Anomaly Detection using Autoencoders in High Performance Computing Systems

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).Comment: 9 pages, 3 figure

Investigation of Cell-material Interactions by Scanning Probe Microscopy

The morphology of cells changes consistently with the surface where they adhere. As reported in the literature, surfaces with micrometric and nanometric patterns affect the cell morphology, as well as surfaces with peculiar chemical functionalities. In order to control both morphology and chemistry of the surface, mono-molecular layers of small organic molecules (specifically Pentacene, α-sexithiophene and PDI8-CN2) were deposited on SiOx substrates by means of Organic Molecular Beam Epitaxy (OMBE). Through the partial annealing method, SiOx substrates were fully covered with a mono-molecular layer, as confirmed by Atomic Force Microscopy measurements (surface coverage of about 98%). Such molecules enable SiOx substrates to become biocompatible and to have flat morphologies with selective chemical functionalities. Epithelial cells were cultivated on such samples and their structure and shape has been investigated by optical and fluorescence microscope and Scanning Electrochemical Microscopy (SECM). In the last years, Scanning Probe Microscopies (SPM) techniques have been increasingly used to investigate important biological issues. In order to best apply to the characteristics of these techniques in biological and medical fields, the probes used for imaging have to be: i) cheap and disposable as they can be easily contaminated and damaged during their use; ii) small enough to resolve the investigated biological phenomena or object; iii) reproducible in their aspect and imaging capability. In this work a new fabrication method was proposed and the probes obtained comply with all the aforementioned requirements

Design and optimization of a semi-active suspension system for railway applications

The present work focused on the application of innovative damping technologies in order to improve railway vehicle performances in terms of dynamic stability and comfort. As a benchmark case-study, the secondary suspension stage was selected and different control techniques were investigated, such as skyhook, dynamic compensation, and sliding mode control. The final aim was to investigate which control schemes are suitable for optimal exploitation of the non-linear behavior of the actuators. The performance improvement achieved by adoption of the semi-active dampers on a standard high-speed train was evaluated in terms of passenger comfort. Different control strategies have been investigated by comparing a simple SISO (single input single output) regulator based on the skyhook damper approach with a centralized regulator. The centralized regulator allows for the estimation of a near optimal set of control forces that minimize car-body accelerations with respect to constraints imposed by limited performance of semi-active actuators. Simulation results show that best results is obtained using a mixed approach that considers the simultaneous applications of model based and feedback compensation control terms