Evaluation of the quantiles and superquantiles of the makespan in interval valued activity networks

This paper deals with the evaluation of quantile-based risk measures for the makespan in scheduling problems represented as temporal networks with uncer tainties on the activity durations. More specifically, for each activity only the interval for its possible duration values is known in advance to both the sched uler and the risk analyst. Given a feasible schedule, we calculate the quantiles and the superquantiles of the makespan which are of interest as risk indicators in various applications. To this aim we propose and test a set of novel algorithms to determine rapid and accurate numerical estimations based on the calculation of theoretically proven lower and upper bounds. An extensive experimental campaign compu tationally shows the validity of the proposed methods, and allows to highlight their performances through the comparison with respect to the state-of-the-art algorithms

An efficient decomposition approach for surgical planning

This talk presents an efficient decomposition approach to surgical planning. Given a set of surgical waiting lists (one for each discipline) and an operating theater, the problem is to decide the room-to-discipline assignment for the next planning period (Master Surgical Schedule), and the surgical cases to be performed (Surgical Case Assignment), with the objective of optimizing a score related to priority and current waiting time of the cases. While in general MSS and SCA may be concurrently found by solving a complex integer programming problem, we propose an effective decomposition algorithm which does not require expensive or sophisticated computational resources, and is therefore suitable for implementation in any real-life setting. Our decomposition approach consists in first producing a number of subsets of surgical cases for each discipline (potential OR sessions), and select a subset of them. The surgical cases in the selected potential sessions are then discarded, and only the structure of the MSS is retained. A detailed surgical case assignment is then devised filling the MSS obtained with cases from the waiting lists, via an exact optimization model. The quality of the plan obtained is assessed by comparing it with the plan obtained by solving the exact integrated formulation for MSS and SCA. Nine different scenarios are considered, for various operating theater sizes and management policies. The results on instances concerning a medium-size hospital show that the decomposition method produces comparable solutions with the exact method in much smaller computation time

Coherent 100G Nonlinear Compensation with Single-Step Digital Backpropagation

Enhanced-SSFM digital backpropagation (DBP) is experimentally demonstrated and compared to conventional DBP. A 112 Gb/s PM-QPSK signal is transmitted over a 3200 km dispersion-unmanaged link. The intradyne coherent receiver includes single-step digital backpropagation based on the enhanced-SSFM algorithm. In comparison, conventional DBP requires twenty steps to achieve the same performance. An analysis of the computational complexity and structure of the two algorithms reveals that the overall complexity and power consumption of DBP are reduced by a factor of 16 with respect to a conventional implementation, while the computation time is reduced by a factor of 20. As a result, the proposed algorithm enables a practical and effective implementation of DBP in real-time optical receivers, with only a moderate increase of the computational complexity, power consumption, and latency with respect to a simple feed-forward equalizer for dispersion compensation.Comment: This work has been presented at Optical Networks Design & Modeling (ONDM) 2015, Pisa, Italy, May 11-14, 201

The Prostacyclin Analogue Iloprost as an Early Predictor of Successful Revascularization in Diabetic Patients Affected by Critical Limb Ischemia and Foot Ulcers

Abstract Purpose The aim of this study is to evaluate the role of Iloprost as an early predictor of successful revascularization in patients affected by ischemic diabetic foot ulcers (DFUs). Methods Consecutive patients with ischemic DFUs with persistent low TcPO2 ( All patients underwent Iloprost infusion and TcPO2 has been recorded at days 3, 14 and 30. According to the TcPO2 reported at day 3, patients were divided into two groups: group A (patients with TcPO2 ≥30mmHg) and group B (patients with TcPO2  Results Twenty-five patients have been included, 12/25 (48%) in Group A and 13/25 (52%) in Group B. There were no significant differences at the baseline and one day after PTA between the two groups while TcPO2 values recorded in Group A at days 3, 14 and 30 after Iloprost infusion were significant higher in comparison to the Group B (χ = 0.005). The rate of re-PTA were respectively 33,3% (Group A) and 53,8% (Group B) (p = 0.03). Conclusions Iloprost may be an early predictor of successful revascularization in patients affected by critical limb ischemia (CLI) and DFUs

An Optimal Control Approach to Learning in SIDARTHE Epidemic model

The COVID-19 outbreak has stimulated the interest in the proposal of novel epidemiological models to predict the course of the epidemic so as to help planning effective control strategies. In particular, in order to properly interpret the available data, it has become clear that one must go beyond most classic epidemiological models and consider models that, like the recently proposed SIDARTHE, offer a richer description of the stages of infection. The problem of learning the parameters of these models is of crucial importance especially when assuming that they are time-variant, which further enriches their effectiveness. In this paper we propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We formulate the problem in terms of a functional risk that depends on the learning variables through the solutions of a dynamic system. The resulting variational problem is then solved by using a gradient flow on a suitable, regularized functional. We forecast the epidemic evolution in Italy and France. Results indicate that the model provides reliable and challenging predictions over all available data as well as the fundamental role of the chosen strategy on the time-variant parameters.Comment: 12 pages, 7 figure

