Looking for Light Pseudoscalar Bosons in the Binary Pulsar System J0737-3039

We present numerical calculations of the photon-light-pseudoscalar-boson conversion in the recently discovered binary pulsar system J0737-3039. Light pseudoscalar bosons (LPBs) oscillate into photons in the presence of strong magnetic fields. In the context of this binary pulsar system, this phenomenon attenuates the light beam emitted by one of the pulsars, when the light ray goes through the magnetosphere of the companion pulsar. We show that such an effect is observable in the gamma-ray band since the binary pulsar is seen almost edge-on, depending on the value of the LPB mass and on the strenght of its two-photon coupling. Our results are surprising in that they show a very sharp and significant (up to 50%) transition probability in the gamma-ray ($>$ tens of MeV) domain. The observations can be performed by the upcoming NASA GLAST mission.Comment: to appear in Phys. Rev. Let

a novel energy efficiency metric for model based fault diagnosis of telecommunication central offices

Abstract A novel energy metric is presented, to be adopted for monitoring and diagnosis of telecommunication (TLC) central offices (COFs). Such an activity is motivated by the TLC players need to substantially reduce their energy demand, both to increase their market competitiveness and meet the stringent green-house gas (GHG) emission regulations. The proposed metric, the utilization factor (UF), was thus defined according to the energy break-down of TLC-COFs. Then, suitable data-processing techniques were applied to develop a diagnosis-oriented UF model. Model accuracy, found to be always capable of guaranteeing UF estimation errors safely below 15 % for all non-faulty COFs, was proven adequate to perform model-based fault detection and isolation of relevant malfunctioning, such as abnormal data acquisition and non-optimal energy management

Linked Data approach for selection process automation in Systematic Reviews

Background: a systematic review identifies, evaluates and synthesizes the available literature on a given topic using scientific and repeatable methodologies. The significant workload required and the subjectivity bias could affect results. Aim: semi-automate the selection process to reduce the amount of manual work needed and the consequent subjectivity bias. Method: extend and enrich the selection of primary studies using the existing technologies in the field of Linked Data and text mining. We define formally the selection process and we also develop a prototype that implements it. Finally, we conduct a case study that simulates the selection process of a systematic literature published in literature. Results: the process presented in this paper could reduce the work load of 20% with respect to the work load needed in the fully manually selection, with a recall of 100%. Conclusions: the extraction of knowledge from scientific studies through Linked Data and text mining techniques could be used in the selection phase of the systematic review process to reduce the work load and subjectivity bia

Intensity Thresholds and the Statistics of the Temporal Occurrence of Solar Flares

Introducing thresholds to analyze time series of emission from the Sun enables a new and simple definition of solar flare events, and their interoccurrence times. Rescaling time by the rate of events, the waiting and quiet time distributions both conform to scaling functions that are independent of the intensity threshold over a wide range. The scaling functions are well described by a two parameter function, with parameters that depend on the phase of the solar cycle. For flares identified according to the current, standard definition, similar behavior is found.Comment: 5 pages, 4 figures, revtex

Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning

Abstract Fueled by advertising companies' need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users' privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters. We apply our methodology on a dataset of more than 400, 000 JavaScript files accessed by about 1, 000 volunteers during a one-month long experiment to observe adoption of fingerprinting in a real scenario. We compare approaches based on both static and dynamic code analysis to automatically detect fingerprinters and show they provide different angles complementing each other. This demonstrates that studies based on either static or dynamic code analysis provide partial view on actual fingerprinting usage in the web. To the best of our knowledge we are the first to perform this comparison with respect to fingerprinting. Our approach achieves 94% accuracy in small decision time. With this we spot more than 840 fingerprinting services, of which 695 are unknown to popular tracker blockers. These include new actual trackers as well as services which use fingerprinting for purposes other than tracking, such as anti-fraud and bot recognition