From the Sakai-Sugimoto Model to the Generalized Skyrme Model

We derive the generalized Skyrme model as a low-energy effective model of the Sakai-Sugimoto model. The novelty with the past is the presence of the sextic term equal to the topological charge squared. This term appears when the $\omega$ meson, and the tower of states on top of it, are integrated out. We claim that, in the small 't Hooft coupling limit, the instanton is well described by a Skyrmion arising from the low energy effective Lagrangian of the Sakai-Sugimoto model. The sextic term plays a dominant role in this limit. Moreover, when a pion mass term is added we recover the BPS Skyrme model in the small 't Hooft coupling limit.Comment: 17 pages, 6 figures. v2: minor correction

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

ExaMon-X: a Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

In recent years, the Industrial Internet of Things (IIoT) has led to significant steps forward in many industries, thanks to the exploitation of several technologies, ranging from Big Data processing to Artificial Intelligence (AI). Among the various IIoT scenarios, large-scale data centers can reap significant benefits from adopting Big Data analytics and AI-boosted approaches since these technologies can allow effective predictive maintenance. However, most of the off-the-shelf currently available solutions are not ideally suited to the HPC context, e.g., they do not sufficiently take into account the very heterogeneous data sources and the privacy issues which hinder the adoption of the cloud solution, or they do not fully exploit the computing capabilities available in loco in a supercomputing facility. In this paper, we tackle this issue, and we propose an IIoT holistic and vertical framework for predictive maintenance in supercomputers. The framework is based on a big lightweight data monitoring infrastructure, specialized databases suited for heterogeneous data, and a set of high-level AI-based functionalities tailored to HPC actors’ specific needs. We present the deployment and assess the usage of this framework in several in-production HPC systems

Neutron electric dipole moment from gauge/string duality

We compute the electric dipole moment of nucleons in the large $N_c$ QCD model by Witten, Sakai and Sugimoto with $N_f=2$ degenerate massive flavors. Baryons in the model are instantonic solitons of an effective five-dimensional action describing the whole tower of mesonic fields. We find that the dipole electromagnetic form factor of the nucleons, induced by a finite topological $\theta$ angle, exhibits complete vector meson dominance. We are able to evaluate the contribution of each vector meson to the final result - a small number of modes are relevant to obtain an accurate estimate. Extrapolating the model parameters to real QCD data, the neutron electric dipole moment is evaluated to be $d_n = 1.8 \cdot 10^{-16}\, \theta\;e\cdot \mathrm{cm}$. The electric dipole moment of the proton is exactly the opposite.Comment: Latex, 4 pages; v2: minor corrections, few comments adde

ALDO: An Innovative Digital Framework for Active E-Learning

In this paper, we propose ALDO (Active e-Learning by DOing), a novel, advanced digital framework supporting integrated facilities for effective, active e-Learning. The ALDO framework includes an active repository for collecting/sharing relevant materials, collaborative editing services for enriching so collected “raw” materials, and advanced data visualization tools (e.g., interactive maps, graphs, and timelines) to explore the spatial and temporal dimension of specific data contexts. Although the present research was carried out within the European Horizon 2020 Project DETECt (Detecting Transcultural Identity in European Popular Crime Narratives), focusing on the specific data context of European crime narrative, the generality of ALDO technological framework makes it suitable for any type of study/teaching activity. More in details, ALDO consists of a multi-functional digital infrastructure (back-end) for the integration of collaborative editing and e-Learning activities in formal and informal educational contexts. The platform supports effective services for collecting, sharing, retrieving, and analyzing data, together with advanced online collaboration tools, an e-Learning platform and advanced data visualization tools, all made available to teachers/students through a dedicated Web portal (front-end). The design and creation of above tools and services for teaching, together with their uses, are presented and discussed through a series of real examples taken from DETECt

CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy

Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions

Cataplexy Detection: Neurologists, You Are Not Alone!

Narcolepsy with cataplexy is a severe lifelong disorder characterized, among the others, by the sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). In this extended abstract, we present two methodologies for the automatic analysis of patients’ videos able to assist neurologists in diagnosing the disease and/or detecting attacks. Indeed, recent findings demonstrated that the detection of abnormal motor behaviors in video recordings of patients undergoing emotional stimulation is effective in characterizing the disease symptoms. Such motor behaviors (ptosis, mouth opening, head drop) are however to be discovered by neurologists through manual inspection of patients’ videos. Automatic content-based video analysis is clearly of immediate help here. Experimental results conducted on real data support the effectiveness of the presented automated techniques