the feasibility of our model in real-time detection through the simulation experiment, and robustness against a typical adversarial attack
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