The multiplex structure of interbank networks

The interbank market has a natural multiplex network representation. We employ a unique database of supervisory reports of Italian banks to the Banca d'Italia that includes all bilateral exposures broken down by maturity and by the secured and unsecured nature of the contract. We find that layers have different topological properties and persistence over time. The presence of a link in a layer is not a good predictor of the presence of the same link in other layers. Maximum entropy models reveal different unexpected substructures, such as network motifs, in different layers. Using the total interbank network or focusing on a specific layer as representative of the other layers provides a poor representation of interlinkages in the interbank market and could lead to biased estimation of systemic risk.Comment: 41 pages, 8 figures, 10 table

Interbank markets and multiplex networks: centrality measures and statistical null models

The interbank market is considered one of the most important channels of contagion. Its network representation, where banks and claims/obligations are represented by nodes and links (respectively), has received a lot of attention in the recent theoretical and empirical literature, for assessing systemic risk and identifying systematically important financial institutions. Different types of links, for example in terms of maturity and collateralization of the claim/obligation, can be established between financial institutions. Therefore a natural representation of the interbank structure which takes into account more features of the market, is a multiplex, where each layer is associated with a type of link. In this paper we review the empirical structure of the multiplex and the theoretical consequences of this representation. We also investigate the betweenness and eigenvector centrality of a bank in the network, comparing its centrality properties across different layers and with Maximum Entropy null models.Comment: To appear in the book "Interconnected Networks", A. Garas e F. Schweitzer (eds.), Springer Complexity Serie

Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach

In this work, we propose a compositional data-driven approach for the formal estimation of collision risks for autonomous vehicles (AVs) while acting in a stochastic multi-agent framework. The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation. In our proposed setting, we first cast the original collision risk problem for each agent as a robust optimization program (ROP). Solving the acquired ROP is not tractable due to an unknown model that appears in one of its constraints. To tackle this difficulty, we collect finite numbers of data from trajectories of each agent and provide a scenario optimization program (SOP) corresponding to the original ROP. We then establish a probabilistic bridge between the optimal value of SOP and that of ROP, and accordingly, we formally construct the sub-barrier certificate for each unknown agent based on the number of data and a required level of confidence. We then propose a compositional technique based on small-gain reasoning to quantify the collision risk for multi-agent AVs with some desirable confidence based on sub-barrier certificates of individual agents constructed from data. For the case that the proposed compositionality conditions are not satisfied, we provide a relaxed version of compositional results without requiring any compositionality conditions but at the cost of providing a potentially conservative collision risk. Eventually, we also present our approaches for non-stochastic multi-agent AVs. We demonstrate the effectiveness of our proposed results by applying them to a vehicle platooning consisting of 100 vehicles with 1 leader and 99 followers. We formally estimate the collision risk by collecting data from trajectories of each agent.Comment: This work has been accepted at IEEE Transactions on Control of Network System

A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness

Autonomous Vehicles (AVs) have the potential to provide numerous societal benefits, such as decreased road accidents and increased overall transportation efficiency. However, quantifying the risk associated with AVs is challenging due to the lack of historical data and the rapidly evolving technology. This paper presents a data-driven framework for comparing the risk of different AVs' behaviors in various operational design domains (ODDs), based on counterfactual simulations of "misbehaving" road users. We introduce the concept of counterfactual safety margin, which represents the minimum deviation from normal behavior that could lead to a collision. This concept helps to find the most critical scenarios but also to assess the frequency and severity of risk of AVs. We show that the proposed methodology is applicable even when the AV's behavioral policy is unknown -- through worst- and best-case analyses -- making the method useful also to external third-party risk assessors. Our experimental results demonstrate the correlation between the safety margin, the driving policy quality, and the ODD shedding light on the relative risk associated with different AV providers. This work contributes to AV safety assessment and aids in addressing legislative and insurance concerns surrounding this emerging technology

Comparative Safety Performance of Autonomous- and Human Drivers: A Real-World Case Study of the Waymo One Service

This study compares the safety of autonomous- and human drivers. It finds that the Waymo One autonomous service is significantly safer towards other road users than human drivers are, as measured via collision causation. The result is determined by comparing Waymo's third party liability insurance claims data with mileage- and zip-code-calibrated Swiss Re (human driver) private passenger vehicle baselines. A liability claim is a request for compensation when someone is responsible for damage to property or injury to another person, typically following a collision. Liability claims reporting and their development is designed using insurance industry best practices to assess crash causation contribution and predict future crash contributions. In over 3.8 million miles driven without a human being behind the steering wheel in rider-only (RO) mode, the Waymo Driver incurred zero bodily injury claims in comparison with the human driver baseline of 1.11 claims per million miles (cpmm). The Waymo Driver also significantly reduced property damage claims to 0.78 cpmm in comparison with the human driver baseline of 3.26 cpmm. Similarly, in a more statistically robust dataset of over 35 million miles during autonomous testing operations (TO), the Waymo Driver, together with a human autonomous specialist behind the steering wheel monitoring the automation, also significantly reduced both bodily injury and property damage cpmm compared to the human driver baselines

