An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning.

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

Heart rate variability biofeedback as a behavioral neurocardiac intervention to enhance vagal heart rate control.

BACKGROUND: Patients with coronary heart disease (CHD) who experience depressed mood or psychological stress exhibit decreased vagal control of heart rate (HR), as assessed by spectral analysis of HR variability (HRV). Myocardial infarction and sudden cardiac death are independently associated with depression and stress, as well as impaired vagal HR control. This study examined whether a behavioral neurocardiac intervention to reduce stress or depression can augment cardiovagal modulation in CHD patients. We hypothesized that (1) cognitive-behavioral training with HRV biofeedback would augment vagal recovery from acute stress, and (2) vagal regulation of HR would be inversely associated with stress and depression after treatment. METHODS: This randomized controlled trial enrolled 46 CHD patients from 3 clinics of CHD risk reduction in Toronto and Vancouver, Canada. Subjects were randomized to five 1.5-hour sessions of HRV biofeedback or an active control condition. Outcome was assessed by absolute and normalized high-frequency spectral components (0.15-0.50 Hz) of HRV, and by the Perceived Stress Scale and Centre for Epidemiologic Studies in Depression scale. RESULTS: Both groups reduced symptoms on the Perceived Stress Scale (P = .001) and Centre for Epidemiologic Studies in Depression scale (P = .004). Hierarchical linear regression determined that improved psychological adjustment was significantly associated with the high-frequency index of vagal HR modulation only in the HRV biofeedback group. Adjusted R 2 was as follows: HRV biofeedback group, 0.86 for stress (P = .02) and 0.81 for depression (P = .03); versus the active control group, 0.04 (P = .57) and 0.13 (P = .95), respectively. CONCLUSION: A novel behavioral neurocardiac intervention, HRV biofeedback, can augment vagal HR regulation while facilitating psychological adjustment to CHD.

A comparison of algorithms for detection of spikes in the electroencephalogram.

Identification of the short transient waveform, called a spike, in the cortical electroencephalogram (EEG) plays an important role during diagnosis of neurological disorders such as epilepsy. It has been suggested that artificial neural networks (ANN) can be employed for spike detection in the EEG, if suitable features are provided as input to an ANN. In this paper, we explore the performance of neural network-based classifiers using features selected by algorithms suggested by four previous investigators. Of these, three algorithms model the spike by mathematical parameters and use them as features for classification while the fourth algorithm uses raw EEG to train the classifier. The objective of this paper is to examine if there is any inherent advantage to any particular set of features, subject to the condition that the same data are used for all feature selection algorithms. Our results suggest that artificial neural networks trained with features selected using any one of the above three algorithms as well as raw EEG directly fed to the ANN will yield similar results.