On the complexity of the boundary layer structure and aerosol vertical distribution in the coastal Mediterranean regions: A case study

The planetary boundary layer structure in the coastal areas, and particularly in complex orography regions such as the Mediterranean, is extremely intricate. In this study, we show the evolution of the planetary boundary layer based on in situ airborne measurements and ground-based remote sensing observations carried out during the MORE (Marine Ozone and Radiation Experiment) campaign in June 2010. The campaign was held in a rural coastal Mediterranean region in Southern Italy. The study focuses on the observations made on 17 June. Vertical profiles of meteorological parameters and aerosol size distribution were measured during two flights: in the morning and in the afternoon. Airborne observations were combined with ground-based LIDAR, SODAR, microwave and visible radiometer measurements, allowing a detailed description of the atmospheric vertical structure. The analysis was complemented with data from a regional atmospheric model run with horizontal resolutions of 12, 4 and 1 km, respectively; back-trajectories were calculated at these spatial resolutions. The observations show the simultaneous occurrence of dust transport, descent of mid-tropospheric air and sea breeze circulation on 17 June. Local pollution effects on the aerosol distribution, and a possible event of new particles formation were also observed. A large variability in the thermodynamical structure and aerosol distribution in the flight region, extending by approximately 30km along the coast, was found. Within this complex, environment-relevant differences in the back-trajectories calculated at different spatial resolutions are found, suggesting that the description of several dynamical processes, and in particular the sea breeze circulation, requires high-resolution meteorological analyses. The study also shows that the integration of different observational techniques is needed to describe these complex conditions; in particular, the availability of flights and their timing with respect to the occurring phenomena are crucial

Optical Time-Frequency Packing: Principles, Design, Implementation, and Experimental Demonstration

Time-frequency packing (TFP) transmission provides the highest achievable spectral efficiency with a constrained symbol alphabet and detector complexity. In this work, the application of the TFP technique to fiber-optic systems is investigated and experimentally demonstrated. The main theoretical aspects, design guidelines, and implementation issues are discussed, focusing on those aspects which are peculiar to TFP systems. In particular, adaptive compensation of propagation impairments, matched filtering, and maximum a posteriori probability detection are obtained by a combination of a butterfly equalizer and four 8-state parallel Bahl-Cocke-Jelinek-Raviv (BCJR) detectors. A novel algorithm that ensures adaptive equalization, channel estimation, and a proper distribution of tasks between the equalizer and BCJR detectors is proposed. A set of irregular low-density parity-check codes with different rates is designed to operate at low error rates and approach the spectral efficiency limit achievable by TFP at different signal-to-noise ratios. An experimental demonstration of the designed system is finally provided with five dual-polarization QPSK-modulated optical carriers, densely packed in a 100 GHz bandwidth, employing a recirculating loop to test the performance of the system at different transmission distances.Comment: This paper has been accepted for publication in the IEEE/OSA Journal of Lightwave Technolog

SAILenv: Learning in Virtual Visual Environments Made Simple

Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world. However, most of the existing platforms to interface algorithms with 3D environments are often designed to setup navigation-related experiments, to study physical interactions, or to handle ad-hoc cases that are not thought to be customized, sometimes lacking a strong photorealistic appearance and an easy-to-use software interface. In this paper, we present a novel platform, SAILenv, that is specifically designed to be simple and customizable, and that allows researchers to experiment visual recognition in virtual 3D scenes. A few lines of code are needed to interface every algorithm with the virtual world, and non-3D-graphics experts can easily customize the 3D environment itself, exploiting a collection of photorealistic objects. Our framework yields pixel-level semantic and instance labeling, depth, and, to the best of our knowledge, it is the only one that provides motion-related information directly inherited from the 3D engine. The client-server communication operates at a low level, avoiding the overhead of HTTP-based data exchanges. We perform experiments using a state-of-the-art object detector trained on real-world images, showing that it is able to recognize the photorealistic 3D objects of our environment. The computational burden of the optical flow compares favourably with the estimation performed using modern GPU-based convolutional networks or more classic implementations. We believe that the scientific community will benefit from the easiness and high-quality of our framework to evaluate newly proposed algorithms in their own customized realistic conditions.Comment: 8 pages, 7 figures, submitted to ICPR 202