The population of merging compact binaries inferred using gravitational waves through GWTC-3

We report on the population properties of 76 compact binary mergers detected with gravitational waves below a false alarm rate of 1 per year through GWTC-3. The catalog contains three classes of binary mergers: BBH, BNS, and NSBH mergers. We infer the BNS merger rate to be between 10 $\rm{Gpc^{-3} yr^{-1}}$ and 1700 $\rm{Gpc^{-3} yr^{-1}}$ and the NSBH merger rate to be between 7.8 $\rm{Gpc^{-3}\, yr^{-1}}$ and 140 $\rm{Gpc^{-3} yr^{-1}}$ , assuming a constant rate density versus comoving volume and taking the union of 90% credible intervals for methods used in this work. Accounting for the BBH merger rate to evolve with redshift, we find the BBH merger rate to be between 17.9 $\rm{Gpc^{-3}\, yr^{-1}}$ and 44 $\rm{Gpc^{-3}\, yr^{-1}}$ at a fiducial redshift (z=0.2). We obtain a broad neutron star mass distribution extending from $1.2^{+0.1}_{-0.2} M_\odot$ to $2.0^{+0.3}_{-0.3} M_\odot$. We can confidently identify a rapid decrease in merger rate versus component mass between neutron star-like masses and black-hole-like masses, but there is no evidence that the merger rate increases again before 10 $M_\odot$. We also find the BBH mass distribution has localized over- and under-densities relative to a power law distribution. While we continue to find the mass distribution of a binary's more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above $\sim 60 M_\odot$. The rate of BBH mergers is observed to increase with redshift at a rate proportional to $(1+z)^{\kappa}$ with $\kappa = 2.9^{+1.7}_{-1.8}$ for $z\lesssim 1$. Observed black hole spins are small, with half of spin magnitudes below $\chi_i \simeq 0.25$. We observe evidence of negative aligned spins in the population, and an increase in spin magnitude for systems with more unequal mass ratio

Interbank markets and multiplex networks: centrality measures and statistical null models

The interbank market is considered one of the most important channels 6 of contagion. Its network representation, where banks and claims/obligations are 7 represented by nodes and links (respectively), has received a lot of attention in 8 the recent theoretical and empirical literature, for assessing systemic risk and 9 identifying systematically important financial institutions. Different types of links, 10 for example in terms of maturity and collateralization of the claim/obligation, can 11 be established between financial institutions. Therefore a natural representation of 12 the interbank structure which takes into account more features of the market, is 13 a multiplex, where each layer is associated with a type of link. In this paper we 14 review the empirical structure of the multiplex and the theoretical consequences of 15 this representation. We also investigate the betweenness and eigenvector centrality 16 of a bank in the network, comparing its centrality properties across different layers 17 and with Maximum Entropy null models

Radiochromic film dosimetry in synchrotron radiation breast computed tomography: a phantom study

This study relates to the INFN project SYRMA-3D for in vivo phase-contrast breast computed tomography using the SYRMEP synchrotron radiation beamline at the ELETTRA facility in Trieste, Italy. This peculiar imaging technique uses a novel dosimetric approach with respect to the standard clinical procedure. In this study, optimization of the acquisition procedure was evaluated in terms of dose delivered to the breast. An offline dose monitoring method was also investigated using radiochromic film dosimetry. Various irradiation geometries have been investigated for scanning the prone patient's pendant breast, simulated by a 14 cm-diameter polymethylmethacrylate cylindrical phantom containing pieces of calibrated radiochromic film type XR-QA2. Films were inserted mid-plane in the phantom, as well as wrapped around its external surface, and irradiated at 38 keV, with an air kerma value that would produce an estimated mean glandular dose of 5 mGy for a 14 cm-diameter 50% glandular breast. Axial scans were performed over a full rotation or over 180°. The results point out that a scheme adopting a stepped rotation irradiation represents the best geometry to optimize the dose distribution to the breast. The feasibility of using a piece of calibrated radiochromic film wrapped around a suitable holder around the breast to monitor the scan dose offline is demonstrated