ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks

The explosion in volume and heterogeneity of data communication network measurements opens the door to the massive applica- tion of machine learning and artificial intelligence technology in networking. While machine learning is today systematically and successfully applied in many other data-driven domains, its appli- cation is in an infancy stage of development in the networking domain. The ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big- DAMA, fosters the research and development of novel analytical approaches and technical solutions that can exploit Big Data tech- nology in the analysis of complex communication networks such as the Internet

GRB970228 as a prototype for short GRBs with afterglow

GRB970228 is analyzed as a prototype to understand the relative role of short GRBs and their associated afterglows, recently observed by Swift and HETE-II. Detailed theoretical computation of the GRB970228 light curves in selected energy bands are presented and compared with observational BeppoSAX data.Comment: 2 pages, 1 figure, to appear in the proceedings of "Swift and GRBs", Venice, 2006, Il Nuovo Cimento, in pres

Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective

Combining a hardware approach with a multiple classifier method can deeply improve system performance, since the multiple classifier system can successfully enhance the classification accuracy with respect to a single classifier, and a hardware implementation would lead to systems able to classify samples with high throughput and with a short latency. To the best of our knowledge, no paper in the literature takes into account the multiple classifier scheme as additional design parameter, mainly because of lack of efficient hardware combiner architecture. In order to fill this gap, in this paper we will first propose a novel approach for an efficient hardware implementation of the majority voting combining rule. Then, we will illustrate a design methodology to suitably embed in a digital device a multiple classifier system having Decision Trees as base classifiers and a majority voting rule as combiner. Bagging, Boosting and Random Forests will be taken into account. We will prove the effectiveness of the proposed approach on two real case studies related to Big Data issues

X-ray thermal emission from the jet of M87 with Chandra

With new calibration data, thermal emission from the jet of radio galaxy M87 is studied with about 700 ks archival data with Chandra. For nucleus, HST-1, knot D, X-ray energy spectra is well fitted with a power law. However, For knot A, a power law model is rejected with a high significance and an X-ray energy spectra is well fitted with a combination model of a power law and an apec model of 0.2 keV and a metal abundance 0.00. Thermal emission from knot A is confirmed.Comment: 20 pages, 10 tables, 2 figure

Classification of traffic flows into QoS classes by unsupervised learning and KNN clustering

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier

A theoretical model of an off-axis GRB jet

In light of the most recent observations of late afterglows produced by the merger of compact objects or by the core-collapse of massive dying stars, we research the evolution of the afterglow produced by an off-axis top-hat jet and its interaction with a surrounding medium. The medium is parametrized by a power law distribution of the form $n(r)\propto r^{-k}$ is the stratification parameter and contains the development when the surrounding density is constant ($k=0$) or wind-like ($k=2$). We develop an analytical synchrotron forward-shock model when the outflow is viewed off-axis, and it is decelerated by a stratified medium. Using the X-ray data points collected by a large campaign of orbiting satellites and ground telescopes, we have managed to apply our model and fit the X-ray spectrum of the GRB afterglow associated to SN 2020bvc with conventional parameters. Our model predicts that its circumburst medium is parametrized by a power law with stratification parameter $k=1.5$.Comment: Presented at the 37th International Cosmic Ray Conference (ICRC2021), Berlin, German