Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Recent results and perspectives on cosmology and fundamental physics from microwave surveys

Recent cosmic microwave background data in temperature and polarization have reached high precision in estimating all the parameters that describe the current so-called standard cosmological model. Recent results about the integrated Sachs-Wolfe effect from cosmic microwave background anisotropies, galaxy surveys, and their cross-correlations are presented. Looking at fine signatures in the cosmic microwave background, such as the lack of power at low multipoles, the primordial power spectrum and the bounds on non-Gaussianities, complemented by galaxy surveys, we discuss inflationary physics and the generation of primordial perturbations in the early Universe. Three important topics in particle physics, the bounds on neutrinos masses and parameters, on thermal axion mass and on the neutron lifetime derived from cosmological data are reviewed, with attention to the comparison with laboratory experiment results. Recent results from cosmic polarization rotation analyses aimed at testing the Einstein equivalence principle are presented. Finally, we discuss the perspectives of next radio facilities for the improvement of the analysis of future cosmic microwave background spectral distortion experiments.Comment: 27 pages, 9 figures. Review Article. International Journal of Modern Physics D, in press. [Will appear also on the proceedings of the Fourteenth Marcel Grossmann Meeting University of Rome "La Sapienza" - Rome, July 12-18, 2015 (http://www.icra.it/mg/mg14/), eds. Robert T. Jantzen, Kjell Rosquist, Remo Ruffini. World Scientific, Singapore

New Methods for Network Traffic Anomaly Detection

In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel matrix. The Hankel matrix is spatio-temporal correlation matrix and has been used in many other domains including climate data analysis and econometrics. Using our approach we can seamlessly integrate the discovery of both spatial and temporal anomalies. Comparison with other state of the art methods in the networks community confirms that our approach can discover both DoS and port scan attacks. The spectral decomposition of the Hankel matrix is closely tied to the problem of inference in Linear Dynamical Systems (LDS). We introduce a new problem, the Online Selective Anomaly Detection (OSAD) problem, to model the situation where the objective is to report new anomalies in the system and suppress know faults. For example, in the network setting an operator may be interested in triggering an alarm for malicious attacks but not on faults caused by equipment failure. In order to solve OSAD we combine techniques from machine learning and control theory in a unique fashion. Machine Learning ideas are used to learn the parameters of an underlying data generating system. Control theory techniques are used to model the feedback and modify the residual generated by the data generating state model. Experiments on synthetic and real data sets confirm that the OSAD problem captures a general scenario and tightly integrates machine learning and control theory to solve a practical problem

Self-Potential Signals Generated by the Corrosion of Buried Metallic Objects with Application to Contaminant Plumes

Large-amplitude (\u3e100 mV) negative electric (self)-potential anomalies are often observed in the vicinity of buried metallic objects and ore bodies or over groundwater plumes associated with organic contaminants. To explain the physical and chemical mechanisms that generate such electrical signals, a controlled laboratory experiment was carried out involving two metallic cylinders buried with vertical and horizontal orientations and centered through and in the capillary fringe within a sandbox. The 2D and 3D self-potential (SP) data were collected at several time steps along with collocated pH and redox potential measurements. Large dipolar SP and redox potential anomalies developed in association with the progressive corrosion of the vertical pipe, although no anomalies were observed in the vicinity of the horizontal pipe. This discrepancy was due to the orientation of the pipes with the vertical pipe subjected to a significantly larger EH gradient. Accounting for the electrical conductivity distribution, the SP data were inverted to recover the source current density vector field using a deterministic least-squares 4D (time-lapse) finite-element modeling approach. These results were then used to retrieve the 3D distribution of the redox potential along the vertical metallic cylinder. The results of the inversion were found to be in excellent agreement with the measured distribution of the redox potential. This experiment indicated that passively recorded electrical signals can be used to nonintrusively monitor corrosion processes. In addition, vertical electrical potential profiles measured through a mature hydrocarbon contaminated site were consistent with the sandbox observations, lending support to the geobattery model over organic contaminant plumes

Geophysics for Mineral Exploration

This Special Issue contains ten papers which focus on emerging geophysical techniques for mineral exploration, novel modeling, and interpretation methods, including joint inversions of multi physics data, and challenging case studies. The papers cover a wide range of mineral deposits, including banded iron formations, epithermal gold–silver–copper–iron–molybdenum deposits, iron-oxide–copper–gold deposits, and prospecting forgroundwater resources