Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks

A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that evidence propagation in this setup can be reduced to standard updating in an augmented credal network, equivalent to a set of consistent Bayesian networks. A characterization of the computational complexity for this task is derived together with an efficient exact procedure for a subclass of instances. In the case of multiple uncertain evidences over the same variable, the proposed procedure can provide a set-valued version of the geometric approach to opinion pooling.Comment: 19 page

Performances of the Italian seismic network, 1985-2002: the hidden thing

Seismic data users and people managing a sesimic network are both interested in the potentiality of the data, with the difference that the former look at stability, the second at improvements. In this work we measure the performances of the Italian Telemetered Seismic Network in 1985-2002 by defining basic significant parameters and studying their evolution during the years. Then, we deal with the geological methods used to characterise or to plan a seismic station deployment in a few cases. Last, we define the gain of the network as the percentage of located earthquakes with respect to the total recorded earthquakes. By analysing the distribution of non-located ("missed") earthquakes, we suggest possible actions to take in order to increase the gain. Results show that completeness magnitude is 2.4 in the average over the analysed period, and it can be as low as 2.2 when we consider non-located earthquakes as well. Parameters such as the distance between an earthquake and the closest station, and the RMS location decrease with time, reflecting improvements in the location quality. Methods for geologic and seismological characterisation of a possible station site also proved to be effective. Finally, we represent the number of missed earthquakes at each station, showing that nine stations control more that 50% of all missed earthquakes, and suggesting areas in Italy where the network might be easily improved.Comment: 17 pages, 1 table, 11 figures. Submitted to Annals of Geophysic

Stationary growth and unique invariant harmonic measure of cylindrical DLA

We prove that the harmonic measure is stationary, unique and invariant on the interface of DLA growing on a cylinder surface. We provide a detailed theoretical analysis puzzling together multiscaling, multifractality and conformal invariance, supported by extensive numerical simulations of clusters built using conformal mappings and on lattice. The growth properties of the active and frozen zones are clearly elucidated. We show that the unique scaling exponent characterizing the stationary growth is the DLA fractal dimension

Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schr\"odinger Equation

We present a machine learning model resting on convolutional neural network (CNN) capable to yield accurate scattering phase shifts in s-wave caused by different three-dimensional spherically symmetric potentials at fixed collision energy thereby bypassing the radial Schr\"odinger equation. We obtain good performance even in the presence of potential instances supporting bound states

Identification of a novel motif in DNA ligases exemplified by DNA ligase IV

DNA ligase IV is an essential protein that functions in DNA non-homologous end-joining, the major mechanism that rejoins DNA double-strand breaks in mammalian cells. LIG4 syndrome represents a human disorder caused by mutations in DNA ligase IV that lead to impaired but not ablated activity. Thus far, five conserved motifs in DNA ligases have been identified. We previously reported G469E as a mutational change in a LIG4 syndrome patient. G469 does not lie in any of the previously reported motifs. A sequence comparison between DNA ligases led us to identify residues 468¿476 of DNA ligase IV as a further conserved motif, designated motif Va, present in eukaryotic DNA ligases. We carried out mutational analysis of residues within motif Va examining the impact on adenylation, double-stranded ligation, and DNA binding. We interpret our results using the DNA ligase I:DNA crystal structure. Substitution of the glycine at position 468 with an alanine or glutamic acid severely compromises protein activity and stability. Substitution of G469 with an alanine or glutamic acid is better tolerated but still impacts upon activity and protein stability. These finding suggest that G468 and G469 are important for protein stability and provide insight into the hypomorphic nature of the G469E mutation identified in a LIG4 syndrome patient. In contrast, residues 470, 473 and 476 within motif Va can be changed to alanine residues without any impact on DNA binding or adenylation activity. Importantly, however, such mutational changes do impact upon double-stranded ligation activity. Considered in light of the DNA ligase I:DNA crystal structure, our findings suggest that residues 470¿476 function as part of a molecular pincer that maintains the DNA in a conformation that is required for ligation

Exploiting Multimode Antennas for MIMO and AoA Estimation in Size-Constrained IoT Devices

This work proposes compact multimode Multiple-Input–Multiple-Output (MIMO) antennas for Angle of Arrival (AoA) estimation in miniaturized Internet of Things (IoT) systems. The method excites different orthogonal radiating modes (TM 21 , TM 02 , and TM 31 modes) for beamforming capabilities, and the AoA performance is investigated using the Multiple Signal Classification (MUSIC) algorithm, executed using numerical and experimental data. The technique is tested at 2.238GHz , while using an antenna diamete

Scalable architecture for online prioritization of cyber threats

This paper proposes an innovative framework for the early detection of several cyber attacks, where the main component is an analytics core that gathers streams of raw data generated by network probes, builds several layer models representing different activities of internal hosts, analyzes intra-layer and inter-layer information. The online analysis of internal network activities at different levels distinguishes our approach with respect to most detection tools and algorithms focusing on separate network levels or interactions between internal and external hosts. Moreover, the integrated multi-layer analysis carried out through parallel processing reduces false positives and guarantees scalability with respect to the size of the network and the number of layers. As a further contribution, the proposed framework executes autonomous triage by assigning a risk score to each internal host. This key feature allows security experts to focus their attention on the few hosts with higher scores rather than wasting time on thousands of daily alerts and false alarms